Thermosyphon is an effective heat transfer device which is widely used all over the world for its ease of use, feasible with different environmental challenges. In this research article, the experimentation with different boiling point of working fluids water and R134a has been used in modified thermosyphon. The modified thermosyphon comprises of the cone frustum attached between the condenser and adiabatic section to hold up for high heat inputs. It is noted from the experiment that the working fluids have unique heat transfer capability with regard to thermal properties for applied heat input. The thermal resistance for different fill ratios with water and R134a in thermosyphon was experimented and the optimality in the fill ratio is identified. Due to the modification in the condenser, R134a condenser performs better for both low and high heat inputs but water works well only in high heat inputs. To predict and compare the temperature outputs at the evaporator, adiabatic and condenser sections of the thermosyphon, machine learning algorithm and optimization technique has been deployed and the output is measures for its accuracy, false positive, predictive positive value and effective performance. It is noted both from the experimental and algorithmic approach that the experiment produces less false positive rate which is ≤ 2% and true positive rate which is ≥ 98%, accuracy of the outputs which are ≥ 98%. The optimized outcome also stabilizes the experimental setup strongly and generates an effective performance rate which is ≥ 95%.

热虹吸管是一种有效的传热设备，其易用性在世界范围内得到了广泛应用，在各种环境挑战下都可行。在这篇研究文章中，在沸腾水和R134a中使用不同沸点的实验已被用于改性热虹吸管中。改进的热虹吸管由锥台组成，锥台连接在冷凝器和绝热段之间，可承受高热量输入。从实验中注意到，就施加的热量输入而言，工作流体具有独特的传热能力。对水和R134a在热虹吸管中不同填充比的热阻进行了实验，并确定了填充比的最佳性。由于冷凝器的修改，R134a冷凝器在低热量输入和高热量输入时均表现更好，但水仅在高热量输入时效果良好。为了预测和比较热虹吸器的蒸发器，绝热和冷凝器部分的温度输出，已部署了机器学习算法和优化技术，该输出是其准确性，假阳性，预测性正值和有效性能的度量。从实验方法和算法方法都注意到，实验产生的假阳性率（≤2％）和真阳性率（≥98％）更少，输出的准确度≥98％。优化的结果还可以极大地稳定实验设置，并产生≥95％的有效性能。为了预测和比较热虹吸器的蒸发器，绝热和冷凝器部分的温度输出，已部署了机器学习算法和优化技术，该输出是其准确性，假阳性，预测性正值和有效性能的度量。从实验方法和算法方法都注意到，实验产生的假阳性率（≤2％）和真阳性率（≥98％）更少，输出的准确度≥98％。优化的结果还可以极大地稳定实验设置，并产生≥95％的有效性能。为了预测和比较热虹吸器的蒸发器，绝热和冷凝器部分的温度输出，已部署了机器学习算法和优化技术，该输出是其准确性，假阳性，预测性正值和有效性能的度量。从实验方法和算法方法都注意到，实验产生的假阳性率（≤2％）和真阳性率（≥98％）更少，输出的准确度≥98％。优化的结果还可以极大地稳定实验设置，并产生≥95％的有效性能。预测性正值和有效表现。从实验方法和算法方法都注意到，实验产生的假阳性率（≤2％）和真阳性率（≥98％）更少，输出的准确度≥98％。优化的结果还可以极大地稳定实验设置，并产生≥95％的有效性能。预测性正值和有效表现。从实验方法和算法方法都注意到，实验产生的假阳性率（≤2％）和真阳性率（≥98％）更少，输出的准确度≥98％。优化的结果还可以极大地稳定实验设置，并产生≥95％的有效性能。