arXiv subjects

Alexander Metelkin

Publications and source records attributed to Alexander Metelkin.

3 recordsLinked to original sources

arXiv2026

Effects of permeability on hindered settling of porous particles

We investigate the settling behavior of suspensions of highly porous and permeable particles in the viscous regime using particle-resolved direct numerical simulations (DNS). The simulations employ a coupled Euler-Lagrange framework that accounts for particle permeability. The results show that the settling behavior of permeable particles follows the classical power-law relationship of Richardson-Zaki in terms of their settling velocity, but particles with higher permeability settle faster as the particle volume fraction increases. At a particle volume fraction of 30 percent, the difference in settling speed is up to 106 percent between the least and most permeable particles investigated in this study. We explain this effect by the alteration of counter flows induced by the fluid displacement of settling particles. Quantitative analysis of the mean vertical fluid velocity confirms that suspensions composed of more permeable particles generate weaker counterflows, posing less resistance to the settling motion. We furthermore show how velocity fluctuations and self-diffusivity depend on the permeability of the porous particles and the particle volume fraction. Both quantities increase with volume fraction and are largest for the least permeable particles, except at the highest volume fraction, where reduced settling velocities reverse this trend. The influence of particle permeability also reveals two effects in the particle microstructure. First, the analysis showed that particle clustering decreases with increasing permeability. Second, the overall probability of finding a neighbor within the lubrication range is lowest for the least permeable particles. We attribute this to the weakening of repulsive pressure forces between tumbling particles with increased permeability.

physics.flu-dyn↗

arXiv2025

A Data-Driven Approach for Predicting Hydrodynamic Forces on Spherical Particles Using Volume Fraction Representations

Particle-laden flows are simulated at various scales using numerical techniques that range from particle-resolved Direct Numerical Simulations (pr-DNS) for small-scale systems to Lagrange point-particle methods for laboratory-scale problems, and Euler-Euler approaches for larger-scale applications. Recent research has been particularly focused on the development of both physics-based and data-driven closures to enhance the accuracy of the Lagrangian point-particle approach by leveraging highly resolved data from pr-DNS. In this study, a data-driven methodology is presented for the prediction of hydrodynamic forces acting on spherical particles immersed in an ambient flow field, where neighboring particle information is represented by volume fractions. The volume fractions are computed on an auxiliary grid with cell sizes on the order of the particle diameter. The volume fraction values in the vicinity of each particle are used as input features for the data-driven model to predict the corresponding hydrodynamic forces and moments. The training data was generated by a series of pr-DNS of flow through arrays of randomly distributed, fixed-position particles at various Reynolds numbers and particle volume fractions. The data-driven model is built using Fully Connected Neural Networks (FCNN). Improved prediction accuracy of hydrodynamic forces and torques is demonstrated in comparison to FCNN models that rely on direct particle position inputs. In addition, the proposed volume-fraction-based approach exhibits greater flexibility than previously introduced models by accommodating systems with particles of different sizes and shapes.

physics.flu-dyn↗

arXiv2024

On the parameters of common settling velocity models for porous sediment aggregates

The settling behavior of sediment aggregates is a critical factor influencing the transport of fine-grained sediments in riverine and marine environments. Due to the small size and fragile structure of cohesive sediment aggregates, direct measurement of their porosity and permeability is challenging. While porosity is often estimated using settling velocity relations, permeability is frequently overlooked. This study examines the impact of considering non-negligible permeability on the properties of flocs. We compare aggregate properties by calibrating experimental data to two settling models in a dilute regime: one assumes a fractal structure of aggregates and neglects permeability, while the other assumes constant porosity and permeability. Our results demonstrate that both models describe the experimental data of highly porous aggregates with similar accuracy. We further investigate aggregate dynamics in more complex flow conditions using numerical simulations employing a volume penalization method to geometrically resolve flocs. We compare the behavior of permeable and impermeable flocs in dense suspension regimes and during dilute settling in density-stratified environments. Our findings reveal that permeability significantly influences settling dynamics in complex scenarios and should be considered when determining aggregate properties.

physics.flu-dyn↗