Aiming at meeting the requirement of high heat flux electronics cooling, the geometric complexity of manifold microchannels as an energy-saving technique has been increasing rapidly. It is well worth exploring optimal prediction models for the heat transfer and flow characteristics of manifold microchannels to reduce computing resources and time costs in the optimization process. In this paper, hybrid prediction approaches based on artificial neural networks are proposed for manifold microchannels. Four optimization algorithms, namely, genetic algorithm, particle swarm optimization algorithm, artificial hummingbird algorithm, and zebra optimization algorithm were adopted to enhance the prediction performance. Initially, all prediction models were trained and tested by a numerical database including six input parameters, and the thermal resistance and pumping power were taken as the target outputs. Compared to the original method, hybrid prediction models show higher accuracy and reliability on the numerical data with regression coefficients beyond 0.987. The prediction model optimized by the artificial hummingbird algorithm shows the best performance with the mean average error for thermal resistance and pumping power decreased by 88.7% and 81.4% respectively. Additionally, the runtime of all prediction models is compared and the model optimized by the zebra optimization algorithm takes the longest time, but is still two orders of magnitude shorter than that of the traditional simulation method. Finally, the generalizability of all prediction models is further validated by a database amassed from six experimental studies on manifold microchannels. The results showed that three hybrid models can provide accurate outputs with regression coefficients beyond 0.84 which differs from the poor prediction of the original method. The model optimized by the zebra optimization algorithm shows superior prediction performance for six individual studies, two fluids, three fin shapes, and wide flow rate conditions. The trained hybrid prediction models in this study perform higher accuracy and can be cost-effective and reliable prediction tools for the rapid optimization process of various manifold microchannel applications.
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