The pulsating heat pipe is a very promising heat dissipation device to address the challenge of higher heat-flux electronic chips,as it is characterised by excellent heat transfer ability and flexibility for miniaturi...The pulsating heat pipe is a very promising heat dissipation device to address the challenge of higher heat-flux electronic chips,as it is characterised by excellent heat transfer ability and flexibility for miniaturisation.To boost the application of PHP,reliable heat transfer performance evaluationmodels are especially important.In this paper,a heat transfer correlation was firstly proposed for closed PHP with various working fluids(water,ethanol,methanol,R123,acetone)based on collected experimental data.Dimensional analysis was used to group the parameters.It was shown that the average absolute deviation(AAD)and correlation coefficient(r)of the correlation were 40.67%and 0.7556,respectively.For 95%of the data,the prediction of thermal resistance and the temperature difference between evaporation and condensation section fell within 1.13K/Wand 40.76K,respectively.Meanwhile,an artificial neural networkmodelwas also proposed.The ANN model showed a better prediction accuracy with a mean square error(MSE)and correlation coefficient(r)of 7.88e-7 and 0.9821,respectively.展开更多
基金This work is funded by National Natural Science Foundation of China(No.51906216).
文摘The pulsating heat pipe is a very promising heat dissipation device to address the challenge of higher heat-flux electronic chips,as it is characterised by excellent heat transfer ability and flexibility for miniaturisation.To boost the application of PHP,reliable heat transfer performance evaluationmodels are especially important.In this paper,a heat transfer correlation was firstly proposed for closed PHP with various working fluids(water,ethanol,methanol,R123,acetone)based on collected experimental data.Dimensional analysis was used to group the parameters.It was shown that the average absolute deviation(AAD)and correlation coefficient(r)of the correlation were 40.67%and 0.7556,respectively.For 95%of the data,the prediction of thermal resistance and the temperature difference between evaporation and condensation section fell within 1.13K/Wand 40.76K,respectively.Meanwhile,an artificial neural networkmodelwas also proposed.The ANN model showed a better prediction accuracy with a mean square error(MSE)and correlation coefficient(r)of 7.88e-7 and 0.9821,respectively.
