Prediction of the interfacial disturbance wave velocity in vertical upward gas-liquid annular flow via ensemble learning

Chaofan Li, Yajing Song, Long Xu, Ning Zhao, Fan Wang, Lide Fang and Xiaoting Li

Energy, 2022, vol. 242, issue C

Abstract: Interfacial disturbance wave velocity is an important parameter for the study of momentum transfer between the gas core and the liquid film at the two-phase interface, which directly affects the calculation of frictional pressure drop. Since an exact analytical solution of the interfacial disturbance velocity cannot be derived by the two-phase flow theory equations, an ensemble learning framework for the disturbance wave velocity is constructed and a new model is proposed, which is appropriate for predicting different flow conditions in vertical two-phase flow. The dimensionless velocity-related parameters of interfacial disturbance waves are obtained by feature selection based on the interfacial shear force model, and the grid search method is equipped to tune the important parameters. By comparing the current and literature data prediction results, the extrapolation and applicability of the ensemble learning model are further verified. For the Extra Tree model, the Mean Absolute Percentage Error of the optimized Extra Tree model is less than 20%, and the relative measurement uncertainty is within ±25% for 95.67% of the results. It shows that the proposed ensemble learning framework provides a novel approach in the study of interfacial wave spatiotemporal parameters.

Keywords: Annular flow; Near-infrared measurement sensors; Disturbance wave velocity; Ensemble learning; Cross-correlation analysis (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:242:y:2022:i:c:s0360544221032394

DOI: 10.1016/j.energy.2021.122990

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