基于人工神经网络模型的超临界RP-3热物性计算

为准确得到超临界压力下RP-3的热物性，基于人工神经网络（ANN）方法建立超临界RP-3的密度、黏度、比定压热容和导热系数的计算模型。以广义对应态法则计算得到的RP-3热物性结果训练神经网络，并耦合了实验误差模型得到修正后的ANN模型。计算温度变化范围为300～800 K，压力变化范围为3～6 MPa。结果表明：ANN模型能准确地预测超临界RP-3的热物性，且计算精度比广义对应态法则计算得到的结果提高了16.3%。在压力为5 MPa的工况下，ANN模型预测的密度、黏度、比定压热容和导热系数的回归系数均大于0.99，与实验结果平均相对误差分别为1.5%、4.1%、0.9%和0.7%。

Calculation of thermophysical properties of supercritical RP-3 based on artificial neural network model

In order to accurately obtain the thermophysical properties of RP-3 under supercritical pressure, the calculation models of density, viscosity, specific heat capacity at constant pressure and thermal conductivity of supercritical RP-3 were established based on artificial neural network （ANN） method. The RP-3 thermophysical properties obtained by the extended corresponding state were used to train the neural network, and the modified ANN model was obtained by coupling the experimental error model. The calculated temperature range was 300?800 K, and the pressure range was 3?6 MPa. The results showed that the ANN model can accurately predict the thermophysical properties of supercritical RP-3, and the calculation accuracy was 16.3% higher than that of the extended corresponding state. At the pressure of 5 MPa, the regression coefficients of density, viscosity, specific heat capacity at constant pressure and thermal conductivity predicted by the ANN model were all greater than 0.99. The mean relative errors with the experimental results were 1.5%, 4.1%, 0.9% and 0.7%, respectively.