2012 Freeman Scholar Lecture: Computational Fluid Dynamics on Graphics Processing Units

2013 ◽  
Vol 135 (6) ◽  
Author(s):  
S. P. Vanka
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Graphics Processing Units ◽  
Stokes Equations ◽  
Multicore Processors ◽  
Fluid Flows ◽  
Data Access ◽  
Central Processing ◽  
Data Parallel ◽  
Graphics Processing

This paper discusses the various issues of using graphics processing units (GPU) for computing fluid flows. GPUs, used primarily for processing graphics functions in a computer, are massively parallel multicore processors, which can also perform scientific computations in a data parallel mode. In the past ten years, GPUs have become quite powerful and have challenged the central processing units (CPUs) in their price and performance characteristics. However, in order to fully benefit from the GPUs' performance, the numerical algorithms must be made data parallel and converge rapidly. In addition, the hardware features of the GPUs require that the memory access be managed carefully in order to not suffer from the high latency. Fully explicit algorithms for Euler and Navier–Stokes equations and the lattice Boltzmann method for mesoscopic flows have been widely incorporated on the GPUs, with significant speed-up over a scalar algorithm. However, more complex algorithms with implicit formulations and unstructured grids require innovative thinking in data access and management. This article reviews the literature on linear solvers and computational fluid dynamics (CFD) algorithms on GPUs, including the author's own research on simulations of fluid flows using GPUs.

scholarly journals Computational Fluid Dynamics Using Graphics Processing Units: Challenges and Opportunities

2011 ◽  
Author(s):  
S. Pratap Vanka ◽  
Aaron F. Shinn ◽  
Kirti C. Sahu
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Fluid Flow ◽  
Graphics Processing Units ◽  
Review Paper ◽  
Fluid Flows ◽  
New Paradigm ◽  
The Past ◽  
Challenges And Opportunities ◽  
Graphics Processing

A new paradigm for computing fluid flows is the use of Graphics Processing Units (GPU), which have recently become very powerful and convenient to use. In the past three years, we have implemented five different fluid flow algorithms on GPUs and have obtained significant speed-ups over a single CPU. Typically, it is possible to achieve a factor of 50–100 over a single CPU. In this review paper, we describe our experiences on the various algorithms developed and the speeds achieved.

An Implicit Harmonic Balance Method in Graphics Processing Units for Oscillating Blades

2015 ◽  
Vol 138 (3) ◽  
Author(s):  
Javier Crespo ◽  
Roque Corral ◽  
Jesus Pueblas
Keyword(s):  
Finite Difference ◽  
Harmonic Balance ◽  
Graphics Processing Units ◽  
Stokes Equations ◽  
Computational Cost ◽  
Harmonic Balance Method ◽  
Transonic Compressor ◽  
Central Processing ◽  
Speed Up ◽  
Graphics Processing

An implicit harmonic balance (HB) method for modeling the unsteady nonlinear periodic flow about vibrating airfoils in turbomachinery is presented. An implicit edge-based three-dimensional Reynolds-averaged Navier–Stokes equations (RANS) solver for unstructured grids, which runs both on central processing units (CPUs) and graphics processing units (GPUs), is used. The HB method performs a spectral discretization of the time derivatives and marches in pseudotime, a new system of equations where the unknowns are the variables at different time samples. The application of the method to vibrating airfoils is discussed. It is shown that a time-spectral scheme may achieve the same temporal accuracy at a much lower computational cost than a backward finite-difference method at the expense of using more memory. The performance of the implicit solver has been assessed with several application examples. A speed-up factor of 10 is obtained between the spectral and finite-difference version of the code, whereas an additional speed-up factor of 10 is obtained when the code is ported to GPUs, totalizing a speed factor of 100. The performance of the solver in GPUs has been assessed using the tenth standard aeroelastic configuration and a transonic compressor.

Practical Examples of Automated Development of Efficient Parallel Programs

2021 ◽  
pp. 180-216
Keyword(s):  
Graphics Processing Units ◽  
Dynamic Models ◽  
Multicore Processors ◽  
Central Processing ◽  
Design And Synthesis ◽  
Cuda Technology ◽  
Design Generation ◽  
Graphics Processing ◽  
Rewriting Rules ◽  
Target Platform

In this chapter, some examples of application of the developed software tools for design, generation, transformation, and optimization of programs for multicore processors and graphics processing units are considered. In particular, the algebra-algorithmic-integrated toolkit for design and synthesis of programs (IDS) and the rewriting rules system TermWare.NET are applied for design and parallelization of programs for multicore central processing units. The developed algebra-dynamic models and the rewriting rules toolkit are used for parallelization and optimization of programs for NVIDIA GPUs supporting the CUDA technology. The TuningGenie framework is applied for parallel program auto-tuning: optimization of sorting, Brownian motion simulation, and meteorological forecasting programs to a target platform. The parallelization of Fortran programs using the rewriting rules technique on sample problems in the field of quantum chemistry is examined.

Sailing Yacht Rig Improvements Through Viscous Computational Fluid Dynamics

2005 ◽  
Author(s):  
Vincent G. Chapin ◽  
Romaric Neyhousser ◽  
Stephane Jamme ◽  
Guillaume Dulliand ◽  
Patrick Chassaing
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Wind Tunnel ◽  
High Speed ◽  
Stokes Equations ◽  
Wind Tunnel Test ◽  
Physical Modelling ◽  
Optimized Design ◽  
Linear Interaction ◽  
Sailing Yacht

In this paper we propose a rational viscous Computational Fluid Dynamics (CFD) methodology applied to sailing yacht rig aerodynamic design and analysis. After an outlook of present challenges in high speed sailing, we emphasized the necessity of innovation and CFD to conceive, validate and optimize new aero-hydrodynamic concepts. Then, we present our CFD methodology through CAD, mesh generation, numerical and physical modelling choices, and their validation on typical rig configurations through wind-tunnel test comparisons. The methodology defined, we illustrate the relevance and wide potential of advanced numerical tools to investigate sailing yacht rig design questions like the relation between sail camber, propulsive force and aerodynamic finesse, and like the mast-mainsail non linear interaction. Through these examples, it is shown how sailing yacht rig improvements may be drawn by using viscous CFD based on Reynolds Averaged Navier-Stokes equations (RANS). Then the extensive use of viscous CFD, rather than wind-tunnel tests on scale models, for the evaluation or ranking of improved designs with increased time savings. Viscous CFD methodology is used on a preliminary study of the complex and largely unknown Yves Parlier Hydraplaneur double rig. We show how it is possible to increase our understanding of his flow physics with strong sail interactions, and we hope this methodology will open new roads toward optimized design. Throughout the paper, the necessary comparison between CFD and wind-tunnel test will be presented to focus on limitations and drawbacks of viscous CFD tools, and to address future improvements.

Optimization of Biomimetic Propulsive Kinematics of a Flexible Foil Using Integrated Computational Fluid Dynamics–Computational Structural Dynamics Simulations

2018 ◽  
Vol 141 (6) ◽  
Author(s):  
Jiho You ◽  
Jinmo Lee ◽  
Seungpyo Hong ◽  
Donghyun You
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Young’S Modulus ◽  
Phase Angle ◽  
Structural Dynamics ◽  
Stokes Equations ◽  
Young's Modulus ◽  
Immersed Boundary ◽  
Computational Structural Dynamics ◽  
Dynamics Simulations

A computational methodology, which combines a computational fluid dynamics (CFD) technique and a computational structural dynamics (CSD) technique, is employed to design a deformable foil whose kinematics is inspired by the propulsive motion of the fin or the tail of a fish or a cetacean. The unsteady incompressible Navier–Stokes equations are solved using a second-order accurate finite difference method and an immersed-boundary method to effectively impose boundary conditions on complex moving boundaries. A finite element-based structural dynamics solver is employed to compute the deformation of the foil due to interaction with fluid. The integrated CFD–CSD simulation capability is coupled with a surrogate management framework (SMF) for nongradient-based multivariable optimization in order to optimize flapping kinematics and flexibility of the foil. The flapping kinematics is manipulated for a rigid nondeforming foil through the pitching amplitude and the phase angle between heaving and pitching motions. The flexibility is additionally controlled for a flexible deforming foil through the selection of material with a range of Young's modulus. A parametric analysis with respect to pitching amplitude, phase angle, and Young's modulus on propulsion efficiency is presented at Reynolds number of 1100 for the NACA 0012 airfoil.

Development of SIMPLE-Like Algorithms for Incompressible Navier-Stokes Equations in Liquid Processing of Materials

2012 ◽  
Vol 184-185 ◽  
pp. 944-948 ◽  
Author(s):  
Hai Jun Gong ◽  
Yang Liu ◽  
Xue Yi Fan ◽  
Da Ming Xu
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Stokes Equations ◽  
Simple Algorithm ◽  
Navier Stokes ◽  
Incompressible Fluid Flow ◽  
Numerical Accuracy ◽  
Simple Scheme ◽  
Navier Stokes Equations ◽  
Computational Fluid Dynamics Cfd

For a clear and comprehensive opinion on segregated SIMPLE algorithm in the area of computational fluid dynamics (CFD) during liquid processing of materials, the most significant developments on the SIMPLE algorithm and its variants are briefly reviewed. Subsequently, some important advances during last 30 years serving as increasing numerical accuracy, enhancing robustness and improving efficiency for Navier–Stokes (N-S) equations of incompressible fluid flow are summarized. And then a so-called Direct-SIMPLE scheme proposed by the authors of present paper introduced, which is different from SIMPLE-like schemes, no iterative computations are needed to achieve the final pressure and velocity corrections. Based on the facts cited in present paper, it conclude that the SIMPLE algorithm and its variants will continue to evolve aimed at convergence and accuracy of solution by improving and combining various methods with different grid techniques, and all the algorithms mentioned above will enjoy widespread use in the future.

A Condensed Phase Computational Fluid Dynamics Model for Polymer Melt Flow

2008 ◽  
Author(s):  
Qing Tang ◽  
Michael Bockelie
Keyword(s):  
Fluid Dynamics ◽  
Heat Flux ◽  
Computational Fluid Dynamics ◽  
Condensed Phase ◽  
Stokes Equations ◽  
Free Surface Flows ◽  
Polymer Materials ◽  
High Heat Flux ◽  
Time Step ◽  
Temperature Dependent Viscosity

This paper presents a condensed phase computational fluid dynamics (CFD) based tool for modeling the processes of melting, flow and gasification of thermoplastic materials exposed to a high heat flux. Potential applications of the tool include investigating the behavior of polymer materials commonly used in personal computers and computer monitors if exposed to an intense heat flux, such as occurs during a fire. The finite-volume based model uses a three-dimensional body-fitted time dependent grid formulation to solve the unsteady Navier Stokes equations. A multi-grid method is used to accelerate convergence at each time step. Sub-models are included to describe the temperature dependent viscosity relationship and in-depth gasification and absorption of thermoplastic materials, free surface flows and surface tension. A series of test cases have been performed and the model results are compared to experimental data to investigate the impacts of different sub-models, boundary conditions, material properties and problem configurations on the accuracy, efficiency and applicability of the modeling tool.

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