A data-driven stochastic model for velocity field and phase distribution in stirred particle-liquid suspensions

Hamzah Sheikh; Chiya Savari; Mostafa Barigou

doi:10.1016/j.powtec.2022.117940

A data-driven stochastic model for velocity field and phase distribution in stirred particle-liquid suspensions

Hamzah Sheikh, Chiya Savari, Mostafa Barigou^*

^*Corresponding author for this work

Chemical Engineering

Research output: Contribution to journal › Article › peer-review

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Abstract

A computationally efficient Lagrangian stochastic model driven by short 3D experimental trajectories determined by a technique of positron emission particle tracking, has been developed to study two-phase particle-liquid flow in a mechanically agitated vessel and unravel the complex behaviour of both phases. Using a small set of trajectory driver data, the stochastic model is used in conjunction with a particle-wall collision model to simulate the full velocity field and spatial distribution of particles. The performance of a first and a second order model is evaluated in particle suspensions of various concentrations. Both models are able to predict local phase velocities to a high degree of accuracy. Predictions of spatial particle distribution are reasonable by the first order model but very accurate by the second order model. Furthermore, the latter is able to accurately predict the two-phase velocity field and spatial phase distribution under flow conditions outside the experimental range.

Original language	English
Article number	117940
Number of pages	19
Journal	Powder Technology
Volume	411
Early online date	14 Sept 2022
DOIs	https://doi.org/10.1016/j.powtec.2022.117940
Publication status	Published - Oct 2022

Bibliographical note

Funding Information:
This work was supported by EPSRC Programme Grant EP/ R045046 /1: Probing Multiscale Complex Multiphase Flows with Positrons for Engineering and Biomedical Applications (PI: Prof. M. Barigou, University of Birmingham).

Publisher Copyright:
© 2022 The Authors

Keywords

Particle-liquid flow
Lagrangian trajectory
Mixing
Stirred vessel
Stochastic model

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10.1016/j.powtec.2022.117940Licence: Creative Commons: Attribution (CC BY)

SheikhH2022Data-drivenFinal published version, 8.12 MBLicence: Creative Commons: Attribution (CC BY)

Probing Multiscale Complex Multiphase Flows with Positrons for Engineering and Biomedical Applications
Barigou, M. & Parker, D.
Engineering & Physical Science Research Council
1/10/18 → 30/09/24
Project: Research Councils

Cite this

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title = "A data-driven stochastic model for velocity field and phase distribution in stirred particle-liquid suspensions",

abstract = "A computationally efficient Lagrangian stochastic model driven by short 3D experimental trajectories determined by a technique of positron emission particle tracking, has been developed to study two-phase particle-liquid flow in a mechanically agitated vessel and unravel the complex behaviour of both phases. Using a small set of trajectory driver data, the stochastic model is used in conjunction with a particle-wall collision model to simulate the full velocity field and spatial distribution of particles. The performance of a first and a second order model is evaluated in particle suspensions of various concentrations. Both models are able to predict local phase velocities to a high degree of accuracy. Predictions of spatial particle distribution are reasonable by the first order model but very accurate by the second order model. Furthermore, the latter is able to accurately predict the two-phase velocity field and spatial phase distribution under flow conditions outside the experimental range.",

keywords = "Particle-liquid flow, Lagrangian trajectory, Mixing, Stirred vessel, Stochastic model",

author = "Hamzah Sheikh and Chiya Savari and Mostafa Barigou",

note = "Funding Information: This work was supported by EPSRC Programme Grant EP/ R045046 /1: Probing Multiscale Complex Multiphase Flows with Positrons for Engineering and Biomedical Applications (PI: Prof. M. Barigou, University of Birmingham). Publisher Copyright: {\textcopyright} 2022 The Authors",

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doi = "10.1016/j.powtec.2022.117940",

language = "English",

volume = "411",

journal = "Powder Technology",

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AU - Sheikh, Hamzah

AU - Savari, Chiya

AU - Barigou, Mostafa

N1 - Funding Information: This work was supported by EPSRC Programme Grant EP/ R045046 /1: Probing Multiscale Complex Multiphase Flows with Positrons for Engineering and Biomedical Applications (PI: Prof. M. Barigou, University of Birmingham). Publisher Copyright: © 2022 The Authors

PY - 2022/10

Y1 - 2022/10

N2 - A computationally efficient Lagrangian stochastic model driven by short 3D experimental trajectories determined by a technique of positron emission particle tracking, has been developed to study two-phase particle-liquid flow in a mechanically agitated vessel and unravel the complex behaviour of both phases. Using a small set of trajectory driver data, the stochastic model is used in conjunction with a particle-wall collision model to simulate the full velocity field and spatial distribution of particles. The performance of a first and a second order model is evaluated in particle suspensions of various concentrations. Both models are able to predict local phase velocities to a high degree of accuracy. Predictions of spatial particle distribution are reasonable by the first order model but very accurate by the second order model. Furthermore, the latter is able to accurately predict the two-phase velocity field and spatial phase distribution under flow conditions outside the experimental range.

AB - A computationally efficient Lagrangian stochastic model driven by short 3D experimental trajectories determined by a technique of positron emission particle tracking, has been developed to study two-phase particle-liquid flow in a mechanically agitated vessel and unravel the complex behaviour of both phases. Using a small set of trajectory driver data, the stochastic model is used in conjunction with a particle-wall collision model to simulate the full velocity field and spatial distribution of particles. The performance of a first and a second order model is evaluated in particle suspensions of various concentrations. Both models are able to predict local phase velocities to a high degree of accuracy. Predictions of spatial particle distribution are reasonable by the first order model but very accurate by the second order model. Furthermore, the latter is able to accurately predict the two-phase velocity field and spatial phase distribution under flow conditions outside the experimental range.

KW - Particle-liquid flow

KW - Lagrangian trajectory

KW - Mixing

KW - Stirred vessel

KW - Stochastic model

U2 - 10.1016/j.powtec.2022.117940

DO - 10.1016/j.powtec.2022.117940

M3 - Article

SN - 0032-5910

VL - 411

JO - Powder Technology

JF - Powder Technology

M1 - 117940

ER -

A data-driven stochastic model for velocity field and phase distribution in stirred particle-liquid suspensions

Abstract

Bibliographical note

Keywords

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Projects

Probing Multiscale Complex Multiphase Flows with Positrons for Engineering and Biomedical Applications

Cite this