Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization

TLDR

Response surface methods have been used for a variety of applications in aerospace engineering, particularly in multidisciplinary design optimization. The study investigates using kriging models instead of second‑order polynomial response surfaces to build global approximations for designing an aerospike nozzle, aiming to assess challenges in constructing accurate kriging models for multidisciplinary design optimization. The authors perform error analysis and graphical comparison of response surface and kriging models, and solve four optimization problems using both approximations to evaluate their suitability for multidisciplinary design optimization. Kriging models employing a constant global model and Gaussian correlation function produce slightly more accurate global approximations than second‑order polynomial response surfaces.

Abstract

Response surface methods have been used for a variety of applications in aerospace engineering, particularly in multidisciplinary design optimization. We investigate the use of kriging models as alternatives to traditional second-order polynomial response surfaces for constructing global approximations for use in a real aerospace engineering application, namely, the design of an aerospike nozzle. Our objective is to examine the difeculties in building and using kriging models to create accurate global approximations to facilitate multidisciplinary design optimization. Error analysis of the response surface and kriging models is performed, along with a graphical comparison of the approximations. Four optimization problems are also formulated and solved using both sets of approximation models to gain insight into their use for multidisciplinary design optimization. We end that the kriging models, which use only a constant “global” model and a Gaussian correlation function, yield global approximations that are slightly more accurate than the response surface models.