Dimensional Analysis in Error Reduction for Prediction of Nucleate Boiling Heat Flux by Artificial Neural Networks for Limited Dataset


 Artificial Neural Networks (ANNs) have been used to predict nucleate boiling heat flux by learning from a dataset of twelve experimental parameters across 231 independent samples. An approach to reduce the number of parameters involved and to increase model accuracy has been proposed and implemented. The proposed approach consists of two steps. In the first step, a feature importance study is performed to determine the most significant parameters. In the second step, dimensional analysis is performed on these important parameters. Neural network analysis is then conducted based on dimensionless parameters. The results indicate that the proposed feature importance study and dimensional analysis can significantly improve ANNs performance. It also show that model errors based on the reduced dataset is considerably lower than those based on the initial dataset. The study based on other machine learning models also show the reduced dataset generate better results. The results conclude that ANNs outperforms other machine learning algorithms and outperforms a well-known boiling correlation equation. Novel results quantifying contribution percentage for each term with respect to the two boiling regimes (surface tension dominated (SDB) and boiling dominated (BDB)) have been reported and it shows that surface tension and liquid subcooling are the most significant parameters in SDB regime with a combined contribution percentage of 60%, while wall superheat and gravity are the most significant parameters in BDB regime with a combined contribution percentage of 70% .