We demonstrate an adaptive sampling approach for computing the probability of a rare event for a set of three-dimensional airplane geometries under various flight conditions. We develop a fully automated method to generate parameterized airplanes geometries and create volumetric mesh for viscous CFD solution. With the automatic geometry and meshing, we perform the adaptive sampling procedure to compute the probability of the rare event. We show that the computational cost of our adaptive sampling approach is hundreds of times lower than a brute-force Monte Carlo method.

翻译：我们展示了一种适应性抽样方法,用于计算不同飞行条件下一组三维飞机几何的罕见事件的概率。我们开发了一种完全自动化的方法,产生参数化飞机的几何和为粘结的CFD溶液创建体积网格。通过自动几何和网格,我们运用适应性抽样程序来计算稀有事件的概率。我们显示,我们适应性取样方法的计算成本比蒙特卡洛的粗力法低数百倍。