Parametric optimization of microchannel heat exchanger using socio-inspired algorithms

Abstract

Miniaturized products and systems have emerged as game-changing innovations with huge potential in the modern period with increasing emphasis on sustainable development and green energy. Automotive, astronomical, electronics, and medical research are just a few of the industries where micro electro mechanical systems (MEMS) have found use. In addition to that, microchannel heat exchangers (MCHX) have been created in response to the growing demand for effective cooling solutions for these small systems. Optimization of these MCHX is important for improving the overall system efficiency. In this work, two popular socio inspired evolutionary algorithms viz. teaching learning-based optimization (TLBO) and cohort intelligence (CI) are applied for optimizing three objectives such as power density, compactness factor, and heat transfer with pressure drop (HTPD) for air-water MCHX. The results obtained are significantly improved when compared with genetic algorithm (GA). Moreover, both the techniques are observed to be robust. This study investigates the use of socio-inspired artificial intelligence (AI) algorithms to support the design and optimization of heat exchangers, highlighting their potential to address complex engineering challenges more efficiently.