Using artificial neural network to predict velocity of sound in liquid water as a function of ambient temperature, electrical and magnetic fields

One of the main thermophysical properties of liquid water is velocity of sound. However, the effect of different externalities on velocity of sound in liquid water is not well known. Therefore, in current study, by designing an artificial neural network (ANN) velocity of sound in liquid water under different externalities is predicted. Selected externalities are ambient temperature from 272.65 K to 348.43 K, different electrical fields in range of 0 V/m to 4.03E + 9 V/m and magnetic fields in range of 0-10.0594 T. To prepare of reference dataset for entry to ANN, numerical and experimental data as macroscopic reference data are extracted from microscopic characteristic of water HB strength. In order to achieve an appropriate ANN, ANN architecture sensitivity analysis is conducted by using an iterative algorithm. Learning procedure in the selected feed-forward back propagation ANN is done by hyperbolic transfer functions. Also, Levenberg-Marquardt algorithm is utilized for the optimization process. ANNs output showed that the maximum MSE in prediction of velocity of sound is 0.00066. Also, the minimum of correlation coefficient in prediction of velocity of sound is 0.99131. Based on the ANNs outputs, weights and bias, an equation to predict of velocity of sound in liquid water under intended externalities is proposed. Also, according to weight sensitivity analysis input of electrical fields with 63% relative importance percentage has a grater impression on the response variable of velocity of sound in liquid water.