SBIR/STTR Award attributes
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.
