Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : December 06, 2016 - December 07, 2016
An optimization approach employing Kriging-based response surface method and Multi-Objective Genetic Algorithm in the design of MCHX with a wavy fin is presented. New CFD-based prediction models for the Colburn factor (j) and friction factor (f) of wavy fins based on 671 results obtained from the CFD simulations are generated by Kriging response surface method. The new models agree better with CFD results than prediction models derived by multiple regression method, which describes 100% of the points within 5% of CFD j factor data and 95.7% of the points for f factor. Design optimization for MCHXs in refrigeration cycles are also performed using MOGA and Kriging models. It was observed that Pareto fronts of objective functions such as pressure drop, LMTD, power consumption, and HX weight are identified by searching optimum solutions in the entire design space without ending with local solutions
