An Unified Approach to Structural Health Management and Damage Prognosis of Metallic Aerospace Structures Prognosis of Aircraft and Space Devices, Components, and Systems Air Force Office of Scientific Research University of Cincinnati, Cincinnati, Ohio February 19 and 20, 2008 Grant Number: FA95550-06-1-0309 Program Manager: Dr. Victor Giurgiutiu Aditi Chattopadhyay Department of Mechanical and Aerospace Engineering Arizona State University
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An Unified Approach to Structural Health Management and Damage Prognosis of Metallic
Aerospace Structures
Prognosis of Aircraft and Space Devices, Components, and SystemsAir Force Office of Scientific Research
University of Cincinnati, Cincinnati, Ohio
February 19 and 20, 2008
Grant Number: FA95550-06-1-0309
Program Manager: Dr. Victor Giurgiutiu
Aditi Chattopadhyay
Department of Mechanical and Aerospace Engineering
Arizona State University
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MURI
Structural Health Monitoring & Prognosis of Aerospace Systems
Douglas Cochran•Statistical signal processing•Theory of sensing•Mathematical modeling
James B. Spicer•Materials process monitoring & control
•Ultrasonics
•High-temperature characterization
Dan Inman•SHM
• Wireless sensing and damage assessment
•Membrane optics
Roger G. Ghanem•Risk assessment•Stochastic mechanics•Computational mechanics•Inverse problems and optimization
Antonia Papandreou•Signal processing •Sensing & Information Processing •Detection & Estimation
MURI RESEARCH TEAMASU TeamAcademic Professionals: Jun Wei, Narayan Kovvali
Graduate Students : Debejyo Chakraborty, Clyde Coelho, Chuntao Luo, Subhasish Mohanty, Donna Simon, Sunilkumar Soni, Rikki Teale, Christina Willhauck
USC TeamAcademic Professional: Maarten Arnst
Graduate Students : Maud Comboul, Sonjoy Das, Arash Noshadravan
JHU TeamAcademic Professional: Seyi Balogun
Graduate Students : Lindsay Channels, Travis DeJournett
VT TeamAcademic Professional: Benjamin L. Grisso
Graduate Student : Mana Afshari
BENEFIT TO DOD/INDUSTRY TECHNICAL APPROACH
•Physically based models to characterize damage nucleation & growth
•Characterize wave propagation in hotspot
•Optimally integrate sensor network
•Waveform design & damage detection
•Sensor management schemes for detection/classification
•Stochastic models to account for uncertainties
•Estimation of remaining useful life
•Validation on test structures
• Connect microscopic damage to macroscopic scale monitoring
• Sensor sensitivity
• Sensor/host structure coupling
• Hierarchical information management
• Hybrid approach for life estimation
• Precursor to damage/first failure to inspection
OBJECTIVES•Computationally efficient multiscale modeling techniques for characterizing the damage state of a material (including nucleation and growth)
• Damage detection and classification techniques for sensor integration and instrumentation
• Prognosis capabilities for predicting failure probability and remaining useful life
• Testing, validation and application
BASIC RESEARCH ISSUES
• Improved techniques will facilitate assessment of health of metallic aircraft structures
• Project outcome will help surmount some of the technical challenges, complementing ongoing activities at AFRL
• Research results will help establish improved IVHM systems
• Future aircraft systems can benefit from integration with prognosis programs focused on current aircraft.
• Advancements in damage analysis, detection & classification are useful sustainable infrastructure and electronic system monitoring
TASK DESCRIPTIONS AND PERSONNEL
A. ChattopadhyayP. Peralta
Roger Ghanem
Task 1•Material Charecterization•Multiscale model to predict damage nucleation & growth
• Collaboration with AFRL: • Mark Derriso, Structural Health Assessment Team Leader, AFRL/VA
• Provides data from AFRL experimental set-ups• Frequent meetings with Mark and his team: discuss MURI progress
and relevant AFRL problems needed to help transition of our work to real systems
• Meetings with Jim Larsen (AFRL/MLLMN) and Kumar Jata (AFRL/MLLP) • Collaboration with Boeing Phantom Works (Eric Haugse)
• Hotspot Program with AFRL (involves actual F-18 testing in Arizona for transition to real systems).Participants: AFRL, Boeing Phantom Works, Accelent Technologies, Metis Design
• Members from AFRL, US Air Force Academy, United Technologies Research Center, Boeing, Next Generation Aeronautics, Lockheed Martin Aeronautics Company, National Transportation Safety Board, NRL, Naval Surface Warfare Center, NASA GRC, NASA LaRC, NASA ARC, US Army ARDECOM, Los Alamos National Lab.
RVE for Grains/ Particles3D Grain/ Particle size
distributions
Material
Characterization
Multiscale
ModelingMicrostructure Reconstruction
Short Crack Growth in the Mesoscale
Structure Level Fatigue Simulation
Representative Microstructure (FEM)
Metallography Microscale
Damage Initiation
Physically-based Multiscale Modeling
TECHNICAL APPROACH
Strain fields ahead of fatigue cracks in wrought Al alloys: in-situ testing and DIC
Nanoindentation of precipitates in wrought Al alloys
Load stage
Loaded specimen
Load Direction and Rolling Direction (RD)
MATERIALS CHARACTERIZATIONMultiscale Material Characterization
Crack tipCrack tip
Use Electron Backscatter Diffraction (EBSD) along with serial sectioning: 2-, 2.5- and 3-D
“Artificial” microstructures are also being generated
Same grain size (100 µm) different grain size distribution Large (300 µm) grain size
2.5-D 3-D
MATERIALS CHARACTERIZATIONMicrostructure Reconstruction and Representation
2-D
Use microstructure representation and meshing tools: defects can be included
Results show effects of microstructural variability on local fields
2.5-D 3-D
MATERIALS CHARACTERIZATIONMicrostructurally Explicit Finite Element Models
2-D
2-D
3-D
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INTERACTION OF RELEVANT SCALES IN MULTISCALE MODELING
FUTURE WORKTask 1:• Predict damage nucleation & propagation using modified fatigue damage criteria.• Simulate sensor signals & study their interaction with cracks using distributed point
source method (DPSM) – a wave based approach.
Task 2:• Adaptive signal processing and classification using active and multi-task learning
methodologies.• Use of data from new sensors and physics based FEM modeling to train damage
detection and classification algorithms.
Task 3:• Formulate multivariate prognosis models that incorporate physical-based models to
account for load sequence effects.• Incorporate material and sensor signal variability into prognosis framework.• Develop a prognosis approach for crack nucleation based on "virtual sensors" (output
from multiscale modeling) to estimate life spend to grow "detectable" damage.
Task 4:• Perform testing on instrumented samples with complex geometry (lug joints, bolted
joints) to gather statistical information on failure modes, sensor performance and to collect data for model validation (integration with Tasks 1, 2, and 3).
• Develop a test article for use with the biaxial load frame to obtain statistical information under both complex geometries and complex loading (integration with Tasks 1, 2, and 3).