Stargel - Multi-Scale Structural Mechanics and Prognosis - Spring Review 2012

Multi-Scale Structural
Mechanics and
Prognosis

09 MAR 2012

Dr. David Stargel
Program Manager
AFOSR/RSA
Integrity  Service  Excellence Air Force Research Laboratory

9 March 2012 DISTRIBUTION A: Approved for public release; distribution is unlimited.
1

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

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*

LIST SUB-AREAS IN PORTFOLIO: Focus w/in sub-areas
Novel flight structures Computing
Multi-scale modeling and prognosis Predicting
Structural dynamics Enabling

* From Wikipedia
DISTRIBUTION A: Approved for public release; distribution is unlimited.
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Thrust Areas

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Challenges

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Collaborations

• 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

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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

8

AFRL Notional Digital Twin Roadmap

9

A Common Vision of Future
Capabilities
Planned Capabilities

Hypersonic Strategic Long-Duration Autonomous Space
Bombers Reconnaissance Vehicles Vehicles

Shared Technical Challenges

Computational Damage Mechanics
Structural Health Management
Experimental Damage Mechanics
Risk-Based Design
Materials Engineering & Processing
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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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Transformational Computing in Aerospace
Science & Engineering
AFOSR PMs: Douglas Smith & David Stargel
In consultation with Curcic, Fahroo, & Luginsland

(T–CASE)

Today, 2011
Tomorrow, 2015

Future, 2025… • To create transformational approaches in
computing for aerospace science and
engineering
 Novel micro- • Multi-disciplinary approach including novel
architectures? computer architectures, system software,
 Hybrid/complementary and mathematical algorithms
photonic methods? • Emphasis on
 Quantum-based • Multi-scale modeling & structural
systems? mechanics
 Bio-computing? • Complex flow physics modeling &
 Neuro-morphic control
computing?

14

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 University of Pittsburgh Team
Lead PI: Dr. David Meyer Lead PI: Dr. Peyman Givi
Today
Project Title: Project Title:
Applications of Quantum Computing in Tomorrow Speedup for Turbulent
Quantum
Aerospace Science and Engineering Combustion Simulations
Team Disciplines: Team Disciplines:
Mathematics, Computational Science, Mechanical Eng., Materials Science,
Structural Eng., Mechanical and Physics, Quantum Theory, Simulation and
Aerospace Eng., Chemistry, Physics Modeling
Approaches: Approaches:
(1) Combine four quantum subroutines (1) Quantum algorithms that operate on
into quantumFuture … for efficiently
algorithm general purpose quantum computers
solving systems of linear equations (2) Avenues for quantum simulation on
(2) Application of quantum search quantum devices
algorithms for use in optimization
problems DISTRIBUTION A: Approved for public release; distribution is unlimited. 15

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 Some Fundamental Basic Science Issues
Develop a framework for “optimal” model • Logical accounting of relevant uncertainty
selection, performance assessment, updating, sources
and uncertainty assessment in aerospace • Consistent transition probability model that
structural modeling propagates uncertainty through the decision-
making process
A sound uncertainty management methodology • Optimization strategy of complex, likely non-
- Mechanism Model smooth decision surfaces
- Uncertainty Quantification and Propagation • Determination of the cost function
- Uncertainty Updating form(s)…application-specificity
- Verification and Validation

FEM and Usage Model
Information Dynamic monitoring update
Uncertainty Analysis

Yongming Liu, Clarkson University
Concurrent structural fatigue Probabilistic
Uncertainty Bayesian Verification &
damage prognosis under Physical Fatigue
Modeling Updating Validation
uncertainties Variability Prognosis

Damage
Model Inspection RUL
Mechanism
Uncertainty SHM update
Analysis
The ideal future…
• Completely known physics with no (or negligibly little) uncertainty
• A much Much MUCH greater computational capacity
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Enabling Methodologies

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Active Knits for Radical Change Air Force
Structures
PI: Dr. Diann Brei, University of Michigan
GRANT # FA9550-09-1-0217

Contraction Rolled Furling Twisting
Garter Stitch Stockinette Stitch I-Cord

Forward Backward
Loop Loop
Backward
Loop
Backward A
Rear Connecting
Loop
Ridge A wire

Accordion Arching
Rib Stitch Seed Stitch

Backward Forward
Loop Loop

Backward
Forward
Loop
Loop

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Analytical Model

Novelty of approach: includes operational transitions, friction, load path, and active materials
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Flow Control Applications
Flaps, Spoilers, Vortex Generators Synthetic Jets
• Change effective shape of wing • Constant disturbance delays
midflight boundary layer separation
• Effective at leading and • Traditional jet mechanisms
trailing edge of the wing increase design complexity
• Large size and weight prohibit (http://www.aerospaceweb.org) (Holman et al., 2005) • Piezoelectric active jets are
Flap and VGS Synthetic Jet
full integration of distributed promising but debond at high
actuators over wing frequencies
Benefits
(Collis, 2004)
• Reduce Drag (Smith, 1998; Cattafesta, 2001, Crook 1999)

• Enhance Lift
Bumps and Spars Roughness Elements
• Improve Maneuverability
• Contour bumps theoretically • Distributed surface texturing
reduce transonic drag ~15% • Increase Fuel Economy
• Reduce turbulent skin friction
• Spars theoretically reduce • Expand Mission Variety drag up to 30%
shear stresses by 9.9% • Difficult to create distributed
• Actively varying height mid- actuation across surface of
flight and creating large wing
deformations difficult (Stanewsky, 2001)
(Bein et al., 2000) Leading Edge Distributed
(Milholen, 2004; Stanewsky, 2001) Contour Bump Roughness Elements (Dearing, 2007; Lambert, 2006)

Technological Needs: Large Displacements, High Pressure, Distributed
Actuation
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Rib Stitch Architecture and
Operation
Rib Stitch Architecture
Knit Purl Forward Rib: Backward Rib:
Column Column Knit Loops Purl Loops

Rear Forward
Ridges Ridges

http://www.spin-knit-dye.com

Traditional Fiber Textile SMA Wire Schematic

Rib Stitch Operational Mechanism Balanced
Force Couples
F F

• Martensite Compressed State
- Applied load flattens ridges F F
- Leg connecting knit to purl loops bends
horizontally in the less stiff state F Unbalanced
Force Couples
• Austenite Expanded State F

- Material stiffens and straightens, recovering plastic F
F

deformation from Martensite State Forward Rib
(Knit Loops)
- Increased stiffness and unbalanced force couples Backward Rib

cause the forward ribs to lift and backward ribs to (Purl Loops)

depress Rib Stitch Actuation Mechanism

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Rib Stitch Prototype Fabrication
and Testing
Prototypes Prototype Testing
16 wales
1k 2p 2k 2p 2k 2p 2k 2p 1k
Stainless
Steel Rods

Linear
Ball Bearings

14 courses
72 mm

Slider Plate

Base Plate Rib Stitch
Encoder Strip Knit Prototype

Experimental Setup
140 mm 100
*Area = 0.010 Austenite Expanded
Rib Stitch Prototype m2
19.6 g
Mass = 90
80 Heat
Martensite Compressed

70 6 5
Applied Load Cool Increase

Force (N)
60
(Fapp) Applied Load Load
(2*Fapp) 50 Heat
2*D Act
Plate
40 4 3
D Act Apply
Rib
Plate 30 Cool
Plate
Knit Load
2*hMcomp 20
>hMcomp Rib
Rib
Knits
Knit
10 Heat
0 1 2
a) Stacked Rib Stitch Configuration b) Nestled Rib Stitch Configuration
0 5 10 15 20
Stacked Rib Nestled Rib Knit Prototype Height (mm)
Knit Actuator Actuator Experimental Procedure

23

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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AFRL/AFOSR Workshop on Compliant
Mechanisms in Micro Air Vehicle Design
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
 Methodology  Laminar emergent mechanism
 Design synthesis  Smart/adaptive structure & actuator
 Performance definition, calculation and  Fabrication
measurement  Softening or statically balanced compliant
 Passive shape change and complex motion mechanism
generation  Flapping Micro Air Vehicle
 Multi-DOF compliant mechanism
 Origami

March 26th and 27th, 2012, Tec^Edge, Dayton, Ohio

Workshop Chairs: Dr. David Stargel (AFOSR) and Dr. James Joo (RBSA)
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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
Mathematical Rigor
• Four themes: +
Artistic Inspiration
– A: Compliant Mechanisms Active Design
– B: Active Materials Materials Theory
Origami
– C: Bio-origami
– D: Foldable Structures and Micro-structures
Adaptive Morphing
• Required Elements: System (AMS)
– 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

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Multi-Scale Structural Mechanics
Summary
Past Present
• Three core thrusts with the integrating vision of a Virtual Future
Twin Concept
• Novel Flight Structures
Few tests represent
• Multi-scalefleet
aircraft Modeling and Prognosis Each aircraft has its
• Structural Dynamics CAE supplements own virtual twin
experimental fleet models
• 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
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Questions?

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Stargel - Multi-Scale Structural Mechanics and Prognosis - Spring Review 2012

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