A novel case of honeycomb shaped pin fin heat sink: CFD-data driven machine learning models for thermal performance prediction

This research explores a useful thermal engineering application utilizing novel honeycomb-shaped pin fins heat sink (PFHS). 3D incompressible flow and heat transfer are examined using standard k-ԑ turbulence model for Reynolds numbers ( Re ) ranging from 8547 to 21367. Using the PFHS with 1.5 mm side length and pitch arrangement of ( S = 25 × 25), the Nusselt Number ( Nu ) increased by 171 % at a Re of 21367. Furthermore, Cu-Diamond composite material raises Hydrothermal performance factor ( HTPF ) to 3.304. At a Re of 8547, honeycomb heat sink has 200 % greater HTPF than the baseline. Predictive modeling of HTPF , Nu , and pressure drop ( ΔP ) was done using Artificial Neural Network (ANN), Multiple Linear Regression (MLR), Extreme Gradient Boosting Regression (XGBR), and Light Gradient Boosting Machine Regression (LGBMR). The four models with the highest R 2 and lowest MRE on the test dataset performed best. Both models were implemented using Keras and Sklearn. XGBR and MLR have superior HTPF prediction accuracy (R 2 test = 0.977, MRE (%) = 1.1 and 0.924, MRE (%) = 2.1). XGBR and MLR predicted the Nu well with R 2 test values of 0.979 and MRE percentages of 1.9 and 3.9. R 2 test of 0.999 and MRE (%) of 0.3 indicate that XGBR predicts pressure reduction.