Optimization of Thermal and Pressure Drop Performance in Circular Pin Fin Heat Sinks Using the TOPSIS Method

Jemit Adhyaru, Mohan Uma, Vedagiri Praveena and Prabhu Sethuramalingam ()
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Jemit Adhyaru: Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India
Mohan Uma: Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India
Vedagiri Praveena: Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India
Prabhu Sethuramalingam: Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India

Energies, 2024, vol. 17, issue 24, 1-36

Abstract: This study aims to optimize the thermal performance of pin fin heat sinks by minimizing the maximum temperature of the heat source. Using ANSYS ICEPAK, simulations were conducted for various design parameters, including the number of fins, inlet flow rate, and fin thickness, across circular fins in both inline and staggered arrangements. The circular staggered configuration with 36 fins (3 mm thick) and a flow rate of 6 CFM (Cubic Feet per Minute) achieved the lowest temperature of 34.96 °C, outperforming the inline arrangement. The Taguchi method helped strike a balance between heat transfer and pressure drop, revealing that flow rate has a greater influence when varied compared to the number of fins and fin thickness. An optimal configuration was identified with 36 fins and a flow rate of 4 CFM, which was less sensitive to operational variations. Analysis of Variance (ANOVA) revealed that inlet flow rate significantly impacts heat sink performance, while polynomial regression models demonstrated strong generalization capabilities, with Root mean square error (RMSE) of 8.92%. These findings provide reliable predictive tools and practical insights for optimizing heat sink designs in electronics cooling applications. By utilizing the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, the coefficient of relative closeness (Cn*) is plotted as a main effect. Referring to the multi-objective optimization-based TOPSIS method, it is found that the attributes are partly from the inlet flow rate (Q) are 63.4% of the number of fins (Nf) (25.05%).

Keywords: heat sink; Taguchi analysis; regression analysis; ANOVA; temperature; pressure drop; machine learning; TOPSIS (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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