考虑随机和认知混合不确定性的 CFD 模型修正方法

A type of model updating framework is proposed, aiming at the challenge of CFD model updating under mixed aleatory and epistemic uncertainty. The framework integrates mixed uncertainty quantification, global sensitivity analysis and parameter updating strategy. The method of mixed uncertainty quantification is established based on evidence theory, and sensitivity analysis index——change rate of probability envelope area for mixed uncertainty is constructed based on evidence theory. A parameter updating method based on the likelihood samples strategy is proposed. For the CFD numerical simulation of the three-dimensional wing ONERA M6, the probability envelope representation of the lift coefficient is obtained by quantifying mixed uncertainty, considering epistemic uncertainty of the turbulence model coefficients and aleatory uncertainty of the incoming flow conditions. Based on this, the global sensitivity analysis is carried out to explore the key turbulence model coefficients that have a great impact on the output, so as to reduce the complexity and calculation of the model updating. The key coefficients are updated according to the likelihood samples strategy. The updated CFD simulation results following parameter iterative updating show a strong degree of consistency with the experimental data, demonstrating the efficacy of the suggested CFD model updating technique.