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

Machine learning methods for particle stress development in suspension Poiseuille flows

Journal Article · Fri Dec 01 23:00:00 EST 2023 · Rheologica Acta

DOI:https://doi.org/10.1007/s00397-023-01413-z· OSTI ID:2293476

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Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Brown University, Providence, RI (United States)
Sandia National Laboratory (SNL-NM), Albuquerque, NM (United States)
Pasteur Labs., Brooklyn, NY (United States)
Brown University, Providence, RI (United States)

Numerical simulations are used to study the dynamics of a developing suspension Poiseuille flow with monodispersed and bidispersed neutrally buoyant particles in a planar channel, and machine learning is applied to learn the evolving stresses of the developing suspension. The particle stresses and pressure develop on a slower time scale than the volume fraction, indicating that once the particles reach a steady volume fraction profile, they rearrange to minimize the contact pressure on each particle. Here we consider how the stress development leads to particle migration, time scales for stress development, and present a new physics-informed Galerkin neural network that allows for learning the particle stresses when direct measurements are not possible. The particle fluxes are compared with the Suspension Balance Model with good agreement. We show that when stress measurements are possible, the MOR-physics operator learning method can also capture the particle stresses.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Science Foundation (NSF); USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC05-76RL01830; NA0003525

OSTI ID:: 2293476

Alternate ID(s):: OSTI ID: 2311556

Report Number(s):: PNNL-SA--182934; SAND--2023-14545J

Journal Information:: Rheologica Acta, Journal Name: Rheologica Acta Journal Issue: 10 Vol. 62; ISSN 0035-4511

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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