Numerical and multi-objective optimal design of bionic corrugated plate-fins heat exchangers by extreme learning machine algorithm

Abstract The structural characteristics of the fins determine the performance of heat exchanger. An outstanding fin structure exhibits high heat transfer performance and low flow resistance, which are conflicting outcomes. Through natural evolution, fish have developed a shape structure suitable for swimming. Inspired by this, this research presents a biomimetic corrugated fin structure based on the fins of eels. The extreme learning machine algorithm is used to construct a surrogate model. The NSGA-III multi-objective optimization method is chosen to optimize the corrugated fin heat exchanger. In comparison with the traditional corrugated fin, the optimized biomimetic fin shows 4.7% enhancement in heat transfer performance and 6.1% decrease in the resistance coefficient. Based on the simulation results, the characteristics of the internal flow field of the fin are analyzed from the perspectives of velocity, temperature, and pressure. This study also incorporates the field synergy theory. By constructing the field synergy equation to calculate the synergy angle between velocity and temperature, the performance advantages of the biomimetic fin are further verified. The proposed novel fin can offer a new alternative for high-performance heat exchangers, and the simulation optimization method can provide a new approach for engineers to design heat exchanger fins.