Strategic Multiscalel White Book 2012

White BookWhite BookWhite BookWhite Book

“Strategic Multiscale:

A New Frontier For

R&D and Engineering

Alessandro Formica

March 2012

Alessandro Formica – March 2012 All rights reserved

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TABLE OF CONTENTS

1. Introduction………………………………………………………………………………... pag. 3

2. Strategic Multiscale Framework…………………………………………………………. pag. 5

2.1 R&D and Engineering Scenario: From Computational To Strategic Multiscale………….. pag. 5

2.2 Strategic Multiscale Framework Architecture………………………………………………… pag. 9

2.3 Strategic Multiscale Framework Goals…………………………………………………………. pag. 10 3. Integrated Multiscale Science - Engineering Framework…………………………….. pag. 12

3.1 Architecture………………………………………………………………………………………. pag. 12

3.2 Multiscale Data, Information and Knowledge Analysis and Management System………pag. 13

3.3 Multiscale Science – Engineering Information Space………………………………………. pag. 20

3.4 Multiscale Modeling & Simulation as Knowledge Integrators and Multipliers…………. pag. 25

3.5 Multiscale Multiresolution Experimentation, Testing and Sensing………………………. pag. 29

3.6 Methodologically Integrated Multiscale Science – Engineering Strategies……………… pag. 34 3.6.1 The Information – Driven Concept………………………………………………………………... pag. 34

3.6.2 Methodological Integration Schemes, Maps and Strategies……………………………………… pag. 37

3.6.3 Multiscale Knowledge – Based Virtual Prototyping and Testing………………………………… pag. 42

3.7 Designing the R&D and Engineering Process………………………………………………. pag. 43 3.7.1 R&D and Engineering Analysis and Design Process Architecture………………………………. pag. 43

3.7.2 R&D and Engineering Analysis and Design Strategy Management System…………………… pag. 44

3.7.3 Integrated Multiscale Science – Engineering Analysis Strategies………………………….. pag. 46

3.8 Applications…………………………………………………………………………………………pag. 50 3.8.1 Multiscale Systems Engineering………………………………………………………………… pag. 50

3.8.2 Multiscale Processing and Manufacturing……………………………………………………….. pag. 54

4. Integrated Multiscale Science – Engineering Technology, Product and Process Development (IMSE-TPPD) Framework……………………………………………………….. pag. 64

4.1 Overview and Architecture………………………………………………………………………… pag. 64

4.2 Computer Aided R&D and Engineering/Processing (CARDE) Framework…………….. pag. 68

4.3 Innovative Technology and System Development Analysis and Planning Framework…… pag. 69

4.4 Multiscale Science – Engineering Knowledge Integrator and Multiplier (KIM) and.

Computing Information Communication Infrastructural Framework………………………....... pag. 73

About the Author…………………………………………………………………………….. pag. 76

Contacts……………………………………………………………………………………….. pag. 78


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1. Introduction

Relationships between science and engineering, basic and applied research, technology development,

engineering and manufacturing are deeply changing. At the same time, dramatic advances in Computing

Information and Communication (CIC) technologies are reshaping the Research, Industry Scenario and

Cooperative Environments. Accordingly, a new language and theoretical framework to understand and

manage this complex process and drive technology innovation and complex systems design well into the

21st century, is a reasonable need. However, significant methodological advances are needed to take full

advantage of the Computing, Information and Communication (CIC) technological “Revolution” and

effectively cope with educational, industrial, economic, environmental and societal challenges. A new

Integrated Multiscale Multidisciplinary Science - Engineering approach can be regarded as a strategic goal.

The fundamental thesis of this White Book is that, to meet 21st century innovative technology development

and complex systems engineering analysis and design challenges, we need important improvements in

Methodology and the way Information is dealt with inside R&D and Engineering. This development process

can be started by implementing what we call a “Strategic” view of the Multiscale concept and method.

Computational Multiscale is today widely regarded as a “New Frontier” for Computational Science and

Engineering. “Strategic Multiscale” can be a “New Frontier” for R&D and Engineering/Manufacturing

Strategies and Organization.

Strategic Multiscale is not only a new methodology, but a unifying paradigm to enable integration of

science and engineering as it was defined by Villermaux, Ka, Ng, Formica, in the mid of nineties. Central

elements of the Strategic Vision of Multiscale are a new concept of Modeling and Simulation as “Knowledge

Integrators and Multipliers” and “Unifying Paradigm” for Scientific and Engineering Knowledge Domains

and Methodologies and a new set of Multiscale Science - Engineering Data Information and Knowledge

Schemes and Strategies. This Vision directly leads to the extension of the multiscale concept to the

experimental, testing and sensing worlds and a comprehensive integration of a full spectrum of multiscale

computational, experimental, testing and sensing methodologies and related knowledge domains. The

ultimate goal is to define more general “Methodologically Integrated Multiscale Multidisciplinary R&D and

Engineering Strategies”.

The “Strategic Multiscale” Vision embodies three Frameworks:

� “Integrated Multiscale Science – Engineering Framework” which represents the theoretical,

conceptual and methodological basis (Chapter 3)

� “Integrated Multiscale Science – Engineering Technology, Product and Process Development (IMSE-TPPD) Framework” (Chapter 4) which is constituted by:

− a set of Software Environments that implement theories, methods and concepts described in the

previously quoted Framework (Paragraphs 4.2 and 4.3)

− The “Multiscale Knowledge Integrator And Multiplier Computing, Information and Communication

(CIC) Infrastructural Environment” (Paragraph 4.4)

� “ Multiscale Science – Based Education, Information and Communication “Language” and Framework” which describes the application of the Strategic Multiscale concepts and methods to the

Education, Information and Communication Areas. Multiscale “Language” and Framework are

described in the “Multiscale Science – Based Education, Information and Communication Framework”

White Book.


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General References

David L. McDowell, Jitesh H. Panchal, Hae-Jin Choi. Carolyn Conner Seepersad, Janet K. Allen, Farrokh

Mistree, 2010. Integrated Design of Multiscale, Multifunctional Materials and Products - Published by

Elsevier .

Oden , J.T. , Belytschko , T. , Fish , J. , Hughes , T.J.R. , Johnson , C. , Keyes , D. , Laub , A. , Petzold , L. ,

Srolovitz , D. , Yip , S. , 2006 . Simulation-based engineering science: Revolutionizing engineering science

through simulation . In : A Report of the National Science Foundation Blue Ribbon Panel on Simulation-

Based Engineering Science . National Science Foundation : Arlington, VA .

Olson , G.B. 1997 . Computational design of hierarchically structured materials . Science, 277 ( 5330 ) ,

1237 – 1242 .

Alessandro Formica. Fundamental R&D Trends in Academia and Research Centres and their Integration

into Industrial Engineering Report drafted on behalf of European Space Agency, July 2000

Alessandro Formica, Multiscale Science – Engineering Integration A New Frontier for Aeronautics, Space

and Defense, Italian Association of Aeronautics and Astronautics (AIDAA), March 2003


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2. Strategic Multiscale Framework

2.1 R&D and Engineering Scenario: From Computational to Strategic Multiscale

In mid of nineties several researchers in the Chemical Engineering field (Sapre and Katzer, Lerou and Ng,

and Villermaux) and the author of this White Book (Alessandro Formica) highlighted the need of a

comprehensive multiscale approach as a key strategic step to establish a new “Unifying Paradigm” to enable

a better correlation between scientific and engineering advances and knowledge domains. This vision was highlighted, at the 5

th World Congress of Chemical Engineering (1996), San Diego, CA,

USA, by the late lamented Prof. Jacques Villermaux (at that time Vice President of European Federation of

Chemical Engineering) Later on, Prof. Charpentier, past European Federation of Chemical Engineering

President, illustrated similar concepts:

Taking advantage of these conceptual advances, in the White Book “Multiscale Science – Engineering

Integration – A New Frontier for Aeronautics, Space and Defense (May 2003) sponsored and published by

Italian Association of Aeronautics and Astronautics (AIDAA), Formica outlined the concept of “Strategic

Multiscale” and he described a first version of the related Integrated Framework.

Strategic Multiscale is the theoretical and methodological basis to change R&D and Engineering

organization, structure and strategies correlating, inside a coherent Framework, science and engineering

analytical, computational and experimental, testing and sensing methodologies and techniques. New

Frameworks and new Data, Information and Knowledge Management Systems, based on the multiscale

science - engineering integration concept, can contribute to organize scientific knowledge in such a way as

to make it directly applicable Technology Development and Engineering/Manufacturing/Processing and

redefine knowledge transfer along the whole chain: basic research, applied research, technology

development and integrate R&D, engineering, manufacturing and operational testing.

Multiscale as “Unifying Paradigm for Chemical Engineering

Prof. Charpentier, past European Federation of Chemical Engineering (EFCE) President, at the 6th

World Congress of Chemical Engineering - Melbourne 2001, described his Vision of Multiscale as

“Strategic Paradigm” for Chemical Engineering.

We report his words :

“One key to survival in globalization of trade and competition, including needs and challenges, is the

ability of chemical engineering to cope with the society and economic problems encountered in the

chemical and related process industries. It appears that the necessary progress will be achieved via a

multidisciplinary and time and length multiscale integrated approach to satisfy both the market

requirements for specific end use properties and the environmental and society constraints of the

industrial processes and the associated services.

This concerns four main objectives for engineers and researchers:

(a) total multiscale control of the process (or procedure) to increase selectivity and productivity,

(b) design of novel equipment based on scientific principles and new methods of production: process

intensification,

(c) manufacturing end-use properties for product design: the triplet ‘processus-product-process’

engineering,

(d) implementation of multiscale application of computational modeling and simulation to real-life

situations: from the molecular scale to the overall complex production scale.”


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The drivers for a new vision of Multiscale come from some specific features which characterize modern

R&D and Engineering/Manufacturing Scenario. Three key issues, in particular, play a key role:

a) Integration of Science and Engineering (nanotechnology is only the most evident sign of this process)

b) Performance and Optimization pushed to the limits

d) Growing complexity of engineering systems and of the related R&D and Engineering Processes

These issues heavily condition innovative technology and system development times, costs and risks and

programs organization, structure and management.

a) Science-Engineering Integration: Science has become a key Technological and Engineering

Variable Science – Engineering Integration means that Scientific knowledge is increasingly at the root of new

technology developments in key fields such as materials, materials processing, electronics, communication,

information processing, computing, optics, propulsion, clean by design solutions,…..

Technological Systems integrate a wide spectrum of sub-systems, components and devices working not only

at the classical engineering scales (macro and meso), but, also, at scientific space and time scales (micro and

nano). Nano and Micro technologies are territories where science and engineering meet together. Nano

technologies open the way to the definition of a new generation of “inherently hierarchical multiscale

materials, devices and systems”. We are seeing the birth of new fields: “Quantum Engineering” and

“Hierarchical Multiscale Nano To Macro Engineering”

b) Performance, Requirements and Optimization pushed to the limits Pushing performance, requirements and optimization to the limits means that understanding, predicting, and

controlling systems dynamics is increasingly dependent on understanding, predicting, and controlling a

hierarchy of physical (chemical and biological) mutually interacting phenomena occurring at a wide range of

space and time scales. In this context, small phenomena at the lowest scales can have a major impact on the

behaviour of a macro system. Accordingly, classical homogenization and averaging procedures, as well as

semi empirical or simplified formulations, which worked well in the past, are, in many cases, no longer up to

the challenge

c) Complexity “Complexity” is a very general, if not generic at all, term. It is possible to relate “Complexity” to our

capability of understanding, predicting and controlling the dynamics of a “System. In the context of this

White Book, we consider five interrelated types of complexity :

� Physics Complexity (directly related to multiscale and the science-engineering integration issue) Multiscale Multiphysics hierarchies of physical phenomena and processes underlie the behaviour of

systems, sub-systems, components, devices and states of matter (materials, fluids, plasmas).

� Requirements, Functional and Operational Complexity Widening range of functions to be performed

by systems, widening operational envelope and widening spectrum of requirements to be met (energy

efficiency, environmental compliance, safety, development and operational costs, life – cycle issues,…)

� System Complexity A technological System is constituted by a full hierarchy (from macro to nano) of

subsystems, components and devices which use a wide range of different technologies (mechanical, bio,

info, electronics, optoelectronics, fluidics,…) and operate across a widening range of scales. That

implies a network of interactions among the full hierarchy of subsystems, components and devices which

span a wide spectrum of different space and time scales and, globally, condition the macro life – cycle

performance of the system

� R&D and Engineering Process Complexity This kind of complexity can be identified as: the

“Fragmentation Issue” for R&D and Engineering. Said in more specific terms, it refers to the always

continuously growing spectrum of models, methods, data, and information characterizing R&D and

Engineering Processes. The “Fragmentation Issue” heavily condition architecture, organization, and

structure of the R&D and Engineering processes associated with innovative technology and system

development.


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� Uncertainties Management Complexity Uncertainties are related to any aspect of the R&D and

Engineering process. The lack of a comprehensive and rigorous strategy to deal with uncertainties, in a

systematic way, inside the R&D and Engineering process, seriously limits our capability of reliably

predicting systems behavior, selecting alternative technological and engineering solutions, validating

computational and experimental, testing and sensing methods/techniques, defining the right mix among

theory, modeling & simulation, and experimentation & testing. Uncertainty is a function of physics

(scales and disciplines), systems and process complexity. Sources of Uncertainties (the list is not

exhaustive):

− Physics: If you do not know physics it is very difficult to assess model reliability, predictive

capabilities and applicability conditions.

− Geometry: The continuous reduction of the dimension of devices, components, and subsystems

makes even small geometric errors ever more critical

− Manufacturing: The ever growing relevance of even very small structural and compositional

variations on properties and performance of processed/manufactured materials and parts.

− Modeling: Uncertainty characterizes modeling hypotheses, input data, initial and boundary

conditions.

− Operational Environment (Loading Conditions) : Interactions between an high-tech system and its

operational environment involve highly complex physical phenomena and a multitude of different

nominal and off-nominal cases and situations.

− System Complexity: Interactions among devices, component, and subsystems making up a high-tech

systems involve a highly entangled pattern of multiscale, multimedia, multidisciplinary phenomena

which is practically impossible to characterize in a deterministic way.

− Information Uncertainty. Last but not least, we take into account what we can call the “Information

Uncertainty Challenge”. An often neglected uncertainty is linked to the determination of what

information is needed in critical tasks of the R&D and engineering process, what is the needed level

of accuracy, what is the range of validity and reliability level of (computational and experimental &

testing) models, what is the right mix between modeling & simulation and experimentation & testing

to accomplish tasks.

Two particularly critical issues are : “known unknowns” (unknown solutions to known problems) and the so

called “unknown unknowns” (unknown sources of uncertainty). Both have a critical relevance for R&D and

Engineering.

Fig.1 from “Modeling and Simulation In Support of T&E and Acquisition” Dr. Frank Mello, OSD/DOT&E

Presented at: The International Congress & Exhibition on Defense Test and Evaluation and Acquisition:

The Global Marketplace Vancouver, British Columbia, Canada


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Multiscale Development Stages

It is possible to identify four fundamental stages in the development of Multiscale and Science – Engineering

Integration:

a) Computational Multiscale methods to address specific R&D and Engineering issues. It is the basic

development stage. There is an increasing activity to improving existing computational multiscale methods

and develop new schemes and strategies (adaptive, concurrent, hierarchical,….)

b) Integrated Computational Multiscale Framework to address complex R&D and Engineering tasks.

Materials and Biology are key application fields. It can be considered as the “State of the Art”.

c) Extension of the Multiscale approach to the Experimentation, Testing and Sensing fields. Activities are

underway

d) “ Strategic Multiscale”, based upon the “Strategic Vision of Multiscale”, which could be a starting point to

change organization and structure of the R&D and Engineering landscape. A first sketch of a possible

structure of this kind of Frameworks and related application fields is outlined in this White Book.

The theoretical and methodological basis of the “Strategic Multiscale” Vision is constituted by the following

key elements:

� The Multiscale concept and method as basic theoretical and methodological element, extended, in this

context, to the experimental, testing and sensing fields

� A new “Vision” of Multiscale Modeling & Simulation as “Knowledge Integrators and Multipliers” and

“Unifying Paradigm” for Scientific and Engineering Methodologies and Knowledge Domains. In this

perspective “Modeling & Simulation” integrate the full spectrum of science and engineering

methodological approaches and knowledge environments.

� The “Multiscale Science-Engineering Information Space” concept to integrate data, information and

knowledge from computational models and methods and experimental, testing and sensing models and

techniques

� The “Information – Driven Analysis” concept and scheme which, together with the Science –

Engineering Information Space” concept is a key element to shape Multiscale Methodologically

Integrated R&D and Engineering Analysis Strategies

� New Multiscale Science – Engineering Data, Information and Knowledge Management Systems based

upon the Multiscale Maps concept

� New methods to “Design” the R&D and Engineering Process

The “Integrated Multiscale Science-Engineering Framework” represents the basis to perform the transition:

�� From traditional CAD, CAE, CAM systems to Multiscale Science - Engineering CAD, CAE, CAM

systems

�� From “Integrated Product and Process Development (IPPD)” Frameworks to a new generation of

Integrated Multiscale Science – Engineering Technology, Product and Process Development (IMSE-

TPPD)”Frameworks

It is of fundamental importance to highlight that the definition of an “Integrated Multiscale Science-

Engineering Framework” does not mean that specific aspects and peculiarities of Basic Research, Applied

Research, Technology Development and Engineering should be canceled and/or neglected and that all the

activities in these different Scientific and Engineering domains should be tightly correlated and inserted

inside global rigid schemes. In this White Book “Multiscale Science – Engineering Integration” means that :

− Scientific Knowledge should be structured in such a way as to make it directly applicable inside

Engineering processes and domains (Science – Driven Engineering)

− Requirements, performance, properties can be propagated along the Technology Readiness Level (TRL)

development scheme following a two – way approach (from TRL 0 to TRL 9 and from TRL 9 to TRL 0)

− Requirements, performance, properties can be linked over the full spectrum of scales and the hierarchy

of system architectural elements for complex systems.

Note: Multiscale is a general term, it incorporates, as a special case, classical single scale methods and

models. Multiscale stands for Multiscale Multiresolution Multiphysics in the most general case.


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2.2 Strategic Multiscale Framework Architecture

The overall “Strategic Multiscale Framework” is constituted by three specific interrelated Frameworks:

A. “ Integrated Multiscale Science - Engineering Framework” which describes the fundamental concepts

and methods to develop upon new R&D and Engineering Strategies and related SW Environments

(Chapter 3)

B. “Integrated Multiscale Science – Engineering Technology, Product and Process (IMSE-TPPD)

Framework” which represents the Integrated Product and Process Development (IPPD) Frameworks next

Generation (Chapter 4)

− Computer Aided R&D and Engineering (CARDE) Framework

− Innovative Technology and System Development Analysis and Planning Framework

− “Multiscale Knowledge Integrator and Multiplier Computing, Information and

Communication Infrastructural” Environment

C. “Multiscale Science – Based Education, Information and Communication Framework” described in

the homonymous White Book


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2.3 Strategic Multiscale Framework Goals

As quoted in the Introduction, the fundamental goal of the Strategic Multiscale is to change in a qualitative

way, organization, structure and strategies of the R&D and Engineering landscape, catalyze and foster a

spectrum of innovation trends:

Innovation for the Computing, Information and Communication (CIC) Fields

� Fostering the design, development and application of a new generation of CIC HW Systems based

upon Multiscale Nano To Macro Engineering Architectures and Technologies

� Fostering the design, development and application of a new generation of Integrated SW Frameworks which realize:

− a comprehensive Multiscale Science – Engineering Integration [already on the way]

− a comprehensive Methodological Integration (multiscale computation, experimentation, testing and

sensing) [to a large extent to be still comprehensively developed]

and that incorporate new Multiscale Science – Engineering Data, Information and Knowledge Analysis,

Integration and Management Schemes

� Catalyzing the development of new SW Frameworks for the “Modeling” of complex Integrated R&D

and Engineering Processes (Designing the R&D and Engineering Processes)

� Catalyzing the development of new SW Frameworks for the Modeling of Complex Systems for the

full Life Cycle and the whole spectrum of Operational Conditions including the extreme and accident

ones

Innovation for Computing, Information and Communication (CIC) Infrastructures

� A New Generation of Computational Centers (Chapter 4) The Vision of “Modeling and Simulation as “Knowledge Integrators and Multipliers” and “Unifying

Paradigm” for the full spectrum of R&D and Engineering Methodologies (Experimentation, Testing and

Sensing) open the way to the creation of a New Generation of “Multiscale Multidisciplinary Science – Engineering Knowledge Integrator and Multiplier” Centers. In this new context Computational

Centers integrate themselves with Experimental and Testing Facilities and Field Monitoring Systems

operating over a full range of scientific and engineering scales.

� A new Generation of Cyberinfrastructures (Chapter 4) The new generation of Cyberinfrastructures which can be referred to as “Multiscale Multidisciplinary

Knowledge Integrator and Multiplier Cyberinfrastructural Environments” foresee a comprehensive on-

line integration of the full spectrum of Scientific and Engineering Theoretical, Computational,

Experimental, Testing and Sensing Teams and the related Facilities. The Unifying Conceptual Context is

offered by the new Modeling and Simulation Vision (Modeling and Simulation as Knowledge Integrators

and Multipliers) and the related Knowledge Management schemes.

Innovation For Technology and Engineering

� Promoting and Easing the development of New Fields for R&D and Engineering: Multiscale

Nano To Macro Experimentation, Testing and Sensing (Paragraph 3.5) Advances in computational modeling and technological fields have created the opportunity to extend

the multiscale approach from the computational world to the experimental, testing and sensing ones.

Activities are already started in Europe (Max Planck, and European Synchrotron Research Facility,

for instance), US and Japan. What is needed today are integrated large scale and scope initiatives.

This new field entails the development of new multiscale experimental and testing characterization

protocols, technologies and operational modes and new multiscale sensing network architecture and

operational methodologies. Methodologically Integrated Multiscale Science – Engineering Strategies

described in this document allow to take full advantage, in a synergistic way, of progress in both the

fields: Modeling & Simulation and Experimentation, Testing and Sensing, to define a real new way

to do Research, Technology Innovation and Engineering.


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� New Integrated Multiscale Nano To Macro Technological and Engineering Solutions (Multiscale Nano To Macro Technology and Engineering: From Multiscale Analysis to Multiscale Design)

New Methodologically Integrated Analysis and Design Strategies and New Integrated Data, Information

and Knowledge Analysis and Management Frameworks put the bases to design “inherently”

Hierarchical Multiscale Nano To Macro Systems (atoms, materials, structures components and

products) which is a fundamental condition to fully exploit in the industrial environment the

potentialities of Nano and Micro Technologies. “Multiscale Systems”, are. systems organized

following a Hierarchical strategy where structures at the different scales interact in a synergistic way

to determine an extended spectrum of functionalities and performance.

The EU NMP (Sixth Framework) Integrated Multiscale Process Units Locally Structured Elements

(IMPULSE 2005 – 2009) Program is a very interesting example of this trend. IMPULSE is Europe’s

flagship R&D initiative for radical innovation in chemical production technologies. In the Materials

Field, the Hierarchic Engineering of Industrial Materials (HERO-M) Center has been recently set up at

the Royal Institute of Technology, Stockholm

Innovation For Research, Technology Development and Engineering Process Organization, Structure and Strategies � Knowledge Transfer along the R&D and Engineering Technology Readiness Levels Scale

Strategic Multiscale Strategies and Frameworks allow to Organize and Structure Scientific Knowledge is

such a way to make it directly applicable to Analyze, Design and Manufacturing Innovative Technology

and Industrial Systems. New Data, Information and Knowledge Management Systems (Paragraph 3.2),

based upon the multiscale science-engineering integration concept, can contribute to redefine knowledge

transfer along the whole chain: basic research, applied research, technology development and

integration, engineering, manufacturing, operational testing. That leads to accelerate the pace of the

insertion of research achievements inside technology development and engineering design and improve

effectiveness and efficiency of the whole process. Multiscale means “Multiscale Multiphysics”.

Multiscale is intrinsically Multidisciplinary and Interdisciplinary as to become a very powerful integrator

of knowledge.

� Development of Methodologically Integrated Multiscale R&D and Engineering Strategies and Frameworks

The concept of Modeling & Simulation as “Knowledge Integrators and Multipliers”, the “Multiscale

Data, Information and Knowledge Analysis and Management System and the development of Multiscale

Experimentation and Testing Strategies open the way to the definition of “Methodologically Integrated

Multiscale Strategies and Frameworks” (Paragraph 3.6) to implement a full integration of different

Multiscale Multilevel Computational, Experimental, Testing and Sensing methods and techniques not

only in the computational models development and validation phases, but, also, in the application one

� Development of Methodologies and Frameworks to “Design” The R&D and Engineering Process Growing complexity of the R&D and Engineering Processes calls for the definition of more formal

methods to structure and organize this kind of Processes. New Strategies are described in the Paragraph.

3.7


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3. Integrated Multiscale Science - Engineering Framework

3.1 Architecture

Main elements of the Conceptual and Methodological Framework are:

� Multiscale Science - Engineering Data, Information and Knowledge Analysis and Management System

� Multiscale Science – Engineering Information Space

� Modeling & Simulation as “Knowledge Integrators and Multipliers” and Unifying Paradigm for

Scientific and Engineering Methodologies and Knowledge Domains

The role of Multiscale as “Unifying Paradigm and Language” for Science and Engineering was discussed

by Alessandro Formica, some years ago in the book - Computational Stochastic Mechanics In a Meta-

Computing Perspective – December 1997 - Edited by J. Marczyk – pag. 29 – Article: A Science Based

Multiscale Approach to Engineering Stochastic Simulations.

� Information – Driven Multiscale Science – Engineering Analysis Concept and Schemes

� Methodologically Integrated Multiscale Science – Engineering Methodologies

� New Methods, Tools and Strategies to Design the R&D and Engineering Process

� Integrated Multiscale R&D and Engineering Analysis Strategies


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3.2 Multiscale Science – Engineering Data, Information and Knowledge Management System

A critical issue for a wide diffusion of the science – based engineering analysis and design approach in the

industrial field is the availability of Software Environments (CAD/CAE/CAM) specifically conceived for

multiscale science – engineering strategies and applications. Today, notwithstanding the growing diffusion

of multiscale inside university, research, and even industry, software environments (CAD/CAE/CAM)

specifically conceived to implement multiscale science-engineering integration visions and strategies are in

their starting phase. The lack of software environments specifically conceived to implement a multiscale

science-engineering integration strategy represents a “fundamental” hurdle to a large scale implementation

of multiscale inside innovative technology development and engineering/manufacturing/processing fields.

The new Data, Information and Knowledge Management System proposed in this White Book rests on the

concept of “Multiscale Multiresolution Multi Abstraction Level Map. The Multiscale Multiresolution

Multi Abstraction Levels Map concept here described is an extension of the “Map” concept discussed by

Formica in the Multiscale Science – Engineering Integration: A new Frontier for Aeronautics, Space and

Defense White Book published on March 2003 by Italian Association of Aeronautics and Astronautics,.

Definition: Multiscale Multiresolution Multi Abstraction Level Maps are “ Multiscale Multiresolution

Multi Level Information and Knowledge Structures” describing complex networks of relationships and

interdependencies between a large spectrum of “Information Variables” characterizing “Systems Structure

and Dynamics”. Relationships and interdependencies between “Information Variables” are worked out

applying several mathematical techniques such as multivariate analyses and neural networks to raw data

coming from a wide range of “Data Sources” (analytical and computational models, data bases,

experimentation, testing and sensing). covering the full spectrum of scales (from atomistic to macro) and the

full spectrum of disciplines. “Multiscale Maps” structure Data and Information and, accordingly, they

represent a step to turn Information into Knowledge. Representations can be static and dynamic. Multi

Abstraction means that Maps can be set up and integrated applying several aggregation and clustering

schemes. A cluster of Multiscale Maps aggregated following a specific aggregation scheme can define what

can be called a “Knowledge Domain”. “Knowledge Domains” can be organized in a “Hierarchical Way”.

Maps are organized in a hierarchical way. For instance: a “Physical Knowledge Domain” linked to a

specific Process (Hypervelocity Impact, Combustion or Explosion, for instance) can be constructed by

assembling a range of Multiscale Physical Maps describing more elementary physical (chemical and

biochemical) phenomena (fracture, fragmentation, phase change,..) related to a specific material or

component of a System.

Multiscale Maps are built integrating/fusing (statistical methods, neural networks,…) data from a wide

range of sources:

− a spectrum of scientific and engineering teams,

− a wide range of methodologies,

− a spectrum of analysis and design tasks in the different stages of the whole Technology Development

and Engineering process.

Multiscale Maps incorporate error analyses and uncertainty quantification methods.

“Multiscale Maps” allow for an effective insertion and management of the more fundamental knowledge

(basic and applied research) inside Technology Development and Engineering phases. At each phase,

Multiscale Maps are built taking full advantage of the knowledge get in the previous phase.


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Several typologies of Maps are foreseen which describe relationships between:

− Multiscale Analysis and Design Variable Maps tracking relationships between Analysis and Design

Variables . Multiscale Analysis and Design Variable Maps are built applying statistical analysis schemes

(multivariate, PCA) or other techniques like neural networks to data coming from several sources: data

bases, computation, analytical theories, experimentation, testing, sensing. Data integration and fusion

techniques are applied to reconcile and integrate data coming from different sources characterized by a

range of accuracy and reliability degrees. Multiscale Analysis and Design Variable Maps describe

relationships between variables and parameters used to characterize “Systems Behaviour” over a full

range of space and time scales.

− Multiscale Physics Maps describing relationships between Physical, Chemical and Biochemical

Phenomena and Processes

− Multiscale Architectural/Structural Maps describing relationships between the hierarchy of Sub-

Systems, Components, Devices, Materials and Elementary Structures constituting a System (or System

of Systems) of arbitrary level of complexity.

− Multiscale Monitoring and Control Maps describing the networks of Monitoring anc Control devices

and Systems

− Multiscale Functional Maps describing relationships between System Architectural/Structural Elements

and Functions performed

− Multiscale Requirements - Performance – Property – Structure Maps describing relationships between

Requirements, Performance, Structural Elements and related Properties over the whole scales and

resolution levels. .

− Multiscale Performance – Property – Structure - Processing Maps describing the impact of

Processing techniques over the network of Performance, Structure - Property relationships over the

whole scales and resolution levels.

Multiscale Maps represents a key element of a new Multiscale Computer Aided Research, Development

and Engineering (CARDE) Systems.

Main objectives:

� Developing new schemes allowing for a more in-depth analysis and structuring of data, information

and knowledge and related correlations and interdependencies

� Integrating the full spectrum of “Data Sources” (Data Bases, Analytical Theories, Computational

Models, Experimentation , Testing and Sensing). The “Information Space” and the “Modeling and

Simulation as Knowledge Integrators and Multipliers” concepts and methods ease this kind of

Integration

� Developing new CAD/CAE Environments specifically conceived to Design new Hierarchical

Multiscale Nano To Macro Multifunctional Systems in the context of an Integrated Science –

Engineering Approach

� Developing new Integrated Science – Engineering CAD/CAE Environments able to be applied

inside the whole R&D and Engineering Process.

Multiscale Maps are indexed and related to specific R&D and Engineering Tasks and Phases and Design

Hypotheses and Decisions

The Multiscale Science – Engineering Data, Information and Knowledge Management System records,

organizes and manages all the previously defined Maps. Each Map is characterized by a set of Tags which

link it to a specific task and phase inside the R&D and Engineering Analysis and Design Process.


15

Fig. 2 Physics Map Example (from “Overview of the Fusion Materials Sciences Program Presented by S.J.

Zinkle, Oak Ridge National Lab Fusion Energy Sciences Advisory Committee Meeting February 27, 2001

Gaithersburg”)

This figure depicts a “ Information Structure” like the proposed Multiscale Physics Maps. In this case the

Multiscale Physics Map describes relationships between physical phenomena and chemical/physical

structural transformations linked to Radiation Damage Process for Metals

A cluster of Multiscale Physics Maps, linked to specific physics phenomena or processes, can define what

can be called a “Physical (Chemical and Biochemical) Phenomena and Processes Knowledge Domain”.

“Knowledge Domains” aggregated following horizontal and vertical ways.

Knowledge Domains are managed by the Multiscale Science – Engineering Data, Information and

Knowledge Management System.


16

Multiscale Multiresolution Multilevel Architectural and Structural Maps

Any “System” of arbitrary degree of complexity (an air transportation system, an energy production system,

an aerospace vehicle, a chemical plant, a structure, a nanotechnology device, a nanostructured material), can

be recursively broken down in a set of simpler (macro, meso, micro, nano and atomistic) “Architectural and

Structural Elements”. We distinguish two kinds of interconnected Systems: Technological Systems and

Natural Systems where the Technological System (or System of Systems) operates.

Fig. 3 Two dimensional multilevel multiscale view of an aircraft. (from the “Validation Pyramid and the

failure of the A-380 wing” Presentation given by I. Babuska (ICES, The University of Texas at Austin), F.

Nobile (MOX, Politecnico di Milano, Italy), R. Tempone (SCS and Dep. of Mathematics’, Florida State

University, Tallahassee) in the context of the Workshop “Mathematical Methods for V&V SANDIA ,

Albuquerque, August 14-16, 2007

Three new features distinguish this kind of Maps and related Multiscale Multilevel Science – Engineering

CAD Systems:

− Multiscale Multilevel Architectural/Structural Element Networks Analysis and Description. New CAD

Systems should describe the full set of multiscale multilevel (inside a single scale) Architectural and

Structural Elements of a System (or System of Systems) - including the “Operational Environment” -

and related interconnections. Interconnection Elements describe two – way interactions between

Elements. This feature is of particular importance if we like to assess the impact of the System upon the

environment where it operates and the effects of the Environment on the System for the whole Life Cycle

and the whole spectrum of operational conditions including extreme ones and accidents.

− Zooming and Selected Multilevel Multiscale view capabilities. Users should have the possibility to select

a full spectrum of views at different levels of resolution, scales and abstraction. Multiple views should be

visualized in order not to lose connections among different levels of abstraction, resolution and scales.

The zooming function should allow users to transition from a levels of abstraction, levels and scales in an

interactive way.

− Multi Abstraction Levels: we can select groups (clusters) of architectural/structural elements of different

typologies over a spectrum of scales and resolution levels as needed to carry out specific analyses and

design tasks.


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This kind of “Maps” gives a comprehensive picture of the:

– “Architectural and Structural Elements” which constitute a “system” and related interconnections: from

the System (or System of Systems) down to elementary structures (atoms/molecules, groups of atoms

and molecules)

– Materials, Energy, Chemical and Biochemical Substances Flow (pollutants emitted toward the Natural

System for instance,) among the “Elements” constituting the System or System of Systems

– Analysis and Design Variables their relationships and interdependencies and links between “ Analysis

and Design Variables” and Architectural and Structural Elements

– Properties of the full set of Architectural and Structural Elements

– Performance and Requirements for the full set of Architectural and Structural Elements. Performance are

calculated and/or measured during the R&D and Engineering Process while, Requirements are imposed

by designers.

Architectural and Structural Maps evolve along the Technology Development and Engineering Analysis and

Design Process thanks to Analysis and Design Modules and “Strategy Modules”. “Maps” are built using the

available knowledge; as analysis and design activities proceed, they are interactively modified. Different

Maps can be linked to different Architectural Hypotheses and Decisions for different purposes and tasks

during the R&D and Engineering Process. Maps are recorded, organized and managed in specific

“Architectural and Structural Map Data Bases”. Architectural and Structural Elements Maps are related

to:

− Functional Maps

− Monitoring and Control Maps

− Physics and Process Maps

Multiscale Monitoring and Control Maps

This kind of Maps gives a comprehensive picture of the Multiscale Multiresolution Networks of Monitoring

and Control Devices and Systems their interconnection schemes and their functionalities and operational

modes. Multiscale Monitoring and Control Maps are related to:

− Architectural and Structural Maps

− Physics Maps (effects of Control actions)

Multiscale Functional Maps

We define two types of Functional Maps.

− The first one, which can be called “Direct Functional Map”, describes “Functions” carried out by the

System and the full hierarchy of its Elements. Direct Functional Maps link Architectural/Structural

Elements to Functions and they describe what functions are performed by Architectural/Structural

Elements.

− The second one, which can be called “Inverse Functional Map” relates Functions to

Architectural/Structural Elements over the full spectrum of hierarchy levels

Functional Maps are linked to:

− Architectural and Structural Maps

− Physics and Processes Maps

“Functional Maps” defined during the Technology Development and Engineering Process are recorded,

organized and managed by specific “Functional Maps Data Bases”. Maps are indexed in such a way as to

relate them to specific R&D and Engineering Phases and Tasks.


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Multiscale Physics Maps

We use the term “Physics” to indicate a more or less complex cluster of elementary physical and biochemical

phenomena/processes occurring inside a scale or developing over a spectrum of scales,

Phenomena/processes are, for instance, failure, stress corrosion cracking erosion, phase

transformation,…… A Process can be broken down in a full hierarchy of more elementary Processes and

Phenomena. The distinction between “processes” and “phenomena” is, to some extent, arbitrary. It is a

matter of opportunity. Phenomena and Processes can concern more Architectural/Structural Elements.

Physics Maps are linked to:

− Architectural/ Structural and Functional Maps.

− Monitoring and Control Maps

− Requirements - Performance – Property – Structure Maps

− Performance – Property – Structure - Processing Maps

“Physics Maps” are “software environments” which describe :

� the full set of physical (biological and chemical, as needed) phenomena and processes which rule the

dynamics of Architectural/Structural Elements of a “System” under analysis/design for a specific

Task and their interactions inside a scale and over different scales.

� The full hierarchy of (geometrical, physical and biochemical) Architectural/Structural

transformations related to a specific set of Phenomena/Processes linked to a specific R&D and

Engineering Task .

� Relationships between the full hierarchy of processes, phenomena and Architectural/Structural

transformations for a specific Task

Maps are indexed in such a way as to relate them to specific R&D and Engineering Phases and Tasks.

Physics Maps are linked to Integration Strategy Maps described in the Paragraph 3.6. Integration Strategy

Maps describe what Computational Models, Experimentation, Testing and Sensing Techniques/Procedures

are applied to analyze specific physical phenomena/processes and their interconnection networks and

sequence of execution. Physics Maps are built using the available knowledge, as R&D and Engineering

proceed, they are interactively modified.

“Physics Maps” defined during the R&D and Engineering Process are recorded, organized and managed by

specific “Physics Maps Data Base”.

Integration of the previously defined Multiscale Maps allow to correlate:

− functions to physical phenomena and processes (linking Multiscale Functional Maps with Multiscale

Physics Maps

− Properties (Multiscale Architectural/Structural Maps) to Physics (Multiscale Physics Maps)


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Performance – Properties – Structure – Processing Maps

The definition of the Performance – Properties – Structure – Processing relationships has become a

cornerstone of the modern Materials Science and Engineering and R&D and Engineering at all.

Prof. Gregory Olson, Northwestern University has been one of the pioneers of this strategy. Prof. Olson

described this approach in a Science Magazine article: Vol. 277 (29 August 1997) pp. 1237-1242.

Fig. 4 (from Prof. Olson – Northwestern University) illustrates the application of a Performance – Properties

- Structure – Processing Map to the design of new alloys.

Performance – Properties –Structure - Processing Maps are indexed in such a way as to relate them to

specific R&D and Engineering, Phases and Tasks. Performance – Properties - Processing – Structure Maps

defined during the R&D and Engineering Process for different purposes and tasks are organized and

recorded in the “Performance – Properties - Structure - Processing Map Data Bases” The Multi

Abstraction Level feature of the Maps can be seen in the figure: each box is a specific abstraction level. Each

Box refer to a cluster of processes occurring over u spectrum of scales and resolution levels.

This kind of software environments contribute to characterize and manage relationships between processing

and manufacturing activities and the resulting architecture/structures

These Maps identify :

� defects (typology, physical and chemical characteristics, density and distribution : statistical and

deterministic analysis) linked to specific processes and manufacturing activities and steps

� bio – chemical and structural features and transformations linked to specific processes and manufacturing

conditions, procedures and technologies

This kind of Maps are related to Multiscale Physics Maps


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3.3 Multiscale Science - Engineering Information Space

This concept was presented by Alessandro Formica in the Report “Fundamental R&D Trends in Academia

and Research Centres and Their Integration into Industrial Engineering” (September 2000), drafted for

European Space Agency (ESA). The “Multiscale Science-Engineering Information Space” is associated to

any analytical, computational model/method, and experimental, testing and sensing procedure and technique

applied to a specific task. The “Multiscale Science-Engineering Information Space” defines:

− what spectrum of information about physical/biological/chemical phenomena and processes

− at what level of accuracy and reliability

can be get by a computational model or experimental/testing/sensing technique/procedure applied in a

specific context for a specific task.

A set of “model variables” characterize analytical and computational models. A set of “method variables”

characterize the specific method applied to perform simulations. A set of “system variables” characterizes

the system to be modeled and simulated or subjected to experimental, testing and sensing analyses. A set of

“experimental, testing and sensing variables” characterizes experimental, testing and sensing techniques and

procedures.

The ”Science – Engineering Information Space” also applies to cluster of computational models and

experimental/testing/sensing techniques/procedures linked through multiscale multiphysics coupling

schemes. In this case we can define “coupling scheme parameters” which describe the method used to

couple models and/or experimental/testing/sensing techniques/procedures.

− With the term “system” we refer to the system (materials, device, component,….) under analysis.. A set

of “variables” describe the geometrical, biological, chemical and physical structure of the system.

− With the term “Operational Environment”, we refer to “External Fields and Loading Conditions”

− With the term “model” we refer to the mathematical/computational representation of the “system” under

investigation. A set of “variables” characterize and describe the models (boundary conditions, external

fields, space and time dimensions, discretization techniques, particles number and typology,…….). In

the proposed framework we extend the concept of “Model” to the Experimental/Testing/Sensing world

as explained in the Paragraph 3.4

− With the term “method” we refer to the specific deterministic and statistical analytical and computational

method (Monte Carlo. Classical Molecular Dynamics, Quantum Molecular Dynamics, Density

Functional Theory, Dislocations Dynamics, Cellular Automata,…).

− With the term “experimental/testing/sensing technique and procedure variables” we refer to the

“variables” which describe technical characteristics of the experimental and testing apparatus and the

specific operational modes and conditions (globally referred to as “procedure”)

Information Space Construction To build the “Information Space” of a specific (single scale or multiscale) computational model with

reference to a specific system and analysis task (fracture, delamination, oxidation,…), we perform a set of

simulations, varying in a systematic way parameters/variables which characterize the physical (chemical and

biochemical) phenomena/processes of interest in the context of a specific task including external forces.

Then, we validate computational models using a set of experiments, tests and sensing measures to track the

“boundaries” of the Information Space and evaluate accuracy and reliability (Uncertainty Quantification –

UQ). Information Spaces can be built also for experimental, testing and sensing techniques and procedures.

In this case a “Cross Validation” strategy is applied which foresee the comparison of a spectrum of

experimentation, testing and sensing techniques.


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The “Information Space”, should also include Multiscale Analysis and Design Variable and Multiscale

Physics Maps worked out during the previously described construction process.

It is possible to apply different schemes to build the “Information Space” for a specific task. For instance:

− fixing model and methodology variables and varying external conditions and/or system variables

(typology and architecture of a material or device)

− fixing external conditions and system variables and varying model and/or methodology variables (for a

molecular dynamics model: simulation time, force fields typology, number of particles,…).

− any other possible combinations

The Information Space, for each specific computational model/method (or cluster of models: multiscale

multiphysics) applied to a specific task includes information about the computing resources needed to

perform simulations and the experimental, testing and sensing techniques used to validate it

Information Space Relevance

Three considerations underlie the definition of the “Multiscale Science – Engineering Information Space”

concept and method:

� rationally correlating advances for models/methods and multiscale multiphysics coupling schemes with

the capability of getting information thought to be important to carry out specific R&D and Engineering

tasks.

� rationally defining the role of models/methods and related multiscale multiphysics coupling schemes

inside a more general R&D and Engineering analysis and design process and the interdependencies

among different models, methods, techniques and coupling schemes.

� formally tracking and planning the development path (roadmap) for models, methods, techniques and

related coupling schemes as linked to specific R&D and Engineering analysis and design tasks, and

assessing the relative importance of the different models/methods and related coupling schemes to get

some Information at a specific level of accuracy and reliability.

We can consider an aerodynamic design task, for instance. The ability to run a 30/50-million grid points

Navier Stokes simulation in the same lapse of time, or less, as a 1-million grid points simulation, is surely an

important result from an engineering analysis and design point of view. But, what is the relative “weight”

between model dimension and physics (turbulence) modeling as function of a particular task (calculation of

aerodynamic coefficients, for instance) at a certain level of accuracy and reliability?

In this way, can we get more reliable and accurate information instrumental to reducing cost and

development time and introduce innovative technological solutions? The answer is not so straightforward.

Turbulence plays a key role in flow dynamics phenomena of critical importance for the design of a wide

range of systems. Suppose the biggest simulation model used the same turbulence model (or a slight

modification) as the one employed in the smallest one, what is the relationship among the number of grid

points, turbulence modeling (model variables) and the capacity of getting the needed engineering

information at the right level of accuracy (for instance : CP - CL or vortex dynamics – look at the V-22 vortex

ring state story) ? Is the number of grid points or the turbulence modeling the dominant knowledge factor

from a designer point of view?

The situation becomes even more critical when the physics and chemistry to be taken into account are highly

complex (aerothermodynamics and combustion, for example). It is sufficient to think at a combustion

chamber or an hypersonic vehicle. Several variables such as complex thermo chemical phenomena, the

interaction between turbulence and chemistry, multiphase and phase change phenomena, condition the

information space linked to a model.

We introduce, now, the “Range of Validity” concept for the “Multiscale Science-Engineering Information

Spaces” associated to models/methods and experimental, testing and sensing techniques and procedures.


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“Range of Validity” is the range of the “Multiscale Science-Engineering Information Space” inside which

we can get a set of information from specific models/methods and experimental, testing and sensing

procedures/techniques and possible coupling schemes at a certain level of accuracy and reliability

(uncertainty quantification).

It is of fundamental relevance to determine how the “Range of Validity” changes as model, method,

experimental & testing and coupling scheme variables change. The “range of validity” is a key element to

determine (for a specific task) :

� how “good” computational models and experimental, testing and sensing techniques and coupling

schemes should be to get Information we think to be needed to carry out a task at a predefined error and

uncertainty level.

� how to define the right mix of computational models/methods and experimental & testing

procedures/techniques and coupling schemes to get what we think to be the right information at the right

level of accuracy and uncertainty to perform a specific R&D and Engineering analysis and design task..

Fig. 5 (Center for Computational Materials Design – NSF) describes a framework to define in a formal way

the “Range of Validity (or Applicability Domain)” of a model

The “Multiscale Science-Engineering Information Space” formalizes what, today, is being performed in an

empirical and semi-empirical way. Such a formal procedure allows us to rigorously evaluate the relative

weight of the several “model/method/technique variables” as function of the “Information Space” and the

best research/development paths for computational models/methods and experimental & testing techniques

to address specific challenges.

The “Multiscale Science-Engineering Information Space” concept and method enables researchers and

designers to jointly define development roadmaps for computational models and experimental, testing

and sensing techniques.


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The need of defining the “Information Space” associated to computational method and experimental

techniques, in the context of the Verification & Validation process, has been analyzed, for instance, by Tim

Trucano in “Uncertainty in Verification and Validation: Recent Perspective Optimization and Uncertainty

Estimation, Sandia National Laboratories Albuquerque, NM 87185-0370 SIAM Conference on

Computational Science and Engineering, February 12-15, 2005, Orlando, Florida - SAND2005-0945C”.

Fig. 6 The figure (from the previously quoted document) illustrates the “Information Space concept

Thanks to the “Multiscale Science – Engineering Information Space” concept and method, it is possible to

define “Costs/Benefits Function” for models/methods and related coupling schemes as referred to different

Technology Development and Engineering tasks. “Benefits” are referred to the Information get and “Costs”

to the resources needed to develop, validate and apply models/methods/techniques/coupling schemes. This

kind of Function could be useful to Technology Development and Engineering Project Managers to better

manage and allocate human, organizational and financial resources.

The “Multiscale Science – Engineering Information Space” and the “Range of Validity” concepts can play a

role to develop new Verification and Validation (V&V) strategies and methods. Uncertainty Quantification

(UQ) is a key challenge for Computational Science and Engineering. We would like to underline this

Challenge concerns not only “Science”, but, also Engineering, taking into account the ever more strong

relationships between the two fields: Computational Engineering is increasingly built upon Computational

Science. Multiscale is a clear demonstration and application of this trend. UQ and “Quantification of Margin

of Uncertainty (QMU)” [performance (measured) vs. requirements (set)] , are becoming (have already

become) one of the new driver and objective for the Computational World. The Predictive Science

Academic Alliance Program (PSAAP) managed by US National Nuclear Security Agency (NNSA) is a clear

example of application of these statements.

The “Multiscale Science-Engineering Information Space” is of fundamental importance to define and

implement “Methodologically Integrated Multiscale Science-Engineering Strategies” which foresee the

simultaneous use of several different single and multiscale computational models and methods, and several

different single and multiscale experimental techniques working over a full range of scales.

The “Multiscale Science – Engineering Information Space is becoming of increasingly importance for

Science and Engineering because for a specific tasks is common using a spectrum of computational models

and a spectrum of experimental techniques and methods. Integration calls for rigorous methodologies to

determine what kind of Information can be get from computations and what from experimentation, testing

and sensing.


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According to the previous analysis, the “Multiscale Science-Engineering Information Space” concept and

method is instrumental to identify:

� shortcomings and limitations of computational models/methods and related multiscale multiphysics

coupling schemes for specific R&D and Engineering tasks

� development lines (roadmaps) for computational models and methods and multiscale coupling schemes

to achieve specific R&D and Engineering objectives

� shortcomings and limitations and development lines (roadmaps) for experimental, testing and sensing

techniques and procedures and related multiscale multiphysics coupling schemes

� integrated roadmaps for jointly developing multiscale multiphysics analytical, computational and

(multiscale) experimental, testing and sensing techniques to deal with specific R&D and Engineering

Tasks

� integrated strategies for jointly applying multiphysics multiscale analytical, computational and

(multiscale) experimental, testing and sensing techniques/procedures to deal with specific R&D and

Engineering Tasks


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3.4 Multiscale Modeling and Simulation as Knowledge Integrators and Multipliers and Unifying Paradigm for Scientific and Engineering Methodologies and Knowledge Domains

The “Vision” of “Multiscale Modeling & Simulation” as “Knowledge Integrators and Multipliers” (KIM)

and “Unifying Paradigm” for Scientific and Engineering Knowledge Domains and (Experimentation,

Testing and Sensing) Methodologies characterizes the “Integrated Multiscale Science-Engineering

Framework” and it represents the conceptual context inside which the Framework is applied to R&D and

Engineering Processes. The KIM notion was presented by Alessandro Formica in the: “HPC and the

Progress of Technology : Hopes, Hype, and Reality” – RCI. Ltd Management White Paper – February 1995

“Multiscale Multiphysics Modeling and Simulation” can be regarded as “Knowledge Integrators and

Multipliers” (KIM) and “Unifying Paradigm” for Scientific and Engineering Knowledge Domains and

Methodologies because Multiscale Models are able to integrate and synthesize, in a coherent framework,

Data, information, and Knowledge from:

�� a number of disciplines,

�� a wide range of scientific and engineering time and space domains,

�� multiple scientific and engineering models (science-engineering integration) linked by a spectrum of

coupling schemes.

�� A wide spectrum of Computational, Experimentation, Testing and Sensing Multiscale Science –

Engineering Data and Information Spaces built during the development, validation, application and I

improvement phases of the same Multiscale Models

�� by several Maps generated by a wide range of methodologies (analytical theories, computation,

experimentation, testing and sensing) during the development, validation, application and improvement

phases of the same Multiscale Models

In this vision, we propose to extend the concept of “Model” to include not only its mathematical

formulation, but, also, Information Spaces and Maps linked to it for specific tasks.

Multiscale Information Spaces and Multiscale Maps embody and organize Data, Information and

Knowledge get by the full spectrum of analytical theories, a set models at different scales and the related

experiments, tests and sensing measures used to develop, validate and improve them.

It is to be highlighted that all the existing Modeling and Simulation concepts, application strategies and

methodologies, such as “Virtual Prototyping” , “Simulation - Based Design”, “Simulation - Based

Acquisition”, Simulation Based Engineering Science (SBES) and “Virtual Engineering”, can be considered

as particular cases of this more general concept and strategy.

We would like to highlight that the “KIM” concept puts Modeling and Simulation and, accordingly, HPC,

at the centre of the R&D and Engineering Process much more than the classical “Virtual Prototyping”

and “Simulation Based Engineering Science” concepts

The concept of “Model” as “Knowledge Integrator” is certainly not new. This view, in the mid of nineties,

was clearly described in the chemical engineering field by James H. Krieger, in the article “Process

Simulation Seen As Pivotal In Corporate Information Flow” - Chemical & Engineering News, March 27,

1995. The text reported the following statement of Irving G. Snyder Jr., director of process technology

development, Dow Chemical : "The model integrates the organization. It is the vehicle that conveys

knowledge from research all the way up to the business team, and it becomes a tool for the business to

explore different opportunities and to convey the resulting needs to manufacturing, engineering, and

research." . In the same article other companies such as BNFL and Du Pont expressed similar points of view.

Note: Continuous advances in computational modeling and computing power makes it possible to build

computational models which simulate the experimental or testing apparatus, the system to be probed and

related interactions. This kind of modeling is an interesting asset to plan experimentation, testing and

sensing and analyze results.


26

Key element of the KIM Vision is the extension of the concept of “Model” to the Experimental, Testing and

Sensing World as detailed in the following:

Even if attention to the integration issue is positively increasing, particularly for models development and

verification and validation phases, there are still conceptual and methodological relationships not thoroughly

examined between challenges and advances in modeling and simulation, and progress and challenges in

experimental, testing and sensing techniques. Experience is showing us that ever more complex and large

scale computations call for increasingly sophisticated and expensive experimental techniques both in the

model development, validation and improvement phases. Advances in modeling and simulation are

intimately linked to progress in experimental, testing and sensing methods and techniques and vice versa. A

direct correlation and strong mutual dependencies, in the model development, validation and improvement

phases, exist between the two fields sometimes regarded as antithetic. It is important to take into account

that, if computational methods and computing technologies are continuously progressing, also experimental,

testing and sensing techniques are making continuous significant progress.

It is sufficient to think at the impact on materials research that the Scanning Tunneling Microscopy (STM)

and Atomic Force Microcopy (AFM) techniques have had.

It is important to highlight that Computational Development Strategies should be jointly conceived with

Experimentation, Testing and Sensing Development Strategies and vice versa.

Furthermore more and more complex and powerful 3D and 4D experimental, testing and sensing techniques

increasingly call for complex computational models to interpret, analyse and organize data and define

integrated measurement and characterization strategies.

The Concept of Experimental, Testing and Sensing “Model”

In the proposed theoretical and methodological framework it is necessary to extend the concept of

“Model” from the Computational to the Experimental, Testing and Sensing World. In the context of the

Experimental, Testing and Sensing World, for “Model”, as referred to a specific Experimental, Testing,

Sensing activity carried out with specific techniques, working in a specific operational mode and

probing a specific system for a specific task, we mean an “Information and Knowledge Structure” that

define:

− Characteristics (structure, composition, initial dynamics state, boundary conditions, external loadings)

of the System to be probed

− Characteristics of the equipment in terms of resolution, scale, physical and biochemical phenomena

which can be probed

− Characteristics of the specific Experimental, Testing and Sensing operational conditions and modes

applied for specific R&D and Engineering Tasks

− The “Multiscale Science – Engineering Information Space” related to it

− Multiscale Physics Maps .

As in the Computational World, it is easy to define the concept of “Multiscale Experimental, Testing and

Sensing Model”. In this case the “Information/Knowledge” Structure refers to a cluster of different

equipments and it embodies information about:

− Interaction schemes among the different equipments

− Data and Information Flow among the different equipments


27

A priority target is to develop a unified conceptual context to synergistically take advantage of advances in both the fields and not only for the computational models development and validation phases, as it occurs today, but, also, in the application phase. All of that in the context of Integrated Frameworks and Strategies

An effective R&D and Engineering Strategy should find the way to synergistically take advantage of

advances in both the fields.

In several cases, today, advanced HPC/Modeling/Simulation and experimental/testing/sensing programs are

conceived and managed as separated realities. This situation can lead to costs increase and hamper and

limit the effectiveness of both the programs. The new Vision reconcile development streams and roadmaps in

the two fields.

In the R&D and Engineering Process, today and, more and more, in the future, we have to integrate a full

spectrum of (interdependent and interlinked) scientific and engineering models and codes with a wide

spectrum of experimental, testing and sensing (scientific and engineering) data with a full spectrum of

scientific and engineering analytical formulations. Data get from experimentation, testing and sensing covers

several physical and biochemical disciplines and domains and several different space and time scales. It is

clear that, increasingly, we have to deal with very complex interaction patterns “intra” the experimentation,

testing and sensing world, “intra” the computational modeling world and “inter” the experimentation,

testing, sensing and computational modeling worlds. “Multiscale Science – Engineering Information Spaces,

Multiscale Maps and the Kim vision can be a first step to realize this integration.

The KIM concept is a fundamental theoretical and methodological basis. Methodologically Integrated

Multiscale Science - Engineering Strategies are built upon it. The following example is a notable

demonstration of the possibility opened by Integrated Computational and Experimental Strategies

ORNL - neutrons and simulations reveal details of bioenergy barrier –

OAK RIDGE, Tenn., June 15, 2011 — A first of its kind combination of experiment and simulation at the Department of Energy's Oak

Ridge National Laboratory is providing a close-up look at the molecule that complicates next-

generation biofuels.

Lignin, a major component of plant cell walls, aggregates to form clumps, which cause problems

during the production of cellulosic ethanol. The exact shape and structure of the aggregates, however,

have remained largely unknown.

A team led by ORNL's Jeremy Smith revealed the surface structure of lignin aggregates down to 1

angstrom—the equivalent of a 10 billionth of a meter or smaller than the width of a carbon atom. The

team's findings were published in Physical Review E. "We've combined neutron scattering experiments

with large-scale simulations on ORNL's main supercomputer to reveal that pretreated softwood lignin

aggregates are characterized by a highly folded surface," said Smith, who directs ORNL's Center for

Molecular Biophysics and holds a Governor's Chair at University of Tennessee. Lignin clumps can

inhibit the conversion of biofuel feedstocks—for example, switchgrass—into ethanol, a renewable

substitute for gasoline. …..

The complementary techniques of simulation on ORNL's Jaguar supercomputer and neutron scattering

at the lab's High Flux Isotope Reactor enabled Smith's team to resolve lignin's structure at scales

ranging from 1 to 1,000 angstroms. Smith's project is the first to combine the two methods in biofuel

research. "This work illustrates how state-of-the-art neutron scattering and high-performance

supercomputing can be integrated to reveal structures of importance to the energy biosciences," Smith

said. The research was supported by DOE's Office of Science and used the resources of the Leadership

Computing Facility at ORNL under a DOE INCITE award. Team members include ORNL's Sai

Venkatesh Pingali, Volker Urban, William Heller, Hugh O'Neill and Marcus Foston and Arthur

Ragauskas from Georgia Institute of Technology.


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Classical Modeling & Simulation Application Strategies in the innovative technology development field are

significantly hampered and limited the following fundamental contradiction:

“when we develop innovative technologies and innovative engineering solutions, we often enter a territory

where theories are not well developed and reliable, and the availability of experimental and testing data is

fragmented or lacking at all. Accordingly, we face a fundamental and intrinsic problem: Modeling &

Simulation is the reference strategy to limit risks, costs, and development times by heavily reducing the

resort to complex and expensive experimental and testing activities. However, contrary to what happens in

the mature or evolutionary technology environment, we cannot adopt this strategy because we still need

very significant experimental and testing activities to develop and validate the needed computational

models.”

That is what is called a classical “Catch 22” situation: (i.e.) a situation which involves intrinsic

contradictions.”

This contradiction is certainly not ignored. In the presentation “Modeling and Simulation in the F-22

Program” held on 3 June 98, Bgen Michael Mushala, F-22 System Program Director, highlighted this issue.

We quote his exact words :

A Catch 22 :

>> Increased Reliance on Simulation Requires High Confidence in the Modeling

>> High Confidence in the Modeling Requires High Quality Flight Test Data

How to get out of this contradiction? We think that single scale and independent computational and

experimentation, testing and sensing science and engineering strategies are not up to the challenge, A partial

way forward can be the application of the new Vision of Modeling and Simulation and, in particular, of

some of its key constitutive elements:

� Multiscale Maps

� the “Multiscale Science – Engineering Information Space” concept. which enables the definition in a

formal way of what kind of information at what level of accuracy and reliability can be get by single

and multiscale computational, experimental, testing and sensing models and techniques.

� A new concept of computational model which include not only mathematical and physical (chemical

and biochemical, as needed) formulations, but, also, Data, Information and Knowledge (Multiscale

Maps) linked to it when applied to a specific task

� The extension of the “model” concept to the experimental, testing and sensing world

� Definition of the “Applicability Conditions” and “Predictability Criteria” for (single and multiscale)

Computational models which guide the application of Modeling and Simulation and their

integration with experimentation, testing and sensing (Methodologically Integrated Multiscale

Application Strategies) [Paragraph 3.6]


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3.5 Multiscale Multiresolution Multiphysics Experimentation, Testing and Sensing

− The term “Multiscale” means the ability to probe phenomena and processes occurring over a spectrum of

space and time scales

− The term “Multiresolution” refers to the analysis of phenomena and processes inside a single scale, but

with a range of different resolution degrees

− The term “Multiphysics” means the analysis of a spectrum of phenomena and processes referred to

different physical and biochemical domains, inside a specific scale and resolution degree or over a range

of scales.

The following issues are motivating the birth and they are driving the development of the Multiscale

Multiresolution, Multiphysics Experimental, Testing and Sensing fields:

� Just the continuous development of Computational Multiscale has put the basis and established the need

to extend, in a systematic way, the Multiscale Concept and Method to the experimental, testing and

sensing fields.

� The development and validation of ever more complex multiscale computational models and methods

increasingly call for the integration of data, information and knowledge from a wide range of

experimental, testing and sensing equipments working over an extended spectrum of space and time

scales and physical domains. We can state that a direct relationship between the Computational and

Experimental, Testing and Sensing Multiscale World exists. Advances in Multiscale Computational

Models and Methods is directly linked to advances in experimental, testing and sensing multiscale. The

development of Multiscale Multiresolution Experimentation, Testing and Sensing techniques open a

whole new Application World to Multiscale Modeling and Simulation.

� Hierarchical Multiscale Materials and Systems made up of a wide spectrum of sub-systems,

components, devices and basic structural elements call for Multiscale Integrated Experimental, Testing

and Sensing techniques and strategies to get an in-depth and Comprehensive understanding of their

dynamics.

� The behaviour of Materials, Devices and Systems inside widening operational envelopes and related

requirements for “Extreme Performance” levels (Extreme Engineering) is critically dependent upon a

full spectrum of multiscale physical and biochemical phenomena. Furthermore, the classical approach to

Life Cycle issues (damage, fracture, properties degradation, corrosion, failure..) is increasingly showing

specific limits. This situation makes a science – based (multiscale) experimental, testing and sensing

approach a specific target for Technology Development and Engineering.

� New and more powerful experimental, testing and sensing equipments are continuously developed.

Technology advances allow, today, to design experimental, testing and sensing equipments with inherent

capabilities to probe systems over an extended range of scales: Free Electron Laser and X Ray

Synchrotron, are two significant examples of this trend. Advances in wireless and wired sensor network

and the Integration of Distributed Processing and Sensing put the bases to the design of a new Generation

of Multiscale Sensor Networks. Technological Advances put the bases to design and implement

Multiscale Multidisciplinary Cyberinfrastructures which connect a wide spectrum of experimental,

testing and sensing systems working over a full spectrum of space and time scales.


30

Fig. 7 (from the Presentation: “Dynamic Behavior of Materials”, G. Ravichandran, Graduate Aerospace

Laboratories (GALCIT) California Institute of Technology (Caltech), TST Meeting, May 20, 2011)

This figure illustrates the complex set of experimental resources needed to validate a Multiscale

Computational Model and Framework

The research has been carried out in the context of the Hypervelocity Impact (HVI) Program at Caltech

funded and managed by National Nuclear Security Agency (NNSA). Prof. Michael Ortiz is the Program

Director

There is a specific parallelism between developments in the Computational World and developments in the

Experimental/ Testing/Sensing one. In the Computational World we see a growing number of methods and

techniques able to model and simulate phenomena at an increasingly level of detail over an extended range

of space and time scales.

The same trend is characterizing experimentation, testing and sensing. As in the Computational context the

challenge, now, is to devise “integrated strategies” to fully exploit these new potentialities so, in the

Experimental, Testing and Sensing World, we have the same challenge: devising integrated strategies.

The next logical step is a full integration of the two Worlds as envisaged in this White Book to shape

Methodologically Integrated R&D and Engineering Strategies.


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For “Multiscale Experimental/Testing/Sensing Techniques” we mean:

�� Single Experimental/Testing/Sensing Equipments able to probe “Systems” over a range of space and

time scales.

�� Integration of multiple experimental/testing/sensing equipments. In this case data get from a set of

experimental/testing equipments is integrated to give a comprehensive picture of phenomena/processes.

Multiscale Maps can represent an interesting tool to accomplish this task. Integration can be

implemented over Cyberinfrastructures. .

Fig. 8 (from the “Engineering Microstructural Complexity in Ferroelectric Devices Project

(Multidisciplinary Research University Initiative US Army Research Office)) describes the combination of

experimental and testing equipments needed to characterize the Multiscale Dynamics of a Ferroelectric

Device.

Multiscale Experimentation, Testing and Sensing and related Modeling & Simulation and Data, Information

and Knowledge Analysis and Management Systems have the following objectives in the context of R&D and

Engineering, Materials Characterization, Development, Operational Testing and Monitoring of any kind of

Natural, Human and Technological System:

� Track relationships and interdependencies between phenomena, processes, properties, performance

over a wide range of scales and disciplinary domains (Multiscale Maps)

� Integrate and Fuse Data, Information and Knowledge (Maps) from a spectrum of Disciplinary Areas

(Horizontal Integration)

� Integrate and Fuse Data, Information and Knowledge from multiple Resolution Levels and Scales

(Vertical Integration)

� Integrate and Fuse Data, Information and Knowledge from a wide spectrum of techniques and

equipments: in several cases we can use, for instance, experimental data to complement and analyze

testing and sensing data (Methodological Integration)


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European Synchrotron Research Facility (ESRF) and Multiscale Experimental Research

The X-Ray Imaging (XRI) beamlines address a large variety of topics with a high scientific and societal

impact. Among these topics we can mention the biomedical research (imaging, radiotherapy, drug action,

metallic particles in biological materials), the multiscale investigation of the most important fossils, cultural

heritage studies, in-situ observation of the growth/failure of materials, porous or granular materials and

storage materials for nuclear waste. The multiscale approach is considered essential for the scientific success

of SR-based imaging: the projected XRI BLs will provide spatial resolutions over four orders of magnitude

(10-8

– 10-4

m), and a wide range of photon energies (2 to 150 keV, as well as infrared).

Synchrotron Radiation from European Synchrotron Radiation Facility (ESRF) has been already applied

several times in a multiscale mode. Scientists from Max Planck Institute (Germany) and the ESRF

discovered the way deformation at the nanoscale takes place in bones by studying it with synchrotron X-

rays.. A bone is made up of two different elements: half of it is a stretchable fibrous protein called collagen

and the other half is a brittle mineral phase called apatite.. In order to understand how this construction is

achieved and functions, scientists from the Max Planck Institute of Colloids and Interfaces in Potsdam

(Germany) ESRF used X-rays from ESRF to see for the first time the simultaneous re-arrangement of

organic and inorganic components at a micro and nanoscale level under tensile stress.

Scientists carried out experiments on ID2 beam line at the ESRF. They tracked the molecular and

supramolecular rearrangements in bone while they applied stress using the techniques of X-ray scattering

and diffraction in real time. The high brilliance of the X-ray source enabled the tracking of bone deformation

in real time. Researchers looked at two length scales: on one side they observed the 100 nanometers sized

fibres, and on the other, the crystallites embedded inside the fibre, which are not bigger than 2 to 4

nanometers. This Multiscale approach is relevant for the whole Biomaterials field.

Integration of Experimental Facilities working with different techniques and covering a spectrum of

phenomena and space and time scales.

ESRF was also involved, jointly with the Laboratoire de Physique et Mechanique des Materiaux CNRS, the

Institut Laue-Langevin and the Institute of Physics of the ASCR, v.v.i. Laboratory of Metals – Praha – Czech

Republic, in the Multiscale analysis by neutron and synchrotron X-ray diffraction of the mechanically-

induced martensitic transformation of a CuAlBe shape memory alloy. The objectives were to determine

stresses and orientations evolutions from macro to micro scale during a stress-induced martensitic

transformation

Multiscale Synchrotron Techniques For Environmental Sciences: nucleation and growth New material synthesis largely depends on novel synthetic routes which are often constrained by increased

environmental concerns. A way to overcome this issue is to perform chemical reactions in miniature volumes

(e.g. microfluidic devices). Scattering techniques offer interesting possibilities in green chemistry,

particularly to monitor reactions at the nanoscale, thereby allowing the conditions to be optimized. In

environmental sciences, control of aerosol growth resulting from the combustion of fossil fuels is a major

issue. Recently, synchrotron scattering experiments have been used to elucidate the mechanism of soot

formation and their multiscale structure in flames and at the exhaust of a diesel engine. These systems can be

classified as open non-equilibrium systems with complex self organisation and intermittent behaviour.

Multiscale Synchrotron Techniques for Pyrolysis Another example is the in situ spray pyrolysis used for the synthesis of nanomaterials. In all these cases, a

combination of SAXS and USAXS provide insight to intermittent structural development from a few

nanometres (nucleation) to micron scale (aggregates and agglomerates). Similar studies can be extended to

dusty plasmas to directly probe charged nanoparticles and their long-range correlations. However, the

systems mentioned above are in a highly non-equilibrium state and to probe the transient processes at large

length scales a long pinhole type USAXS instrument is required. Access to a very wide range of structural

levels from molecular to microscopic scales has potential applications in smart materials vigorously pursued

for addressing the grand challenges in energy research .


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Strategic Research Agenda for Multiscale Experimentation, Testing and Sensing:

� Development of new Experimental and Testing Systems which are, inherently, Multiscale

Multiresolution, i.e. able to operate over different scales and or at different resolution levels inside a

scale.

� New Techniques to integrate, fuse and analyze Data from a spectrum of Single and Multi Scale

Experimental, Testing and Sensing Systems

� The development of new Strategies and related Frameworks to “rationally” integrate a wide range

of Single and Multi Scale Experimental, Testing and Sensing Systems for specific R&D and

Engineering Tasks. The extension of the “Model” concept to the Experimental, Testing and Sensing

world and the “Information Space” concept can contribute to achieve this objective

� Development of new “two – way” Strategies and related Frameworks to integrate a wide range of

Single and Multi Scale Experimental and Testing Equipments for specific Technology Development

and Engineering Tasks

� The design of new Integrated Schemes and related Frameworks to realize a comprehensive two-way

integration between Multiscale Computational and Multiscale Experimental, Testing and Sensing

Methodologies, Strategies and Environments to define really “Methodologically Integrated

Multiscale R&D and Engineering” Strategies and Frameworks.

Multiscale Computational Modeling and Multiscale Experimentation Integration Materials Research Society Bulletin

An important recognition of the key strategic relevance of the development of multiscale experimental

techniques and their integration with multiscale computational modeling comes from the article “Three-

Dimensional Materials Science: An Intersection of Three-Dimensional Reconstructions and Simulations

(Katsuyo Thornton and Henning Friis Poulsen, Guest Editors), published in the Materials Research

Society (MRS) Bulletin June 2008.

“..For example, by combining a nondestructive experimental technique such as 3D x-ray imaging on a

coarse scale, FIB-based 3D reconstruction on a finer scale, and 3D atom probe microscopy at an even

finer scale, one has an opportunity to capture materials phenomena over six orders of magnitude in

length scale. This will bring materials researchers closer to the ultimate dream of a direct validation of

multiscale models, both component by component and ultimately as an integrated simulation tool. In

conjunction with the advances on the modeling side, such comprehensive experimental information is

seen as very promising for establishing a new generation of models in materials science based on first

principles…..”


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3.6 Methodologically Integrated Multiscale Science – Engineering Strategies

3.6.1 The Information – Driven Concept

The relevance of “Information”, as a key element to shape R&D and Engineering Strategies, is winning an

increasing attention. Several studies have been performed, for instance, by Jitesh H. Panchal, Janet K. Allen,

David L. McDowell and colleagues at Georgia Institute of Technology. Alessandro Formica highlighted the

role of Information to drive modeling and simulation strategies in the White Paper “HPC and the Progress of

Technology : Hopes, Hype and Reality” published in US by RCI Ltd on February, 1995. In this document

he discussed the concept of “Engineering Information Analysis”. The issue was also dealt with in the context

of the Accelerated Insertion of Materials (AIM) Program (1999) managed by US DARPA. The following

text is drawn from DARPA Proposer Information Pamphlet BAA 00-22 clearly describes the theme and

related challenges:

“The need for an “Information-Driven” strategy . “….There are many interrelated technical challenges

and issues that will need to be addressed in order to successfully develop new approaches for accelerated

insertion. These include, but are not limited to, the following:

The construction of the designer’s knowledge base: What information does the designer need and to what

fidelity? How does one coordinate models, simulations, and experiments to maximize information content?

What strategies does one use for design and use of models, computations, and experiments to yield useful

information? How can redundancies in the data be used to assess fidelity ? The development/use of models

and simulation: What models are required to be used and/or developed in the context of the designer

knowledge base? How can models of different time and length scales be linked to each other and to

experiments? How can the errors associated with model assumptions and calculations be quantified? How

can models be used synergistically with experimental data ?

The use of experiments: Are there new, more efficient experimental approaches that can be used to

accelerate the taking of data? How can experiments be used synergistically with models? How can legacy

data and other existing data base sources be used ?

The mathematical representation of materials: How can one develop a standardized mathematical language

to: describe fundamental materials phenomena and properties; formulate reliable, robust models and

computational strategies; bridge interfaces; and identify gaps between models, theory and experimental

materials science and engineering? How can this representation be used to develop hierarchical principles

for averaging the results of models or experiments while still capturing extremes ?……”

In the context of the “Integrated Multiscale Science – Engineering Framework”, “Information” is a key

element which, to a large extent, drives and shapes R&D and Engineering Strategies.

The term “Information – Driven” means that R&D and Engineering strategies have to address what can be

called “The Information Challenge for R&D and Engineering” :

– What Information at what level of accuracy and reliability (uncertainty quantification) is needed to

accomplish a task

– What Relationships and Interdependencies between analysis and design variables should be tracked over

a full range (as needed) of space and time scales to accomplish a task

– What kind of information sources (analytical, computational, experimental, testing and sensing

models/techniques) are needed and how they can be combined to get the previously identified

information


35

Accordingly, the following key issues define the “The Information - Driven Analysis Scheme for R&D

and Engineering”

�� What Information at what level of accuracy and reliability is thought to be needed to accomplish a R&D

and Engineering task . “Thought to be needed” means that the process is iterative, we start with some

hypotheses and just Multiscale Science Engineering Strategies and related Data, information and

Knowledge Analysis schemes and tools give us the possibility to improve evaluation about the

Information needed to execute the task. Example : What Information (what physical and chemical

phenomena and processes related to materials, structures and chemically reacting flows and their

interactions) at what level of accuracy and uncertainty should we know to analyze the dynamics of a

Thermal Protection Systems of an Hypersonic Vehicle for a specific operational environment?

� What physical length scales and related physical and biochemical phenomena rule the dynamics of the

“system” under analysis, what is the relative weight, what are relationships and interdependencies

between phenomena and processes inside a scale and between different scales (to be described thanks to

Multiscale Maps).

� What Information at what level of accuracy and reliability can existing analytical, computational models

experimental, testing and sensing techniques and related coupling scheme give us (to be described using

the “Multiscale Science – Engineering Information Space”).

�� How good analytical and computational models, experimental, testing and sensing techniques and related

coupling schemes should be to get the previously identified information thought to be needed to

accomplish a task. How “good” means evaluating how much “physical realism” should be incorporated

into the models and what scales hierarchy has to be taken into account. Not in all the cases, of course,

we really need complex multiscale methodologies going down to the Schrödinger equations: simple

single scale models can be accurate and reliable enough.

Note: This kind of Information is critical to evaluate what new analytical and computational models and

what new experimental, testing and sensing procedures/techniques should be developed and integrated to

deal with a specific analysis task. It is important to identify not only what we know, but, in particular,

what we do not know, what we should know, how we should know it (what combination of scientific

and engineering methodologies and technologies should be needed). In this context, the “lack of

Knowledge” becomes and important element to guide Strategies.

�� What is the right combination and the right sequence of application (Integration Strategy: Designing the

Analysis and Design Processes) of single and multi scale analytical and computational, models/methods

and single and multi scale experimental, testing and sensing procedures/techniques to get Information

thought to be needed to accomplish a specific analysis/design task. A critical step for the “Rational

Design” of R&D and Engineering Processes is a proper selection, integration, and sequencing of

computational and analytical models and experimental/testing/sensing techniques and procedures with

varying degrees of complexity and resolution to deal with a specific “Task”. To do that we have to define

the “Multiscale Science-Engineering Information Space” associated to computational models and

experimental, testing and sensing procedure/technique and related coupling schemes. Application

Strategies defined in the Paragraph 3.6.2 and Integration Strategy Maps guide the Integration Strategy.

� Furthermore, another very critical issue is that we need a rational approach to link advances in the

different methods at the different scales with the new information we need to meet challenges in the

different tasks in the different stages of the R&D and engineering process. How do we effectively and

timely evaluate the impact of scientific methodological and information advances at an atomic,

molecular, and grain (for materials) level on new technological and engineering solutions if we do not

have conceptual and methodological (multiscale) frameworks to link methods and information at the

different scales: from atomic to continuum? The “Multiscale Science-Engineering Information Space”

can represent a first step to deal with these critical issues. If we like to shape new cooperative schemes

between industry, from one side, and academia and research, from the other side, we have to define

specific methodologies to evaluate the “industrial and technological value” of new scientific

methodological advances.


36

Max-Planck Institut für Eisenforschung Düsseldorf, Germany

Methodologically Integrated Multiscale Strategies

Fig. 9 Multiscale Computational and Experimental/Characterization Integrated Strategy applied by the

Max-Planck Institut für Eisenforschung in the Materials field.

It is clear the central role played by the integration of the Computational Multiscale World with the World of

Multiscale Characterization/Experimentation


37

3.6.2 Methodological Integration Schemes, Maps and Strategies The “Multiscale Science – Engineering Information Space” and the “Information – Driven” concept

(described in the paragraph 3.6.1) allow us to define new “Applicability Conditions” and “Predictability Criteria” for Computational Models to shape “Application Strategies” for Modeling and Simulation and

their integration with related “Experimentation, Testing and Sensing Application Strategies”

The final goal is the development of “Methodologically Integrated Multiscale Science - Engineering Strategies” which represent a very important element of the New Framework here described.

The definition of “Applicability Conditions” and related “Predictability Criteria” for computational models

implies the ability of establishing specific rules and schemes that allow researchers, designers, and planners

to evaluate, with a high degree of reliability, where, when, and to what extent, it is possible to safely

(quantifying in probabilistic terms risks and uncertainties) substitute modeling & simulation for

experimentation and testing and where, when, how and to what extent we need to integrate modeling &

simulation with experimentation, testing and sensing for specific tasks.

Applicability Conditions. Two basic conditions which rule the development and the implementation of

predictive models and their integration with experimental and testing techniques can be defined:

� researchers and engineers are able to formulate hypotheses about what Information is needed to

accomplish a R&D and Engineering task:

� what physical length scales and phenomena/processes and relationships/interdependencies are

important for specific R&D and Engineering tasks and purposes.

� at what level of accuracy and reliability phenomena/processes should be modeled and simulated

� researchers and engineers are able to define the range of validity of the models and, inside this range,

the degree of accuracy and reliability of the same models.

Applicability Conditions can also be applied to the Experimental, Testing and Sensing Fields. A detailed

comparison of the “Information” which can be get by the respective analyses with the “Information” we

think it is needed to accomplish a specific Task is an important element to shape “Methodologically

Integrated” Strategies

Predictability Criteria When we discuss about predictive capabilities of models in the R&D and Engineering context, we should

carefully take into account two critical issues: predictive consequence and confidence.

�� Predictive Consequence: what is the impact of errors/uncertainties for specific tasks? Errors/uncertainties

can be relatively large but their impact can be low. On the contrary, errors and uncertainties can be

limited but their impact can be very large.

�� Predictive Confidence: how to assess models errors and uncertainties in order to evaluate the level of

confidence? [Multiscale Science – Engineering Information Space and Verification & Validation

methods]

Application Conditions and Predictability Criteria are important “Guiding Principles” to define Multiscale

Modeling and Simulation Application Strategies and to shape “Methodologically Integrated Multiscale

Science – Engineering Strategies”.

The final objective is to define “Integration Strategy Maps” which describe:

� What analytical theories, single and multi scale computational models and what single and multi scale

experiments, tests and sensing systems and models have to be selected to deal with a specific task

� What is the order of execution and the overall Integration Scheme as shaped by the “Applicability

Conditions” and “Predictability Criteria” (Multilevel Network of Computational, Experimental, Testing

and Sensing Models/Methods and Techniques)

� What is the flow of input and output data and information/knowledge (Maps) among the full spectrum of

models and experiments/tests/sensing models and techniques.


38

Several hypotheses can be taken into account and interactively changed during application.

For each specific task, “Integration Strategy Maps” describe:

� The full set of Analytical Theories/Formulations, Computational, Experimental, Testing and Sensing

Models/Methods/Techniques applied to deal with specific task

� The order of execution and Integration Scheme: Multilevel Network of Multiscale Analytical,

Computational, Experimental, Testing and Sensing Models and Techniques.

� Multiscale Science – Engineering Information Spaces

� Input and Output Data and the related Flow between “Models”

� Multiscale Maps

We consider three “Integration Areas”

1) Inside the Experimentation, Testing and Sensing World: Integration of a full set of single and

multiscale experiments, tests and sensing measurements (performed with a number of equipments)

applied to accomplish a specific R&D and Engineering tasks

2) Inside the Computational World: Integration of a full set of single and multiscale multiphysics

computational models applied to accomplish a specific R&D and Engineering task

3) Between the Experimental, Testing, Sensing and the Computational Word: Integration of a full set of

single and multiscale experimental , testing and sensing techniques with a full spectrum of

computational models experiments and tests to accomplish R&D and Engineering tasks.

“Integration Strategy Maps” are built applying the “Information – Driven Analysis Strategy”, the

“Multiscale Science – Engineering Information Space” concept and method, the “Applicability Conditions”

and the “Predictability Criteria”.

“Integration Strategy Maps” defined during the R&D and Engineering Process are recorded, organized and

managed by a specific Integration Strategy Maps Data Base


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Integration Strategy Maps can be linked to and integrated with Physics Maps, “Requirements, Property,

Structure, Performance” and “Processing, Property, Structure, Performance” Maps

Fig. 10 Integration Strategy Map (from US Department of Energy (DoE) Fusion Materials program: Aspects

of Multiscale Modeling Primary Damage and Rate Theory Models Presentation – R. E. Stoller – Metals and

Ceramics Division Oak Ridge National Laboratory)

This Figure describes a possible combination of the proposed Multiscale Map of Physics and Multiscale

“ Integration Strategy Map” of Computational Models and Experiments & Tests

The next page Box synthetically describes the key role and significance of a comprehensive integration

between Computation and Experimentation to address challenging R&D and Engineering issues. Integration

should be planned and applied not only inside the Computation Verification & Validation (V&V) process,

but, also, in the core of the R&D and Engineering activities. Multiscale Computation (Modeling &

Simulation) and Multiscale Experimentation Integration can deeply change structure and strategies of the

R&D and Engineering Process.


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Frontiers in Energy Research (US Department of Energy): January 2012

Experiment and Theory: The Perfect Marriage

Scientists combine measurements and calculations to explain energy’s most puzzling problems

Gareth S. Parkinson Experimentalists and theorists working together represent the best approaches to securing our energy

future. In establishing 46 Energy Frontier Research Centers, the U.S. Department of Energy moved to

expedite the rate of scientific discovery by encouraging teamwork in a community more accustomed to

relying on individual brilliance. This approach to science funding, which takes its lead from the

common proverb individuals play the game, but teams beat the odds, brings together scientists with

diverse backgrounds and skill sets to solve the most pertinent problems in energy research. The spirit of

collaboration is exemplified by the way in which theorists and experimentalists, often seen as different

breeds of scientist, are combining their very different approaches to attack the most highly complex

problems. Effective synergy between experimentalists and theoreticians is important in modern science

because the type of information provided by each method is fundamentally different, but

complementary. The experimentalists obtain precise measurements of the properties of a system, but

often struggle to explain complex phenomena without making assumptions about the system.

Theoreticians, on the other hand, use computer simulations to model the processes at work, but need

guidance from experiment to know if they are on the right track. While an integrated approach often

results in a deeper understanding of the system under study, bringing experimentalists and theorists

around the same table also greatly speeds up the discovery process by slashing the time between theory-

experiment iterations. In the absence of direct collaboration, experimental and theoretical groups

working on the same problem learn of each other’s progress through the scientific literature or at annual

meetings. Working together from the outset allows areas of agreement and disagreement to be quickly

identified, and a consensus can be more quickly reached.

There are many examples of the integrated experiment-theory approach bearing fruit in the EFRCs. For

instance, recent research into lithium batteries, conducted in the Nanostructures for Electrical Energy

Storage Center, combines experimental methods and calculations based on quantum mechanics to show

that coating lithium electrodes with an insulating aluminum oxide layer could significantly extend

lithium battery lifetimes. The results demonstrate that a fundamentally different electron flow process

occurs in the presence of the insulating alumina film, leading to significantly slower rates of electrolyte

decomposition inside the battery.

A second example, from the Center for Molecular Electro catalysis, combines experiment and theory to

better understand a promising method for converting hydrogen molecules into electricity, as would be

done in a fuel cell. Experimentalists measured the rate at which a novel nickel-containing catalyst

molecule is able to move protons that arise from the splitting of hydrogen. The theoreticians in the

project simulated how protons move within the molecule, and determined that the main bottleneck in

the process occurs when the catalyst molecule changes its shape.

Innovation central In addition to revolutionizing energy technologies, the EFRCs are tasked with the creation of a new

generation of tools for penetrating, understanding and manipulating matter on the atomic and

molecular scales. In the Center for Atomic-Level Catalyst Design, researchers are focused on

developing the experimental and theoretical tools required to understand how catalysts convert one

molecule into another, such as when carbon monoxide is converted to carbon dioxide in a modern car

exhaust. The key issue holding back significant progress in this area is that the current methods for

understanding catalytic reactions at the atomic scale can only handle extremely simplified model

systems that do not necessarily bear any resemblance to the real catalysts doing the job. Center director

James Spivey explains: “Typically only reactions on ideal catalyst surfaces can be simulated. Such

surfaces do not represent real catalysts. We are attacking this problem.”

In this issue of the EFRC newsletter, several excellent examples of experiment-theory collaborations

are highlighted. As will become clear on reading the articles, this integrated approach has yielded

success across the entire breadth of topics covered by the EFRCs, and represents one of the best

approaches currently available to achieve the rapid advancements required to secure our energy future.


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The Predictivity and Validation Issues

The National Nuclear Security Program (NNSA), in the context of the Advanced Simulation and

Computing (ASC) Initiative, established the Predictive Science Academic Alliance Program

(PSAAP) focusing on the emerging field of predictive science—the application of verified and

validated computational simulations to predict the behavior of complex systems where routine

experiments are not feasible. The goal of these emerging disciplines is to enable scientists to make

precise statements about the degree of confidence they have in their simulation-based predictions.

Five PSAAP Centers have been created:

California Institute of Technology: Center for the Predictive Modeling and Simulation of High-

Energy Density Dynamic Response of Materials; Purdue University: Center for Prediction of

Reliability, Integrity and Survivability of Microsystems (PRISM); Stanford University: Center for

Predictive Simulations of Multi-Physics Flow Phenomena with Application to Integrated Hypersonic

Systems; University of Michigan: Center for Radiative Shock Hydrodynamics (CRASH); University

of Texas at Austin: Center for Predictive Engineering and Computational Sciences (PECOS)

The following text, drawn from the Presentation “Can Complex Material Behavior be Predicted?

Given by Prof. Michael Ortiz, Caltech PSAAP Center Director, at the DoE NNSA Stockpile

Stewardship Graduate Fellowship Program Meeting Washington DC, July 14, 2009, illustrates

objectives and approach underlying the general PSAAP Strategy and Methodology concerning

Validation and Predictivity challenges:

PSAAP Caltech High-Energy-Density Dynamic Response of Materials (Hypervelocity Impact

Application Field) Center objective:

− rigorous certification of complex systems operating under extreme conditions. l

Overarching Center objectives:

− Develop a multidisciplinary Predictive Science methodology focusing on high-energy-density

dynamic response of materials

− Demonstrate Predictive Science by means of a concerted and highly integrated experimental,

computational, and analytical effort that focuses on an overarching ASC-class problem:

Hypervelocity normal and oblique impact at velocities up to 10km/s

Overarching approach:

− A rigorous and novel Quantification of Margin of Uncertainty (QMU) methodology will drive

and closely coordinate the experimental, computational, modeling, software development,

verification and validation efforts within a Yearly Assessment format

Two issues deserve to be highlighted:

− The central role of the “Uncertainty Quantification” and “Quantification of Margin of

Uncertainty” issues in the context of the Computational Models Validation effort to shape R&D

and Engineering activities. This vision can be, to some extent, related to the previously illustrated

concepts: Multiscale Science – Engineering Information Space, Range of Validity and

Information Driven R&D and Engineering Strategy

− The key role of Computational, Analytical and Experimental Efforts Integration. New

(multiscale) experimental techniques and analytical (theoretical) developments are fundamental to

develop and apply new and more powerful (predictive) computational models and strategies. The

Vision is in line with our “Methodologically Integrated R&D and Engineering” approach


42

3.6.3 Multiscale Knowledge – Based Virtual Prototyping In the new conceptual and methodological context, classical “Virtual Prototyping” concept should be

complemented by a new concept which can be referred to as “Multiscale Science – Engineering Knowledge

– Based Virtual Prototyping”. Classical concepts can be regarded as a particular case of this more general

concept and strategy. Classical “Virtual Prototyping” approach is applied when “Applicability Conditions”

can be met and “Predictability Criteria” can be reliably evaluated also thanks to the “Knowledge” gained

with Multiscale Science - Engineering Maps and “Multiscale Science – Engineering” Information Space”

concept and method. For this reason we can add the term “Knowledge – Based”.

The previously described theoretical and methodological apparatus allows us to formulate rational

hypotheses about what experiments, tests and sensing measures are really needed to get the information we

think to be necessary to characterize the behaviour of a “System” at a predefined level of accuracy and

reliability, and, accordingly, assess the “risk” associated to replace experimentation, testing and sensing

with computation for a specific task. This a fundamental condition to replace in a “rational” way testing with

computation. In this new context, we can design and plan highly complex Multiscale Multilevel Testing

Strategies guided by “Multiscale Computational Models” and related Multiscale Maps and Multiscale

Information Spaces. Data, Information and Knowledge “flow” in a seamless and fully integrated way

between the Testing and Computational Worlds and vice versa. Not only, with the new theoretical and

methodological apparatus, we can easily integrate inside Testing Strategies even “Information Capabilities”

of several Experimental and Sensing Facilities. Multiscale Maps from Experimentation can also contribute to

understand possible Testing Anomalies and Problems. This kind of integrated analyses can, in turn, suggest

new Experimental activities.

An interesting application field for methods and tools described in this document is what can be called “Multiscale Science – Engineering System Testing”. A problem is to transfer, in a structured way,

information and knowledge get from testing at a scale to the higher scales of a System along the whole chain:

from testing carried out to characterize behaviour of materials (basic constituents of any System) [coupon

testing] to testing of devices, components, sub-systems and the global system. We should correlate

Multiscale Maps for all the scales and resolution levels. Correlation works in both the directions: bottom –

up and top – down:

− Bottom – Up: Multiscale Maps built from materials testing can be a useful basis to develop upon testing

strategies for devices. Multiscale Maps built from devices testing are applied to improve testing

strategies for components and so on along the scale.

− Top – Down: results from testing at a scale can be better analyzed taking advantage of Multiscale Maps

get from testing at lower scales


43

3.7 Designing the R&D and Engineering Process

3.7.1 R&D and Engineering Analysis and Design Process Architecture

A first step to “Design the R&D and Engineering Process” is to identify the “elements” which characterize

its structure and track relationships and interdependencies:

�� R&D and Engineering Process Architecture Any R&D and Engineering Project/Process can be decomposed into Multilevel Networks of Phases

(Time Intervals inside which specific activities are accomplished) Each Phase can be subdivided into a

Multilevel Network of R&D and Engineering Strategy Modules.

�� R&D and Engineering Strategy Modules Architecture

The Architecture (Strategy) to carry out a generic “R&D and Engineering Project” is described by a

multilevel network of R&D and Engineering Strategy Modules

Any R&D and Engineering Strategy Module can be, recursively, decomposed into Multilevel Networks

of simpler R&D and Engineering Analysis and Design Modules. At the lowest level, R&D and

Engineering Analysis and Design Modules can be decomposed into Multilevel Networks of Tasks. At

the lowest Task Level, Integration Strategy Maps are defined. A specific R&D and Engineering

Strategy Management System manages and organizes all of that

�� System Architecture/Structure (detailed over the full set of levels/scales, as needed)

− Multilevel Multiscale Network of Architectural/Structural Elements: Systems (or System of

Systems) – Sub Systems – Components – Devices – Basic Structures (Materials, Fluids,

Plasmas)

�� R&D and Engineering variables (projected over the full set of System Architectural/Structural

Elements at all the levels and scales)

− Requirements

− Performance

− Properties

− Functions

− Requirement - Performance – Structure – Property Relationships

− Performance - Property -Structure – Processing Relationships

− Architectural/Structural Element – Function Relationships

− Analysis and Design Variables

Relationships and interdependencies are described by the full set of Multiscale Maps.

�� R&D and Engineering Project Performing Entities

All the activities needed to achieve objectives inside each R&D and Engineering Phase are accomplished

by a Multilevel Network of “Physical Entities” and “Human Entities:

� Human and Management Entities:

− Organizations/Institutions

− Teams

� Physical Entities

− Theoretical, Computational, Experimental, Testing, Sensing Centers and Facilities,

Manufacturing Facilities, Cyberinfrastructural Frameworks

The Multilevel Network of Entities defines what can be called a “Multiscale Science – Engineering

Collaboratory Framework”


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3.7.2 R&D and Engineering Analysis and Design Strategy Management System

Te following elements characterize the R&D and Engineering Analysis and Design Strategy Management

System:

� Strategy Modules

� Analysis Modules

� Design Modules

� Hypothesis and Decision Modules which keep track of the spectrum of Architectural/Structural,

Analysis and Design Hypotheses and Final Decisions adopted during the R&D and Engineering Process.

� Tasks

� Integration Strategy Maps described in the Paragraph 3.6.2 and applied inside the lowest Hierarchical

Analysis Tasks Level

The R&D and Engineering Analysis and Design Strategy Management System allows to track, organize and

manage of the previously identified elements and their relationships and interdependencies.

Strategy Modules

Strategy Modules are constituted by Multilevel Networks of R&D and Engineering Analysis and Design

Modules and Tasks.

− R&D and Engineering Analysis and Design Modules (each R&D and Engineering Analysis and

Design Module (linked to a specific Phase) can be decomposed into a multilevel network of more

elementary Analysis and Design Modules. At the lowest network level, R&D and Engineering Analyses

and Design Modules can be decomposed into a multilevel network of Tasks

− Tasks (each Task can be decomposed into a multilevel Network of more elementary Tasks)

− Hypothesis and Decision Modules

R&D and Engineering Design Modules

Any R&D and Engineering Design Module can be broken down in a multilevel network of lower level R&D

and Engineering Design Modules

R&D and Engineering Design Modules describe:

�� The full set and hierarchy of R&D and Engineering Design Modules and Tasks linked to them for each

Phase

�� The full set of Architectural/Structural and Functional Maps linked to them

�� R&D and Engineering Objectives, Analysis and Design Variables, and Analysis – Design Variable

relationships

�� The network of Analysis Modules linked to any R&D and Engineering Design Module

R&D and Engineering Design Modules are recorded and managed in a specific “R&D and Engineering

Design Modules Data Base”

R&D and Engineering Analysis Modules

R&D and Engineering Analysis Modules in each Phase are organized in a Multilevel Network of more

elementary Analysis Modules. Any Analysis Modules of high level can embody “Analysis Modules” of a

lower level. “Analysis Modules” can embody a multilevel network of “Analysis Tasks”.

At the lowest level, “Analysis Tasks” define what Analysis Strategies (multilevel network of analytical

formulations, computational models and experimental/testing/sensing Models/Techniques) are applied to

achieve Analysis Objectives. “Integration Strategy Maps”, described in the Paragraph 3.6.2, describe these

strategies. Analysis Modules and Tasks are linked to Design Modules and Tasks

It is possible to develop Analysis Modules tailored for specific issues and tasks such as durability or

producibility of composites or metallic or hybrid materials or structures, or the dynamical analysis of a sub-

system, a component or a device.


45

Analysis Modules track and organize Data, Information and Knowledge inside and between the different

tasks in the different phases of the Technology Development and Engineering process and, for each task,

correlate data and information with information sources (experiments, tests, computations, analytical

formulations)

R&D and Engineering Analysis Modules and Tasks are recorded and managed in a specific “R&D and

Engineering Analysis Modules and Tasks Data Base”

Computational Code and Model Libraries

Libraries are software environments which allow to catalogue and manage a whole set of :

�� Computational codes which implement a spectrum of methods [Molecular Dynamics, Coarse Grained

MD, Monte Carlo, Density Functional Theory, Phase Fields, Dislocation Dynamics, Continuum Finite

Elements,….]

�� Computational Models and related links to Tasks where they are applied

�� Single and Multiscale Multiphysics coupling methods and schemes.

For each Computational Model (linked to a specific Task), Library describes:

− The specific Computational method applied

− Characteristic of the modeled “System”

− Model Dimension

− Boundary and Initial Conditions

− Maps

− Links to the tasks where it has been employed

− The specific coupling scheme(s) among a cluster of models in case of Multiscale Techniques

Experimental, Testing and Sensing Technique/Equipment and Model Libraries

Libraries are software environments which allow to catalogue and describe:

�� Experimental, Testing and Sensing Techniques (STM, AFM, TEM, SEM,…) and related specific

application methods which implement them

For each Experimental, Testing and Sensing Technique/Equipment, Libraries detail:

� Characteristics, applicability conditions and what kind of information can be get from them for specific

application domains and conditions

� Single and Multiscale coupling methods and schemes.

For each Experimental/Testing/Sensing Model linked to a specific Task, Library describes:

− The specific Experimental/Testing/Sensing technique applied

− Characteristic/State of the probed “System”

− Experimental/Testing/Sensing operational mode

− Maps

− Links to the tasks where it has been employed

− The specific coupling scheme(s) among a cluster of models in case of Multiscale Techniques


46

3.7.3 “Integrated Multiscale Science – Engineering Analysis Strategies”

“Integrated Multiscale Science – Engineering Analysis Strategies” are implemented inside the “Analysis

Modules” described in the Paragraph 3.7.2 and they are a key element to support Design Strategies embodied

in the “Design Modules”.

Integrated Multiscale Science – Engineering Strategies are absolutely general Analysis Strategies, they can

be applied to any task, in any context for any purpose in any phase of the R&D and

Engineering/Manufacturing Process.

Analysis Strategies can be applied to analyze dynamics of:

�� any “System” and the interaction among all of its components (any level and scale)

�� interactions between the “System” and other “Systems” (System of Systems)

�� interactions between a “System” and the “Environment” where it operates for nominal and off-nominal

(accident included) situations.

We would like to state that, in this document, multiscale stands for “multiscale multiphysics” and that

multiscale is a general term and it embodies, as a special case, classical single scale models and analyses

which can, in turn, take advantage of “Reduced Order Models” built upon multiscale analysis schemes. The

term “Integrated” is used because R&D and Engineering Strategies are based upon a full integration of

computational and experimental, testing and sensing models, techniques and strategies.

It should be highlighted that for “Multiscale Computational Models” we mean not only “Classical

Computational Models”, but, also, “Multi and Single Scale Agent Based Models.

A key goal of Multiscale Information - Driven Strategies is to develop a hierarchy of Multiscale Multiresolution Multiphysics (Computational, Experimental, Testing and Sensing) Models. Each model should be characterized by the level of complexity thought to be needed to get the Information to accomplish specific tasks: no more no less. Citing Einstein: A model must be as simple as possible, but not simpler

“Integrated Multiscale Science – Engineering Analysis Strategies” synthesize and take advantage of all the

concepts and methods described in the previous paragraphs:

� Data, Information and Knowledge Structures and Analysis Schemes (Multiscale Knowledge Domains

and Multiscale Maps)

� The “Multiscale Science – Engineering Information Space” and “Information – Driven” concepts

� The “Modeling and Simulation” as “Knowledge Integrators and Multipliers”“ and “Unifying Paradigm

for “Scientific and Engineering Methodologies” and “Knowledge Domains” concept and the related

“Methodologically Integrated Multiscale Science – Engineering Strategies” .

Analysis Strategies take advantage of the full spectrum of Multiscale Methods (hierarchical, concurrent,

adaptive,..). The full spectrum of Multiscale schemes can be applied in an integrated way to achieve specific

objectives. The “Computational Materials Design Facility (CMDF), developed at Caltech and MIT,

introduced the term “Multi Paradigm” for this scheme. Top – Down Analyses are integrated, as needed, with

Bottom – Up analyses.

Four application schemes for Multiscale Analysis Strategies can be devised:

� Multiscale Scientific Analyses finalized to “Understand” Physical and Bio - Chemical Phenomena

and Processes and their Relationships A spectrum of multiscale computational, experimental, testing and sensing methods linked using a full

range of coupling schemes (multi paradigm approach) are applied to gain a unified understanding of

scientific and engineering phenomena/processes and elucidate relationships and interdependencies between

phenomena, processes and system architectural/structural elements inside a scale and across different scales.

Multiscale Maps give a coherent view of the network of relationships and interdependencies among

“System Dynamics” variables turning data from different sources into Knowledge


47

� Multiscale Science – Engineering Analyses. Computational Scientific (Quantum – Atomistic – Micro) Models are directly coupled (On – Line Coupling)

with Meso and Macro (Engineering) Computational Models in order to have an Integrated Multiscale Nano

To Macro Analysis Framework . This kind of scheme has been already developed. A issue to be taken into

account in this case is that this Application Line and Approach is, normally, expensive from a computational

point of view.

� Reduced – Order Modeling, Sub – Grid Models and Constitutive Equations Development Reduced-Order Models, Sub – Grid Models and Constitutive Equations are built, taking advantage of

Knowledge get by Multiscale Scientific Analyses described in the previous item. Constitutive Equations and

Sub – Grid Models are inserted inside classical Engineering codes. This approach can also be referred to as

“Multiscale Off – Line Approach”. Multiscale Scientific Analyses are an important element to build

“Hierarchies of Multilevel Multiscale Computational, Experimental, Testing and Sensing

Models/Techniques. In this perspective, Reduced – Order Models and “Hierarchies” can be regarded as a

synthesis and integration of science and engineering. The fundamental objective is to improve reliability,

range of validity, and effectiveness of models applied in the different phases of the Research, Technology

Development and Engineering Process and for Systems and Life – Cycle Engineering issues. It is important

to highlight that Knowledge get from Multiscale Scientific Analyses is captured and organized not only by

reduced order modeling, but also by Multiscale Maps. This kind of strategy allows to directly insert

“Multiscale Knowledge” inside classical Engineering/Manufacturing/Processing models and codes. These

flexible integration strategies allow Engineering Teams to use, in a systematic way, scientific knowledge

without having to directly manage the complex modeling and simulation process of basic physical and

chemical phenomena. Such task would require highly specialized knowledge which is, normally, outside the

reach of designers.

� Integrated Multiscale R&D and Engineering Strategies The previously indicated approaches, with particular reference to the On and Off –Line Schemes, can be

integrated in an interactive way inside a more general strategy. Some tasks can be executed with the

Multiscale Scientific Analysis approach. Some other tasks can be carried out by applying Reduced – Order

Science Based Modeling. It is important to emphasize that the application of Multiscale Strategies demands

some not secondary modifications in the projects organization, structuring and management. In particular, a

fundamental element is the definition of “Integrated Multiscale Multidisciplinary Teams”.

Integration develops over three lines:

�� Disciplines: physics, chemistry, electronics, biology,

�� Scales: specialists who operate in various in Scientific and Engineering areas

�� Methodology: specialists who operate in the three methodological contexts: Theory, Computational,

Experimentation & Testing

A fundamental issue for all the Multiscale Strategies is to adopt an “Adaptive and Multi Step Selection of Details and Resolution”

Integrated Multiscale R&D and Engineering Analysis Strategies develop over the following phases and

steps:

1) Definition of Multiscale Analysis Process Architecture

– Definition of the “System” Architecture

– Identification of the reference scales of the “System” to be analyzed (the selection is linked to

specific analysis objectives and tasks)

– Definition of Functions to be performed by the “System” for the full hierarchy of its “Elements”

– Definition of the [Requirements - Performance – Properties – Architecture/Structure Relationships]

– Definition of the overall Architecture of the Analysis Process: Multilevel Network of R&D and

Engineering Phases, Analysis Modules and Tasks and related relationships and interdependencies.

More hypotheses can be worked out. Hypotheses are tuned and/or modified following Analysis

results.


48

This first step is accomplished setting up some hypotheses built upon the knowledge available at the

starting time.

2) Analysis Strategies Definition Multiscale Maps, at the starting time, are built using existing information and knowledge and processing

available data (historical data bases). Then, Analyses deliver new data that allow to iteratively and

interactively modify first Map hypotheses.

For each Task of the previously identified Tasks Network:

– Identification of physical and bio - chemical structures over the selected scales which are thought

to be relevant for the “Objectives” of the Analysis [Architectural/Structural Maps] – first working

hypothesis

– Identification of bio - chemical and physical phenomena/processes and their interdependencies

underlying and characterizing the dynamics of a system and thought to be relevant to meet with

the “Objectives” of the Analysis for the full range of the selected scales [Physics Maps] - first

working hypothesis

– Definition of the “Requirements - Performance – Properties – Architecture/Structure” relationships

inside a scale and over the range of the selected scales [Requirements - Performance – Properties –

Architecture/Structure Map] - first working hypothesis

– Identification of “ Processing – Architecture/Structures” relationships (if it is needed in the analysis

process) inside a scale and over the range of the selected scales [Processing – Architecture/Structure

Map] - first working hypothesis

– Definition of what kind of Information is thought to be needed to achieve Analysis Objectives for

the Analysis Tasks. [“Thought to be needed” means that the process is iterative and interactive, we

start with some hypotheses and just Multiscale Science Engineering Analyses give us the possibility

of improving our evaluation]

– assessment of what Information at what level of accuracy and reliability can, existing analytical

theories, computational models, experimental testing and sensing techniques and related coupling

schemes, deliver (evaluation performed using the “Multiscale Science – Engineering Information

Space” and historical available Information).

– Definition of how good” (Multiscale Science – Engineering Information Analysis) analytical and

computational models, experimental, testing and sensing models and techniques and related

coupling schemes should be to get the previously identified information . That means evaluating if,

where, when and to what extent we have to take into account a hierarchy of scales and develop and

apply new multiscale models and new reduced order models instead of existing single and

multiscale models. Not in all the cases, of course, we should go down until Schrödinger equations

from the continuum. Don’t Model Bulldozers with quarks (Goldenfeld and Kadanoff, 1999)

– Identification of what new analytical and computational models and what new experimental,

testing and sensing techniques should be developed

[Note : A New Approach to Analysis. The “Multiscale Science – Engineering Information Space”

and the “Information – Driven Analysis” concepts and methods help us to identify not only what we

know, but in particular, what we do not know, what we should know, how we should know it (what

combination of new scientific and engineering methodologies and technologies should be needed)]

– Development (if it is needed) of new analytical theories, experimental, testing and sensing

techniques and computational (reduced order models included) models, definition of the related

coupling schemes inside a scale and between different scales.

– Identification of experimental, testing and sensing techniques needed to validate computational

models.

– Definition of Verification and Validation strategies for all the models and the coupling schemes


49

− Definition of “Methodologically Integrated Strategies”: what is the right combination and the right

sequence of application of single (including reduced order models and analytical formulations) and

multiscale computational models and single and multi scale experimental, testing and sensing

models/techniques to get Information thought to be needed to accomplish specific analysis tasks

[Integration Strategy Map]

Note: from a general point of view, it can be advisable to adopt a “Multiscale Multiphysics Multilevel

Multistep Adaptive” Modeling and Experimental, Testing and Sensing Strategy. Multistep Adaptive means

that we start with some simple models, experiments and tests to get a first acquaintance of the dynamics of

the system. The analysis of data, information and knowledge (Multiscale Maps) get from a first run makes it

possible to adaptively increase complexity levels) of models, experiments and tests only as needed for

specific tasks.

3) Analysis Execution

For each Task:

�� A first run of Computations, Experimentations, Tests and Sensor measures is performed

�� in and out data and information flow linked to the selected computational models and experimental,

testing and sensing models/techniques is tracked

�� Data from Computations, Experimentations , Testing and Sensing is analyzed, correlated and organized

using Multiscale Maps

�� All Maps, and Strategy Modules are updated, as needed, following analysis results

�� New Multiscale Methodologically Integrated Strategies (if it is deemed to be necessary) are formulated

�� Evaluations of the impact of the results over Analysis and Design Hypotheses and the Architecture of

Technology Development and Engineering Analysis and Design Modules are carried out

The Process is iterative. We can have more iteration levels:

�� Inside each Analysis Task of a specific Analysis Module

�� Between Tasks inside a specific Analysis Module: results of a Task analysis can change analysis

conditions for the other Tasks

�� Inside the Multilevel Hierarchy of Modules (Architecture of the Multilevel Network of Modules can be

changed)

Specific Modules and Tasks can be devoted to develop Reduced – Order Models, Sub – Grid Models and

Constitutive Equations.


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3.8 Integrated Multiscale Science – Engineering Framework Applications

3.8.1 Multiscale Systems Engineering

This paragraph is devoted to a synthetic analysis of the application of the Integrated Multiscale Science –

Engineering Framework to a field of growing relevance for a wide range of Engineering fields such as

Chemical/Processing Engineering, Civil Engineering, Aerospace Engineering.

Integration among a wide range of technologies and a full spectrum of sub-systems, components, devices,

and materials is, today, a fundamental challenge in the analysis, development and design of high-tech

systems. In the future, the widening use of a full hierarchy of nano, micro, meso technologies, devices and

components will make this issue even more critical. System Engineering will, more and more become a

Hierarchical “Multiscale” Systems Engineering. Nanotechnology, Nano To Macro Integration and

Multiscale (Computational, Experimental, Testing and Sensing) will be the catalysts for this process

Because, today, it begins to be possible to analyze the dynamics of systems at multiple scales, the next step

is to use “Integrated Multiscale Science - Engineering Strategies” to design hierarchical systems at multiple

scales. That means being able to design systems in such a way as to make multiple “structures/elements” at

different scales cooperating to produce an increasingly wider spectrum of properties and functions and

higher performance levels.

Multiscale System Design This Figure, from MIT, clearly illustrates the “Multiscale System Design” Concept. This Concept and

Design Strategy allows to better meet with a widening spectrum of tighter and tighter Requirements

(Environmental Compliance, Efficiency, Safety, Security, Operational Flexibility,…) by increasing

Functionalities, Design Parameters and related Solutions, Architectures and Process variables.


51

In the “Multiscale System Engineering” field, Analyses Challenges are linked to the following issues:

�� Analysis of Requirements over the full spectrum of scales (Multiscale Requirements Traceability)

�� Analysis of the “Requirements – Performance – Architecture/Structure – Property” relationships and

interdependencies over the full spectrum of scales

�� Analyzing Multiscale Interactions among different elements at the same scale

�� Analyzing Multiscale Interactions among “System Architectural Elements” working at different scales:

from Macro To Nano.

To address these challenges is fundamental to develop Hierarchies of Multiscale Multilevel “Variable

Fidelity” Computational Models and Experimental/Testing/Sensing Models and Techniques and efficient

and reliable coupling schemes between codes/models based upon a range of physico mathematical

representations and principles.

New technological solutions (micro and nanotechnologies) and tighter and tighter requirements pose specific

challenges which change in a qualitative, not quantitative, way the approach to analysis, simulation and

design in the Hierarchical Multiscale Nano To Macro Systems Engineering scenario:

�� From a general point of view, the overall performance and operating behavior of systems will be more

and more determined by how multiscale and multi-physics phenomena interact in multi-component and

multimedia environments. The general trend towards miniaturization (micro and nano technologies)

makes it necessary for CAD/CAE/CAM systems to take into account, inside a fully integrated context,

an ever wider range of geometric and physical scales Separated single scale models have only a partial

validity if we like to predict in a correct way the overall behavior of a complex system under real

operating conditions, in particular when side effects, extreme and off-nominal conditions occur. Many

side effects stem from small-scale geometric details and media interactions that are not comprehensively

and adequately modeled by constitutive (engineering scale) equations. All that makes simulations using

classical engineering codes and separated sets of engineering and scientific codes, very difficult and of

limited reliability.

�� Off-nominal physical behaviour, such as fatigue, fracture, damage and corrosion a s well as off-nominal

dynamics of components, sub-systems and systems (or system of systems) in extreme operational

conditions (accidents included) occur at multiple space and time scales. The problem is classically

addressed by resorting to expensive physical prototypes for sub-systems and components, and setting up

lengthy, and not in all the cases really exhaustive and conclusive, testing activities. In most of the cases

Unified Multiscale and Multilevel variable fidelity hierarchies of models exist that describe behavior

across this huge scale range are applied. Instead, we have separate and non-communicating models at

the different scales and fidelity levels.

The development, using “Integrated Multiscale Science – Engineering Frameworks” of “Integrated

Hierarchies of Multiscale Multilevel Computational Models and Experimental, Testing and Sensing models/techniques” can be considered as a key target.

A multiscale system design approach calls for, but, at the same time, opens the way to new strategies for

complex systems monitoring and control. A combination of a new generation of multiscale sensors and

distributed computing systems, can lead to innovative monitoring and control schemes. New multiscale

sensors will be able to deliver not only "averaged" data and information, as in the past, about space and time

variations of key physical and technological variables (pressure, temperature, chemical composition,....) but

the detailed map of local values and rates at different levels of resolution and time and space scales. This

kind of information can be used to develop and validate off-line physical models no longer based on an

empirical and semi-empirical (averaged) knowledge but on a first principles understanding of the physical

reality. Highly detailed real-time models to control technological systems will grow out of this new level of

understanding and will run on an array of distributed computing systems.


52

Complex Materials, in particular Nanostructured Materials, can be regarded as “Multiscale Hierarchical

Systems”. Biological and Bio – Inspired Materials are significant examples of this consideration.

Fig. 11 (from Raabe, Sachs, Romano, Acta Mater. (53, 2005, 4281) and Sachs, Fabritius, Raabe,

Journal of Structural Biology (161, 2008, 120) )

Fig. 12 (from Raabe, Sachs, Romano, Acta Mater. (53, 2005, 4281) and Sachs, Fabritius, Raabe,

Journal of Structural Biology (161, 2008, 120) )

Both the figures are related to the research carried out at the Max Planck Institut for Eisenforschung,

Germany and they show the multiscale hierarchical structure of chitin compounds.

Biological and Bio-Inspired Materials and Structures as “Multiscale Hierarchical

Systems”


53

Research about the Multiscale Hierarchical Structure of Biological Materials and Advances in Multiscale

Computational Modeling and Multiscale Characterization techniques led to the birth of a new Field referred

to as “Materiomics”.

“Materiomics is an emerging field of science that provides a basis for multiscale material system

characterization, inspired in part by natural, for example, protein-based materials.

Materiomics is defined as the study of the material properties of natural and synthetic materials by

examining fundamental links between processes, structures and properties at multiple scales, from nano to

macro, by using systematic experimental, theoretical or computational methods. This term has been coined

in analogy to genomics, the study of an organism's entire genome. Similarly, materiomics refers to the study

of processes, structures and properties of materials from a fundamental, systematic perspective by

incorporating all relevant scales, from nano to macro, in the synthesis and function of materials and

structures. The integrated view of these interactions at all scales is referred to as a material's materiome.

The broader field of materiomics encompasses the study of a broad range of materials, which includes

biological materials and tissues, metals, ceramics and polymers. Among others, materiomics finds

applications in elucidating the biological role of materials in biology, for instance in the progression and

diagnosis or the treatment of diseases. It also includes the transfer of biological material principles in

biomimetic and bioinspired applications, and the study of interfaces between living and non-living systems”

“By incorporating concepts from structural engineering, materials science and biology our lab's research

has identified the core principles that link the fundamental atomistic-scale chemical structures to functional

scales by understanding how biological materials achieve superior mechanical properties through the

formation of hierarchical structures, via a merger of the concepts of structure and material. Our work has

demonstrated that the chemical composition of biology's construction materials plays a minor role in

achieving functional properties. Rather, the way components are connected at distinct scales defines what

material properties can be achieved, how they can be altered to meet functional requirements, and how they

fail in disease states….”.

[From the Laboratory for Atomistic and Molecular Mechanics, PI: Markus J. Buehler, Ph.D. Esther and

Harold E. Edgerton Associate Professor of Civil and Environmental Engineering, Center for Materials

Science and Engineering, Center for Computational Engineering, Massachusetts Institute of Technology

website]

Fig. 13 Multiscale Modeling and Multiscale Characterization Techniques (Markus J. Buehler)


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3.8.2 Multiscale Processing and Manufacturing The advent of Nano Engineering, Nano Manufacturing and 3D (Nano)Manufacturing technologies put the

bases for the development of Hierarchical Multiscale Manufacturing and Processing. The whole “Integrated

Multiscale Science – Engineering Framework” theoretical and methodological apparatus can be directly

applied in these fields.

It is interesting to highlight that the Processing area has been and it is one of the most sensitive and reactive

field to the multiscale challenge. For this reason we dedicate a special attention to this area illustrating some

of the applications of the multiscale strategy.

The “Strategic View of Multiscale” is, to a large extent, born in the Chemical Engineering field in the mid of

nineties (Multiscale as “Unifying Paradigm for Science and Engineering).. Even the concept of “Science –

Based Industry” was born the Chemical field.

6th World Congress of Chemical Engineering - Melbourne 2001 The Triplet "Processus-Product-Process" Engineering: The Future of

Chemical Engineering

Prof. Jean-Claude Charpentier – Key note Speaker Dept. Chem. Eng/CNRS Ecole Supérieure de Chimie Physique Electronique de Lyon

“Industry used to be king, now the customer is. In year 2001, to adapt the chemical engineering

approach to the needs of process industries and meet market demands, the offer is technological.

Being a key to survival in globalization of trade and competition, including needs and challenges, the

evolution of chemical engineering is received and its ability to cope with the problems encountered

by chemical and related process industries is appraised.

It appears that the necessary progress is coming via a multidisciplinary and time and length multi-scale approach that will allow us to satisfy both the market requirements for specific end-use properties and the environmental and social constraints of the industrial processes.

This will be obtained with breakthroughs in molecular modeling, scientific instrumentation and

powerful computational tools. This concerns four main objectives for engineers and researchers:

� to increase productivity and selectivity through intelligent operations, intensification and

multiscale control of processes;

� to design novel equipment based on scientific principles and new methods of production;

� to extend chemical engineering methodology to product-oriented engineering, i.e.

manufacturing end-use properties: the triplet "processus-product-process" engineering

� to implement multi-scale application of computational chemical engineering modeling and

simulation to real-life situations: from the molecular scale to the overall complex product scale

in order to analyze and optimize the supply chains. “


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Fig. 14 The Chemical Supply Chain

(from Prof Charpentier presentation given in year 2007 at the ESCAPE 17 Conference)

This figure synthetically describes the new approach envisaged by Prof. Charpentier in year 2001. This

strategy is now referred to as “Process Intensification”.

The limits of the classical approach to Chemical Engineering Design were well described, some years ago,

by the words of Dr. Irving G. Snyder Jr., director of process technology development at Dow Chemical. He

highlighted : "In the chemical engineering field we often know that A plus B makes C but, in many cases, we

do not know the transient intermediates that A and B go through in producing C; the reaction mechanisms of

all the by-product reactions; which of all the steps in the reaction mechanism are kinetically controlled,

which mass-transfer controlled, and which heat transfer controlled; if the reaction is homogeneous, what

takes place at every point in the reactor at every point in time; and if the reaction is heterogeneous, the

diffusion characteristics of raw materials to the catalyst surface or into the catalyst, as well as the reaction,

reaction mechanism, and by-product reactions within the catalyst, the diffusion characteristics of products

away from the catalyst, and the nature of heat transfer around the catalyst particle".

Intelligent operations and multiscale control of processes. The implementation of multiscale modeling jointly with the use of computer-based control schemes and

array of advanced multiscale sensors would allow to control events not only at the classical macro scale but

at the microscale level (detailed local temperature and composition control) and also at the

nanoscale/molecular levels. New multiscale models for predictive control and science-based technologies

will make possible a very accurate control of reaction conditions with respect to mixing, quenching, and

temperature profile. This science-based scheme significantly differs from the classical one that imposes

boundary conditions and lets a system operate under spontaneous reaction and transfer processes. The

multiscale control of processes solution would lead to an increased productivity and selectivity and opens the

way to a "smart chemical engineering" to meet, at the same time, tight economic and environmental

requirements.


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In systems in which process variables at different scales are highly coupled, controlling any single variable

will generally require the development of an integrated multiscale vision and the controller to use multiple

sensor inputs, and multiple actuator outputs. Innovative (multiscale) sensing and control strategies can be

derived from descriptions based on fundamental principles and simulations linking different phenomena at

a wide range of scales. There are clearly opportunities for new mathematical algorithmic research as well as

new sensor design and development. The full conceptual and methodological apparatus described in the

“Integrated Multiscale Science-Engineering Framework” can be applied to these issues and problems.

Specific Application fields:

� Analysis, modeling, design and characterization of processing and manufacturing units and design of

innovative processes and processing/manufacturing units and systems

� Analysis, modeling and characterization of structural and physico-chemical transformations which

characterize the full materials - manufacturing – assembly chain

� Analysis, modeling and characterization of interactions between processing and manufacturing units and

the environment for all the nominal and off-nominal operational conditions, including accidents. (this

issue is becoming more and more critical and conditioning)

Design of New Equipments Based on Scientific Knowledge and New Modes of Production The development of an integrated conceptual framework, which links basic scientific understanding to

engineering and technological issues, makes it possible to conceive innovative equipments based on first

principles. The design of new operating modes in chemical engineering and manufacturing can be linked to

a science-based approach. Innovative engineering applications of reversed flow, cyclic processes, unsteady

operations, extreme conditions, high-pressure technologies, and supercritical media, are largely dependent

on:

− The ability to couple scientific and engineering knowledge and models.

− The development of integrated science – engineering model based predictive control schemes

− The development and application of new micro and nano sensors and Integrated Sensor Processing

devices

− The development of new devices (MEMS and NEMS, micro-reactors, micro separators, and micro

analyzers) is, making possible accurate control of reaction conditions with respect to mixing, quenching,

and temperature profile.

Intelligent Processing of Materials (IPM) Intelligent Processing of Materials (IPM) is widely considered as the reference methodology for advanced

materials and manufacturing processes. IPM is closely linked to a multiscale understanding of materials

physics and biochemistry and its space-time transformations.

The strategy which underlies IPM is to model micro and meso (and more and more also nano, taking into

account the development of the Nanomanufacturing field) structural evolution during processing, sense

micro/nano structural changes in real time, and use a model-based control strategy to achieve the desired

micro, meso and nano structure in the finished product. A key objective is to develop advanced physical-

based models for relating the fundamental laws that govern the processes controlling the evolution of

microstructures and nanostructures and the resulting physico-chemical properties. Despite continuous

advances in control technologies in the materials and manufacturing processes, manufactured parts and

components contains several defects at a wide range of scales. These defects have a major impact on the

engineering properties of materials and structures due to increasingly tight design constraints and

requirements.

Multiscale Performance – Properties – Structure – Processing Maps are a key objective of the Integrated

Multiscale Science – Engineering Framework.


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Multiscale Sustainable Manufacturing Analysis

The following text is drawn from the Article: Multiscale Characterization of Automotive Surface Coating

Formation for Sustainable Manufacturing - Chinese Journal of Chemical Engineering, 16(3) 416-423 (2008)

- Jie XIAO, Jia LI, Cristina Piluso and Yinlun HUANG, Department of Chemical Engineering and Materials

Science, Wayne State University, Detroit, MI 48202, USA)

Note: The text illustrate the potentialities of Integrated Multiscale Analyses to asses performance of

Manufacturing Processes, their efficiency and environmental impact. Methodology employed holds a general

value and it can be applied to a wide range of Manufacturing issues and fields.

INTRODUCTION In automotive coating manufacturing, paints of different types are applied on a vehicle’s surface to generate

a basecoat and a clear coat (together called topcoat, 10-60 micrometers). Each layer is commonly developed

in two consecutive operational steps: paint spray and film curing. In operation, vehicle bodies are moved

steadily one by one by a conveyor through a spray booth and then an oven. In paint spray, the emulsified

paint particles (1-10 � micrometers in size) fly at a high speed (initial speed at 70-80 ms-1) from the spray

devices to the target vehicle panels . The particles missing the panels will be swept away by the air to the

drain through the grids on the floor. In production, material transfer efficiency, wet film topology, and

volatile organic compound (VOC) emissions are the main economic, quality, and environmental concerns.

The oven for film curing is usually divided into a number of zones to allow for the use of different heating

mechanisms (i.e., radiation from the oven walls and hot air convection) under different conditions. In the

end, the cured coating with the desired properties is developed on the vehicle surface; hopefully, it is defect

free (at any length scales). Besides, energy consumption and VOC emission are expected to be at a

minimum.

Paint spray and coating curing experience various technical challenges. First, high productivity requires

vehicle bodies to continuously move on a conveyor while being sprayed and baked. This makes it extremely

difficult to ensure coating thickness uniformity and a defect free finish. Second, almost all product

performance variables and a number of key process variables are not measurable during manufacturing. This

has forced the current quality control practice to rely only on post-process sampling, which causes various

quality problems, leads to inefficient energy and material utilization, and generates excessive amounts of

waste and pollutants. Third, which is probably the most challenging issue, is the knowledge disconnection

between macroscale bulk production, finer-scale material properties, and product behavior. In production,

operational settings (at the macroscopic level) are always adjusted based on experience; hence the true

manufacturing optimality cannot be realized in reality. In this article, a definition of the sustainable

manufacturing of paint-based automotive coatings is presented first. An integrated multiscale modeling and

simulation methodology is then introduced for characterizing paint spray and film curing. The resulting

multiscale system models can describe simultaneously product and process dynamic behaviors at the

different spatial and temporal scales, thus enabling comprehensive and deep analyses on the multiple-stage

coating manufacturing. Model-based simulation has revealed various usually inconceivable opportunities for

sustainable manufacturing. A paint material application for automotive surface coating manufacturing is

sustainable if the resulting products meet quality specifications (on appearance, durability, and styling), and

the manufacturing consumes the minimum amount of materials and energy, and has the minimum adverse

environmental impact (i.e., minimum VOC emission and paint-containing wastes).

Integrated multiscale paint spray model The following scheme shows a general model structure where two sets of models exist: (1) the macroscale

spray-booth air flow and electric field models, and (2) the mesoscale particle flying and collision models. In

addition, three following multiscale integration approaches are needed (see the rectangular box with one

corner cut):

(1) an approach for developing a (macro) coating topology from a static spray pattern, (2) an approach for

coupling continuous (macro) booth condition models with discrete (meso) particle models, and (3) an

approach for creating (meso) surface roughness from a static spray pattern.


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Integrated multiscale coating curing model The detailed curing model structure is shown in next scheme, where there is a macro-scale oven model set, a

mesoscale film physical behavior model set, and a micro-scale chemical behavior model (all in rectangular

box). In addition, three model integration approaches are needed to generate the multiple information

necessary for studying coating behavior and manufacturing performance (see those rectangular boxes with

one corner cut).


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Performance assessment models The economic, environmental, and social impacts of the coating manufacturing are assessed based on

production cost, productivity, energy and material use efficiencies, waste reduction performance, and

product quality (i.e., end users’ satisfaction). Figs. 2 and 3 include the performance assessment models (see

the rectangular boxes with rounded edges), each of which takes the information from the macro, meso,

and/or microscale models. For example, for oven curing, an energy model needs the information of energy

used for maintaining the required oven wall temperature, convection air temperature and velocity. An

environmental quality model can be used to calculate VOC emission due to solvent evaporation. The product

quality models are for quantifying macro-to-microscale coating physical/chemical properties, appearance,

and durability.

MULTISCALE INFORMATION UTILIZATION The models listed above can describe various types of phenomena occurring in product manufacturing at

different time/length scales. In this section, two major integration tasks are described to explain the

approaches for handling the information generated from the paint spray and coating curing model sets.

Coating topology generation A real paint application process is very complicated, where each vehicle body moves at a certain line speed

and the spray devices move in different ways (e.g., the spray bells above the vehicle roof move side by side

at a certain frequency and amplitude). For a given spray device, the number of paint particles from each bell

is on the order of 109 per second. Thus, for example, to paint a 1.3-1.8 m2 roof panel with three bells within

27 s, the total number of particles sprayed can be on the order of 1011. In model-based simulation, particle

collision and spray bell oscillation must be considered in order to have a better approximation of real spray

operations.

Due to these complexities, it is impractical to simulate particles on the order of 1011 directly for a multiple-

bell operation when studying the generation of a coating layer of about 70 m on a panel. On the other hand, it

should be reasonable to use the paint-spray (mesoscopic) information obtained from a static spray pattern

(i.e., the simulation based on the fixed locations of bells and receiving panels) repeatedly in a constructive

way to generate a coating layer (macroscopic) on the panel. This allows the use of a superposition approach

to add the static spray patterns in the pathway that a bell movement follows.

Macro (curing environment)–micro (network structure formation) coupling It is reasonable to assume that the heat generated/ consumed by cross-linking reactions can be neglected in

the initial study, which means that a coupling between the curing environment and the network structure

formation is only one-way. Note that film temperature dynamics in curing can be derived by a CFD solver.

By compromising solution resolution and computational expense, the total vehicle surface area is divided

into 20 zones in this work. In each zone, the average temperature is passed for one MC simulation. It is

assumed that in each zone, those homogeneously mixed chemical species (i.e., the polymers and crosslinkers

in this case) are reacted at the same film temperature. Using periodic boundary conditions, the crosslinked

network structure formed in each zone can be predicted by using only a few thousand molecules. In this

manner, a total of 20 MC simulations can generate a network structure in the coating that covers the

complete vehicle surface.

INTEGRATED ANALYSIS OF PROCESS AND PRODUCT PERFORMANCE The comprehensive analyses on paint spray and coating curing have revealed various opportunities for

achieving sustainable coating manufacturing. Part of the results is briefly presented below.

Paint spray system analysis Four cases with different downdraft settings are studied. It is found that downdraft mainly affects both booth

air quality and energy consumption. Increasing downdraft will decrease VOC emissions in the spray booth

but consume more energy. Also, three cases are investigated to assess the effect of different initial

distributions of particle sizes on the performance of product and process. It reveals quantitatively that smaller

sized particles can produce a better coating topology, but result in lower material efficiency and worse

environmental quality. Thus, the initial particle size distribution should be properly controlled to achieve a

better tradeoff between product performance and process performance


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Coating curing system analysis Three paint materials having different initial number average molecular weight (MW) are investigated. The

study has revealed that under the same curing condition, a decrease of the initial resin MW can lead to a

decrease of the final effective crosslink density. It means that a lower MW resin requires a higher curing

temperature or a longer curing time. Consequently, a decrease of the resin MW will consume more energy in

curing, although the amount of emissions can be reduced. This information will be valuable for identifying

the most desirable material formulation with well acceptable material application conditions.

CONCLUSIONS Polymeric coating manufacturing on vehicle surfaces is one of the most sophisticated and expensive steps in

automotive assembly. Most known studies on coating quality through paint spray and coating curing have

focused on the product’s macroscopic behavior, and for those lab-based studies, the operation simulated has

been limited to the use of many ideal operational settings. Thus, many important issues in production, such

as energy consumption, material use efficiency, and work-zone environmental quality, which are key

indicators of manufacturing sustainability, can hardly be addressed. This article has illustrated that all the

major process and product issues in paint spray and coating curing can be simultaneously addressed properly

by means of a multiscale system modeling and analysis approach. The integrated process and product

performance analysis introduced in this article illustrates its potential in generating important product and

process information that is critical for achieving sustainable manufacturing in automotive surface coating

applications.


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Multiscale Manufacturing of Three-Dimensional Polymer-Based Nanocomposite Structures - Louis Laberge Lebel and Daniel Therriault - École Polytechnique of Montreal, Canada

A multiscale approach Due to the several orders of magnitude involved in the fabrication of nanocomposite devices, an efficient

manufacturing technique must address the challenges at the nano-, micro- and macroscales. Figure shows

this multiscale concept for the creation of a 3D scaffold structure using a single-walled carbon nanotube

(SWNT) and a polymer nanocomposite. At the nanoscale, the dispersion of SWNTs should respond to the

targeted usage of the nano-reinforcement. Individualization of the nanoparticles, so the particles are in

contact with the matrix only, might be desirable when nanoscale properties are to be present in the final

product. Conversely, slight contact between the nanoparticles is needed when the percolation phenomena

through the entire domain are needed. In both cases, the interaction with the host polymer must be

controlled. At the microscale, the production of nanocomposite structures must allow a control over the

orientation of high aspect-ratio nanoparticles such as SWNTs. The arrangement of the nanocomposite

microscale structures in 3D permits the localization and orientation of the nano-reinforcement in a

macroscale product.

Nanoscale The nanoscale poses important manufacturing challenges such as the dispersion of the nanoparticles and

the close interaction of the nanoparticles with the host polymer matrix. In a dispersed state, the distance

between particles in the matrix must be controlled to achieve the targeted properties. For mechanical

reinforcement, every nanoparticle should be separated from each other to maximize the interface between

the matrix and the nano-reinforcement, thus enhancing the available area for stress transfer to the nano-

reinforcement. Aggregated nanoparticles are often responsible for underperformances

Microscale The arrangement of nanocomposite material in structures typically at the micron to several hundred

micron range offers several advantages. First, the material needed is reduced when the cost is an issue. In

addition, the microstructures manufacturing techniques allow a better control on the nanoparticle

disposition due to their microscale confinement. For example, high aspect ratio nanoparticles, such as

CNTs, can align themselves along the flow direction with the help of the high shear achievable in small-

scale manufacturing. Several techniques are used to produce nanocomposite at the microscale.

Microinjection molding (MIM) is an emerging method to manufacture microscale devices from polymer

nanocomposites

Macroscale Different techniques exist to manufacture nanocomposite products at the macroscale. A polymer

nanocomposite can be simply molded in a shape before hardening either by cooling or by the effect of

curing reaction. This relatively simple technique could find applications in traditional fiber reinforced

composites by modifying the matrix-dominated properties.

Conclusion The fabrication of high-performance nanocomposite materials and complex 3D structures must overcome

the different challenges at the nano-, micro-, and macroscale. Dispersion and interaction with the polymer

matrix are of paramount importance at the nanoscale. The microscale manufacturing techniques should

provide a control over the orientation of high aspect-ratio nanoparticles such as carbon nanotubes. Finally,

proper assembly technique of microstructures should be developed to create functional devices at the

macroscale. The manufacturing techniques explained in this chapter, i.e. the infiltration of 3D

microfluidic networks and UV-assisted direct writing, represent new avenues for the creation of 3D

reinforced micro- and macrostructures that could find applications in organic electronics, polymer-based

MEMS, sensors, tissue engineering scaffolds and aerospace structures.


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Environmental Engineering

The “Environmental Issue” has emerged as one of the critical challenges facing the industrial world. Efforts

to reduce the pollution generated by industrial activities rely in several cases on the "end-of-the-pipe"

control strategy. In this context, green chemistry means essentially pollution cleanup and waste management

technologies. A more radical and innovative approach, which can be defined "clean by design", entails the

re-design of chemical and manufacturing processes and units to eliminate at the root the formation of

pollutants and toxic by-products.

The “Integrated Multiscale Science – Engineering Framework” and “Integrated Multiscale Science –

Engineering Cyber-infrastructures” can be applied to the Environmental fields in the following areas:

�� Study of the multiscale spectrum of physical and biochemical phenomena/processes and the complex

pattern of relationships and interdependencies between them which rule the dynamics of “Environmental

and Climatological Systems”. This kind of analysis are instrumental to design multiscale monitoring

infrastructures and related data analysis schemes

�� Study of the multiscale (space and time) spectrum of physical and bio-chemical processes and the

complex pattern of relationships and interdependencies between them which underlie dynamics of civil,

infrastructural and industrial units and plants for the whole Life – Cycle and for nominal and off –

nominal conditions, accidents included. This is an important precondition to:

−−−− implement the "clean by design" approach aimed at eliminating at the root (atomic and molecular

level) the formation of pollutants and toxic by-products.

−−−− develop innovative technology and system engineering solutions.

�� Study of interaction dynamics of different industrial, civil, infrastructural systems and the environment

(System of Systems Analysis and Design)

�� Study of multiscale two - way interactions between industrial systems and the environment (humans

included) for nominal and extreme conditions (Safety, Security, Design of “Inherently Resilient and

Green Systems).

�� Designing new multiscale environmental monitoring systems able to integrate and interpret data

(Multiscale Knowledge Maps) from a wide range of sensors working over a full spectrum of space and

time scales.


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Integrated Hierarchical Multiscale Nano To Macro Monitoring Systems: From Space to Atoms and Molecules

An important goal of the Strategic Multiscale is the design of Integrated Multiscale Monitoring (and

Control) Systems which take full advantage of new Nano and Micro Sensor and Integrated Sensor &

Processing (ISP) technologies. The following Box is related to a National Institute of Standards and

Technologies (NIST) Report which highlight this need and challenge as a very critical issue: Greenhouse

Gases Multiscale Multiresolution Monitoring is becoming an increasingly important issue for the modern

Society in order to reliably assess the impact (footprint) of all the Human and Industrial activities over

Ecosystems.

The term “Integrated” means three Integration Streams:

� Multiscale Multiresolution Space Integration

� Multiscale Multiresolution Time Integration

� “Sensor Systems - Experimental Facilities” Integration and “Sensor Systems – Experimental Facilities –

Computational Centers” Integration. The growing complexity of the Networks of Physical and

Biochemical Phenomena and Processes to be Monitored and Analyzed calls for Integrated Data Analysis

and Interpretation Strategies which can be carried out by Multiscale Multidisciplinary Computational

Models acting as “Knowledge Integrators and Multipliers”. Multiscale Multidisciplinary Models

integrate and fuse data from a wide range of sources (Sensors and Laboratory Facilities) to turn a

“Tsunami” of Data into useful Knowledge. It should be taken into account that more Data does not

necessarily means more Information and Knowledge.

Greenhouse gases: The measurement challenge

The continuing increase in the level of carbon dioxide and other "greenhouse gases" in the Earth's

atmosphere has been identified as a cause for serious concern because it may radically accelerate

changes in the Earth's climate. Developing an effective strategy for managing the planet's greenhouse

gases is complicated by the many and varied sources of such gases, some natural, some man-made, as

well as the mechanisms that capture and "sequester" the gases. A new report sponsored by the National

Institute of Standards and Technology (NIST) focuses on one of the key challenges: defining and

developing the technology needed to better quantify greenhouse gas emissions. The new report,

"Advancing Technologies and Strategies for Greenhouse Gas Emissions Quantification," is the result of

a special workshop in the NIST Foundations for Innovation series, convened in June 2010, to bring

together greenhouse gas experts from government, industry , academia and the scientific community to

address the technology and measurement science challenges in monitoring greenhouse gases. A wide

variety of techniques are used for measuring greenhouse gas emissions and, to a lesser extent, the

effectiveness of "sinks"—things like the ocean and forests that absorb greenhouse gases and sequester

the carbon.

The problem is that developing an effective global strategy for managing greenhouse gases requires

a breadth of measurement technologies and standards covering not only complex chemical and

physical phenomena, but also huge differences in scale. These range from point sources at electric

power plants to distributed sources, such as large agricultural and ranching concerns, to large -scale

sinks such as forests and seas.

Satellite - based systems, useful for atmospheric monitoring, must be reconciled with ground-based

measurements. Reliable, accepted international standards are necessary so governments can compare

data with confidence, requiring a lot of individual links to forge an open and verifiable chain of

measurement results accepted by all.


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4. Integrated Multiscale Science – Engineering Technology, Product and Process Development (IMSE-TPPD) Framework

4.1 Overview and Architecture

Running programs in Europe, US and Japan are putting the bases for the definition and implementation of a

new generation of “Integrated Product and Process Development” (IPPD) Frameworks which can be termed:

“Integrated Multiscale Science – Engineering Technology, Product and Process Development” (IMSE-

TPPD) Frameworks. We add the term “Technology” because, in the context of “Science – Engineering

Integration”, we would like to stress links between science, technology development, engineering and

manufacturing.

An interesting example of this strategic direction has been the “Integrated Computational Materials

Engineering” (ICME) Initiative promoted by US National Academy of Sciences and TMS. ICME is

supported by Universities, Research Centers and Industry. In Europe several EURATOM programs are

pursuing similar objectives. Outside, the Materials and Processing Area, the EU “Virtual Physiological

Human” is a noteworthy initiative which foresees the development of a large scale and scope “Integrated

Multiscale Framework” for the Biomedical field.

Materials and Nanostructured Devices and Systems are, more and more, inherently, “Multiscale Systems”,

i.e. systems organized following a hierarchical strategy where structures at the different scales interact in a

synergistic way to give an extended spectrum of functionalities and performance. The development of new

Multiscale Frameworks can give the birth of a new field: Multiscale Technology, Engineering and

Processing/Manufacturing..

This issue is fundamental to meet with an extended range of requirements (efficiency, safety and

environmental compliance). A very interesting example of this strategic approach has been the EU NMP

(Sixth Framework) Integrated Multiscale Process Units Locally Structured Elements (IMPULSE 2005 –

2009) Program. IMPULSE is Europe’s flagship R&D initiative for radical innovation in chemical

production technologies. Created in the framework of the SUSTECH program of CEFIC, IMPULSE was a

specifically targeted program aimed at creating a totally new strategy for the design and operation of

production systems for the chemical (and related) process industries.

IMPULSE aimed at developing a new approach to competitive and eco-efficient chemicals production: Structured Multiscale Design.

The multiscale design approach of IMPULSE provides intensification locally only in those parts of a

process and on the time and length scale where it is truly needed and can produce the greatest benefit.

IMPULSE aimed at the integration of innovative process equipment such as microreactors, compact heat

exchangers, thin-film devices and other micro and/or meso-structured components, to attain radical

performance enhancement for whole process systems in chemical and pharmaceutical production.


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“Multiscale Multidisciplinary Science – Engineering Cyber Knowledge Integrator and

Multiplier Extended Enterprise”.

The classical “Integrated Product and Process Development (IPPD)“ Framework is linked to the “Extended

Enterprise” concept. The new IMSE-TPPD Framework, proposed in this document, can be related to a new

industrial, economic and societal scenario which can be called “Multiscale Multidisciplinary Science –

Engineering Cyber Extended Enterprise”.

− Multiscale Multidisciplinary Science-Engineering means that “Integrated Multiscale Science-

Engineering Frameworks” shape R&D and Engineering, Planning, Operation and Management

activities and that Civil, Industrial, Environmental and Societal Infrastructures are organized

applying Integrated Multiscale Hierarchical Nano To Macro Engineering Architectures

− Cyber Knowledge Integrator and Multiplier means that the “Multiscale Science-Based Enterprise”

concept is implemented over “Multiscale Science – Engineering Knowledge Integrators and

Multipliers Cyberinfrastructural Environments (on line connection among Computational,

Experimental, Testing, Sensing and Theoretical Centers and Facilities)”

− Extended Enterprise means that the IMSE-TPPD Framework shape a new “University – Research

– Industry – Society Cooperative Environment”. This new kind of “Cooperation Contexts” enables

researchers, designers, public and private managers and politicians to synthesize a wide spectrum of

different resources, methods and operational schemes and define comprehensive strategies to meet

common objectives and goals. Multiscale Frameworks can be instrumental to improve correlation

between operational requirements, engineering requirements and technological and scientific

advances promoting accelerating in such a way technological and engineering innovation

Fig. 15 “Multiscale Multidisciplinary Science – Engineering Cyber Extended Enterprise”. (from the

Presentation : Opportunities and Barrier Issues in Carbon Nanocomposites - R. Byron Pipes, NAE, IVA

Goodyear Endowed Professor - University of Akron - National Science Foundation Composites Workshop -

June 9-10, 2004)


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The IMSE-TPPD Framework

� Enables a long term, systematic, organic, and effective involvement of the scientific community inside

real operational innovation technology programs with specific tasks, responsibilities, and profits

� Compels industry to redefine the whole technology development and engineering process taking full and

systematic advantage of science knowledge and progress

The “Multiscale Multidisciplinary Science – Engineering Cyber Knowledge Integrator and Multiplier Extended Enterprise” concept can offer scientists, researchers, public and private managers and politicians

a “unified context” to better understand the complex pattern of relationships and interdependencies among

the wide range of different aspects and issues which characterize the research and technological innovation

world and, accordingly, synthesize widely scattered efforts and forge more effective “unified strategies” to

deal with problems of increasing complexities.

The IMSE-TPPD Framework (this one described in this document is only a first proposal not the ultimate

solution) allows to take into account, inside a unified context, all the phenomena, from atomic and

molecular scales to the engineering and operational ones which rule materials design, processing and

application including life-cycle and sustainability issues. Integration of atomic/molecular scales with the

micro, meso and macro worlds is a fundamental challenge for a wide industrial application of the most

innovative nanotechnologies in the materials, engineering and processing areas. Multiscale Collaborative

Frameworks realize a real “two-way” science-engineering integration from an industrial point of view and

put the bases to create a new “Multiscale Quantum - based Engineering World”.

The IMSE-TPPD Framework deals with the following areas:

�� Multiscale Research and Technology Development Processes

�� Multiscale Engineering Analysis and Design: the design of “Inherently” Hierarchical Multiscale

Technological and Engineering Devices, Components and Systems is a key target

– Mission and Scenario Analysis

– (Multiscale) Requirements Definition

– Design (Conceptual, Preliminary and Detailed Design)

– System/subsystem model or prototype demonstration in a relevant environment

– System prototype demonstration in an operational environment

– Full System completed and “qualified” through test and demonstration

�� Multiscale System Engineering

�� Multiscale Monitoring and Control

�� Multiscale Life – Cycle Engineering

�� Multiscale Safety and Security Engineering

�� Multiscale Manufacturing and Processing

�� Multiscale Environmental R&D and Engineering (Green Engineering and Analysis of the Impact of

Product and Processes on the Environment for nominal and off nominal operating conditions, accidents

included)

� Multiscale d Testing

�� Multiscale Innovative Technology and Systems Development Planning


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Integrated Multiscale Science – Engineering Technology, Product and Process Development

(IMSE-TPPD) Framework Architecture

Key Architectural Elements are:

�� Computer Aided R&D and Engineering (CARDE) Framework which implements the Integrated

Multiscale Science – Engineering Framework (Paragraph 4.2)

� Innovative Technology and System Development Analysis and Planning Framework or “Virtual

Multi Space and Time Scale R&D and Engineering Machine” (Paragraph 4.3)

� Multiscale “Knowledge Integrator and Multiplier” Cyberinfrastructural [Computing, Information

and Communication (CIC)] Environment (Paragraph 4.4)


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4.2 Computer Aided R&D and Engineering (CARDE) Framework

The Computer Aided R&D and Engineering (CARDE) Framework(CAD and CAE Frameworks next

generation) implements the full spectrum of concepts and methodologies described in the Chapter 3

“Integrated Multiscale Science Engineering Framework”. Key Elements:

� Multiscale Science – Engineering Data, Information and Knowledge Analysis and Management

System

� Multiscale Multiphysics Computer Aided Design (CAD) System (based upon Architectural and

Functional Maps)

� R&D and Engineering Process Strategy Definition System (Designing the R&D and Engineering Process)

� Methodologically Integrated Multiscale Science – Engineering Analysis Environments

� Application Specific Modules (Life – Cycle, Safety & Security, Manufacturing and Processing,

Environmental Impact,…)

� Multiscale Visualization Modules

Software Environments run over Multiscale “Knowledge Integrator and Multiplier”

Cyberinfrastructural [Computing, Information and Communication (CIC)] Environments


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4.3 Innovative Technology and System Development Analysis and Planning Framework or “Virtual Multi Space and Time Scale R&D and Engineering Machine”

Fig. 16 NASA Technology Readiness Level (TRL) Scale. This scale describes the several phases of an

“Innovative Technology and Systems Development Process”.

The previous representation has a general value. It can be applied to any technological and engineering

sector

Any R&D and Engineering Process can be seen as a “Multi Space and Time Scale Process” and it can be

modeled and simulated by Multiscale Computational Frameworks. The fundamental goals of new

planning processes based upon the “Integrated Multiscale Science-Engineering Framework” are to :

� improve effectiveness and reliability of alternative “System Architectures” selection process by

identifying in a more comprehensive and reliable way problems linked to interactions between the

“System” and the Operational Environment and among a wide spectrum of subsystems, components and

devices which constitute the overall “System Architecture” which is, increasingly an inherent Multiscale

Multilevel Architecture..

� improve evaluation of the impact of advances in fundamental scientific knowledge over the development

of innovative technology solutions and systems architectures (Bottom – Up approach)

� improve assessment of how “System Requirements” propagate down the TRL chain following a “Top

Down” approach In this prospect, the definition of performance levels and operational requirements for

the system, enables, following Multiscale Multilevel analysis schemes, the identification of what are the

needed features and performance of sub-systems, components, and devices and their relationships.


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� improve assessment of the Science-Engineering Information/Knowledge needed to accomplish each step

(from TRL 1 to TRL 9) and to transition in a successful way from a step to the next one

� improve assessments of what information can be get by using existing analytical theories, computational

models and experimental & testing techniques, and what not

� improve identification of the needed development paths in analytical theories, computational models,

and experimental & testing techniques and what mix of resources (theories, modeling & simulation,

experimental & testing techniques) are needed to develop the envisaged sub-system or component.

� Improve Organization of Information/Knowledge inside each TRL Phase in such a way as to make it

directly and comprehensively usable and applicable in the next one along the scale.

Two development lines can be followed:

a) Bottom – Up Approach: the starting point is progress in innovative technologies and devices and

components (advances can be real or hypothesized)

b) Top – Down Approach: more ambitious operational and performance requirements to be met represent

the starting point

To accomplish the previously quoted tasks, we can use the full methodological and theoretical apparatus of

the “Integrated Multiscale Science-Engineering Framework” to build a “Virtual Multiscale Space-Time

Machine” or “Virtual R&D and Engineering Systems Development Analysis and Planning Framework”. The term “Virtual” means that in both the cases:

� A model of the planned system and the operational environment (and of the hierarchy of sub-systems,

components, devices and materials) is developed using available data, information and knowledge that

are being organized using the “Multiscale Science – Engineering Data, Information and Knowledge

Management System”. Information are being progressively updated and improved as we transition from

one phase to another one in an incremental way. As data and information, along the TRL chain, become

available from computation, experimentation, testing and sensing, they are inserted into the models by

taking full advantage of the KIM concept and method.

� A model of the R&D and Engineering Process (Designing the R&D and Engineering Process) is built.

The Model is progressively updated. At the starting date, it is possible to use simplified models

“Virtual Analyses” can proceed following two strategies:

� “top-down” (from a complex structure and operational environment to its constituents) : requirements

are set for a system at a certain scale (access scale) and the analysis is performed for the hypothetic

system (several hypothesis are taken into account and modeled) considering the scales which are under

the one for which requirements (and accordingly levels of performance) are being set

� “bottom-up” (from fundamentals to a complex structure and its operational environment): hypotheses

are formulated about the architecture of the system for scales over the initial scale taken into account.

Models are developed. The analysis proceeds by evaluating how and to what extent performance and

properties calculated and or measured at a certain scale (nano scale, for instance) influence dynamics and

architecture/structure at the scale immediately higher (micro scale, for instance) and so on. This kind of

approach is instrumental to build technology roadmaps and innovative technology development plans

The approaches can be interactively and iteratively combined. Several different scenarios can be taken into

account and evaluated (What if Strategy). Sensitivity Analyses can also be carried out.


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In this “Virtual Context”, “Multiscale Science-Engineering Information-driven Strategy” is critical to

identify what kind of Information at what level of accuracy and fidelity should be needed to characterize the

complex technological dynamics of the set of subsystems, components and devices and their interactions.

Fig. 17 (from NASA) clearly illustrates the concept of Virtual Multiscale Machine in the Aerospace field.

Fig. 18 (from Georgia Institute of Technology) well describes the Multiscale Technology and Engineering

Development and Integration Scenario for Complex Systems: From Atoms to Assembly, Product , Industrial

System and Ecosystems


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It is important to highlight that even if we are not able to develop highly detailed multiscale models, the new

proposed Framework can represent a valuable tool. Simplified models can allow researchers and engineers

to jointly perform simple, but still meaningful analyses to identify critical items in the Innovative

Technology Development process and shape more effective cooperation between scientists and engineers.

A critical issue is that present innovative technology development strategies are, in several cases, not fully

able to assess the (multiscale science-engineering) information needed to develop, validate, and integrate

key technologies in more complex systems. Present innovative technology development strategies are not

fully “information-driven” or, to better say, Multiscale Science-Engineering Information-driven.

The “Science-Engineering Information Space” and the Modeling & Simulation as “Knowledge Integrators

and Multipliers” concepts and methods allow us to define, besides the classical Technology Roadmaps, new

Integrated Multiscale Science-Engineering Information-Driven Theoretical and Methodological (computational, experimental, testing and sensing) Roadmaps which enable researchers, designers and

managers to jointly identify critical scientific and engineering resources needed to develop innovative

technological systems and shape more effective university-research-industry cooperative scenarios inside a

unified and coherent conceptual context.

Roadmaps of computational methodologies are being already drafted, but they are not fully “Information-

Driven” and, normally, not comprehensively integrated with experimental, testing and sensing roadmaps.

Computational and experimental & testing roadmaps are drafted separately without well defined links and

interdependencies.

Said in other words, roadmaps do not comprehensively identify and specify what information at what level

of accuracy and fidelity is needed to reach new engineering and technological achievements and what

information at what level of accuracy and fidelity we can get from the new outlined models and methods. Or,

at least that is accomplished only or mainly at a qualitative level.

The “Strategic Value of the “Integrated Multiscale Science-Engineering Framework” is that this kind of

approach enables a more in-depth and timely identification of the “Scientific and Engineering Critical

Issues and Domains and their relationships and interdependencies” in such a way as to allow for the

definition of timely integrated science-based (or science-engineering) strategies.


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4.4 Multiscale Science - Engineering Knowledge Integrator and Multiplier Computing, Information and Communication (CIC) Infrastructural Framework

New Cyberinfrastructures (GRIDS) (or Computing, Information and Communication Infrastructures)

represent the “Infrastructural and Technological Layer” for the Integrated Multiscale Science – Engineering

Technology, Product and Process Development (IMSE-TPPD) Frameworks.

� Multiscale Science – Engineering means that:

� the “Integrated Multiscale Science – Engineering Framework” can be used to design the Architecture of

Cyberinfrastructures: what kind of resources are interconnected with specific functionalities and

performance) conceived for specific Research, Environmental, Engineering, Manufacturing, Monitoring

and Control purposes.

� Methodologically Integrated Multiscale Science – Engineering Strategies (Paragraph 3.6) shape

Cyberinfrastructure Operational Modes. Specific Resources and Services can be activated, tailored and

integrated in an adaptive way for specific Tasks. This new kind of Cyber Infrastructure links together

the full spectrum of Computational, Experimental, Theoretical, Testing Centers and Networks of Earth

and Space based sensor systems according to Unified Multiscale Science - Engineering Strategies.

Fig. 19 (from US Department of Energy) A possible representation of Multiscale Multidisciplinary

Science – Engineering Cyberinfrastructure.


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� Knowledge Integrator and Multiplier means that

� the “Multiscale Modeling and Simulation as Knowledge Integrators and Multipliers and Unifying

Paradigm for Scientific and Engineering Methodologies (analytical, experimental, testing and sensing)

and related Knowledge Domains” can give the birth to a New Generation of Computational Centers with

extended functionalities and capabilities referred to as “Multiscale Computational Science - Engineering Knowledge Integrator and Multiplier Centres”. These Centres would be based upon

the new central concept of “Multiscale Multidisciplinary Modeling and Simulation as Knowledge

Integrators and Multipliers” and “Unifying Paradigm” for the full spectrum of Scientific and Engineering

Knowledge Domains and (analytical, experimental, testing, sensing) Methodologies. MethodologicallyA

“two – way” partnership among the new envisaged Computational Centers and Experimental, Testing

and Sensing Centers, Systems and Facilities is a distinguishing feature of this new vision. Furthermore,

Computational Centers, following the “Knowledge Integrators and Multipliers” view will become a key

node and catalyst of multiple interaction patterns between the Experimental, Testing and Sensing

Worlds. Significant technological advances allow to design and implement remote control techniques for

experimental, testing and sensing systems. Accordingly, new Computational Centers can easily interact

with extended Virtual Distributed Environments which integrate a wide spectrum of equipments and

facilities allowing a network of multiple cooperations. New previously illustrated concepts, methods and

frameworks lead to a new set of Functionalities for the Centres:

a) Integrated Environments for jointly (cooperating with Analytical, Experimental, Testing and

Sensing, Teams) “Designing” Integrated Computational and Experimental, Testing and Sensing

Frameworks and Strategies

b) Integrated Environments for the construction of Multiscale Multidisciplinary Science – Engineering

“Knowledge Domains” which turn Data coming from a full spectrum of scientific and engineering

sources (data bases, computations, analytical formulations, experimentation, testing and sensing)

into Multiscale Multidisciplinary Knowledge Domains

c) Integrated Environments for the Development and Validation of advanced Multiscale Computational

Models and Frameworks

A new level of Integration is made possible by the KIM Vision Integration develops along the following

lines:

� Scale integration (Multiscale Science and Engineering Integration) involving teams inside University,

Research Centres, Industry, dealing with research and engineering issues at different scales and

resolution levels. The design and implementation of Multiscale Science – Engineering

Cyberinfrastructures or GRIDs can give a real boost to the development of Multiscale Multiresolution

Experimental, Testing and Sensing technologies, procedures and strategies.

� Data, Information and Knowledge Integration: integration of data, information and knowledge from a

full spectrum of sources: theory, experimentation, testing and sensing) to build Multiscale Multiphysics

Science – Engineering Maps

� Teams and Methodologies lntegration: teams employing the full spectrum of methodologies (theory,

computation, experimentation/testing/sensing) for a wide range of disciplines (mathematics, physics,

chemistry, biology, electronics,..) work together to apply unified strategies for specific Tasks.

New Computational Centers become “Portals” to a Composite Multidisciplinary Multiscale Science –

Engineering World


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Integration of Supercomputing and Experimental Resources

Blue Collar Computing™ - Polymer Portal Collaboration Program:

Blue Collar Computing™ (BCC) is a collaborative program sponsored by the Ohio Supercomputer

Center (OSC) to help industry gain easy and affordable access to advanced computing technologies.

With support from the Ohio Board of Regents, OSC launched Blue Collar Computing™ in 2004. The

Ohio Supercomputer Center built an Integrated Virtual Environment called “Polymer Portal

Collaboration” which enables researchers and designers to effectively use and integrate a wide

spectrum of computational, experimental/testing and data repository resources. It could be a valuable

example of a New Generation of Supercomputing Centers. The following figures illustrate Polymer

Portal functionalities:


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About the Author

- Alessandro Formica, born in Milano (Italy) 3/20/51

- University Education : Nuclear Engineering at Polytechnic of Milan.

- Office : Via Piazzi, 41 – 10129 Turin, Italy

- Home : Via Sismondi, 4 – 20133 Milan, Italy

� Professional Skills - Alessandro Formica has more than thirty years of experience in the following

fields:

− Analysis, Design and Management of Computer-Aided Engineering, Computing, Information and

Communication, Modeling and Simulation, R&D and Engineering Projects and Initiatives (Aerospace

and Defense, Chemical and Environmental Engineering, Materials, High Performance Computing,….)

in the European and International scenario.

− Design and Management of European and International Cooperations

− Design and Management of European and International Events (Conferences and Workshops)

− Scenario, Marketing and Development Trend Analyses

− Design of Large Scale Projects and related Innovative Visions

� Professional Experiences:

− ARS S.p.A. (ENI Group R&D and Engineering Company), Director of Advanced Projects;

− Engineering Systems International, Head of Italian Branch;

− Singapore Government Industrial Group, Consultant;

− RCI Ltd. [US based International Consortium, operating in the Modeling & Simulation and High

Performance Computing areas], European Scientific Director, Director of Business Development and

Strategic Initiatives;

− RCI Consulting Company, European Scientific Director

− Executive Office of US President, Consultant;

− HPC companies (Cray, Convex, Gould), Consultant;

− European Space Agency, Consultant;

− Alenia Space, Consultant;

− Swiss Center for Scientific Computing (CSCS), Consultant;

− Computer Sciences Corporation, Consultant

− Alenia Aeronautica, Consultant;

− Torino Wireless, Large Projects Direction Consultant

− Polytechnic of Milano, Consultant

− Polytechnic of Turin, Consultant,

− Polytechnic of Turin School of Doctorate Lecturer for Multiscale Science – Engineering Integration;

− EUMAT (European Union Materials Technology) Platform Working Group 2 Modeling and Simulation

member;

− Polimeri Europa (ENI Group Chemical Company), Consultant;

− Nanoshare consultant (Nanoshare is a new company promoted by University and Research Italian

Ministry and involving Rome University “Tor Vergata” and people from Rome University “La

Sapienza”).

The “Strategic Multiscale” White Book synthesizes several years of studies and consulting activities by the

author in the field of Multiscale Science – Engineering integration and its application to Research,

Technology Development and Engineering. Studies on Multiscale started at the beginning of the nineties

when Alessandro Formica held the position of RCI Ltd (US based HPC International Consortium) European

Scientific Director. In the Report “Fundamental R&D Trends in Academia and Research Centres and Their

Integration into Industrial Engineering” (September 2000), drafted for European Space Agency (ESA), a first

version of an “Integrated Multiscale Science - Engineering Framework” was outlined and its impact on R&D

and Engineering analyzed.


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The White Book “Multiscale Science – Engineering Integration – A New Frontier for Aeronautics, Space

and Defense (May 2003) sponsored by Italian Association of Aeronautics and Astronautics (AIDAA)

introduced the concept of “Strategic Multiscale” and a more refined version of the related Integrated

Framework.. A Framework version specifically conceived for Industrial Applications: “Integrated

Multiscale Science – Based Technology, Product and Process Development” was drafted in the context of

the consulting cooperation with Alenia Aeronautica and Finmeccanica Group (November 2006). Multiscale

Analyses and Studies were also carried out on behalf of Polytechnic of Milan and Turin and in cooperation

with University of Rome “La Sapienza” and University of Rome “Tor Vergata”.

This White Book synthesizes several years of studies and consulting activities by the author in the field of

Multiscale Science – Engineering integration and its application to Research, Technology Development and

Engineering. Studies on Multiscale started at the beginning of the nineties when Alessandro Formica held the

position of RCI Ltd (US based HPC International Consortium) European Scientific Director. In the Report

“Fundamental R&D Trends in Academia and Research Centres and Their Integration into Industrial

Engineering” (September 2000), drafted for European Space Agency (ESA), a first version of an “Integrated

Multiscale Science - Engineering Framework” was outlined and its impact on R&D and Engineering

analyzed. The White Book “Multiscale Science – Engineering Integration – A New Frontier for Aeronautics,

Space and Defense (May 2003) sponsored by Italian Association of Aeronautics and Astronautics (AIDAA)

introduced the concept of “Strategic Multiscale” and a more refined version of the related Integrated

Framework.. A Framework version specifically conceived for Industrial Applications: “Integrated

Multiscale Science –Based Technology, Product and Process Development” was drafted in the context of the

consulting cooperation with Alenia Aeronautica and Finmeccanica Group (November 2006). Multiscale

Analyses and Studies were also carried out on behalf of Polytechnic of Milan and Turin and in cooperation

with University of Rome “La Sapienza” and University of Rome “Tor Vergata”.


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Contacts

Alessandro Formica

Via Piazzi, 41

10129 Torino

Italy

Phone. +39 338 71 52 564 +39 342 1350 390

E-mail: [email protected] and [email protected]

Documents

Strategic Multiscalel White Book 2012