RBF-based mesh morphing improvement using Schur complement applied to rib shape optimization

Abstract

Purpose This paper aims to improve the radial basis fuction mesh morphing method. During a shape optimization based on computational fluid dynamic (CFD) solvers, the mesh has to be changed. Two possible strategies are re-meshing or morphing. The morphing one is advantageous because it preserves the mesh connectivity, but it must be constrained. Design/methodology/approach RBF mesh deformation is one of the most robust and accurate morphing method. Using a greedy algorithm, the computational cost of the method is reduced. To evaluate the morphing performances, a rib shape optimization is performed using the NSGA-II algorithm coupled to kriging metamodels based on CFD. The morphing method is then compared to a re-meshing strategy. Findings The authors propose a method, based on Schur complement, to speed-up the greedy process. By using the information of the previous iteration, smaller linear systems are solved and time is saved. The optimization results highlight the interest of using a morphing-based metamodel regarding the resolution time and the accuracy of the interpolated solutions. Originality/value A new method based on Schur complement is addressed to speed-up the greedy algorithm and successfully applied to a shape optimization.

Multi-objective surrogate model-based optimization of a small aircraft engine air-intake duct

“…23 Kriging-based optimization was performed by Mastrippolito et al (2019) on a rib shape. 24 To our best knowledge, usage of surrogates supported by mesh morphing techniques in ducted flow optimization is still not excessively exploited in the literature. Marchlewski et al (2020) employed this approach in a U-shaped intake multi-objective optimization study.…”