Stargel - Multi-Scale Structural Mechanics and Prognosis - Spring Review 2012
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Transcript of Stargel - Multi-Scale Structural Mechanics and Prognosis - Spring Review 2012
1 DISTRIBUTION A: Approved for public release; distribution is unlimited. 9 March 2012
Integrity Service Excellence
Dr. David Stargel
Program Manager
AFOSR/RSA
Air Force Research Laboratory
Multi-Scale Structural
Mechanics and
Prognosis
09 MAR 2012
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2012 AFOSR SPRING REVIEW
NAME: David Stargel
BRIEF DESCRIPTION OF PORTFOLIO:
FLIGHT STRUCTURES: Fundamental basic research into
structural mechanics problems relevant to the US Air Force
LIST SUB-AREAS IN PORTFOLIO:
Novel flight structures
Multi-scale modeling and prognosis
Structural dynamics
Structural mechanics or Mechanics of structures is the computation of
deformations, deflections, and internal forces or stresses (stress equivalents)
within structures, either for design or for performance evaluation of existing
structures*
* From Wikipedia
Focus w/in sub-areas
Enabling
Computing
Predicting
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Thrust Areas
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Challenges
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• NASA - Ed Glaessgen/Steve Smith
• ARO/ARL - David Stepp/Jim Chang
• ONR - Ignacio Perez/Liming Salvino/David Shifler
• NSF – Christina Bloebaum
• DTRA – Su Peiris
• MURI on Uncertainty – Fariba Fahroo
• Mathematics for Multi-Scale Modeling – Fariba Fahroo
• AOARD/EOARD/SOARD
• Transformational Computing – John Luginsland/Tatjana Curcic
• MURI on Hybrid Structures –Joycelyn
Harrison/Ali Sayir
Collaborations
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Structural Mechanics Vision of Future Weapon Systems
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Digital Twin Vision
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Key Workshop Recommendations
1. Material Scale Modeling – Develop high fidelity 3D microstructures of heterogeneous materials
– Need better representation of mechanics in homogenization-derived
reduced order models
2. Deterministic Multiscale Modeling – Develop a computational environment with flexibility to accommodate
different methodologies in conjunction with actual physics and
mathematics of the different domains
– Explore new up-scaling and down-scaling strategies along with advances
in multiple-temporal-scale modeling
3. Uncertainty Quantification – Explore holistic combination of deterministic and probabilistic modeling
– Enhance probabilistically-based sensitivity methods to identify important
variables
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AFRL Notional Digital Twin Roadmap
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A Common Vision of Future
Capabilities Planned Capabilities
Hypersonic Strategic
Bombers
Long-Duration
Reconnaissance Vehicles
Shared Technical Challenges
Computational Damage Mechanics
Experimental Damage Mechanics
Structural Health Management
Materials Engineering & Processing
Autonomous Space
Vehicles
Risk-Based Design
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National Multi Scale Foundational
Research Plan Process
The “Plan for the Plan”
• Phase 1- 2011: Education
• Inform damage mechanics community of the plan and ensure
participation
• Develop framework for plan organization
• Phase 2 – early 2012: Organization
• Further refine thrust area plan details
• Establish database of current funded efforts
• Estimate funding requirements and shortfalls to achieve
stated plan goals
• Phase 3 – late 2012: Utilization
• Use identified funding requirements and proof of collaboration
between agencies to advocate for increased resources for
multi-scale damage mechanics research
Each Agency will continue to utilize existing funding instruments
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Comprehensive Technical Objectives
– Computational Damage Mechanics
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Challenging and exciting scientific opportunities
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Future, 2025…
Tomorrow, 2015 Today, 2011
AFOSR PMs: Douglas Smith & David Stargel
In consultation with Curcic, Fahroo, & Luginsland
(T–CASE)
• To create transformational approaches in computing for aerospace science and engineering
• Multi-disciplinary approach including novel computer architectures, system software, and mathematical algorithms
• Emphasis on • Multi-scale modeling & structural
mechanics • Complex flow physics modeling &
control
Novel micro-
architectures?
Hybrid/complementary
photonic methods?
Quantum-based
systems?
Bio-computing?
Neuro-morphic
computing?
Transformational Computing in Aerospace
Science & Engineering
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Future …
Tomorrow
Today
Transformational Computing in
Aerospace Science & Engineering To create transformational approaches in computing for aerospace
science and engineering. “How can we exploit quantum computing architectures specifically to
advance aerospace computing?” University of California San Diego Team Lead PI: Dr. David Meyer Project Title: Applications of Quantum Computing in Aerospace Science and Engineering Team Disciplines: Mathematics, Computational Science, Structural Eng., Mechanical and Aerospace Eng., Chemistry, Physics Approaches: (1) Combine four quantum subroutines
into quantum algorithm for efficiently solving systems of linear equations
(2) Application of quantum search algorithms for use in optimization problems
University of Pittsburgh Team Lead PI: Dr. Peyman Givi Project Title: Quantum Speedup for Turbulent Combustion Simulations Team Disciplines: Mechanical Eng., Materials Science, Physics, Quantum Theory, Simulation and Modeling Approaches: (1) Quantum algorithms that operate on
general purpose quantum computers (2) Avenues for quantum simulation on
quantum devices
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Forecasting Aircraft Usage for Prognosis LRIR PIs: Ben Smarslok, Eric Tuegel, and Ravi Penmetsa
Background & Motivation
• Material state evolution is nonlinear & history dependent
• Reliable structural prognosis requires the generation of realistic loading and environmental sequences
• Existing techniques focus on a single structural load parameter history
• Used ABAQUS Solver
– Developed scripts to translate CFD pressures
onto the FE mesh
• ~1 Million DOF
• 2.5 hrs of run time using 2 cores of a single
CPU
– 30 Min for actual static analysis
– 2 hrs processing input file
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A Bayesian Experimental Design Approach for Optimization and
Uncertainty Quantification in Aerospace Structural Modeling and
Analysis
PI: Dr. Michael Todd, UCSD
Objective
Develop a framework for “optimal” model
selection, performance assessment, updating,
and uncertainty assessment in aerospace
structural modeling
Some Fundamental Basic Science Issues
• Logical accounting of relevant uncertainty
sources
• Consistent transition probability model that
propagates uncertainty through the decision-
making process
• Optimization strategy of complex, likely non-
smooth decision surfaces
• Determination of the cost function
form(s)…application-specificity
The ideal future…
• Completely known physics with no (or negligibly little) uncertainty
• A much Much MUCH greater computational capacity
Physical
Variability
Information
Uncertainty
Model
Uncertainty
Uncertainty
Modeling
FEM and
Dynamic
Analysis
Damage
Mechanism
Analysis
Probabilistic
Fatigue
Prognosis
Usage
monitoring
Model
update
Bayesian
Updating
Inspection
SHM
RUL
update
Verification &
Validation
A sound uncertainty management methodology
- Mechanism Model
- Uncertainty Quantification and Propagation
- Uncertainty Updating
- Verification and Validation
Yongming Liu, Clarkson University Concurrent structural fatigue
damage prognosis under uncertainties
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Enabling Methodologies
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Accordion Rib Stitch
Arching Seed Stitch
Twisting I-Cord
Rolled Furling Stockinette Stitch
Contraction Garter Stitch
Backward
Loop
Rear
Ridge
Backward
Loop
Connecting
wire
A
A
Forward
Loop
Backward
Loop Forward
Loop
Backward
Loop
Forward
Loop
Backward
Loop
Active Knits for Radical Change Air Force Structures
PI: Dr. Diann Brei, University of Michigan GRANT # FA9550-09-1-0217
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Novelty of approach: includes operational transitions, friction, load path, and active materials
Analytical Model
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Flow Control Applications
Bumps and Spars Roughness Elements
Technological Needs: Large Displacements, High Pressure, Distributed Actuation
(Bein et al., 2000)
Synthetic Jet
• Constant disturbance delays boundary layer separation
• Traditional jet mechanisms increase design complexity
• Piezoelectric active jets are promising but debond at high frequencies
Contour Bump
(Holman et al., 2005)
Flap and VGS
(http://www.aerospaceweb.org)
Synthetic Jets Flaps, Spoilers, Vortex Generators
• Change effective shape of wing midflight
• Effective at leading and trailing edge of the wing
• Large size and weight prohibit full integration of distributed actuators over wing
Leading Edge Distributed
Roughness Elements
• Contour bumps theoretically reduce transonic drag ~15%
• Spars theoretically reduce shear stresses by 9.9%
• Actively varying height mid-flight and creating large deformations difficult
• Distributed surface texturing
• Reduce turbulent skin friction drag up to 30%
• Difficult to create distributed actuation across surface of wing
(Stanewsky, 2001)
Benefits •Reduce Drag
•Enhance Lift
• Improve Maneuverability
• Increase Fuel Economy
•Expand Mission Variety
(Collis, 2004) (Smith, 1998; Cattafesta, 2001, Crook 1999)
(Milholen, 2004; Stanewsky, 2001) (Dearing, 2007; Lambert, 2006)
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Rib Stitch Architecture and
Operation
Rib Stitch Architecture
SMA Wire Schematic
Rib Stitch Operational Mechanism
• Martensite Compressed State - Applied load flattens ridges
- Leg connecting knit to purl loops bends horizontally in the less stiff state
• Austenite Expanded State - Material stiffens and straightens, recovering plastic
deformation from Martensite State
- Increased stiffness and unbalanced force couples cause the forward ribs to lift and backward ribs to depress
Rib Stitch Actuation Mechanism
Forward Rib(Knit Loops)
F
F
F
F
F
F
Balanced Force Couples
Backward Rib(Purl Loops)
F
F
Unbalanced Force Couples
http://www.spin-knit-dye.com
Traditional Fiber Textile
Knit Column
Purl Column
Rear Ridges
Forward Ridges
Backward Rib: Purl Loops
Forward Rib: Knit Loops
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Rib Stitch Prototype Fabrication
and Testing
Experimental Procedure
Experimental Setup
Prototypes Prototype Testing
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20
Fo
rce
(N)
Prototype Height (mm)
Austenite Expanded
Martensite Compressed
2
3
5
1
4
6
Apply
Load
Increase Load
Heat
Heat
Cool
Cool
Heat
Stainless Steel Rods
Linear Ball Bearings
Base Plate
Slider Plate
Encoder StripRib Stitch
Knit Prototype
Rib Stitch Prototype *Area = 0.010 m2 Mass = 19.6 g
140 mm
72 m
m
16 wales
14 co
urses
2 k 2 k 2 k 1 k1 k 2 p 2 p 2 p 2 p
a) Stacked Rib Stitch Configuration
2*hMcomp
2*DAct
Applied Load
(Fapp)
Plate
Rib Knit
Plate
Rib Knit
Stacked Rib
Knit Actuator
b) Nestled Rib Stitch Configuration
Applied Load
(2*Fapp)
>hMcomp
DAct
Plate
Rib Knits
Nestled Rib Knit
Actuator
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Compliant Mechanisms
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Passively Morphing Ornithopter Wing for
Increased Lift and Agility PIs: Dr. James E. Hubbard Jr., U of Maryland and Dr. Mary I. Frecker, Penn State
FA9550-09-1-0632
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The primary objective of this workshop is to
1. Investigate the research challenges associated with applying Compliant Mechanism
(CM) design methodology to flapping Micro Air Vehicle (MAV) designs, with a
extension to general air vehicle designs.
2. Explore past and on-going research in this application area to determine the current
state of the art and to aid in determining future feasibility.
3. Establish collaboration between compliant mechanism design and air-vehicle design
communities in order to leverage current and future research opportunities with the
goal of more affordable and reliable vehicle design.
Suggested topics
March 26th and 27th, 2012, Tec^Edge, Dayton, Ohio
Workshop Chairs: Dr. David Stargel (AFOSR) and Dr. James Joo (RBSA)
AFRL/AFOSR Workshop on Compliant
Mechanisms in Micro Air Vehicle Design
Methodology
Design synthesis
Performance definition, calculation and
measurement
Passive shape change and complex motion
generation
Multi-DOF compliant mechanism
Origami
Laminar emergent mechanism
Smart/adaptive structure & actuator
Fabrication
Softening or statically balanced compliant
mechanism
Flapping Micro Air Vehicle
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ODISSEI: Origami Design for Integration of Self-
assembling Systems for Engineering Innovation Collaborative effort with NSF EFRI Program
AFOSR PMs: Fariba Fahroo, Joycelyn Harrison, Doug Smith, & David Stargel
• Four themes:
– A: Compliant Mechanisms
– B: Active Materials
– C: Bio-origami
– D: Foldable Structures and Micro-structures
• Required Elements:
– ODISSEI-1 – Development of scientific, mathematical, and/or design
theories and methods for folding/unfolding
– ODISSEI-2 – Development of theoretical foundations for self-assembly
at all scales and across scales.
– ODISSEI-3 -Computational discovery and tools to facilitate design of
complex systems through folding and unfolding mechanisms
• PIs are strongly encouraged to include community outreach and
educational opportunities for outreach
Active Materials
Design Theory
Origami
Mathematical Rigor +
Artistic Inspiration
Adaptive Morphing System (AMS)
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Multi-Scale Structural Mechanics
Summary Past Present Future
Few tests represent
aircraft fleet
CAE supplements
experimental fleet models
Each aircraft has its
own virtual twin
• Three core thrusts with the integrating vision of a Virtual
Twin Concept
• Novel Flight Structures
• Multi-scale Modeling and Prognosis
• Structural Dynamics
• Focus program on core concepts of structural mechanics
• Computing
• Predicting
• Enabling
• Program is coordinated and actively collaborating with
other government agencies and within AFOSR
• Exploring new transformational capabilities
• Quantum Computing for Aerospace Sciences
• Origami Engineering
29 9 March 2012 DISTRIBUTION A: Approved for public release; distribution is unlimited.
Questions?