Bayesian-Based Probabilistic Limit Cycle Oscillation Prediction of Aircraft with Stores

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.