Abstract
A rectangular microchannel heat sink is modeled by employing thermal resistance and pressure drop networks. The available correlations for both thermal resistance and pressure drop are utilized in optimization. A multi-objective optimization technique, the prey–predator algorithm, is employed with the objective to find the optimal values for the heat sink performance parameters, i.e., thermal resistance and the pumping power of the heat sink. Additionally, a radial basis function neural network is used to investigate a relationship between these parameters. Full training based on the prey–predator algorithm with the sum of the squared error function is used to achieve the best performance of the model. The analysis of variance method is also employed to test the performance of this model. This study shows that the multi-objective function based on the prey–predator algorithm and the neural networks is suitable for finding the optimal values for the microchannel heat sink parameters. The minimum values of the multi-objective function are found to be “pumping power = 2.79344” and “total thermal resistance = 0.134133”.
Highlights
These days, the size of electronic devices is reducing, whereas the power density is growing continuously
Theof assumed of parameters b with an parameters and operatingare conditions reported and operating conditions reportedare in Table
The output weights are calculated with the hidden layer parameters by using the optimization algorithms
Summary
These days, the size of electronic devices is reducing, whereas the power density is growing continuously. Several researchers including [7,8,9,10,11] used microchannel heat sinks to reduce hydraulic and thermal resistance to improve the overall performance of chips and other electronic devices. We have used multi-objective optimization ( known Pareto optimization), and a set of solutions of optimal values of total thermal resistance, and the pumping power of the heat sink, which depends upon the overall pressure drop in the heat sink, is obtained. The present study is about the optimization of multi-objective functions using PPA to determine the optimal values of total thermal resistance and the pumping power. A literature review related to entropy generation and optimization is described
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