Probabilistic Remaining Useful Life Prediction of Composite Aircraft Components

Award Information
Agency:
National Aeronautics and Space Administration
Branch
n/a
Amount:
$99,968.00
Award Year:
2011
Program:
SBIR
Phase:
Phase I
Contract:
NNX11CD02P
Award Id:
n/a
Agency Tracking Number:
105116
Solicitation Year:
2010
Solicitation Topic Code:
A1.12
Solicitation Number:
n/a
Small Business Information
CT, East Lyme, CT, 06333-1303
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
610056405
Principal Investigator:
Jim Lua
Principal Investigator
(860) 398-5620
jlua@gem-consultant.com
Business Contact:
Jim Lua
Business Official
(860) 398-5620
jlua@gem-consultant.com
Research Institution:
Stub




Abstract
A composite fatigue damage assessment and risk informed prognosis toolkit will be developed by enhancing and integrating existing solution modules within a probabilistic analysis framework. This tool will for the first time be able to address concurrently both microcracking induced stiffness degradation and cyclic loading induced delamination crack growth without remeshing. A physics-based deterministic solver will be developed by integrating a discrete crack network model with a multiaxial fatigue damage accumulation law. An advanced probabilistic analysis framework with the Bayesian Maximum Entropy (BME) updating procedure will be developed for risk informed total life management. The damage detection results will be integrated/fused with the physics based delamination growth prediction tool to form a risk informed damage prognosis and condition based maintenance metrics. Global Engineering and Materials, Inc. (GEM) has secured commitments for technical support from Clarkson University and Boeing, who will provide existing solution modules, supporting data, customization plug-ins, and expertise. The multi-faceted feasibility study consists of developing a method that will enable the prediction of multi-site, multi-mode damage interaction, extracting delamination driving force, characterizing delamination evolution under multiaxial non-proportional loading, and performing risk informed fatigue failure prediction and BME updating when new detection and maintenance data become available.

* information listed above is at the time of submission.

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