A data driven deep neural network model for predicting boiling heat transfer in helical coils under high gravity
نویسندگان
چکیده
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 set up provide 11 g flux 15100 W/m2 and mass velocity range from 40 2000 kg m−2 s−1. current work, total 531 data samples have used ANN model. The was developed Python Keras environment Feed-forward Back-propagation (FFBP) Multi-layer Perceptron (MLP) using eight features (mass flow rate, thermal power, inlet temperature, pressure, direction, acceleration, tube inner surface area, coil diameter) as inputs two (wall coefficient) outputs. composed of three hidden layers number 1098 neurons 300,266 trainable parameters found optimal according statistical error analysis. Performance evaluation conducted based on six verification statistic metrics (R2, MSE, MAE, MAPE, RMSE cosine proximity) between predicted values. results demonstrate that 8-512-512-64-2 best performance predicting characteristics (R2=0.853, MSE=0.018, MAE=0.074, MAPE=1.110, RMSE=0.136, proximity=1.000) testing stage. It indicated utilisation learning, able successfully coils, especially achieved excellent outputs very large value differences.
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ژورنال
عنوان ژورنال: International Journal of Heat and Mass Transfer
سال: 2021
ISSN: ['1879-2189', '0017-9310']
DOI: https://doi.org/10.1016/j.ijheatmasstransfer.2020.120743