A data driven deep neural network model for predicting boiling heat transfer in helical coils under high gravity. (February 2021)

• Record Type: Journal Article
• Authors: Liang, Xing; Xie, Yongqi; Day, Rodney; Meng, Xianhai; Wu, Hongwei
• Is Part Of: International journal of heat and mass transfer. Volume 166(2021)
• Publication Date: 2021-02
• Subjects: Boiling -- heat transfer -- helical coils -- deep neural network -- high gravity; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2020.120743
• Languages: English
• ISSNs: 0017-9310

Abstract

Highlights: The boiling heat transfer in helical coil can be predicted successfully by data-driven models. Data-driven model need different inputs to predict the heat transfer under high gravity conditions. Features such as mass flow rate, represent multiple factors that can reduce the input numbers. The proposed method can greatly reduce the cost of computation fluid dynamic modeling. 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.