This study introduces a predictive hydrodynamic model to investigate the distribution characteristics of synthetic glass particles injected from a time-dependent source in a turbulent free surface flow, while also examining the impact of pulsation on the spatial and temporal distributions of particles. The simulations are conducted using a Computational Fluid Dynamics (CFD) code based on the finite volume approach. The Discrete Phase Model (DPM) is selected to capture the movement of particles. The k-ε turbulence closure model is employed to simulate turbulence generation, and the Volume of Fluid (VOF) method is used to accurately capture the time-varying free surface. The trajectory and deposition of particles are analyzed in detail.

The numerical results demonstrate that the pulsation plays a predominant role at the early stages of glass particles distribution, and that particles transportation can be enhanced due to the synchronization of particle movement with the oscillating potential. It was also observed that the pulsation affects the distribution of the injected material, particularly near the front, and that a significant swirling action is developed compared to constant-rate-injection case.

The findings presented in this study can guide watershed managers in implementing effective and cost-efficient conservation measures; by providing insights into the dynamics of water flow and pollutant dispersion, these results can improve the design of water treatment facilities, optimize sediment transport systems, and enhance pollutant dispersion models.

Realistic pore-scale simulations of flow through porous media frequently use discrete images (pixels in 2D or voxels in 3D) of real-life samples as inputs. Today's commonly held belief is that high-accuracy simulations require high-resolution images, which often result in lengthy scanning and/or simulation times. Yet, decreasing the resolution destroys the simulation accuracy when the features of the sample (e.g., pores) are unresolved. Here, we report the discovery of superstructures in discrete images, which emerge from the sample's features and discrete mesh. These superstructures - and not the original features of the sample - control flow in low-resolution simulations. Consequently, decreases in resolution change the topology (flow “pathways”) and morphology (pore “shapes”) of the discrete image of the sample. Using permeability as an example, we present a new methodology to significantly improve the flow simulation accuracy for both low resolution CT-imaged and computer-generated samples. This methodology is based on the novel concept of “null point”, P0, and voxel-based resolution parameter, \chi. The presented methodology improves extraction of quantitative information from discrete images. Our findings are not limited by image dimensionality, imaging technique, or simulated processes.

Efficiently accounting for uncertainties in computational fluid dynamics (CFD) models remains a crucial challenge. For computing statistical solutions of partial differential equations in fluid flow models, it is promising to combine scalable deterministic solvers based on lattice Boltzmann methods (LBMs), such as OpenLB, with uncertainty quantification (UQ) techniques. By sampling uncertain parameters and conducting many simulations, the uncertainty can be accurately quantified using statistical analysis. However, non-intrusive Monte Carlo (MC) methods are often computationally expensive when applied to CFD problems. In this talk, we propose to employ a generalized polynomial chaos (gPC) expansion method within a stochastic Galerkin (SG) framework on LBM. Our novel SG LBM offers a more efficient alternative to MC for estimating uncertainty in LBM simulations of incompressible fluid flows. Numerical results of a Taylor--Green vortex flow problem validate that the SG LBM achieves comparable accuracy to the MC LBM, while significantly reducing the computational costs. By combining the strengths of both the LBM and gPC-based SG methods, we thus provide a robust and intrinsically efficient framework for UQ in CFD simulations.