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Keywords = gravity-driven granular flow

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13 pages, 1089 KB  
Article
Granular Segregation in Gravity-Driven, Dense, Steady, Fluid–Particle Flows over Erodible Beds and Rigid, Bumpy Bases
by James T. Jenkins and Michele Larcher
Water 2023, 15(14), 2629; https://doi.org/10.3390/w15142629 - 20 Jul 2023
Cited by 2 | Viewed by 1703
Abstract
Kinetic theory is used to propose and solve boundary value problems for fully developed, steady, dense gravity-driven flows of mixtures composed of identical inelastic spheres and water over both inclined erodible beds and rigid, bumpy bases confined by vertical sidewalls. We solve the [...] Read more.
Kinetic theory is used to propose and solve boundary value problems for fully developed, steady, dense gravity-driven flows of mixtures composed of identical inelastic spheres and water over both inclined erodible beds and rigid, bumpy bases confined by vertical sidewalls. We solve the boundary value problems assuming values of the mass density and of the size of the spheres typical of natural materials and show the numerical solutions for the profiles of the mean velocities of the particles and fluid, the intensity of the particle velocity fluctuations, and the granular concentration. In addition, we indicate how the features of the grain velocity fluctuations profile would influence segregation in three situations when the particle phase consists of two sizes of spheres: (1) the spheres are of the same material, and only gradients of temperature influence their segregation; (2) the mass densities of the material of the spheres are such that only gravity influences segregation; and (3) the mass densities are such that the coefficients of the temperature gradients and gravity segregation mechanisms are equal. For spheres of the same material, over a rigid bumpy base, the concentration of larger spheres increases from zero at the bed to the maximum value at the top of the flow; while over an erodible bed, this concentration has its maximum value at both the bed and the top of the flow. Full article
(This article belongs to the Special Issue Granular Flows Modeling and Simulation)
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14 pages, 5168 KB  
Article
Numerical Investigation of Gravity-Driven Granular Flow around the Vertical Plate: Effect of Pin-Fin and Oscillation on the Heat Transfer
by Xing Tian, Jian Yang, Zhigang Guo and Qiuwang Wang
Energies 2021, 14(8), 2187; https://doi.org/10.3390/en14082187 - 14 Apr 2021
Cited by 9 | Viewed by 2684
Abstract
In this paper, the heat transfer of pin-fin plate unit (PFPU) under static and oscillating conditions are numerically studied using the discrete element method (DEM). The flow and heat transfer characteristics of the PFPU with sinusoidal oscillation are investigated under the conditions of [...] Read more.
In this paper, the heat transfer of pin-fin plate unit (PFPU) under static and oscillating conditions are numerically studied using the discrete element method (DEM). The flow and heat transfer characteristics of the PFPU with sinusoidal oscillation are investigated under the conditions of oscillating frequency of 0–10 Hz, amplitude of 0–5 mm and oscillating direction of Y and Z. The contact number, contact time, porosity and heat transfer coefficient under the above conditions are analyzed and compared with the smooth plate. The results show that the particle far away from the plate can transfer heat with the pin-fin of PFPU, and the oscillating PFPU can significantly increase the contact number and enhance the temperature diffusion and heat transfer. The heat transfer coefficient of PFPU increases with the increase of oscillating frequency and amplitude. When the PFPU oscillates along the Y direction with the amplitude of 1 mm and the frequency of 10 Hz, the heat transfer coefficient of PFPU is increased by 28% compared with that of the smooth plate. Compared with the oscillation along the Z direction, the oscillation along the Y direction has a significant enhancement on the heat transfer of PFPU. Full article
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18 pages, 12635 KB  
Article
Using a Data Driven Approach to Predict Waves Generated by Gravity Driven Mass Flows
by Zhenzhu Meng, Yating Hu and Christophe Ancey
Water 2020, 12(2), 600; https://doi.org/10.3390/w12020600 - 22 Feb 2020
Cited by 36 | Viewed by 6195
Abstract
When colossal gravity-driven mass flows enter a body of water, they may generate waves which can have destructive consequences on coastal areas. A number of empirical equations in the form of power functions of several dimensionless groups have been developed to predict wave [...] Read more.
When colossal gravity-driven mass flows enter a body of water, they may generate waves which can have destructive consequences on coastal areas. A number of empirical equations in the form of power functions of several dimensionless groups have been developed to predict wave characteristics. However, in some complex cases (for instance, when the mass striking the water is made up of varied slide materials), fitting an empirical equation with a fixed form to the experimental data may be problematic. In contrast to previous empirical equations that specified the mathematical operators in advance, we developed a purely data-driven approach which relies on datasets and does not need any assumptions about functional form or physical constraints. Experiments were carried out using Carbopol Ultrez 10 (a viscoplastic polymeric gel) and polymer–water balls. We selected an artificial neural network model as an example of a data-driven approach to predicting wave characteristics. We first validated the model by comparing it with best-fit empirical equations. Then, we applied the proposed model to two scenarios which run into difficulty when modeled using those empirical equations: (i) predicting wave features from subaerial landslide parameters at their initial stage (with the mass beginning to move down the slope) rather than from the parameters at impact; and (ii) predicting waves generated by different slide materials, specifically, viscoplastic slides, granular slides, and viscoplastic–granular mixtures. The method proposed here can easily be updated when new parameters or constraints are introduced into the model. Full article
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