Abstract
Uncertainty is pervasive and exerts a profound influence in engineering practice. Uncertainty quantification is a primary task and of paramount importance in uncertainty analysis. To deal with both dependent and independent uncertain parameters in engineering problems, an improved multidimensional parallelepiped model (IMPM) is subsequently developed. Compared with traditional methods that entirely based on statistical features, more accurate uncertainty quantification results can be obtained by means of IMPM. To enhance the efficiency of uncertainty propagation analysis under IMPM, a radial basis function neural network (RBFNN) surrogate model is constructed to replace the original time-consuming finite element simulation model. Eventually, the effectiveness of proposed models and numerical methods is validated through an engineering example involving the coupled vibro-acoustic system considering thermal stress of a hypersonic vehicle.
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