Slurry pipe transport has directed the efforts of researchers for decades, not only for the practical impact of this problem, but also for the challenges in understanding and modelling the complex phenomena involved. The increase in computer power and the diffusion of multipurpose codes based on Computational Fluid Dynamics (CFD) have opened up the opportunity to gather information on slurry pipe flows at the local level, in contrast with the traditional approaches of simplified theoretical modelling which are mainly based on a macroscopic description of the flow. This review paper discusses the potential of CFD for simulating slurry pipe flows. A comprehensive description of the modelling methods will be presented, followed by an overview of significant publications on the topic. However, the main focus will be the assessment of the potential and the challenges of the CFD approach, underlying the essential interplay between CFD simulations and experiments, discussing the main sources of uncertainty of CFD models, and evaluating existing models based on their interpretative or predictive capacity. This work aims at providing a solid ground for students, academics, and professional engineers dealing with slurry pipe transport, but it will also provide a methodological approach that goes beyond the specific application.

This paper presents and discusses the results of the “2022 International Computational Fluid Dynamics Challenge on violent expiratory events” aimed at assessing the ability of different computational codes and turbulence models to reproduce the flow generated by a rapid prototypical exhalation and the dispersion of the aerosol cloud it produces. Given a common flow configuration, a total of 7 research teams from different countries have performed a total of 11 numerical simulations of the flow dispersion by solving the Unsteady Reynolds Averaged Navier–Stokes (URANS) or using the Large-Eddy Simulations (LES) or hybrid (URANS-LES) techniques. The results of each team have been compared with each other and assessed against a Direct Numerical Simulation (DNS) of the exact same flow. The DNS results are used as reference solution to determine the deviation of each modeling approach. The dispersion of both evaporative and non-evaporative particle clouds has been considered in 12 simulations using URANS and LES. Most of the models predict reasonably well the shape and the horizontal and vertical ranges of the buoyant thermal cloud generated by the warm exhalation into an initially quiescent colder ambient. However, the vertical turbulent mixing is generally underpredicted, especially by the URANS-based simulations, independently of the specific turbulence model used (and only to a lesser extent by LES). In comparison to DNS, both approaches are found to overpredict the horizontal range covered by the small particle cloud that tends to remain afloat within the thermal cloud well after the flow injection has ceased.

This paper presents and discusses the results of the “2024 International Computational Fluid Dynamics Challenge on the long-range indoor dispersion of pathogen-laden aerosols” aimed at assessing the ability of different computational codes and turbulence models to reproduce the dispersion of particles produced by a turbulent natural convection flow enclosed in a room sized cubical cavity. A total of 12 research groups from ten different countries have conducted 15 simulations of the same flow configuration by solving the Reynolds averaged Navier–Stokes (RANS) equations, the unsteady Reynolds averaged Navier–Stokes (URANS) equations or using scale adaptive simulations (SAS), large-eddy simulations (LES), or hybrid (URANS-LES) techniques. Results for the velocity field and the particle dispersion provided by the different simulations are compared extensively, including the reference results provided by a direct numerical simulation (DNS). In general, LES and hybrid methods reproduce the time-averaged flow field correctly, the spatial distribution of the turbulence kinetic energy, and the particle dispersion. The performance of SAS is similar to that of LES and hybrid methods while the predictions of the RANS and URANS simulations exhibit larger deviations with respect to DNS. In general, the particle dispersion is better reproduced by simulations that capture correctly the spatial distribution of the turbulence kinetic energy.

The CFD simulation of cyclone separators has remarkably evolved over the past decades. Nowadays, computational models are essential for designing, analyzing, and optimizing these devices. Due to the intrinsic anisotropy of the flow inside these separators, the Reynolds stress model (RSM) has been mostly employed. However, RSM models fail to solve most time and space scales, including those relevant to particle behavior. Consequently, the prediction of the grade collection efficiency may be hindered, particularly for low-Stokes-number particles. For example, the precessing vortex core phenomenon (PVC), a well-known phenomenon that is relevant for particle motion, is not usually captured in Reynolds-averaged Navier–Stokes (RANS) simulations. Alternatively, the large-eddy simulation (LES) has been proven to be a superior approach since it captures many time and space scales that would have been otherwise dissipated, allowing for more accurate predictions of particle collection. However, this accuracy comes at a considerable computational cost. To combine the advantages of these two models, the main objective of this research was to evaluate a new hybrid RSM-LES model applied to the cyclone’s flow. The results were compared to experimental data and with RSM model results. It showed that, compared to a RANS model given by the RSM closure model, the grade collection efficiency curve obtained by the hybrid model is closer to the experimental one, even for the coarser mesh. Beyond that, the results showed that while the improvement in results was not proportional to mesh refinement for RANS modeling, the hybrid model showed significant improvement with mesh refinement.

The present work focuses on the advanced modeling and simulation of gas–solid flows in circulating fluidized bed (CFB) risers using computational fluid dynamics (CFD). Gas–solid flows play a crucial role in various industrial processes, particularly in the recovery of heavy petroleum fractions. The research utilizes the Euler–Lagrange framework with a point-particle approach to model these flows. For dense flow conditions, the gas phase formulation is modified to account for volume fraction. Additionally, a stochastic particle–particle collision model, which is computationally less intensive than deterministic approaches and previously untested for dense flows, is employed. Key findings indicate that the dilute flow formulation remains valid up to a solid mass loading 8 in well-distributed vertical flows. However, in CFB risers, due to the agglomeration of solids into clusters, the dense formulation becomes necessary at lower mass loadings of 4. Comparisons with experiments and simulations using deterministic models demonstrate that the proposed stochastic model accurately predicts dense gas–solid flows in CFB risers up to 22. Finally, the implications of this study are significant for industrial applications, providing a computationally efficient method to accurately model dense gas–solid flows, which is essential for optimizing processes in the petroleum industry.