Multi-Fidelity Uncertainty Propagation Approach for Multi-Dimensional Correlated Flow Field Responses

To quantify uncertainties in the multidimensional correlated flow field responses resulting from uncertain model parameters in computational fluid dynamics (CFD), this paper proposes an uncertainty quantification framework that utilises proper orthogonal decomposition (POD) and a co-Kriging model. A mixed sample snapshot set containing both high- and low-fidelity responses is first formed by transferring high-fidelity responses into the space of low-fidelity responses. The use of POD on this snapshot set reduces the multidimensional correlated responses to reduced-dimension responses, which are coefficients of basis functions. A co-Kriging model is then established between the input parameters and reduced-dimension responses. This comprehensive model enables predictions of multidimensional correlated responses under new input parameters. Through an analysis of a NACA0012 airfoil, it is demonstrated that the proposed multi-fidelity approach offers significant advancements in accuracy and efficiency compared to the single-fidelity model, providing an efficient prediction model for uncertainty propagation analysis.