Application of Artificial Neural Network for Prediction of Local Void Fraction in Vertical Subcooled Boiling Flow

Abstract

This paper presents the feasibility study of potential application of multi-layer feed-forward Artificial Neural Networks (ANN) to predict local void fraction of subcooled boiling flows in vertical upward annular channel. A total of 638 experimental data points performed at KAERI and reported in literature was selected for training and testing ANN model. The seven basic parameters are chosen to be input variables and then the optimal structure of ANN which consist of two hidden layers with 131 neurons was determined based on traditional Trial-and-Error method after balancing the trade-off between the performance and training time. Results showed that the ANN model is capable to accurately predict the local void fraction with R2 value of 0.99931 for training data, R2 value of 0.99483 for testing data and R2 value of 0.99828 for all data. Also, it proved that the ANN training will be more effective with an extensive experimental database.