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Volume 118
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Data-driven prediction and quantification of flow asymmetry and solid concentration redistribution in rectangular-duct slurry transport (Open Access)
Hossein Fatahian *, Nyararai Mukandiwa, Rakesh Mishra, Naeem S. Mian, Esmaeel Fatahian
School of Computing and Engineering, University of Huddersfield, Huddersfield, HD1 3DH, UK
10.1016/j.partic.2026.08.034
Volume 118, November 2026, Pages 260-288
Received 13 June 2026, Revised 24 August 2026, Accepted 31 August 2026, Available online 3 September 2026, Version of Record 17 September 2026.
E-mail: h.fatahian3@hud.ac.uk
Highlights

• New flux-based indices quantify velocity-transport asymmetry in rectangular slurry ducts.

• The upper duct half consistently transports more streamwise velocity than the lower half.

• Coarse iron ore produces the strongest asymmetry, with AIflux = 7.31% and ARflux = 1.088.

• The CFD–ANN framework accurately predicts velocity and concentration profiles.


Abstract

Rectangular-duct slurry transport exhibits coupled particle stratification and velocity-profile asymmetry that cannot be represented adequately by circular-pipe correlations. This study develops a validated CFD–machine-learning framework to quantify these effects and predict concentration and velocity profiles. A three-dimensional mixture model coupled with kinetic-theory and turbulence closures examined the influences of velocity, solids concentration, hydraulic diameter, particle density, and particle size. The resulting 81-case full-factorial CFD database trained separate shallow artificial neural networks, with predictive assessment extended to Support Vector Regression, Random Forest, and power-law regression. Increasing velocity from 2.0 to 3.5 m/s reduced the dimensionless bottom-to-top concentration ratio from approximately 3.5 to nearly 1.0, whereas higher concentration, density, and particle size intensified settling and lower-region accumulation. The proposed flux indices showed that the upper duct half consistently transported more streamwise velocity than the lower half. Coarse iron ore produced the strongest asymmetry, with AIflux = 7.31% and ARflux = 1.088. Pressure loss increased with velocity, concentration, and density, while larger ducts reduced it by more than 50%. For three independent intermediate cases, the velocity ANN achieved mean R2 = 0.99937 and RMSE = 0.00203. The framework enables rapid, CFD-grounded assessment of non-circular slurry systems within the investigated domain and supports design optimisation without repeated full-scale CFD simulations.


Graphical abstract
Keywords
Rectangular-duct slurry transport; Velocity-flux asymmetry; Solid concentration redistribution; CFD-Machine learning; Multiphase flow
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