Application of data-driven design optimization methodology to a multi-objective design optimization problem

Abstract

The data-driven design optimization methodology (DDDOM) is an application of the dynamic data-driven application system concept in the engineering design domain. The DDDOM combines experiments and simulations concurrently and tends to achieve better designs in less time with less effort than traditional methods. This paper presents the application of the DDDOM to a multi-objective design optimization problem—design of a cooling system for electronic elements. In the DDDOM approach, both simulation and experimental data are combined to generate good surrogate models for the objective functions. The ε-constraint optimization method is then performed on the surrogate models and the Pareto set is found. The presented approach provides a new methodology for design optimization.

Keywords:

• Dynamic data-driven application system
• Data-driven design optimization methodology
• Multi-objective design optimization
• Regression model

Acknowledgements

The authors acknowledge the financial support provided by the National Science Foundation under Grant No. NSF-CTS-0121058, monitored by Dr Frederica Darema, Dr C.F. Chen and Dr Michael Plesniak.