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相关论文: GPU acceleration of CaNS for massively-parallel di…

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We introduce CaLES, a GPU-accelerated finite-difference solver designed for large-eddy simulations (LES) of incompressible wall-bounded flows in massively parallel environments. Built upon the existing direct numerical simulation (DNS)…

流体动力学 · 物理学 2024-11-18 Maochao Xiao , Alessandro Ceci , Pedro Costa , Johan Larsson , Sergio Pirozzoli

We present an efficient solver for massively-parallel direct numerical simulations of incompressible turbulent flows. The method uses a second-order, finite-volume pressure-correction scheme, where the pressure Poisson equation is solved…

流体动力学 · 物理学 2019-09-13 Pedro Costa

We present the Fluid Transport Accelerated Solver, FluTAS, a scalable GPU code for multiphase flows with thermal effects. The code solves the incompressible Navier-Stokes equation for two-fluid systems, with a direct FFT-based Poisson…

We present STREAmS, an in-house high-fidelity solver for large-scale, massively parallel direct numerical simulations (DNS) of compressible turbulent flows on graphical processing units (GPUs). STREAmS is written in the Fortran 90 language…

计算物理 · 物理学 2020-04-07 Matteo Bernardini , Davide Modesti , Francesco Salvadore , Sergio Pirozzoli

CaNS-Fizzy -- Fizzy for short -- is a GPU-accelerated numerical solver for massively-parallel Direct Numerical Simulations (DNS) of incompressible two-phase flows. A DNS enables direct access to all flow quantities, resolved in time and…

流体动力学 · 物理学 2025-09-12 G. Lupo , P. Wellens , P. Costa

We present a multi-block finite-difference solver for massively parallel Direct Numerical Simulations (DNS) of incompressible flows. The algorithm combines the versatility of a multi-block solver with the method of eigenfunctions…

计算物理 · 物理学 2022-09-14 Pedro Costa

The AFiD code, an open source solver for the incompressible Navier-Stokes equations ({\color{blue}\burl{http://www.afid.eu}}), has been ported to GPU clusters to tackle large-scale wall-bounded turbulent flow simulations. The GPU porting…

This paper introduces open-source computational fluid dynamics software named open computational fluid dynamic code for scientific computation with graphics processing unit (GPU) system (OpenCFD-SCU), developed by the authors for direct…

流体动力学 · 物理学 2022-12-21 Guanlin Dang , Shiwei Liu , Tongbiao Guo , Junyi Duan , Xinliang Li

A new flow solver scalable on multiple Graphics Processing Units (GPUs) for direct numerical simulation of wall-bounded incompressible flow is presented. This solver utilizes a previously reported work (J. Comp. Physics, vol. 352 (2018),…

计算物理 · 物理学 2018-12-05 Sanghyun Ha , Junshin Park , Donghyun You

This paper explores strategies to transform an existing CPU-based high-performance computational fluid dynamics solver, HyPar, for compressible flow simulations on emerging exascale heterogeneous (CPU+GPU) computing platforms. The…

计算工程、金融与科学 · 计算机科学 2022-12-07 Youngdae Kim , Debojyoti Ghosh , Emil M. Constantinescu , Ramesh Balakrishnan

Multiphase compressible flows are often characterized by a broad range of space and time scales. Thus entailing large grids and small time steps, simulations of these flows on CPU-based clusters can thus take several wall-clock days.…

We present an efficient implementation for running three-dimensional numerical simulations of fluid-structure interaction problems on single GPUs, based on Nvidia CUDA through Numba and Python. The incompressible flow around moving bodies…

流体动力学 · 物理学 2024-12-05 M. Guerrero-Hurtado , J. M. Catalán , M. Moriche , A. Gonzalo , O. Flores

We show feasibility and benefits of porting an adaptive multi-scale kinetic-fluid code to CPU-GPU systems. Challenges are due to the irregular data access for adaptive Cartesian mesh, vast difference of computational cost between kinetic…

计算物理 · 物理学 2015-07-23 Sergey Zabelok , Robert Arslanbekov , Vladimir Kolobov

Immersed boundary methods (IBMs) facilitate the simulation of flows around stationary, moving, and deforming bodies on Cartesian grids. However, extending these simulations to the large grid sizes required for realistic flow problems…

流体动力学 · 物理学 2026-03-16 Sushrut Kumar , Joshua Romero , Jung-Hee Seo , Massimiliano Fatica , Rajat Mittal

We develop a matrix-free Full Approximation Storage (FAS) multigrid solver based on staggered finite differences and implemented on GPU in MATLAB. To enhance performance, intermediate variables are reused, and an X-shape Multi-Color…

数值分析 · 数学 2025-10-14 Jiale Meng , Shuqi Tang , Steven M. Wise , Zhenlin Guo

High-order gas-kinetic scheme (HGKS) has become a workable tool for the direct numerical simulation (DNS) of turbulence. In this paper, to accelerate the computation, HGKS is implemented with the graphical processing unit (GPU) using the…

数值分析 · 数学 2023-01-25 Yuhang Wang , Guiyu Cao , Liang Pan

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…

流体动力学 · 物理学 2014-02-04 Kyle E Niemeyer , Chih-Jen Sung

Computational Fluid Dynamics (CFD) is the simulation of fluid flow undertaken with the use of computational hardware. The underlying equations are computationally challenging to solve and necessitate high performance computing (HPC) to…

分布式、并行与集群计算 · 计算机科学 2024-04-04 Zachary Cooper-Baldock , Brenda Vara Almirall , Kiao Inthavong

An existing hybrid MPI-OpenMP scheme is augmented with a CUDA-based fine grain parallelization approach for multidimensional distributed Fourier transforms, in a well-characterized pseudospectral fluid turbulence code. Basics of the hybrid…

计算物理 · 物理学 2018-08-07 Duane Rosenberg , Pablo D. Mininni , Raghu Reddy , Annick Pouquet

This paper presents a Graphics Processing Units (GPUs) acceleration method of an iterative scheme for gas-kinetic model equations. Unlike the previous GPU parallelization of explicit kinetic schemes, this work features a fast converging…

计算物理 · 物理学 2020-01-08 Lianhua Zhu , Peng Wang , Songze Chen , Zhaoli Guo , Yonghao Zhang
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