Global Meta-Model for System Level Multidisciplinary Design Optimization

This paper presents an efficient global meta-model building technique for solving high fidelitymultidisciplinary design optimization (MDO) problems. The main difficulties associated withMDO are often characterized by interdisciplinary couplings, high computational cost of ananalysis in individual disciplines and a large number of design variables and constraints. Theseissues result in very high overall computational cost limiting applications of MDO to complexindustrial design problems. To address these issues a combination of global meta-model usingmoving least squares (MLSM) and the trust region strategy is introduced. A global meta-modelis used to identify the feasible and infeasible regions and the trust region strategy is used for adetailed search of the feasible region. The technique is demonstrated on a test problem and theeffectiveness of the method for modeling and system level collaborative optimization usinghigh fidelity models is studied. The results show that meta-model based on MLSM provide ahigh degree of accuracy whilst achieving a considerable reduction in computational cost