TY - JOUR
T1 - A data driven deep neural network model for predicting boiling heat transfer in helical coils under high gravity
AU - Liang, Xing
AU - Xie, Yongqi
AU - Day, Rodney
AU - Meng, Xianhai
AU - Wu, Hongwei
N1 - © 2020 Elsevier Ltd. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.ijheatmasstransfer.2020.120743
PY - 2021/2/1
Y1 - 2021/2/1
N2 - 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, which is compared with experimental data. A test rig is set up to provide high gravity up to 11 g with a heat flux up to 15100 W/m 2 and the mass velocity range from 40 to 2000 kg m −2 s −1. In the current work, a total 531 data samples have been used in the ANN model. The proposed model was developed in a 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 results demonstrate that a 8-512-512-64-2 neural network has the best performance in predicting the helical coil characteristics with (R 2=0.853, MSE=0.018, MAE=0.074, MAPE=1.110, RMSE=0.136, cosine proximity=1.000) in the 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, and especially achieved excellent performance in predicting outputs that have a very large range of value differences.
AB - 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, which is compared with experimental data. A test rig is set up to provide high gravity up to 11 g with a heat flux up to 15100 W/m 2 and the mass velocity range from 40 to 2000 kg m −2 s −1. In the current work, a total 531 data samples have been used in the ANN model. The proposed model was developed in a 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 results demonstrate that a 8-512-512-64-2 neural network has the best performance in predicting the helical coil characteristics with (R 2=0.853, MSE=0.018, MAE=0.074, MAPE=1.110, RMSE=0.136, cosine proximity=1.000) in the 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, and especially achieved excellent performance in predicting outputs that have a very large range of value differences.
KW - Boiling
KW - deep neural network
KW - heat transfer
KW - helical coils
KW - high gravity
UR - http://www.scopus.com/inward/record.url?scp=85097414850&partnerID=8YFLogxK
U2 - 10.1016/j.ijheatmasstransfer.2020.120743
DO - 10.1016/j.ijheatmasstransfer.2020.120743
M3 - Article
SN - 0017-9310
VL - 166
JO - International Journal of Heat and Mass Transfer
JF - International Journal of Heat and Mass Transfer
M1 - 120743
ER -