Purpose

Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

Design/methodology/approach

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

Findings

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

Practical implications

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

Originality/value

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

中文翻译：

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使用多方法特征提取和可解释核 Fisher 判别分析进行两相流态识别

目的

识别流态是准确建模两相流的先决条件。本文旨在介绍一种用于流态识别的综合数据驱动工作流程。

设计/方法论/途径

数值两相流模型根据实验数据进行了验证，并用于生成三种不同流态的动态压力信号。首先，采用四种不同的方法进行特征提取：离散小波变换（DWT）、经验模态分解、功率谱密度和时间序列分析方法。使用核费希尔判别分析（KFDA）对每组特征同时进行降维和机器学习（ML）分类。最后，应用沙普利附加解释（SHAP）方法使工作流程可解释。

发现

结果表明，DWT + KFDA 方法的测试和训练准确率最高，分别为 95.2% 和 88.8%。结果还包括虚拟流态图，以方便二维特征的可视化。最后，SHAP分析表明，在DWT的第四和第二信号分解级别提取的最小值和最大值是最好的流量区分特征。

实际影响

该工作流程可应用于装有压力传感器的不透明管道，以实现许多过程工业中发生的两相流的流量保证和自动监控。

原创性/价值

本文提出了一种将动态压力测量与机器学习技术相结合的新型流态识别方法。作者的新颖 DWT + KFDA 方法展示了具有可解释性的流态识别的卓越性能。