This paper describes a novel approach to the analysis of the experimental void fraction time series detected in air–water upward two-phase flows within a vertical channel. Singular Value Decomposition (SVD) is applied to the experimental void fraction time series in order to assess a novel state space spanned by the principal components of the flow dynamics; in fact, this was demonstrated in a previous study to be effective in separating the dominant features from noise-like dynamics and, above all, in evidencing the existence of recurrent behaviours. As recurrence is a typical and fundamental signature of low-order deterministic nonlinear dynamics, the present study aims at reaching a detailed insight on the recurrent structures that characterize the experimental void fraction time series, which were measured during an experimental campaign encompassing the entire spectrum of patterns in vertical air–water upward flows. The main novelty of the present study is the adoption of the tools collectively known as Recurrence Quantification Analysis not directly to the experimental time series, as it is increasingly proposed in the analysis of two-phase flows, but to their most important principal components. The goal is to obtain a more reliable characterization of two-phase flow patterns, which is based only on the relevant features and, hence, is unbiased by noisy high-order dynamics. Reported results show that the present approach indeed provides a powerful tool for the characterisation of the variety of complex dynamics exhibited by two-phase flows, as well as for flow pattern classification.

Two-phase flow characterization through recurrence quantification analysis of the dominant features of experimental dynamics / Pagano, A.; Angeli, D.. - In: INTERNATIONAL JOURNAL OF MULTIPHASE FLOW. - ISSN 0301-9322. - 152:(2022), pp. 1-16. [10.1016/j.ijmultiphaseflow.2022.104102]

Two-phase flow characterization through recurrence quantification analysis of the dominant features of experimental dynamics

Angeli D.
2022

Abstract

This paper describes a novel approach to the analysis of the experimental void fraction time series detected in air–water upward two-phase flows within a vertical channel. Singular Value Decomposition (SVD) is applied to the experimental void fraction time series in order to assess a novel state space spanned by the principal components of the flow dynamics; in fact, this was demonstrated in a previous study to be effective in separating the dominant features from noise-like dynamics and, above all, in evidencing the existence of recurrent behaviours. As recurrence is a typical and fundamental signature of low-order deterministic nonlinear dynamics, the present study aims at reaching a detailed insight on the recurrent structures that characterize the experimental void fraction time series, which were measured during an experimental campaign encompassing the entire spectrum of patterns in vertical air–water upward flows. The main novelty of the present study is the adoption of the tools collectively known as Recurrence Quantification Analysis not directly to the experimental time series, as it is increasingly proposed in the analysis of two-phase flows, but to their most important principal components. The goal is to obtain a more reliable characterization of two-phase flow patterns, which is based only on the relevant features and, hence, is unbiased by noisy high-order dynamics. Reported results show that the present approach indeed provides a powerful tool for the characterisation of the variety of complex dynamics exhibited by two-phase flows, as well as for flow pattern classification.
152
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16
Two-phase flow characterization through recurrence quantification analysis of the dominant features of experimental dynamics / Pagano, A.; Angeli, D.. - In: INTERNATIONAL JOURNAL OF MULTIPHASE FLOW. - ISSN 0301-9322. - 152:(2022), pp. 1-16. [10.1016/j.ijmultiphaseflow.2022.104102]
Pagano, A.; Angeli, D.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1284824
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