Adaptive Variable-Fidelity Analysis and Design Using Dynamic Fidelity Indicators

Abstract

The purpose of the current study is to develop a decision-making model of a dynamic fidelity indicator (DFI) for adaptive variable-fidelity (VF) analysis and to apply it to practical engineering problems, including computational design optimization. The biggest advantage is considerable computation efficiency while satisfying solution accuracy. Bayesian-based DFI and difference-based DFI are formulated as probabilistic model validation metrics to quantify model-form uncertainties attributed to the VF analysis. Two adaptive VF surrogate models of the VF kriging with regression and sample reinterpolation and the simple VF kriging with an additive bridge function are developed. The VF kriging models are integrated with the DFIs into an efficient global optimization design framework for the design problems, which was further improved by the multipoint and multi-objective infill sampling criteria. Validation of the developed framework is carried out with two analytic functions for solution optimality and computational efficiency. As a practical design application, the NLR 7301 multi-element airfoil with a flap is designed for high lift while the model-from uncertainty is defined by different flow solutions of the Euler and Reynolds-averaged Navier–Stokes equations. The results show that the design optimization becomes more efficient with great accuracy by the adaptive VF analysis with the DFIs.