Abstract

In the present study, deep learning neural network model has been employed in many engineering problems including heat transfer prediction. The main consideration of this document is to predict the performance of the boiling heat transfer in helical coils under terrestrial gravity conditions and compare with actual experimental data. Total of 877 data sample has been used in the present neural model. Artificial new Neural Network (ANN) model developed in Python environment with Multi-layer Perceptron (MLP) using four parameters (helical coils dimensions, mass flow rate, heating power, inlet temperature) and one parameter (outlet temperature) has been used in the input layer and output layer in order. Levenberg-Marquardt (LM) algorithm using L2 Regularization to find out the optimal model. A typical feed-forward neural network model composed of three layers, with 30 numbers of neurons in each hidden layer, has been found as optimal based on statistical error analysis. The 4-30-30-1 neural model predicts the characteristics of the helical coil with the accuracy of 98.16 percent in the training stage and 96.68 percent in the testing stage. The result indicated that the proposed ANN model successfully predicts the heat transfer performance in helical coils and can be applied for others operation concerned with heat transfer prediction for future works

Highlights

  • Helical coils are very widespread and have been used in many applications

  • This paper considers the input as helical coils dimension, mass flow rate, heating power, and inlet temperature to predict the output of outlet temperature through the Artificial new Neural Network (ANN) model

  • The accuracy of the model indicated that this model has a high superior performance in the prediction of outlet temperature of boiling heat transfer in helical coils

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Summary

Introduction

Helical coils are very widespread and have been used in many applications. From many works, they indicated that the helical coil has a high heat transfer efficiency, compact design, convenient to produce, and free from thermal deformation. This paper considers the boiling heat transfer, which is the most efficient process. Artificial Neural Networks (ANNs) have been used widely and successfully for modeling and in predicting various engineering problems, including heat transfer. Many works apply ANN of the prediction of heat transfer parameters. The works in [10,11,12] demonstrate the prediction of heat transfer but are not in helical coils. The implementations can be good examples of ANN architecture

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