Research on Kriging-Based Uncertainty Quantification and Robust Design Optimization

An uncertainty quantification method was proposed based on Kriging surrogate model. Due to the advantage that all probability distributions can be employed in the method without limitations, the method can also be easily applied to various design cases. Based on the uncertainty quantification method, a Kriging-based robust design optimization framework is built by coupling Self-adaptive Multi-Objective Differential Evolution algorithm (SMODE) and 3D Reynolds-Averaged Navier-Stokes (RANS) Solver technique. Upon numerical validation, the robust design optimization is carried out with the aim of simultaneously maximizing averaged thermal performance and minimizing variance. In the optimization process, the fillet radius and the inlet Reynolds number are taken as uncertainty parameters. After optimization, the averaged thermal performance of the optimal solution is greatly improved by 11.5% with reduced sensitivity to uncertainty parameters. At last, mechanism behind thermal performance improve of optimal design is explored by detailed flow analysis. © 2019, Science Press. All right reserved.