CFD model confirmation method and product design method

Abstract

The invention provides a CFD model confirmation method based on a multi-credibility deep neural network and application thereof. The uncertainty of key parameters is quantified by using the multi-credibility deep neural network, so that the calculation amount is reduced. According to the method, the uncertainty of the key parameters is quantified by using the multi-credibility deep neural network, so that the calculation amount is reduced, the CFD result is measured under multiple working conditions by using the model measurement method based on the distance method, the influence of the uncertainty on CFD output can be quickly evaluated, so that quick model confirmation is realized, and the technical bottleneck of large computational complexity of CFD uncertainty quantification at present is broken through. According to the method, a parameter checking strategy based on high-quality small samples is established, parameters with cognitive uncertainty in the CFD model are rapidly and effectively corrected, key parameters with cognitive uncertainty can be rapidly and effectively corrected, and therefore the CFD simulation model with high fidelity is constructed.

Description

technical field [0001] The invention relates to the technical field of CFD model confirmation, in particular to a CFD model confirmation method and a product design method. Background technique [0002] At present, computational fluid dynamics (Computational Fluid Dynamics, CFD) numerical simulation has become an indispensable means of product design and development in many cutting-edge fields such as aerospace and national defense security. However, there are many uncertainties in the modeling and simulation of CFD numerical models, such as boundary conditions, geometric shapes, turbulence models, and numerical formats, which seriously affect the credibility of CFD results. Designing with numerical simulations that are quite different from the real results is very likely to cause the real system to fail to meet the expected performance requirements and introduce potential risks. CFD model confirmation is to use mathematical methods to quantify uncertainties such as boundar...

AI Technical Summary

Problems solved by technology

However, the existing methods for quantifying parameter uncertainties are computationally complex and require a large amount of computation; and in the existing CFD model confirmation, the correction direction of uncertain parameters is random, the correction efficiency is low, and it is impossible to quickly build high-fidelity CFD simulation model of

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