Efficient Solutions for Electronic Chip Cooling: Multi-Objective Optimization Using Evolutionary Algorithms with Boron Nitride Nanotube-Based Nanofluid

Abstract

Optimization algorithms have significantly evolved because of advancements in computational capacity. This increase aids in the availability of data to train various artificial intelligence models and can be used in optimizing solutions for electronic chip cooling. In the current study, such a microchannel heat sink (MCHS) is optimized using a Boron Nitride Nanotube (BNN)-based nanofluid as a coolant. Thermal resistance and pumping power are chosen as the objective functions, while geometric parameters such as the channel aspect and width ratio are used as the design variables. Multi-objective multiverse optimizer (MOMVO), an evolutionary algorithm, is used to optimize both objective functions, which are minimized simultaneously. The primary objective of this study is to study the applicability of such advanced multi-objective optimization algorithms, which have not previously been implemented for such a thermal design problem. Based on the study, it is found that the optimal results are obtained with a population size of only 50 and within 100 iterations. Using the MOMVO optimization, it is also observed that thermal resistance and pumping power do not vary significantly with respect to the channel aspect ratio, while pumping power varies linearly with the channel width ratio. An optimum thermal resistance of 0.0177 °C/W and pumping power of 10.65 W are obtained using the MOMVO algorithm.