A Global Two-Layered Meta-Model for Response Statistics in Robust Design Optimization
T Chatterjee (Swansea University), MI Friswell, S Adhikari & R Chowdhury (IIT Roorkee, India)
Engineering Optimization, Vol. 54, No. 1, January 2022, pp. 153-169.
Robust design optimization (RDO) of large-scale engineering systems is computationally intensive and requires significant CPU time. Considerable computational effort is still required within conventional meta-model assisted RDO frameworks. The primary objective of this paper is to further minimize the computational requirements of meta-model assisted RDO by developing a global two-layered approximation based RDO technique. The meta-model in the inner layer approximates the response quantity and the meta-model in the outer layer approximates the response statistics computed from the response meta-model. This approach eliminates both model building and Monte Carlo simulation from the optimization cycle, and requires considerably fewer actual response evaluations than a single layered approximation. To demonstrate the approach, two recently developed compressive sensing enabled globally refined Kriging models have been utilized. The proposed framework is applied to one test example and two real-life applications to clearly illustrate its potential to yield robust optimal solutions with minimal computational cost.
This material has been published in the Engineering Optimization, Vol. 54, No. 1, January 2022, pp. 153-169, the only definitive repository of the content that has been certified and accepted after peer review. Copyright and all rights therein are retained by Taylor & Francis.
Link to paper using doi: 10.1080/0305215X.2020.1861262