Machine learning-based optimization of air-cooled heat sinks

• Published in: Thermal science and engineering progress 2022-09, Vol.34 (C), p.101398, Article 101398
• Main Authors: Shaeri, Mohammad Reza, Sarabi, Soroush, Randriambololona, Andoniaina M., Shadlo, Ameneh
• Format: Article
• Language: English
• Subjects: Air-cooled heat sinks; Artificial neural network; Energy & Fuels; ENGINEERING; Heat sink optimization; Mechanics; Pressure drop; Thermal performance; Thermodynamics; Weight reduction
• ISSN: 2451-9049; 2451-9049
• DOI: 10.1016/j.tsep.2022.101398

•Artificial Neural Networks is used to optimize air-cooled heat sinks.•Optimization is performed over extensive design parameters.•Different optimization objectives are defined based on design priorities.•Generalized method to make effective heat sinks in broad applications is proposed. Machine learning-based models using Artificial Neural Network (ANN) and greedy search algorithm are used to optimize air-cooled parallel plate-finned heat sinks (PPFHSs) subjected to laminar flow over an extensive range of design parameters. The thermal and hydraulic performances of PPFHSs are represented by heat transfer coefficient h and pressure dropΔP, respectively. Optimization objectives for PPFHS designs can vary from industry to industry depending on their design priorities. The present study proposes a novel and generalized optimization method that defines practical optimization objectives and provides an accurate optimization process to design effective PPFHSs for a wide range of industrial applications with different design requirements. Three optimization objectives are presented in this study: (i) the largesth/ΔP, (ii) the largest h within a specified maximum allowed flow rate, and (iii) the lowest weight that maximizes h for operation within the maximum allowed flow rate. While the shortcoming of the first objective is demonstrated, the other two objectives are found to be suitable for designing effective heat sinks (HSs) across different applications. Results suggest a promising trend from the third objective to develop HSs with ∼ 37–68% lower weight, 80–85% reducedΔP, and negligible penalty in h compared with optimized HSs obtained from the second objective. However, since the third objective leads to HSs with thinner fins, structural analysis should be performed to ensure reliable operation of the HSs.