A novel single-parameter two-fluid model for slurry pipe flow: Predictive capability and parameter characteristics

The understanding and prediction of slurry flow behavior in pipeline systems is critical for successful operation and management of various industrial applications due to the inherent complexity of such solid–liquid mixtures. Thus, fundamental experimental investigations and an easy-to-use, efficient computational fluid dynamics model meeting engineering requirements are essential. This study first preliminarily explores the physical process of slurry flow, composed of uniformly graded glass beads at different concentrations, via laboratory tests. These tests use electrical impedance tomography (EIT) and sampling probe techniques to measure particle volume fraction distribution in a 100 mm diameter pipe. A key contribution is a novel, efficient two-fluid model requiring calibration of one parameter, turbulent Schmidt number σ: it offers fast predictions and reasonable estimates of concentration profiles and hydraulic gradients in horizontal pipes post-calibration, assuming full particle suspension (in situ concentration ideally <30% v/v) and neglecting particle–particle interactions. The study finds σ primarily depends on pipe diameter and establishes potential correlations between σ, pipe diameter, and Reynolds number, simplifying calibration across flow conditions and geometries. While not a fundamental theoretical advance, the proposed model enhances predictive capability by minimizing empirical uncertainty and enabling reliable extrapolation beyond validation cases. Moreover, experimental results confirm that EIT provides a qualitative yet robust visualization of particle distribution, demonstrating its promise when integrated with advanced reconstruction algorithms. These findings provide both theoretical insight and practical tools for slurry transport analysis, with particular value in engineering contexts, such as dredging, mining, and offshore pipeline operations.

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Publication Details

Journal
Physics of Fluids
Published
2025-10-01
DOI
https://doi.org/10.1063/5.0291404
Primary Topic
Granular flow and fluidized beds
Type
article
Field-Weighted Citation Impact
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article

A novel single-parameter two-fluid model for slurry pipe flow: Predictive capability and parameter characteristics

Pedro M. Faia, Gianandrea Vittorio Messa, F. A. P. Garcia, Maria G. Rasteiro et al.
Physics of Fluids
Granular flow and fluidized beds
article

A novel single-parameter two-fluid model for slurry pipe flow: Predictive capability and parameter characteristics

Pedro M. Faia, Gianandrea Vittorio Messa, F. A. P. Garcia, Maria G. Rasteiro, Yanchong Duan, R. Silva, Qi Yang
article en

Abstract

The understanding and prediction of slurry flow behavior in pipeline systems is critical for successful operation and management of various industrial applications due to the inherent complexity of such solid–liquid mixtures. Thus, fundamental experimental investigations and an easy-to-use, efficient computational fluid dynamics model meeting engineering requirements are essential. This study first preliminarily explores the physical process of slurry flow, composed of uniformly graded glass beads at different concentrations, via laboratory tests. These tests use electrical impedance tomography (EIT) and sampling probe techniques to measure particle volume fraction distribution in a 100 mm diameter pipe. A key contribution is a novel, efficient two-fluid model requiring calibration of one parameter, turbulent Schmidt number σ: it offers fast predictions and reasonable estimates of concentration profiles and hydraulic gradients in horizontal pipes post-calibration, assuming full particle suspension (in situ concentration ideally <30% v/v) and neglecting particle–particle interactions. The study finds σ primarily depends on pipe diameter and establishes potential correlations between σ, pipe diameter, and Reynolds number, simplifying calibration across flow conditions and geometries. While not a fundamental theoretical advance, the proposed model enhances predictive capability by minimizing empirical uncertainty and enabling reliable extrapolation beyond validation cases. Moreover, experimental results confirm that EIT provides a qualitative yet robust visualization of particle distribution, demonstrating its promise when integrated with advanced reconstruction algorithms. These findings provide both theoretical insight and practical tools for slurry transport analysis, with particular value in engineering contexts, such as dredging, mining, and offshore pipeline operations.

Physics of FluidsVol. 37(10)
University of Coimbra (PT), Politecnico di Milano (IT), Tsinghua University (CN)
Openalex Percentile: Top 70%
Granular flow and fluidized beds
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