Numerical study and optimization of thermal efficiency for a pin fin heatsink with nanofluid flow by modifying heatsink geometry

Abstract

This paper presents a numerical study on the thermal efficiency of a pin fin heatsink (HEK). The working fluid used is an alumina/water nanofluid, which enters the HEK in a laminar flow regime and exits from its surroundings. This study involves varying the distance between circular pin fins, their height, and their diameter. By altering these parameters, we determine the values of thermal resistance (THR) and temperature uniformity (Teta) on the HEK, along with the heat transfer coefficient (HTC). We further optimize the obtained results using artificial intelligence techniques to minimize the THR of the HEK, maximize the HTC, and achieve the best Teta on the HEK. This numerical investigation employs a two-phase approach to model nanofluid flow within the HEK. The optimization process yields predictions with an accuracy of less than 4%. The findings reveal that increasing the height of the pin fins reduces the HTC and the heat capacity of the HEK, while simultaneously improving the Teta on the HEK. Expanding the distance between pin fins enhances the HTC, decreases the THR of the HEK, and further improves the Teta on the HEK. Similarly, augmenting the diameter of the pin fins amplifies the HTC, reduces the THR, and enhances the Teta on the HEK.

Keywords

• Thermal resistance
• Temperature uniformity
• Heatsink
• Two-phase nanofluid
• Machine learning