Void fraction measurement using modal decomposition and ensemble learning in vertical annular flow

The void fraction is a key parameter for calculating the average density and pressure gradient and analyzing the flow conditions in gas-liquid two-phase flow. However, due to the complexity and variability of gas-liquid two-phase annular flow, the void fraction measurement has been an unsolved scientific problem in scientific research and industrial applications. In this study, a new high-precision real-time void fraction prediction model is proposed by combining the energy feature extraction from the empirical modal decomposition (EMD) method, the anomaly filtering from the kernel ridge regression (KRR), and ensemble learning from the extreme gradient boosting (XGBoost). To further validate the prediction performance of the model, it is compared with the lasso regression model (LASSO) based on the EMD decomposition method and a single XGBoost model. The results show that the prediction accuracy can be guaranteed in the case of anomalous energy eigenvalues. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

A new high-precision real-time void fraction prediction model is proposed in this study, combining energy feature extraction from the empirical modal decomposition method, anomaly filtering from kernel ridge regression, and ensemble learning from extreme gradient boosting. The prediction accuracy of the model is guaranteed in the case of anomalous energy eigenvalues.