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Mesh Independent Probabilistic Residual Life Prediction of Metallic Airframe Structures

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNX11CG54P
Agency Tracking Number: 104662
Amount: $99,993.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: A1.03
Solicitation Number: N/A
Timeline
Solicitation Year: 2010
Award Year: 2011
Award Start Date (Proposal Award Date): 2011-02-18
Award End Date (Contract End Date): 2011-09-29
Small Business Information
CT
East Lyme, CT 06333-1303
United States
DUNS: 610056405
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Jim Lua
 Principal Investigator
 (860) 398-5620
 jlua@gem-consultant.com
Business Contact
 Jim Lua
Title: Business Official
Phone: (860) 398-5620
Email: jlua@gem-consultant.com
Research Institution
 Stub
Abstract

Global Engineering and Materials, Inc. (GEM) along with its team members, Clarkson University and LM Aero, propose to develop a mesh independent probabilistic residual life prediction tool for metallic airframe structures. The deterministic solver of this probabilistic analysis tool will be developed by integrating our cutting edge extended finite element toolkit for Abaqus (XFA) with a novel small time-scale fatigue crack growth model for mesh independent fatigue crack growth prediction of a complex airframe structural component subjected to multiaxial and variable amplitude loading. The fast matching and narrow band technique will be implemented to track a curvilinear 3D crack growth without remeshing. Both the versatility and the high computational efficiency will make the XFA an ideal solution model for the probabilistic life prediction where the initial defect shape and location can be treated as random variables without user intervention. After the integration of XFA with a general purpose probabilistic analysis framework (PFA), the resulting probabilistic version of the XFA (PXFA) will enable the following: 1) fatigue reliability assessment of an aging component; 2) evaluation of design variables to meet a targeted reliability level; and 3) provision of operational decision support using SHM data on repair, maintenance, and life extension options.

* Information listed above is at the time of submission. *

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