SBIR-STTR Award

The overall technical objective of the Phase I effort is to develop a Bayesian-based, data driven, probabilistic limit cycle oscillation (LCO) prediction tool that can be used to identify critical and non-critical aircraft with stores configurations. This tool will be built upon a non-deterministic nonlinear structural damping (NSD) model integrated in the ZONA Euler Unsteady Solver (ZEUS). The NSD model is essentially a generalized van der Pol NSD model with several tunable parameters, called the NSD parameters. Using the historical flight test data of various F-16 with store configurations as the training database, probability density function of these NSD parameters can be established via the Bayesian methodology. Simulating random values of these NSD parameters according to these probability density functions and computing the corresponding LCO amplitudes using ZEUS with the non-deterministic NSD model leads to realizations and probability density functions of the LCO amplitudes at various Mach number and altitudes. Once the non-deterministic NSD model is established, the Bayesian-based, data driven, probabilistic LCO prediction tool will have a true LCO predictability of aircraft with stores configurations which can be used to assess, probabilistically, whether the particular stores configuration can be cleared or must be flight tested.Limit Circle Oscillation (LCO),Non-deterministic Nonlinear Structural Damping (NSD) Model,Bayesian Methodology,Aircraft with Stores Configuration,Probability Density Function,Historical Flight Test Data,LCO Predictive Tool,ZONA Euler Unsteady Solve

The overall technical objective of the Phase II effort is to extend the Phase I developments and achieve a fully predictive Bayesian-based, data driven, probabilistic prediction tool of the F-16 limit cycle oscillation (LCO). This tool will rely on the available flight test data on some configurations to predict the LCO amplitude on different configurations within an uncertainty band that account for all uncertainties. These predictions will be achieved using a calibrated finite element model of the F-16 and either ZONA Euler Unsteady Solver (ZEUS) or NASA’s FUN3D code as aeroelastic analysis software whichever is faster while providing the necessary accuracy. Both of these codes will be enriched with the nonlinear structural damping (NSD) model developed earlier by the ZONA/ASU Team which captures the changes in damping taking place when the deformations of the structure become large. A separate set of comparisons of the F-16 behavior predicted by ZEUS with NSD and FUN3D with NSD will elucidate the selection of one aeroelastic software over the other. The NSD model includes two tunable damping parameters a and g. Extending the Phase I efforts, the mean values of these two parameters will be correlated to the flight conditions and F-16 with stores configuration to first permit a deterministic computation of the LCO amplitudes. Next, the damping parameters will be considered as random to model the epistemic and aleatoric uncertainties in the predictions/validations with flight test data. Further, their joint distribution will be estimated in a Bayesian format using available historical flight test data on 10 different configurations. Finally, the posterior distribution and uncertainty bands of the LCO amplitude will be predicted for any new configuration/flight test conditions which could then be used to assess, probabilistically, whether the particular stores configuration can be cleared for flight. The Monte Carlo simulations involved in this last phase will be greatly sped up through the use of the fast LCO amplitude surrogate developed and successfully validated in Phase I in the context of ZEUS with NSD. The validity of this surrogate with FUN3D with NSD data is expected and will be confirmed in Phase II.