Large-Eddy Simulations of Turbulent Incompressible Flows on GPU Clusters

DeLeon, Rey; Jacobsen, Dana A.; Şenocak, İnanç

doi:10.1109/mcse.2012.37

Cited by 24 publications

(11 citation statements)

References 10 publications

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“…DeLeon and coworkers [28,29] recently demonstrated another GPU-based solver for LES of turbulent incompressible flows. The subgrid-scale terms were modeled using the Lagrangian dynamic Smagorinsky model.…”

Section: Turbulent Flowmentioning

confidence: 99%

Recent progress and challenges in exploiting graphics processors in computational fluid dynamics

Niemeyer

Sung

2013

J Supercomput

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The progress made in accelerating simulations of fluid flow using GPUs, and the challenges that remain, are surveyed. The review first provides an introduction to GPU computing and programming, and discusses various considerations for improved performance. Case studies comparing the performance of CPU-and GPU-based solvers for the Laplace and incompressible Navier-Stokes equations are performed in order to demonstrate the potential improvement even with simple codes. Recent efforts to accelerate CFD simulations using GPUs are reviewed for laminar, turbulent, and reactive flow solvers. Also, GPU implementations of the lattice Boltzmann method are reviewed. Finally, recommendations for implementing CFD codes on GPUs are given and remaining challenges are discussed, such as the need to develop new strategies and redesign algorithms to enable GPU acceleration.

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Section: Turbulent Flowmentioning

confidence: 99%

Recent progress and challenges in exploiting graphics processors in computational fluid dynamics

Niemeyer

Sung

2013

J Supercomput

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“…(RANS) equations, Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) were set forth [4][5][6] . In order to carry out large-scale numerical simulation on GPU, implementations on multiple GPUs have been presented in 8,[24][25] .…”

Section: Introductionmentioning

confidence: 99%

“…In order to carry out large-scale numerical simulation on GPU, implementations on multiple GPUs have been presented in 8,[24][25] . It's then important to improve single GPU performance before realizing multi-GPU version.…”

Section: Introductionmentioning

confidence: 99%

Gpu Implementation of a Viscous Flow Solver on Unstructured Grids

Chen

2016

Int. J. Mod. Phys. Conf. Ser.

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Graphics processing units have gained popularities in scientific computing over past several years due to their outstanding parallel computing capability. Computational fluid dynamics applications involve large amounts of calculations, therefore a latest GPU card is preferable of which the peak computing performance and memory bandwidth are much better than a contemporary high-end CPU. We herein focus on the detailed implementation of our GPU targeting Reynolds-averaged Navier-Stokes equations solver based on finite-volume method. The solver employs a vertexcentered scheme on unstructured grids for the sake of being capable of handling complex topologies. Multiple optimizations are carried out to improve the memory accessing performance and kernel utilization. Both steady and unsteady flow simulation cases are carried out using explicit Runge-Kutta scheme. The solver with GPU acceleration in this paper is demonstrated to have competitive advantages over the CPU targeting one.

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“…However, this method cannot capture details of this signal. Direct Numerical Simulation (DNS) has not simplified any part of the Navier-Stokes equations, therefore, the process of this approach is quite resource consuming [5]. Because Large Eddy Simulation (LES) can obtain details of a signal with moderate cost of computational resource, this method has been widely applied in the field of jet noise reduction [6], thermal stripping of jets [7], and researches about turbulent mechanisms [8,9].…”

Section: Introductionmentioning

confidence: 99%

Large Eddy Simulation and Hilbert Huang Transform for fluctuation pressure of high speed train

Wang

Chunju

2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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Fluctuation pressure acting on the surface of high speed trains is the main source of aerodynamic noise. Therefore, it becomes very important to research components of the fluctuation pressure for controlling the aerodynamic noise. In this paper, Large Eddy Simulation (LES) method is applied to calculate the fluctuation pressure acting on a certain type of CRH high speed train and the distribution rule of the fluctuation pressure is studied by Hilbert Huang Transform (HHT) method. Both the fluctuating part and the average part from the surface pressure are extracted by Empirical Mode Decomposition (EMD). A comparison of the distribution rules for fluctuation pressure at two different speed levels is made.

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Large-Eddy Simulations of Turbulent Incompressible Flows on GPU Clusters

Cited by 24 publications

References 10 publications

Recent progress and challenges in exploiting graphics processors in computational fluid dynamics

Recent progress and challenges in exploiting graphics processors in computational fluid dynamics

Gpu Implementation of a Viscous Flow Solver on Unstructured Grids

Large Eddy Simulation and Hilbert Huang Transform for fluctuation pressure of high speed train

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