Implementing Smart Structures Technology in High

Engineering Institute

LA-UR-08-1519 unclassified

Implementing Smart Structures Technology in High Consequence Applications

Charles R. [email protected]

www.lanl.gov/projects/ei

Engineers Australia Lecture SeriesMelbourne, Sydney, Brisbane, Canberra, Australia

December 4,5,8,9, 2008



Abstract

• This talk will begin by citing an example of smart structure technology developed at Los Alamos National Laboratory (LANL) over the last 15 years that have made the transition from research to practice and the barriers faced with such implementation on in situ structures. The example cited has is associated with a very high consequence application because of its defense nature, the hazardous material it is applied to, and the very high costs associated with the test structure. The presentation will then identify general issues that pose challenges when trying to implement new smart structures technology on real world systems. These issues include fundamental difficulties with funding new technology proof-of-principle demonstrations, the need for multidisciplinary technology development, and the need to perform studies in real world environments so that sources of variability can be assessed and quantified. The talk will conclude by raising questions regarding the ability of our current education paradigm to adequately train the next generation of engineers with the skill set necessary to transition smart structures technology from research to practice. Education programs that LANL is developing with the University of California – San Diego will be highlighted as an attempt to address some of the current shortcomings with tradition education models.



AcknowledgementsLANL Engineering Institute:

Matt Bement, Francois Hemez, Gyuhae Park, Kevin Farinholt

LANL Staff:Tom Petersen, John Sandoval, Roger Brocht

Dr. R. Michael Meneghini, St. Vincent Center for Joint Replacement

University Collaborators:Doug Adams (Purdue), Pete Avitabile (U-Mass,Lowell), Joel Conte (UCSD), Phil Cornwell (Rose-Hulman), Sanjoy Dasgupta (UCSD), Rajesh Gupta (UCSD), Dan Inman (Virginia Tech), John Kosmatka (UCSD), Francesco Lanza di Scalea (UCSD), Kincho Law, (Stanford), Nick Lieven (Bristol), Jerry Lynch ( Michigan), Graeme Manson (Sheffield), Tajana Rosing (UCSD), Hoon Sohn (KAIST),

Mike Todd (UCSD), Keith Worden (Sheffield)

Students and Former Students: T. Fasel (Structures, UCSD), Maurizio Gobbato(Structures, UCSD), D. Masceranas (SAIC), C. Olson (NRL), Eric Flynn (Structures, UCSD), Jesse Oliver (Structures, UCSD), Tim Overly (ME, U. Cincinnati), J. Wait (Structures, UCSD), Stuart Taylor (Structures, UCSD), Howard Matt (ATA Eng.) Erik Moro (Structures, UCSD), Elói Figueiredo (Civil Eng. , Univ. of Porto)



Where is Los Alamos?



Where I come From :Los Alamos National Laboratory

• Ensuring the safety and reliability of the U.S. nuclear weapons deterrent

• Reducing the global threat of weapons of mass destruction

• Solving national problems in energy, environment, infrastructure, and health security

Technical Staff Members: 50% PhD, 25% MS, 25% BS

360 Postdocs, 1500 Students

$2.5 billion annual operating budget



Outline

• How we think about Smart Structures and SHM at the Engineering Institute

• An example of “smart structures/SHM” technology developed at Los Alamos

• List some challenges for smart structures/SHM – Warning: contains adult content

• Are we educating the “smart structures/SHM” engineer of the future?


LA-UR-08-1519 unclassifiedSmart Materials Provide:Cradle-to-Grave Total System State Awareness

• Design system functionality in at the material and manufacturing level

– Minimize use of raw materials– Minimize scrap and its associated adverse environmental impact– Minimize energy to develop and manufacture product– Minimize time of conception to finished product (emphasis on small-lot

manufacturing)– Start building “system intelligence” in at the material level– Increased knowledge of initial component and system states

• Monitor and assess in-service system condition – Maximize service life, reliability – Minimize maintenance. Energy demand, environmental impact– System respond s and adapts to environmental and operational

conditions– Continuous feedback to the material design and manufacturing

processes

• Intelligent System Retirement– Predict end-of-life based on system state assessments and future

mission profiles– Maximize recyclable components– Monitor hazardous non-recyclable components– Feedback to material design and manufacturing processes– Feedback to in-service monitoring and assessment process



Skeleton: Adaptive load bearing capacity, self-healing

Muscle: actuation, semi-active and active responses

Senses: mechanical, acoustic, chemical, optical, thermal sensors and networks

Brain: reasoning, prediction, decision making, learning, controls

Digest System: energy conversionwaste management

Nerve system: sensing, data transport and processing

Circulatory System: self-healing, waste MGT, energy transport.

Future Engineered Systems:Cost Effective, Reliable, Energy Efficient, Environmentally benign

Smart Materials Facilitate BiomimeticFunctionality in Engineered Systems



High Explosives Radio Telemetry System

• Develop a system for measuring, transmitting, and receiving data that verifies the flight and terminal-event performance of warheads in delivery environments

• This system consists of fiber optic pressure sensors, conventional strain and acceleration sensors, and the High Explosive Radio Telemetry (HERT) system

• 32 fiber optic sensors, 10 ns sampling resolution, 100 Mb/s transmissionrate, sensor diagnostic capability, 0.6 kg



Basic Flight Trajectory and Downrange Assets

Launch Area

Pacific Coast, USAHawaii

Re-entry Area

South of Wake Is.

Equatorial Pacific

Turn on HERT

@ -9 secondsDetonation

@ height of

burst

Array of 6 Station keeping

buoys with HERT Receivers

600 m from burst

Downrange

Ship with

3 Receivers

16 Km from

burst

Turn on EDTM

After 1st Ignition



Fiber Optic Shock Sensors with Self-Check

• Fiber Optic Shock Sensor– Placed in small machined

groove in HE surface– Generates light upon arrival of

shock wave, blinded from high explosive light

– Prior to shock arrival all sensors are self-checked for integrity

–

Fiber SensorLSO: Cerium-Doped Lutetium Oxyorthosilicate



Ground HERT Explosive Test



Sensor System Loading Environments



HERT Receiver Assets: Autonomous Surface Craft

• HERT data receivers are mounted in ocean-going Autonomous Surface Craft (ASC) positioned near terminal event location

• 900 MHz Communication antenna

• 2.3 GHz HERT data antenna

• Global Position System antenna

• Batteries, 900 MHz Radio, GPS controls, and HERT Receiver inside hull

• Trolling Motors, 2 each



HERT Receiver Assets: Navy Mobile Instrumentation Ship

Three HERT receivers located on Navy Mobile Instrumentation Ship (NMIS)

Millions of dollars and many years in development!



Challenges for Implementing New Smart Structures/SHM Technology

• Need multidisciplinary technology development

• Example from structural health monitoring



Motivation for SHM

Song Su Bridge,Seoul, South Korea (1994, 31 Dead)

Hwy 19 Overpass Collapse Quebec (2006, 5 dead)

I-35 Bridge Collapse in MN (2007, 13 dead)

Mianus Bridge, Greenwich, CT.(June, 1983, 3 dead)

Silver Bridge, Pt Pleasant, WV(1967, 46 dead)

US congress enacts mandatory bridge

inspection

From: http://www.time.com/time/photogallery/0,29307,1649646_1421688,00.htmlhttp://en.wikipedia.org/wiki/Bridge_collapse

http://www.time.com/time/photogallery/0,29307,1649646_1421688,00.html



Sensor Node Development at the Engineering Institute



Active, Hierarchal Wireless Sensor Paradigm



Field Test of Remote Power Delivery Concept



Technologies Being Integrated

• RF-powered sensor design• RF telemetry• Optimal sensor placement

– Sensor correlation analysis– Observability criteria for damage scenarios

• Sensor power strategies• Embedded firmware • UAV-based data acquisition system (external mobile

agent)• System integration and experimental verification• Team includes, Structural Eng., Electrical Eng.,

Computer Scientists, and Mechanical Eng.




• Real world operational, environmental and unit-to-unit variability

• Warning: adult content



Hip Socket

Femoral Head

Femur

Osteo-arthritis

SHM for Hip Arthroplasty



The surgeon impacts the prosthesis



Sensing System added to Femoral Component



Validation Testing at UCSD’s Anatomy Lab



Human Variability Adds Difficulty to This Process

Specimen Sex Age Height (m) Weight (kg) Outcome

#1 Right Female 87 Not Given Not Given Fractured

#1 Left Female 87 Not Given Not Given Fractured

#2 Right Female 84 1.65 57.61 Incomplete

#2 Left Female 84 1.65 57.61 Fractured

#3 Right Male 97 1.68 72.57 Unable to Fracture

#3 Left Male 97 1.68 72.57 Unable to Fracture

#4 Right Male 75 1.57 63.5 Incomplete

#4 Left Male 75 1.57 63.5 Fractured

#5 Right Female 59 1.73 68.04 Incomplete

#5 Left Female 59 1.73 68.04 Incomplete




• Validation tests



Need Long Term Proof of Concept Demonstrations

• Most research projects are funded for about 3 years.• Few actually have a field deployment component to the

study.• This is not sufficient to demonstrate the ability of the

Smart Structures/SHM system to perform for long periods of time.

• Cost benefits of the Smart Structures/SHM system are directly tied to its lifetime in the field.

• Long-term Smart Structures/SHM demonstration projects will have to be done in parallel with conventional technology evaluation.

• However, such projects are expensive!



Need Data from Test Structures• The SHM Catch-22

– Structural system owners will not invest in SHM technology until it is demonstrated on a real world system.

– Real-world structures are generally not available to damage in an effort to develop SHM technology.

• Demonstrations on the ubiquitous laboratory cantilever beam or plate are necessary, but not a sufficient condition for SHM verification.

• Even when structures are made available, the damage introduced is typically not indicative of real-world damage scenarios.

• Demonstrations must incorporate real-world sources of variability.



Non-Technical Barriers to Smart Structures/SHM Transition

• Cost – Multi-disciplinary nature of this technology requires more people with diverse

technical expertise– These costs can be quantified and must be addressed when performing benefit-

cost study• Tenure and Promotion (at least in US Universities)

– Rewards the individual investigator; does not reward team efforts– MS and Ph D students need focused topic for their thesis/dissertation

• Industry’s short research time horizon (12-18 month time to market)• Regulatory agencies are not involved in the technology development

– Government agencies (e.g. FAA, NRC)– Insurance companies

• Government inefficiency– All branches of US military and other government agencies (i.e. Federal Highway

Administration) are trying to develop Smart Structures/SHM technology, but large-scale collaborative inter-agency programs do not exist

• Education is not evolving to address the need for more multi-disciplinary technology integrators

– However, there will always be the need for the technology specialist



Are We Educating the “Smart Structures/SHM” Engineer of the Future?

National Academy of Science report entitled “Rising Above the Gathering Storm” (RAGS) & America Competes ActSee: http://books.nap.edu/catalog.php?record_id=11463http://science.house.gov/legislation/leg_highlights_detail.aspx?NewsID=1938

1) Increase America's talent pool by vastly improving K-12 mathematics and science education;

2) Sustain and strengthen the nation's commitment to long-term basic research;

3) Develop, recruit, and retain top students, scientists, and engineers from both the U.S. and abroad; and

4) Ensure that the United States is the premier place in the world for innovation.

However, there is not one provision that will directly impact engineering and science education curricula at

the university level.

http://books.nap.edu/catalog.php?record_id=11463



Are We Educating the “Smart Structures/SHM” Engineer of the Future?

• Improved economic competitiveness, based in innovation and creative thinking, will not be realized through an increased number of people with advanced degrees that are educated in a system with outdated curricula.

• Curricula and education must evolve along with technology.

• Currently, US universities do an outstanding job at educating the specialist, but their traditional models do not promote the development of multi-disciplinary technology leaders of the future.



The EI Components

• Los Alamos Dynamics Summer School• Multi-Disciplinary Graduate Degree Program• Collaborative Research with UCSD• Annual Workshops

– Produce summary report on state-of-the-art in the respective topics and identify outstanding research issues. (available @ www.lanl.gov/projects/ei)

• Industry Short Courses– SHM– Model Validation and Uncertainty Quantification



DYNAMICS SUMMER SCHOOL

• Get top US-citizen engineering undergraduates enrolled in graduate school.

• Average GPA of these students: 3.7• Approx. 125/130 have gone on to grad

school• 5 have completed their Ph.D.s• Attempt to augment their formal

university education

• Summer School Activities– Week–long tutorials (e.g. Prof. Doug Adams, Nonlinear dynamics)– Guest lectures (e.g. Prof. Jerry Lynch, Wireless Sensor Networks)– All students perform experimental and analytical modal analysis of the same

structure. Results are used to support lecture on model validation and uncertainty quantification.

– Summer long 3-person research project (e.g. zero-power seismic sensor)– Each group produces a conference paper by the end of the summer– Guide to Graduate School and Fellowship Applications– Field Trip (Sandia’s Robotics, Microelectronics and Aging Aircraft Center)



Multidisciplinary Graduate Degree Program

Sensing & Diagnostics

Information Technology

Predictive Modeling

Courses

Nonlinear Dynamics

Array Processing

Nondestructive Evaluation

Statistical Pattern Recognition

Detection Theory

Finite Element

Theory

ContinuumMechanics

Machine Learning

Sensor Networks

Model Validationor

Structural Health Monitoring

Computer ScienceMechanical/AerospaceStructuresElectrical Eng.LANL



Collaborative Research Model

• Support 4-5 graduate students per project– Multi-disciplinary projects involving at least one

faculty PI and Graduate Student from each of the following departments:

• Computer Science and Engineering, • Electrical and Computer Engineering, • Mechanical and Aerospace Engineering, and• Structural Engineering.

– LANL staff work jointly with faculty and co-mentor the graduate students

– Project deliverables must include hardware and software that extend the state of engineering science



Collaborative Research Project Example: Swarming UAVs for Plume Detection, Tracking and Prediction

Aerospace Systems Research

Develop optimal UAVs

For Detection and Tracking Problem

Information Technology & Data

Management Research

Develop swarming control using shared data from all sensor

nodes

Sensors Networks, NDE, Controls, &

Embedded SystemsResearch Center

Develop new network communications

protocols

Multi-scale Predictive Modeling Research Predict

plume dispersion in near real time base on sensor

feedback



Concluding Comments

• This presentation has only raised issues –it has not provided solutions.

• As such, hopefully this presentation will generate discussions of the issues raised.

• Despite the difficulties, the smart materials/SHM community must be focused on transitioning research to practice.

Documents

Implementing Smart Structures Technology in High