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Effect of different numerical approaches on the predictions of the velocity distribution on the (A) XZ plane (Y = 8 mm; the LHS color bar is for DNS and RHS color bar is for all other simulations), and (B) line 1 (located on the XZ plane as shown in the inset) at a Rep of 600. DNS, Direct Numerical Simulation; LES, Large Eddy Simulation; LHS, left‐hand side; Rep, particle Reynolds number; RHS, right‐hand side; RNG, Renormalization Group; RSM, Reynolds stress model; SST, Shear‐Stress Transport; WALE, Wall‐Adapted Local Eddy‐viscosity
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Packed bed reactors are widely used to perform solid‐catalyzed gas‐phase reactions and local turbulence is known to influence heat and mass transfer characteristics. We have investigated turbulence characteristics in a packed bed of 113 spherical particles by performing particle‐resolved Reynolds‐averaged Navier–Stokes (RANS) simulations, Large Edd...
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Citations
... The efficient simulation of fluid flow through porous media is an ongoing research topic, for example in Pan et al. (2004), Yang et al. (2023), Han and Cundall (2013) or Ambekar et al. (2023), to mention a few. For this porous application, we generated a particle bed with the WALBERLA molecular dynamics module MESA-PD, as shown in Rettinger and Rüde (2018). ...
We implement and analyse a sparse / indirect-addressing data structure for the Lattice Boltzmann Method to support efficient compute kernels for fluid dynamics problems with a high number of non-fluid nodes in the domain, such as in porous media flows. The data structure is integrated into a code generation pipeline to enable sparse Lattice Boltzmann Methods with a variety of stencils and collision operators and to generate efficient code for kernels for CPU as well as for AMD and NVIDIA accelerator cards. We optimize these sparse kernels with an in-place streaming pattern to save memory accesses and memory consumption and we implement a communication hiding technique to prove scalability. We present single GPU performance results with up to 99% of maximal bandwidth utilization. We integrate the optimized generated kernels in the high performance framework WALBERLA and achieve a scaling efficiency of at least 82% on up to 1024 NVIDIA A100 GPUs and up to 4096 AMD MI250X GPUs on modern HPC systems. Further, we set up three different applications to test the sparse data structure for realistic demonstrator problems. We show performance results for flow through porous media, free flow over a particle bed, and blood flow in a coronary artery. We achieve a maximal performance speed-up of 2 and a significantly reduced memory consumption by up to 75% with the sparse / indirect-addressing data structure compared to the direct-addressing data structure for these applications.
... The common flow field numerical simulation methods can be categorized into three types: direct numerical simulation (DNS), large eddy simulation (LES), and Reynolds average Navier-Stokes (RANS). 38,39 Direct numerical simulation (DNS) directly solves the NS equation to simulate the evolution of all instantaneous motion quantities in turbulence. While it is the most accurate turbulence simulation method, it requires high grid requirements and consumes significant memory resources and computing costs. ...
Resonant acoustic mixing (RAM) is a widely applied technology that utilizes low-frequency vertical harmonic vibration for fluid transfer and mixing. However, the current research on the mixing mechanism of RAM technology primarily focuses on the initial mixing stages, neglecting the subsequent turbulent transition. This lack of understanding hinders the further improvement of RAM technology. This paper aims to investigate the mixing mechanism of power-law non-Newtonian fluids (NNF) in RAM using the phase field model and the spectral analysis. The study focuses on understanding the facilitating effect of turbulent transition in mixing and explores the influence of the power-law index and the excitation parameter on the mixing characteristics. The results indicate that the flow field experiences Faraday instability due to the intense perturbation during transient mixing. This leads to the fluid mixing through the development of large-scale vortex to small-scale vortex. During this process, the frequency components of the flow field are distributed around the working frequency, demonstrating transient and broad frequency characteristics. The steady state then dissipates energy through the viscous dissipation of small-scale vortices and ultimately relies on the single-frequency components such as submultiples and multiples excited by the nonlinear effect to complete the mixing. The mixing effects of NNF and Newtonian fluids (NF) are essentially the same, but they consume energy in different ways. The mixing uniformity and mixing efficiency of NNF increase with increasing vibration acceleration and decrease with increasing vibration frequency. These findings provide new insights into the RAM mechanism of power-law NNF.
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Multiphase reactors’ performance depends on the mesoscale structures formed due to multiphase hydrodynamics. Examples of mesoscale structures include gas bubbles in a fluidized bed and particle clusters in a riser. Experimental investigation of these mesoscale structures is challenging and expensive. To this end, Computational Fluid Dynamics (CFD) simulations are extensively employed; however, post-processing CFD data to capture mesoscale structures is challenging. This work develops a DBSCAN-based methodology to capture and characterize mesoscale structures from multiphase CFD simulation data. DBSCAN is an unsupervised machine-learning algorithm, which requires the value of two hyperparameters. A simple technique to calculate these hyperparameters is provided and the performance of DBSCAN is assessed on CFD-DEM simulations of bubbling fluidized beds and particle clustering. We demonstrate the computational complexity of DBSCAN to be Ο(n log n), lower than the existing techniques, by testing its scalability on highly resolved grids (up to 100 million grid points).
... Computational fluid dynamics (CFD) is an advanced fluid simulation method, including Reynolds-averaged Navier-Stokes (RANS), large-eddy simulation (LES), and many other models, which can be widely used in various practical applications. [1][2][3] Due to the properties of high computational parallelization, the use of graphics processing units (GPUs) to accelerate computational fluid dynamics (CFD) procedures has become the frontier of the high-performance CFD computing field, 4 as well as some works 5-7 based on using machine learning techniques on GPU to accelerate the CFD simulation procedure. In recent years, Lai et al. 8 achieved a speedup of 500 times through two-layer, parallel computing using the message passing library (MPI) on a 4-GPU supercomputing platform. ...
The granularity of computational fluid dynamics (CFD) generally refers to the point granularity parallelization as a unit of the grid when graphics processing units (GPUs) are utilized as the computing carrier. In commonly deployed implicit time advancement schemes, the parallel dimensionality must be reduced, resulting in the time advancement procedure becoming the only highly time-consuming step in the whole CFD computing procedures. In this paper, a block data-parallel lower-upper relaxation (BDPLUR) scheme based on Jacobi iteration and Roe's flux scheme is proposed and then implemented on a GPU. Numerical experiments are carried out and show that the convergence speed of the BDPLUR scheme, especially when implemented on a GPU, is approximately 10 times higher than that of the original data-parallel lower-upper relaxation (DPLUR) scheme and more than 100 times higher than that of the lower-upper symmetric Gauss‒Seidel (LUSGS) scheme. Moreover, the influence of different Courant-Friedrichs-Lewy (CFL) numbers on the convergence time is discussed, and different viscous matrices are compared. Standard cases are adopted to verify the effectiveness of the BDPLUR scheme.
