Study on frosting characteristics and artificial neural network of parallel cold plates
To reveal and predict the influence of base plate temperature and fin spacing on the frost thickness of parallel cold plates,frosting experiments under different base plate temperatures(-10 to-25 ℃)and fin spacing(1 to 3 mm)were carried out,and an artificial neural network prediction model was established.The results show that compared with the base plate temper-ature of-25 ℃,when the base plate temperature is-10 ℃,the time of frost branch crossing is prolonged by 123.3％,and the frost thickness is reduced by 30.3％.When the fin spacing is 3 mm,the frost thickness shows a step-like increase,and the three linear growth slopes decrease in turn,which are 1.20 ×10-6,0.76 ×10-6 and 0.56 ×10-6,respectively.Through veri-fication of the constructed artificial neural network model,it is found that the correlation coefficient is as high as 0.999 8,and the average absolute relative error was as low as 1.357 9％,which verified the accuracy of the model.In addition,the sensitivi-ty analysis of the model input parameters based on the Garson algorithm reveals that the time factor(accounting for 48.30％)plays a leading role in the frost growth process.
Base plate temperatureFin spacingParallel cold platesFrost thicknessArtificial neural networkGarson al-gorithm
NETL
NSTL
万方数据
