A new paradigm for the automatic generation of workflows ...orap.irisa.fr/wp-content/uploads/2019/04/F43-Orap... · Combinatorial effects in automated process creation and maintenance

A new paradigm for the automaticgeneration of workflows in

Multidisciplinary Design Optimisation

Anne Gazaix, Head of MDO Competence Center, IRT Saint Exupéry

Technical Skill Leader MDO, Airbus Flight Physics

François GallardMDO Architect, IRT Saint Exupéry

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Month 200X Use Tab 'Insert - Header & Footer' for Presentation Title - Siglum - Reference

Development Process Evolution Highly Effective A/C Design

Joint Definition Detail Def.

Design

Data Generation

(e.g. Structural Layout)

(e.g. Systems Layout)

Make Design Work

Joint Definition Detail Def.

Design Data Generation

Loads & Aeroelastics

Performance Optimisation

Structural Optimization

Flexible Aircraft

Optimum Flight Control

Optimum Aircraft Design

Design to Loads

Mission

Optimisation

Faster time to market

More Studies, higher fidelity with managed Risk

Complex multi-disciplinary trade-off and optimization studies

NRC & lead-time reduced (engineering)

RC low, fast ramp-up (manufacturing)

Market & Operations Adaptability (airlines)

Environmental impact: Flow & Noise control

Airbus demand facing market needs

The well known vehicle multi-disciplinary challenge

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Confidential

Sequential design = risk of non-optimal solutions & can lead to antagonistic decisions

Propulsion

Aerodynamics

Mass

Noise

Manufacturing

[Courtesy M. Meaux, Airbus, 2017, « How can Multi-disciplinary Design Optimization (MDO) support R&T Portfolio management ?»]

«The main motivation for using MDO is that the performance of a multi-disciplinary

system is driven not only by the performance of the individual disciplines but also their

interactions»[“Multidisciplinary Design Optimization: A Survey of Architectures », J. Martins, A. Lambey, AIAA Journal, vol. 51, no. 9, pp. 2049-2075, 2013]

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Optimization at system level

m: Aircraft mass

dm/dt: Fuel consumption

SFC: Specific Fuel

Consumption

L/D: Lift-over-Drag ration

g: gravitation

𝑑𝑚

𝑑𝑡= −

𝑆𝐹𝐶.𝑚. 𝑔

𝐿𝐷

Bréguet equation

Aerodynamics

Propulsion Structure

Aircraft (Mission, cost, global performance)

Aerodynamic

shapePropulsion

Airframe

(structure)

System

Sub-systems

© IRT AESE “Saint Exupéry” - All rights reserved Confidential and proprietary document

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System decompositionO

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Challenges in industrial MDO

- Building new geometries versus deforming geometries (fast, robust for large design changes)

- Geometry to mesh generation- Account for intersections between sub-components (their

movement)- Differentiability of the parametrization- Optimization

To produce consistent disciplinary CAD models while enabling different geometry definitions and CAD engines

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Pylon shape template

Challenges in industrial MDO

Automation!

Thousands of design variables, coupling variables and constraints MDO strategies have to be scalable

Analysis tools may be highly costly in CPU time A trade-off accuracy versus restitution time is required

Advanced techniques are necessary

• E.g. surrogate models, use of adjoint, multi-fidelity algorithms and models,

parallelization, clever decomposition strategies

Industrial processes are complex and subject to change MDO implementation has to be practical, flexible and not problem-dependent

Industrial simulation tools may be black boxes MDO strategies have to be non-intrusive

Industrial design variables can be of different types: continuous,

discrete, non-categorial MDO methods have to offer a range of optimization techniques

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Disciplines / Analysis models

The overall system is decomposed into disciplines.

A discipline (or analysis model) solves the equations of the physicsit models.

Disciplines are often combined together to evaluate the objective function and/or the constraints.

The selected combination can be key in the accuracy of the obtained design solution, or key in the efficiency of the optimizationproblem resolution.

[Figure from MIT Course: Multidisciplinary System Design Optimization, IDS.338J, Prof O; de Weck, Prof. K. Willcox, 2010]

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Data-driven engines:

o Automated Work Flow management: execution sequence deduced by the engine from the data flow

o Low tolerance to disciplines input or output changes and high dimensional data

Workflow-driven engines:

o Automated data management : execute disciplines in a predefined sequence, whatever the inputs values are

o Low tolerance to workflow and disciplines changes

High fidelity MDO :o Many inputs & outputs

o Needs deep workflow reconfiguration

=> Need for a new paradigm

Data-driven vs workflow-driven paradigms

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Geometry Meshing SimulationPost-

processing

MDO Formulations + workflow driven as a new engine paradigm

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IDF

MDF

BLISS 97

For a given set of disciplines, design objective and constraints the MDO

formulation defines one or multiple optimization problems.

To define the objective and constraints, sub-processes may be needed,

such as MDAs, which can be implemented in a workflow driven

paradigm.

In GEMS, the MDO formulations offer a range of process definitions

instead of a predefined execution sequence

Compared to classical approaches in industry such as processes

integrated in workflow engines (iSight, ModelCenter), this enables the

full automation of the process creation !

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MDO Formulation Engine (design objectives, constraints, coupling strategies , optimisation algorithms ...)

MDO Formulation Challenge

MDO formulation = mathematicstrategy to definethe optimizationproblem(s) to besolved

Original design problem to besolved

min𝑥, 𝑦

𝑓(𝑥, 𝑦)

𝑠. 𝑡. 𝑅 𝑥, 𝑦 = 0𝑔 𝑥, 𝑦 ≤ 0

governing equations

constraints

objective function

where• 𝑥 are design variables• 𝑦 are coupling variables

?

Structure

Workflow Aerodynamics

Workflow

Structure

OptimisationAerodynamic

optimization windows

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Combinatorial effects in automated process creationand maintenance

• 99 paths in this graph

• With 3 versions of each software, this leads to 3^99 potential processes

This is a major issue for maintenance!

• Re-use of all elementary bricks has to be maximized

• A platform is needed, enabling a fast and flexible reconfiguration of the overall

MDO workflow

Pylon MDO

trade off

Pylon Aero

optimization

Pylon Structure

optimizationPylon MDO

optimization

Multi-objective

optimization

formulation

BLISS 97

formulation

Isight workflow ModelCenter workflow WORMS workflow

DOE method

MDF

formulation

MDO

formulationGradient based

optimizationRSM

ONERA BLISS

formulation

L-BFGS-B SQP SNOPTSobolLHSMacros MGDA Weighted sum

Kriging

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Software Platform for Industrialand Research Optimization

(SPIRO)

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Support the automation of MD design processes in distributed environments

Interoperate disciplinary applications through interfaces to GEMS

Handle data transportation Manage errors and automatic restart Handle configuration management


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in a nutshell

In traditional disciplinary optimization, the optimizer can be plugged to the simulations in a straightforward waySimulator

Optimizer

x

f, c

min f(x)

x c(x)<=0

Simulator 1

Optimizer 1

x1, y2,y3, z

Simulator 2 Simulator 3

f3, y3, c3

Optimizer 2

x2, y1,y3, z x3, y1,y2, z

f2, y2, c2f1, y1, c1

In MDO, there are multiple ways to achieve this wiring

GEMS saves a lot of programing time by automatically generating it according to a catalog of MDO formulations

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GEMS main features

Fully Automate MDO process generation according to a catalog of MDO formulations

=> MDF, IDF, and 6 variants of bi-level formulations are available

Fully automated reconfiguration when changing of MDO formulation

Interface multiple disciplinary simulation and optimization processes

=> For instance industrial fluid dynamics and structural mechanics simulation and optimization tools have been interfaced

Interface multiple platforms

Interface state of the art optimization and DOE algorithms

16 optimization and 20 DOE algorithms are available

Interface surrogate models

Multi-layered parallelism (DOE, sub-processes, MDAs, finite-differences…)

A catalog of data visualizations

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Formulations decoupled from use cases

MDF formulation Bi-level formulation

SS

BJ

Sel

lar

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MDO data as a graph

A graph is produced duringexecution, when relationshipsbetween MDO objects are described.

MDF formulation

Bi level formulation

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In relational databases (SQL), search through relationships between objects requires complex and costly join operations

What Graph databases are use for?

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• Base on graph theory, Graph databases are well

suited to manage highly connected data

WebSearch

Social Networks

SQL Graph

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4 levels of parallelism

1st level of parallelism via Multi- Threading (shared

memory)

2nd level of parallelism via

multi-processing (distributed

memory via process fork)

DOE

3rd level of parallelism at the job scheduler level

4th level of parallelism at the Simulation level (MPI…)OR

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Advanced MDAs

Disciplines dependency

analysis

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Automated

generation of

the MDA

process

Automated discrete

adjoint resolution

=

T∂A/∂ a

∂B /∂ b

∂A/∂ b

∂B/∂ a

∂A/∂ c

∂B /∂ c

∂C /∂ b

∂C/∂ a

∂C /∂ c

λa

λb

λc

∂ F /∂ b

∂ F /∂ a

∂ F /∂ c

T

Partial derivatives come:

from the disciplines if

provided

or generated by GEMS

using complex step or finite

differences

A mix of the three

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Bi-level formulation validation on SSBJ

New MDO formulations derived from ONERA’s variant of BLISS, specifically designed to match

industrial processes and tools constraints.

Here we take the following hypotheses:

disciplinary optimization processes are reused

compatible with adjoint-based optimization for aerodynamics

compatible with mixed discrete and continuous variables optimization for structure

no coupled adjoint available

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Validation of the bi-level on the SSBJ

The bi-level formulation 2x more efficient than state of the art formulation usable in a context where coupled derivatives would not be available

MDF Bi-Level

Calls to disciplines

2856 1349

CPU time. 5,3s 2,9s

MDFBilevel

(system level)

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Towards a bi-level multi-fidelity distributed MDF formulation applied to a pylon aero-structure MDO

Cd* (for multiple Mach, Cl ) M*

COC

OAD constraints( BFL, Vapp, …)

Aeroelasticoptimization

FEM displacement

analysis

CFD forcesanalysis

OAD CoCcomputation

Aerodynamic design variables Z: shared

design variables

System optimization

Mission performance optimization

A/C Mass computation

Aero perfoanalysis

OAD Mission computation

Aero-elasticTailoringfor CoC

minimization

Structural sizing

Loads

Stress analysis

OAD CoCcomputation

Structural design variables

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Conclusion

Industry needs a high level of automatic design space exploration capability. It is key to accelerate the design process and de-risk decision making.

Industry needs reconfigurable workflows and data flows. It is key to create adaptable processes able to cope with market changes.

These requirements are beyond current process integration capabilities limits.

GEMS software is proposed as a disruptive solution enabling to automatically create MDO processes based on a set of MDO formulations

A demonstration of the concept on a multi fidelity engine pylon optimization has been performed.

The generic aspects of GEMS makes its use potentially very broad within and beyond the aeronautics domain.

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A new paradigm for the automatic generation of workflows ...orap.irisa.fr/wp-content/uploads/2019/04/F43-Orap... · Combinatorial effects in automated process creation and maintenance