Adaptive Refined-Model-Based Approach for Robust Design Optimization
Robust design optimization (RDO) has been noteworthy in realizing optimal design of engineering systems in presence of uncertainties. However, computations involved in RDO prove to be intensive for real-time applications. For addressing such issues, a meta-model-assisted RDO framework has been proposed. It has been further observed in such approximation-based RDO frameworks that accuracy of the meta-model is an important factor and even slight deviation in intermediate iterations may eventually lead to false optima. Therefore, two-tier improvement has been incorporated within existing Kriging model so as to ensure accurate approximation of response quantities. Firstly, the trend portion has been refined so that the model is capable of approximating higher order non-linearity. Secondly, a sequential basis selection scheme has been merged during model building, which reduces computational complexity significantly in case of large-scale systems. Implementation of the proposed approach in a few examples clearly illustrates its potential for further complex problems.