High-fidelity chaotic polynomial correction method suitable for CFD uncertainty quantification

Abstract

The invention discloses a high-fidelity chaotic polynomial correction method suitable for CFD uncertainty quantification, and the method is characterized in that the method comprises the steps of obtaining a low-fidelity model at a low-fidelity model level through calculation, and building a rough chaotic polynomial expansion; performing a small amount of calculation on a high fidelity model level; and correcting the low-fidelity model to obtain a final high-fidelity correction model. According to the invention, the statistical information of the obtained high fidelity correction model is slightly different from that of the original high fidelity model; the mean value difference is smaller than three ten thousandths and can be ignored, the error requirement of engineering application is completely met, the calculation time needed by the high-fidelity model established through the method is about 40% of that of an original method, and the calculation cost is greatly saved.

Description

technical field [0001] The invention relates to the field of computational fluid dynamics, in particular to a high-fidelity chaotic polynomial correction method suitable for CFD uncertainty quantification. Background technique [0002] CFD (Computational Fluid Dynamics, Computational Fluid Dynamics) has played an increasingly important role in aerospace, water and land transportation, energy power, atmosphere and ocean and other fields. However, there are a large number of uncertain parameters in CFD, such as turbulence model coefficients, thermophysical parameters and so on. This also leads to significant uncertainties in the simulation results. Uncertain factors may lead to product performance fluctuations, or even functional failure. NASA has investigated 2,500 in-orbit vehicle failures, about 52% of which were caused by uncertain factors. Therefore, in the process of aircraft optimization design and performance evaluation, it is necessary to quantify the influence of ...

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Problems solved by technology

As the dimension of uncertainty parameters increases, the computational demands of these two methods increase sharply, which limits their application in engineering problems, which also makes it difficult for the industrial sector to evaluate the potential impact of numerous uncertain parameters on product performance. harm

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