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A. Kalyan Charan,
C. Venkateshwar Reddy,
C.N. Rohit Sai,
K. Someshwar Rao,
- , Department of Mechanical Engineering, Matrusri Engineering College, Saidabad, Hyderabad, Telangana, India
- , Department of Mechanical Engineering, Matrusri Engineering College, Saidabad, Hyderabad, Telangana, India
- , Department of Mechanical Engineering, Matrusri Engineering College, Saidabad, Hyderabad, Telangana, India
- , Department of Mechanical Engineering, Matrusri Engineering College, Saidabad, Hyderabad, Telangana, India
Abstract
Efficient thermal management is essential for improving the performance and reliability of modern engineering systems and electronic devices. This study presents the design, simulation, and optimization of a pin-fin heat sink using SolidWorks for three-dimensional modeling and ANSYS for thermal and computational fluid dynamics (CFD) analysis. Four different pin-fin geometries, namely square, pentagon, octagon, and circular fins, are considered to evaluate their thermal performance under varying operating conditions. Aluminum is selected as the heat sink material, while air is used as the cooling fluid. The thermal analysis is carried out for inlet temperatures ranging from 400 K to 550 K and airflow velocities varying from 5 m/s to 8 m/s. The performance of each configuration is evaluated in terms of heat transfer coefficient and Nusselt number. The simulation results indicate that increasing airflow velocity significantly enhances heat dissipation due to improved convective heat transfer, while temperature variation has a comparatively smaller influence on thermal performance. To determine the optimal combination of operating parameters, the Taguchi optimization technique with an L16 orthogonal array is implemented using Minitab. Signal-to-noise (S/N) ratio analysis are performed to identify the most influential factors affecting heat transfer behavior. The statistical analysis confirms that airflow velocity is the dominant parameter influencing thermal efficiency, followed by fin geometry. Among the tested configurations, the square pin-fin arrangement exhibits the best thermal performance compared to the other fin shapes. The results show that the optimal operating condition is achieved at an inlet temperature of 400 K and airflow velocity of 8 m/s, where the heat transfer coefficient reaches approximately 40 W/m²K. The square pin-fin geometry demonstrates superior heat dissipation characteristics due to its improved surface interaction with airflow. The findings also reveal that higher airflow velocities improve convective cooling effectiveness, making airflow management a critical factor in heat sink design. In addition to numerical simulation and statistical optimization, a machine learning approach based on Linear Regression (LR) is developed to predict thermal performance and validate the simulation outcomes. The generated CFD dataset is used for training and testing the predictive model, and the predicted values show good agreement with simulation results. The proposed integrated approach combining CFD analysis, Taguchi optimization, and machine learning provides an efficient and reliable framework for enhancing pin-fin heat sink performance while reducing computational time and repeated simulation effort in thermal management applications.
Keywords: Pin-fin heat sink, CFD analysis, thermal management, Taguchi method, ANOVA, Linear Regression, machine learning, heat transfer coefficient
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Trends in Mechanical Engineering & Technology
| Volume | 16 | |
| 02 | ||
| Received | 29/05/2026 | |
| Accepted | 17/06/2026 | |
| Published | 04/07/2026 | |
| Publication Time | 36 Days |