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A data driven deep neural network model for predicting boiling heat transfer in helical coils under high gravity. / Liang, Xing; Xie, Yongqi ; Day, Rodney; Meng, Xianhai; Wu, Hongwei.

In: International Journal of Heat and Mass Transfer, Vol. 166, 120743, 01.02.2021.

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@article{cf17100c4cae42e08ddd3ad80a6b03f2,
title = "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, 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. ",
keywords = "Boiling, deep neural network, heat transfer, helical coils, high gravity",
author = "Xing Liang and Yongqi Xie and Rodney Day and Xianhai Meng and Hongwei Wu",
note = "{\textcopyright} 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",
year = "2021",
month = feb,
day = "1",
doi = "10.1016/j.ijheatmasstransfer.2020.120743",
language = "English",
volume = "166",
journal = "International Journal of Heat and Mass Transfer",
issn = "0017-9310",
publisher = "Elsevier Limited",

}

RIS

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

VL - 166

JO - International Journal of Heat and Mass Transfer

JF - International Journal of Heat and Mass Transfer

SN - 0017-9310

M1 - 120743

ER -