Machine learning methods for particle stress development in suspension Poiseuille flows

Howard, Amanda A.; Dong, Justin; Patel, Ravi; D’Elia, Marta; Maxey, Martin R.; Stinis, Panos

doi:10.1007/s00397-023-01413-z

Rheol Acta

2023

DOI: 10.1007/s00397-023-01413-z

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Machine learning methods for particle stress development in suspension Poiseuille flows

Amanda A. Howard,

Justin Dong,

Ravi Patel

et al.

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Cited by 2 publications

References 79 publications

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Data-driven techniques in rheology: Developments, challenges and perspective

Mangal,

Jha,

Dabiri

et al. 2025

Current Opinion in Colloid & Interface Science

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Data-driven techniques in rheology: Developments, challenges and perspective

Mangal,

Jha,

Dabiri

et al. 2025

Current Opinion in Colloid & Interface Science

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Mass and momentum balance during particle migration in the pressure-driven flow of frictional non-Brownian suspensions

Orsi,

Lobry,

Lemaire

et al. 2024

J. Fluid Mech.

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The transient shear-induced particle migration of frictional non-Brownian suspensions is studied using particle-resolved simulations. The numerical method – the fictitious domain method – is well suited to heterogeneous flows thanks to a frame-invariant formulation of the subgrid (lubrication) corrections that does not involve the ambient flow (Orsi et al., J. Comput. Phys., vol. 474, 2023, 111823). The paper aims to give an accurate quantitative picture of the mass and momentum balance during the flow. The various assumptions and local constitutive laws that together form the suspension balance model (SBM) are thoroughly examined. To this purpose, the various quantities of interest are locally averaged in space and time, and their profile across the channel is extensively studied, with specific attention to the time evolution of the different contributions, either hydrodynamic in nature or from contact interactions, to the shear and normal stresses. The latter, together with the velocity gradient in the wall-normal direction and the volume fraction profile, yield the local constitutive laws, which are compared with their counterpart obtained in homogeneous shear flow. A fair agreement is observed except in a layering area at the boundaries and at the very centre of the channel. In addition, the main assumption of the SBM, i.e. the local relation between the hydrodynamic force on the particles and the particle flux, is meticulously investigated. The hydrodynamic force is found to be mainly a drag, except in the lower range of the probed volume fractions, where a non-drag contribution is observed.

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Machine learning methods for particle stress development in suspension Poiseuille flows

Cited by 2 publications

References 79 publications

Data-driven techniques in rheology: Developments, challenges and perspective

Data-driven techniques in rheology: Developments, challenges and perspective

Mass and momentum balance during particle migration in the pressure-driven flow of frictional non-Brownian suspensions

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