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Clearance of Aircraft Stores Carriage under Uncertainty

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-19-P-2041
Agency Tracking Number: F18B-008-0020
Amount: $149,776.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF18B-T008
Solicitation Number: 18.B
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-05-24
Award End Date (Contract End Date): 2020-05-24
Small Business Information
566 Glenbrook Drive
Palo Alto, CA 94306
United States
DUNS: 172390481
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Goeric Daeninck
 Senior Research Scientist
 (650) 530-2435
Business Contact
 Frankie Farhat
Phone: (650) 898-9585
Research Institution
 Regents of the University of Michigan
 Prof. Karthik Duraisamy Prof. Karthik Duraisamy
3003 S. State Street
Ann Arbor, MI 48109
United States

 (734) 615-7270
 Nonprofit College or University

The main objective of this STTR effort is three-fold. First, to develop and demonstrate in Phase I a Bayesian methodology exploiting flight test data in order to identify critical store carriage tests and clear non-critical store carriage configurations by updated analysis. Second, to extend in Phase II the scope of this methodology to viscous flows with analysis enriched using analytical sensitivities of flutter and LCO with respect to key store parameters. Also, to develop during Phase II a user-friendly software enabling rapid inclusion of test data and the automation of the model updating process. Third, to transition this software to market in Phase III. To this end, the detailed technical objectives for Phase I are: to reformulate the nonparametric probabilistic method for modeling and quantifying model-form uncertainties in a Bayesian framework; to develop a machine-learning-augmented predictive modeling of nonlinear structural damping in a Bayesian setting by effectively using limited data generated from physical experiments such as flight tests; to develop and verify CFD-CSD-based inviscid nonlinear aeroelastic computational models for approximately 10 F-16 configurations; and to use these computational models and flight test data to demonstrate the feasibility of the resulting Bayesian methodology.

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

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