As global warming is rapidly emerging as one of the inherently global issues, one of the renewable energies, i.e. thermo-electric generation, has received attention. In order to increase the efficiency of thermo-electric generation, a maximum temperature difference in plain fins in a heat exchanger is needed, and an appropriate pressure drop is required to ensure stable flow of high temperature fluid. In the present study, the characteristics of the temperature difference and pressure difference for the 2 kW-class thermo-electric generator installed in exhaust pipes of a cogeneration plant were investigated numerically via thermal fluid analysis using ANSYS CFX. Then, size optimization for plain fins of a heat exchanger was carried out using SAS JMP, in which the temperature difference was maximized while meeting the requirement of a given pressure drop condition. A meta-model was generated by using the response surface model, and individual desirability functions were defined to derive the optimal solution that provided the maximal overall desirability function. The result obtained by size optimization showed that the temperature difference of the optimized plain fins of a heat exchanger increased by approximately 27% in comparison with the original model under the given pressure drop condition.

In practical design process, designer needs to find an optimal solution by using full factorial discrete combination, rather than by using optimization algorithm considering continuous design variables. So, ANOVA(Analysis of Variance) based on an orthogonal array, i.e. Taguchi method, has been widely used in most parts of industry area. However, the Taguchi method is limited for the shape optimization by using CAE, because the multi-level and multi-objective optimization can‘t be carried out simultaneously. In this study, a combined method was proposed taking into account of multi-level computational orthogonal array and TOPSIS(Technique for Order preference by Similarity to Ideal Solution), which is known as a classical method of multiple attribute decision making and enables to solve various decision making or selection problems in an aspect of multi-objective optimization. The proposed method was applied to a case study of the multi-level shape optimization of lower arm used to automobile parts, and the design space was explored via an efficient application of the related CAE tools. The multi-level shape optimization was performed sequentially by applying both of the neural network model generated from seven-level four-factor computational orthogonal array and the TOPSIS. The weight and maximum stress of the lower arm, as the objective functions for the multi-level shape optimization, showed an improvement of 0.07% and 17.89%, respectively. In addition, the number of CAE carried out for the shape optimization was only 55 times in comparison to full factorial method necessary to 2,401 times.