An Adaptive Multi-Fidelity Surrogate Model for Uncertainty Propagation Analysis

To quantify the uncertainties in multi-dimensional flow field correlated responses caused by uncertain model parameters, this paper presents an adaptive multi-fidelity model based on gappy proper orthogonal decomposition (Gappy-POD), which integrates the two conventional approaches for enhancing the efficiency of surrogate modeling, namely, multi-fidelity modeling and adaptive sampling algorithms. The challenges surrounding the selection of initial high-fidelity samples and the subsequent incremental augmentation of these samples are addressed. The k-means clustering algorithm is employed to identify locations within the parameter space for conducting high-fidelity simulations, leveraging insights gained from low-fidelity responses. An adaptive sampling criterion, leveraging the low-fidelity projection error derived from the Gappy-POD method, is implemented to progressively augment high-fidelity samples. The results demonstrate that the adaptive model consistently outperforms random sampling methods, highlighting its superiority in terms of accuracy and reliability, providing an efficient and reliable prediction model for uncertainty quantification.