Efficient design optimization of complex system through an integrated interface using symbolic computation. (December 2018) - Kim, Hansu; Kim, Shinyu; Kim, Taekyun; Lee, Tae Hee; Ryu, Namhee; Kwon, Kihan; Min, Seungjae
Highlights: An integrated interface is developed to focus on concept design via Maple software. Outlier analysis, screening, kriging model are adopted to the integrated interface. Symbolic computation is implemented to reduce computational cost. Design optimization of combat vehicle system is applied to the integrated interface. Optimization time is reduced by the integrated interface and symbolic computation. Abstract: A complex system is composed of several subsystems and numerous lower level components. It is inefficient to design the complex system at the system level by using high-fidelity models. A system level approach using a language-based model is required to design such a highly complex system before implementing a detailed design. However, as commercial process integration and design optimization software packages focus on detailed design, which uses the high-fidelity models, it is difficult to perform the design optimization of a highly complex system, such as a combat vehicle. Moreover, as commercial optimization software packages use numerical computation, the gradient calculation cost can increase, and the matrix computation process can be inefficient in terms of optimization time. Therefore, in this study, an integrated interface for efficient design optimization was developed to focus on concept design using a MODELICA language-based model. Additionally, the design variable screening by using analysis of variance, surrogate modeling through sequential design of experiments, and symbolic computation were used to solve the aforementioned problems. These were applied to the design optimization of the combat vehicle system to demonstrate the effectiveness of the integrated interface and symbolic computation. In conclusion, a concept design utilizing a MODELICA language-based model was achieved, and the optimization time achieved by symbolic computation was largely reduced in comparison to the optimization time achieved by numerical computation.