High-resolution prediction of quenching behavior using machine learning based on optical fiber temperature measurement

• Quenching experiments were performed to study subcooling and material effects. • Support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP) models were trained using quenching temperature data. • The optimal MLP model showed good agreement with experimental data for bottom quenching behaviors of Inconel-600 and Monel K500. Quenching heat transfer is a representative complex phenomenon in thermal-hydraulic engineering. Despite the tremendous efforts to precisely predict the quenching behavior, conventional analysis methodology and correlations have exhibited limited prediction capacity on quenching heat transfer according to axial locations of heater rods. The deviations of existing models result from the uncertainty of axial heat conduction with low-resolution temperature measurement and limited regression performance. In this study, machine learning models were trained with optical fiber temperature measurement data having high spatial resolution about quenching to overcome the intrinsic limitation of conventional prediction methods. Support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP) models were trained, and the MLP model showed best prediction performance (R 2 = 0.9999 and test RMSE = 1.41 °C) due to its strong analysis of the underlying relationship between input and output with interpolation capacity. The optimal MLP model (5-30-30-30-1 architecture) showed good agreement with experimental data for bottom quenching behaviors of Inconel-600 and Monel K500, in aspects of minimum film boiling temperature, quench front propagation velocity, and transient boiling curve by providing spatially resolutive quenching behavior that cannot be obtained from conventional correlations and having high uncertainty in existing computational analysis methods. The developed MLP model has the capability to predict the quenching behavior of FeCrAl accident tolerant fuel cladding surface, and better predictability on minimum film boiling temperature (average error of 3.7%) than conventional correlation having 7.1% average error. The suggested methodology using machine learning technique will contribute to innovatively improve the predictability on quenching phenomena with reducing uncertainty.