Evaluation of machine learning models in the classification of pool boiling regimes up to critical heat flux based on boiling acoustics

The present study focuses on the performance of the machine learning methods in classifying the boiling regimes of water up to critical heat flux conditions based on the acoustic characteristics of boiling. The data set is generated by conducting a pool boiling experiment on a wire heater at various heat fluxes varying from 54.95 kW/m2 to 2898.67 kW/m2. A Kanthal D wire of standard wire gauge 36 is used. The data set is divided into three classes: no boiling, nucleate boiling, and critical heat flux to identify the boiling incipience and critical heat flux. Much focus is insisted on identifying critical heat flux as it carries more practical importance in the safety of the cooling systems. Data set size optimization is performed to find the lowest number of records required for each method. Three machine-learning methods are employed to predict the boiling regime, namely, binary decision tree method, decision tree ensemble method and naive Bayes method. Out of these, the decision tree ensemble outperformed the binary decision tree and naive Bayes classifiers. The decision tree ensemble classified the regimes in the given data with the lowest classification error and inference time. The accurate classification of boiling regimes based on boiling acoustics strengthens the safety measures in real-time monitoring of cooling systems.