Khan, U. and Pao, W. and Pilario, K.E.S. and Sallih, N. and Khan, M.R. (2023) Two-phase flow regime identification using multi-method feature extraction and explainable kernel Fisher discriminant analysis. International Journal of Numerical Methods for Heat and Fluid Flow.
Full text not available from this repository.Abstract
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. © 2023, Emerald Publishing Limited.
Item Type: | Article |
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Additional Information: | cited By 1 |
Uncontrolled Keywords: | Discriminant analysis; Extraction; Feature extraction; Fisher information matrix; Learning algorithms; Signal reconstruction; Spectral density; Time series analysis; Two phase flow, Discreate wavelet transform; Discrete-wavelet-transform; Explainable AI; Features extraction; Flow regimes; Flow regimes identification; Kernel fisher discriminant analysis; Two phases flow; Wavelets transform; Work-flows, Discrete wavelet transforms |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 04 Jun 2024 14:11 |
Last Modified: | 04 Jun 2024 14:11 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/19017 |