A data driven deep neural network model for predicting boiling heat transfer in helical coils under high gravity

Abstract

In this article, a deep artificial neural network (ANN) model has been proposed to predict the boiling heat transfer in helical coils under high gravity conditions and compare with actual experimental data. A test rig is set up to provide the high gravity up to 11 g with the heat flux can be up to 15100 W/m2 and the mass velocity range from 40 to 2000 kg m-2 s-1. In the current work, total 531 data samples have been used in the present ANN model. The proposed model was developed in Python Keras environment with Feed-forward Back-propagation (FFBP) Multi-layer Perceptron (MLP) using eight features (mass flow rate, thermal power, inlet temperature, inlet pressure, direction, acceleration, tube inner surface area, helical coil diameter) as the inputs and two features (wall temperature, heat transfer coefficient) as the outputs. The deep ANN model composed of three hidden layers with a total number of 1098 neurons and 300,266 trainable parameters has been found as optimal according to statistical error analysis. Performance evaluation is conducted based on six verification statistic metrics (R^2, MSE, MAE, MAPE, RMSE and cosine proximity) between the experimental data and predicted values. The obtained results demonstrate that 8-512-512-64-2 neural network model has the best performance in predicting the helical coil characteristics with (R2=0.853, MSE=0.018, MAE=0.074, MAPE=1.110, RMSE=0.136 , cosine proximity=1.000) in testing stage. It is indicated that with the utilisation of deep learning, the proposed model is able to successfully predict the heat transfer performance in helical coils, especially achieved excellent performance in predicting the outputs having very large range of value differences.