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We demonstrate an adaptive sampling approach for computing the probability ofa rare event for a set of three-dimensional airplane geometries under variousflight conditions. We develop a fully automated method to generateparameterized airplanes geometries and create volumetric mesh for viscous CFDsolution. With the automatic geometry and meshing, we perform the adaptivesampling procedure to compute the probability of the rare event. We show thatthe computational cost of our adaptive sampling approach is hundreds of timeslower than a brute-force Monte Carlo method.

Aerodynamic Risk Assessment using Parametric, Three-Dimensional Unstructured, High-Fidelity CFD and Adaptive Sampling