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Implementing Smart Structures Technology in
High Consequence Applications
Charles R. Farrar
farrar@lanl.gov
www.lanl.gov/projects/ei
Engineers Australia Lecture Series
Melbourne, Sydney, Brisbane, Canberra, Australia
December 4,5,8,9, 2008
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Abstract
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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.
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Acknowledgements
LANL 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 (RoseHulman), 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)
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Where is Los Alamos?
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Where I come From :
Los Alamos National Laboratory
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• 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
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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?
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Smart Materials Provide:
Cradle-to-Grave Total System State Awareness
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Design system functionality in at the material and
manufacturing level
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Monitor and assess in-service system condition
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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
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
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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
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Smart Materials Facilitate Biomimetic
Functionality in Engineered Systems
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Muscle: actuation,
semi-active and
active responses
Skeleton: Adaptive load
bearing capacity, self-healing
Nerve system: sensing,
data transport and
processing
Circulatory System: selfhealing, waste MGT,
energy transport.
Future Engineered Systems:
Cost Effective, Reliable,
Energy Efficient,
Environmentally benign
Senses: mechanical,
acoustic, chemical,
optical, thermal sensors
and networks Engineering Institute
Digest System:
energy conversion
waste management
Brain: reasoning,
prediction, decision
making, learning,
controls
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High Explosives Radio Telemetry System
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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 transmission
rate, sensor diagnostic capability, 0.6 kg
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Basic Flight Trajectory and Downrange Assets
Downrange
Detonation
Turn on HERT
Ship with
@ height of
@ -9 seconds
3 Receivers
burst
Turn on EDTM
After 1st Ignition
16 Km from
Array of 6 Station keeping
burst
buoys with HERT Receivers
600 m from burst
Re-entry Area
Hawaii
South of Wake Is.
Launch Area
Pacific Coast, USA
Equatorial Pacific
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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
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Fiber Sensor
LSO: Cerium-Doped Lutetium Oxyorthosilicate
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Ground HERT Explosive Test
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Sensor System Loading Environments
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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
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HERT Receiver Assets:
Navy Mobile Instrumentation Ship
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Three HERT receivers located on Navy Mobile
Instrumentation Ship (NMIS)
Millions of dollars and many years in development!
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Challenges for Implementing New Smart
Structures/SHM Technology
• Need multidisciplinary technology
development
• Example from structural health monitoring
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Motivation for SHM
Silver Bridge,
Pt Pleasant, WV
(1967, 46 dead)
US congress enacts
mandatory bridge
inspection
Song Su Bridge,
Seoul, South Korea (1994, 31 Dead)
Mianus Bridge, Greenwich, CT.
(June, 1983, 3 dead)
I-35 Bridge Collapse in MN
(2007, 13 dead)
Hwy 19 Overpass Collapse Quebec
(2006, 5 dead)
From: http://www.time.com/time/photogallery/0,29307,1649646_1421688,00.html
http://en.wikipedia.org/wiki/Bridge_collapse
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Sensor Node Development at the
Engineering Institute
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Active, Hierarchal Wireless Sensor Paradigm
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Field Test of Remote Power Delivery Concept
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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.
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Challenges for Implementing New Smart
Structures/SHM Technology
• Real world operational, environmental and
unit-to-unit variability
• Warning: adult content
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SHM for Hip Arthroplasty
Hip Socket
Femoral
Head
Osteoarthritis
Femur
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The surgeon impacts the prosthesis
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Sensing System added to Femoral Component
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Validation Testing at UCSD’s Anatomy Lab
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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
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Challenges for Implementing New Smart
Structures/SHM Technology
• Validation tests
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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!
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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.
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Non-Technical Barriers to Smart Structures/SHM
Transition
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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 benefitcost study
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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
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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
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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
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Education is not evolving to address the need for more multidisciplinary technology integrators
– However, there will always be the need for the technology specialist
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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 Act
See: http://books.nap.edu/catalog.php?record_id=11463
http://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 longterm 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.
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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 multidisciplinary technology leaders of the future.
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The EI Components
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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
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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
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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)
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Multidisciplinary Graduate Degree Program
Courses
Finite
Element
Theory
Computer Science
Mechanical/Aerospace
Structures
Nonlinear
Electrical Eng.
Dynamics
Predictive LANL
Continuum
Mechanics
Sensing &
Diagnostics
Modeling
Model Validation
or
Structural Health Monitoring
Information
Technology
Machine
Learning
Nondestructive
Evaluation
Array
Processing
Sensor
Networks
Detection Theory
Statistical
Pattern
Recognition
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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:
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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
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Collaborative Research Project Example: Swarming UAVs
for Plume Detection, Tracking and Prediction
Sensors Networks,
NDE, Controls, &
Embedded Systems
Research Center
Multi-scale Predictive
Modeling Research Predict
plume dispersion in near
real time base on sensor
feedback
Develop new network
communications
protocols
Information
Technology & Data
Management
Research
Aerospace Systems
Research
Develop swarming
control using shared
data from all sensor
nodes
Develop optimal UAVs
For Detection and
Tracking Problem
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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.
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