Multifidelity coKriging for High-Dimensional Output Functions with Application to Hypersonic Airloads Computation

摘要

An accurate surrogate model is capable of reproducing high-fidelity results at a fraction of the computational cost. coKriging is an efficient method for constructing surrogate models when two levels of fidelity (low and high) tools are available. This paper extends the functionality of coKriging. First, a practical implementation to high-dimensional output functions is developed by introducing POD-coKriging. Second, coKriging is reformulated to allow different sizes of input/output variables between low- and high-fidelity tools. The extended POD-coKriging method is validated against an analytical problem, and its performance for high-dimensional output functions is demonstrated with the prediction of surface pressure distribution on a two-dimensional deflected panel in hypersonic flow. Finally, properties of the extended POD-coKriging were used to estimate the total cost reduction of sample generation, and it is shown that an order-of-magnitude cost reduction is achievable for practical cases.