Figure - available from: Aiche Journal
This content is subject to copyright. Terms and conditions apply.
Effect of grid resolution on the velocity predictions on the (A) XZ plane at Y = 8 mm, and (B) line 1 (located on the XZ plane as shown in the inset) for RANS simulations performed using the RNG‐κε turbulence model (Rep = 600). RANS, Reynolds‐averaged Navier‐Stokes; Rep, particle Reynolds number; RNG, Renormalization Group
Source publication
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...
Similar publications
Turbine inlet temperatures in advanced gas turbines could be as high as 2000 °C. To prevent ingress of this hot gas into the wheelspace between the stator and rotor disks, whose metals can only handle temperatures up to 850 °C, rim seals and sealing flows are used. This study examines the abilities of large eddy simulation (LES) based on the WALE s...
![Fig. 1 Appendages of an underwater vehicle and its wake structure [2]](publication/381933341/figure/fig1/AS:11431281258222137@1720013946320/Appendages-of-an-underwater-vehicle-and-its-wake-structure-2_Q320.jpg)
Computational fluid dynamics is used to analyze the influence of the horseshoe vortex on the wake features of a simplified geometry representing an underwater vehicle sail (i.e. Rood wing). The sail wake features are of interest as they influence the performance of the downstream components of an underwater vehicle such as the aft appendages and pr...
In recent years, convolutional neural networks (CNNs) have experienced an increasing interest in their ability to perform a fast approximation of effective hydrodynamic parameters in porous media research and applications. This paper presents a novel methodology for permeability prediction from micro-CT scans of geological rock samples. The trainin...
The synthetic turbulence generator (STG) lies at the interface of the Reynolds averaged Navier-Stokes (RANS) simulation and large eddy simulation (LES). This paper presents a STG for the multiple-relaxation-time(MRT) lattice Boltzmann method(LBM) framework at high friction Reynolds numbers, with consideration of near wall modeling. The Reichardt wa...
The paper presents a synthetic turbulence generator (STG) for the lattice Boltzmann method (LBM) at the interface of the Reynolds averaged Naiver-Stokes (RANS) equations and the LBM Large Eddy Simulation (LES). We first obtain the RANS velocity field from a finite volume solver at the interface. Then, we apply a numerical interpolation from the RAN...
Citations
... Ambekar et al.'s PRCFD simulations indicated that local velocity and second-order quantities, such as strain rate and vorticity, predicted by both RANS (with various eddy-viscosity models) and LES, are in reasonable agreement with DNS predictions. Specifically, ε-based RANS models, which focus on turbulent dissipation per unit mass, show good agreement with DNS, while ω-based RANS models, which focus on turbulent dissipation per unit volume, significantly under-predict turbulence quantities by several orders of magnitude due to their inadequacy in handling strongly wall-dominated flows [67]. As for ε-based RANS models, the realizable k-ε RANS model with enhanced wall treatment provides higher accuracy compared to the standard k-ε model, as it improves the turbulent viscosity coefficient and turbulent stress predictions, and can adapt to a wider range of y + value, especially for resolving viscous sub-layer region. ...
For a variety of industrial processes, catalytic fixed-bed reactors are essential and pose problems in respect to the optimization of internal heat distribution. This review assesses the progress made in Particle-Resolved Computational Fluid Dynamics (PRCFD) in analyzing the internal heat transfer in these reactors. Factors affecting heat transfer at the level of the overall bed configuration and at the level of individual particle features are discussed. By comprehensively summarizing the past PRCFD studies on heat transfer analysis in fixed-bed reactors, it provides valuable insights for the further development of PRCFD in this field, to improve the thermal management and guide future optimization of fixed-bed reactors. Moreover, suggestions are provided on simulation pathways that involve combining multiple Computational Fluid Dynamics (CFD) models, such as PRCFD, Particle-Resolved Direct Numerical Simulations (PRDNS), and effective homogeneous CFD, to simulate heat transfer within fixed-bed reactor systems in different scales and scenarios.
... 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.
... In the simulations of the packings described in section 2.1, the properties of water were used covering a wide range of particle Reynolds numbers ( = 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200). This range of the was chosen since the flow will be turbulent for exceeding 200 (Ambekar et al., 2023). The particle Reynolds number is calculated based on the volume-equivalent sphere diameter ( ) as shown in the following equation. ...
... Increasing computational resources and the availability of commercial and open-source software have made Computational Fluid Dynamics (CFD) simulations an integral part of multiphase reactor investigations. 3,[8][9][10][11][12][13][14][15][16] High performance Computing is pushing the bound-aries of the level of detail and the reactor size simulated using CFD. Recently, CFD simulation of a fluidized bed using one billion mesh elements on 36,000 processors was performed. ...
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.
