关键词: 流体力学, 环状流, 界面, 集成学习, 预测

Abstract:

This paper proposed and validated a prediction model for interfacial wave velocity in annular flow within vertical pipes based on ensemble learning. By optimizing the parameters of empirical wavelet transform (EWT) using whale optimization algorithm (WOA), the signal processing accuracy was enhanced. Key features of the interfacial wave velocity signal were extracted from both time and frequency domains based on theoretical models. Further, models such as support vector regression, decision tree regression, random forest regression, least squares boosting (LSBoost) regression, and Bagging regression were combined to develop a high-precision wave velocity prediction model, with gas-liquid interfacial wave velocity as the prediction target under varying flow rates and pressure conditions. The analysis results showed that the proposed model effectively captured the complex flow characteristics in gas-liquid two-phase flow, with the LSBoost regression achieving a root mean square error (RMSE) of 3.96×10^-7, indicating excellent stability and predictive accuracy. The model provided effective support for the analysis of gas-liquid two-phase flow behavior and engineering practice, especially under complex operating conditions, where the model could still predict wave velocity stably. The model hold broad application prospects and potential, with significant value in practical engineering applications.

Key words: fluid mechanics, annular flow, interface, ensemble learning, prediction