Adaptive sampling strategies for non-intrusive POD-based surrogates
Purpose - The purpose of this paper is to propose a novel contribution to adaptive sampling strategies for non-intrusive reduced order models based on Proper Orthogonal Decomposition (POD). These strategies aim at reducing the cost of optimization by improving the efficiency and accuracy of POD data-fitting surrogate models to be used in an online surrogate-assisted optimization framework for industrial design. Design/methodology/approach - The effect of the strategies on the model accuracy is investigated considering the snapshot scaling, the design of experiment size and the truncation level of the POD basis and compared to a state-of-the-art radial basis function network surrogate model on objectives and constraints. The selected test case is a Mach number and angle of attack domain exploration of the well-known RAE2822 airfoil. Preliminary airfoil shape optimization results are also shown. Findings - The numerical results demonstrate the potential of the capture/recapture schemes proposed for adequately filling the parametric space and maximizing the surrogates relevance at minimum computational cost. Originality/value - The proposed approaches help in building POD-based surrogate models more efficiently.
adaptive sampling proper orthogonal decomposition high-fidelity simulations non-intrusive reduced-order model surrogate-based optimization sampling methods simulation