Comparison of response surface and kriging models for multidisciplinary design optimization

TLDR

Response surface and kriging models are two prominent approximation methods for computer experiments, with kriging offering an alternative to polynomial response surfaces. This study compares and contrasts second‑order response surface models and kriging models for approximating deterministic computer analyses. Both models were applied to a multidisciplinary aerospike nozzle design that combines CFD and finite‑element analyses, and their errors were evaluated through graphical comparison and four optimization problems. The second‑order response surface and kriging models produced comparable results.

Abstract

In this paper, we compare and contrast the use of second-order response surface models and kriging models for approximating non-random, deterministic computer analyses. After reviewing the response surface method for constructing polynomial approximations, kriging is presented as an alternative approximation method for the design and analysis of computer experiments. Both methods are applied to the multidisciplinary design of an aerospike nozzle which consists of a computational fluid dynamics model and a finite-element model. Error analysis of the response surface and kriging models is performed along with a graphical comparison of the approximations, and four optimization problems m formulated and solved using both sets of approximation models. The second-order response surface models and kriging models-using a constant underlying global model and a Gaussian correlation function-yield comparable results.