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

Abstract

Quenching heat transfer is a representative complex phenomenon in thermal-hydraulic engineering. Despite the tremendous effort s 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.

Machine Learning Applications and Uncertainty Quantification Analysis for Reflood Tests

“…It can be illustrated that the predictions of the DNN with selected hyperparameters have the highest accuracy compared to LR and RF (see Figures 7-9), delivering an acceptable consistent identification of the most influential physical model. These results trends were also indicated in the previous studies [27,29] where the DNNs tended to capture better predictions compared to RF. However, DNNs often require large datasets to obtain good predictions.…”

“…However, while traditional prediction methods have offered valuable insights, the advent of machine learning presents an opportunity to harness vast amounts of data, refine our predictive capabilities, and achieve unprecedented levels of accuracy. For instance, the machine learning models to predict the velocity of quench front propagation, the minimum film boiling temperature, and the transient boiling curve using fiber temperature measurement data were implemented [27]. Their findings suggest that multilayer perceptrons or deep neural networks (DNNs) can more accurately predict quenching behavior than support vector machine (SVM) and random forest (RF).…”

A Data-Driven Recipe Simulation for Synthetic Rubber Production

“…In this study, the problem was solved using an ensemble model [19]. Because several predictive models have been utilized for temperature prediction, a study comparing ML models and ANN has also been published [22][23][24]. According to previous research, convolution neural networks (CNNs), recurrent neural network (RNNs), and long short-term memory (LSTM) NNs exhibit good performance in predicting the temperature of transmission modes [25].…”

Multi-Objective Optimization towards Heat Dissipation Performance of the New Tesla Valve Channels with Partitions in a Liquid-Cooled Plate

“…Multilayer perceptron (MLP) is a feedforward ANN [23] and has been shown to have strong predictive ability in some studies. Kim et al [24], based on high-spatial-resolution optical fiber temperature measurement data [24] from quenching, utilized three models-SVR, MLP, and random forest (RF). The results displayed a significant improvement of the model performance through the application of MLP.…”