| Autor: |
Kazi Masuk Elahi, Nabil Mohammad Chowdhury, Mohammad Rejaul Haque, Md Mamunur Rashid, Md Meraj Hossain, Tahmid Sadi |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Case Studies in Thermal Engineering, Vol 61, Iss , Pp 105088- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2214-157X |
| DOI: |
10.1016/j.csite.2024.105088 |
| Popis: |
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 R2 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 (R2test = 0.977, MRE (%) = 1.1 and 0.924, MRE (%) = 2.1). XGBR and MLR predicted the Nu well with R2test values of 0.979 and MRE percentages of 1.9 and 3.9. R2test of 0.999 and MRE (%) of 0.3 indicate that XGBR predicts pressure reduction. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
