Development of Multidisciplinary, Multi-Fidelity Analysis and Integration of Aerospace Vehicles

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$600,000.00
Award Year:
2013
Program:
SBIR
Phase:
Phase II
Contract:
FA8650-13-C-2325
Award Id:
n/a
Agency Tracking Number:
F08B-T03-0096a
Solicitation Year:
2008
Solicitation Topic Code:
AF08-BT03
Solicitation Number:
2008.2
Small Business Information
P.O. Box 2287, Brentwood, TN, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
128193997
Principal Investigator:
Animesh Dey
Chief Product Dev. Office
(615) 372-0299
adey@vextec.com
Business Contact:
Jamie Allen
CEO
(615) 372-0299
jallen@vextec.com
Research Institute:
Stub




Abstract
ABSTRACT: The Phase II objective is to develop a probabilistic multi-disciplinary, multi-fidelity uncertainty management analytical tool that links aerodynamic analysis, structural analysis, and fatigue analysis to predict the durability for airframe components and demonstrate that the tool can be used analyze actual airframe structures. The benefit of developing such an uncertainty management tool is to make accurate remaining useful life forecasts of aircraft systems based on actual usage. It is anticipated that the demonstration will be performed on a legacy airframe component. The first year of the project will be focused on developing the necessary computational codes & models to set up the demonstration problem and laying out the basic multi-disciplinary analysis framework to predict fatigue durability. The second year of the project will focus on developing Bayesian analysis techniques which will update usage and damage states (based on real and simulated inspection data) to provide revised estimates on durability. BENEFIT: In the current age of large multi-disciplinary virtual simulation, developing a multi-disciplinary, multi-fidelity uncertainty management tool is useful in determining how to minimize overall uncertainty in analytical predictions. In addition the methodology can be used to optimize for the best use of computational resources to arrive at most robust predictions. The benefit of this methodology lies in creating understanding of how parameter changes at each discipline level affects overall system reliability and uncertainty.

* information listed above is at the time of submission.

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