Thermal performance analysis and optimization of a heat pipe-based electronic thermal management system using experimental data-driven neuro-genetic technique

Abstract

Heat pipes are highly efficient heat transfer devices and are used widely to dissipate heat generated by electronic components, thereby maintaining their operating temperatures and preventing overheating. The present study deals with the thermal performance prediction of a cylindrical heat pipe (length 380 mm) based electronic thermal management system using steady-state experimental investigation. The experiments are conducted under varying operational conditions, including heat inputs (10–50 W), condenser cooling water inlet temperatures (288.15, 293.15, and 298.15 K), and flow rates (10, 25, and 40 LPH). The study shows that the evaporator temperature of the heat pipe consistently rises with the increase in heat inputs for all tested conditions. The lowest evaporator temperature (311.42 K) is noticed for the cooling water inlet temperature of 288.15 K across all heat inputs. Notably, at 10 LPH, the heat pipe supplied with cooling water at 288.15 K exhibits reductions of up to 1.47 % and 2.29 % compared to 293.15 K and 298.15 K, respectively. Similar trends are also observed at 25 LPH and 40 LPH. The heat pipe’s thermal resistance is lowest (0.95 K/W) at a cooling water inlet temperature of 298.15 K. At 10 LPH, there are reductions of 10.19 % and 5.62 % compared to 288.15 K and 293.15 K, and at 40 LPH, reductions are 15.66 % and 7.89 %. The heat pipe’s evaporator heat transfer coefficient also plays a significant role and is maximum for the cooling water inlet temperature of 288.15 K at a flow rate of 10 LPH. To understand the complex behavior of the heat pipe and for better prediction of its thermal performance, a neuro-genetic (experimental data-driven artificial neural network and genetic algorithm) optimization technique is considered in the study. This predicts the optimal combination of operating parameters (heat input, cooling water inlet temperature, and flow rate) to minimize the heat pipe’s evaporator wall temperature which is found to be 312.54 K at a heat input of 10 W, flow rate of 10 LPH, and cooling water inlet temperature of 289.14 K. These input combinations are further validated through the experiments and the error in the heat pipe’s evaporator temperature is found to be 0.66 % only. Overall, this neuro-genetic technique underscores the importance of optimizing operational parameters for enhanced heat pipe performance and offers valuable insights for electronic cooling systems.