Computational fluid dynamics using Graphics Processing Units: Challenges and opportunities

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

Spectral Ewald Acceleration of Stokesian Dynamics for polydisperse suspensions

In this work we develop the Spectral Ewald Accelerated Stokesian Dynamics (SEASD), a novel computational method for dynamic simulations of polydisperse colloidal suspensions with full hydrodynamic interactions. SEASD is based on the framework of Stokesian Dynamics (SD) with extension to compressible solvents, and uses the Spectral Ewald (SE) method [Lindbo & Tornberg, J. Comput. Phys. 229 (2010) 8994] for the wave-space mobility computation. To meet the performance requirement of dynamic simulations, we use Graphic Processing Units (GPU) to evaluate the suspension mobility, and achieve an order of magnitude speedup compared to a CPU implementation. For further speedup, we develop a novel far-field block-diagonal preconditioner to reduce the far-field evaluations in the iterative solver, and SEASD-nf, a polydisperse extension of the mean-field Brownian approximation of Banchio & Brady [J. Chem. Phys. 118 (2003) 10323]. We extensively discuss implementation and parameter selection strategies in SEASD, and demonstrate the spectral accuracy in the mobility evaluation and the overall $\mathcal{O}(N\log N)$ computation scaling. We present three computational examples to further validate SEASD and SEASD-nf in monodisperse and bidisperse suspensions: the short-time transport properties, the equilibrium osmotic pressure and viscoelastic moduli, and the steady shear Brownian rheology. Our validation results show that the agreement between SEASD and SEASD-nf is satisfactory over a wide range of parameters, and also provide significant insight into the dynamics of polydisperse colloidal suspensions.Comment: 39 pages, 21 figure

Characterization of an enzymatic packed-bed microreactor: Experiments and modeling

A micro packed-bed reactor (µPBR) based on two-parallel-plates configuration with immobilized Candida antarctica lipase B in the form of porous particles (Novozym® 435) was theoretically and experimentally characterized. A residence time distribution (RTD) within µPBRs comprising various random distributions of particles placed in one layer was computationally predicted by a mesoscopic lattice Boltzmann (LB) method. Numerical simulations were compared with measurements of RTD, obtained by stimulus-response experiment with a pulse input using glucose as a tracer, monitored by an electrochemical glucose oxidase microbiosensor integrated with the reactor. The model was validated by a good agreement between the experimental data and predictions of LB model at different conditions. The developed µPBR was scaled-up in length and width comprising either a single or two layers of Novozym® 435 particles and compared regarding the selected enzyme-catalyzed transesterification. A linear increase in the productivity with the increase in all dimensions of the µPBR between two-plates demonstrated very efficient and simple approach for the capacity rise. Further characterization of µPBRs of various sizes using the piezoresistive pressure sensor revealed very low pressure drops as compared to their conventional counterparts and thereby great applicability for production systems based on numbering-up approach

Ein Gas-Kinetic Scheme Ansatz zur Modellierung und Simulation von Feuer auf massiv paralleler Hardware

This work presents a simulation approach based on a Gas Kinetic Scheme (GKS) for the simulation of fire that is implemented on massively parallel hardware in terms of Graphics Processing Units (GPU) in the framework of General Purpose computing on Graphics Processing Units (GPGPU). Gas kinetic schemes belong to the class of kinetic methods because their governing equation is the mesoscopic Boltzmann equation, rather than the macroscopic Navier-Stokes equations. Formally, kinetic methods have the advantage of a linear advection term which simplifies discretization. GKS inherently contains the full energy equation which is required for compressible flows. GKS provides a flux formulation derived from kinetic theory and is usually implemented as a finite volume method on cell-centered grids. In this work, we consider an implementation on nested Cartesian grids. To that end, a coupling algorithm for uniform grids with varying resolution was developed and is presented in this work. The limitation to local uniform Cartesian grids allows an efficient implementation on GPUs, which belong to the class of many core processors, i.e. massively parallel hardware. Multi-GPU support is also implemented and efficiency is enhanced by communication hiding. The fluid solver is validated for several two- and three-dimensional test cases including natural convection, turbulent natural convection and turbulent decay. It is subsequently applied to a study of boundary layer stability of natural convection in a cavity with differentially heated walls and large temperature differences. The fluid solver is further augmented by a simple combustion model for non-premixed flames. It is validated by comparison to experimental data for two different fire plumes. The results are further compared to the industry standard for fire simulation, i.e. the Fire Dynamics Simulator (FDS). While the accuracy of GKS appears slightly reduced as compared to FDS, a substantial speedup in terms of time to solution is found. Finally, GKS is applied to the simulation of a compartment fire. This work shows that the GKS has a large potential for efficient high performance fire simulations.Diese Arbeit präsentiert einen Simulationsansatz basierend auf einer gaskinetischen Methode (eng. Gas Kinetic Scheme, GKS) zur Simulation von Bränden, welcher für massiv parallel Hardware im Sinne von Grafikprozessoren (eng. Graphics Processing Units, GPUs) implementiert wurde. GKS gehört zur Klasse der kinetischen Methoden, die nicht die makroskopischen Navier-Stokes Gleichungen, sondern die mesoskopische Boltzmann Gleichung lösen. Formal haben kinetische Methoden den Vorteil, dass der Advektionsterms linear ist. Dies vereinfacht die Diskretisierung. In GKS ist die vollständige Energiegleichung, die zur Lösung kompressibler Strömungen benötigt wird, enthalten. GKS formuliert den Fluss von Erhaltungsgrößen basierend auf der gaskinetischen Theorie und wird meistens im Rahmen der Finiten Volumen Methode umgesetzt. In dieser Arbeit betrachten wir eine Implementierung auf gleichmäßigen Kartesischen Gittern. Dazu wurde ein Kopplungsalgorithmus für die Kombination von Gittern unterschiedlicher Auflösung entwickelt. Die Einschränkung auf lokal gleichmäßige Gitter erlaubt eine effiziente Implementierung auf GPUs, welche zur Klasse der massiv parallelen Hardware gehören. Des Weiteren umfasst die Implementierung eine Unterstützung für Multi-GPU mit versteckter Kommunikation. Der Strömungslöser ist für zwei und dreidimensionale Testfälle validiert. Dabei reichen die Tests von natürlicher Konvektion über turbulente Konvektion bis hin zu turbulentem Zerfall. Anschließend wird der Löser genutzt um die Grenzschichtstabilität in natürlicher Konvektion bei großen Temperaturunterschieden zu untersuchen. Darüber hinaus umfasst der Löser ein einfaches Verbrennungsmodell für Diffusionsflammen. Dieses wird durch Vergleich mit experimentellen Feuern validiert. Außerdem werden die Ergebnisse mit dem gängigen Brandsimulationsprogramm FDS (eng. Fire Dynamics Simulator) verglichen. Die Qualität der Ergebnisse ist dabei vergleichbar, allerdings ist der in dieser Arbeit entwickelte Löser deutlich schneller. Anschließend wird das GKS noch für die Simulation eines Raumbrandes angewendet. Diese Arbeit zeigt, dass GKS ein großes Potential für die Hochleistungssimulation von Feuer hat

Deep Fluids: A Generative Network for Parameterized Fluid Simulations

This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. Due to the capability of deep learning architectures to learn representative features of the data, our generative model is able to accurately approximate the training data set, while providing plausible interpolated in-betweens. The proposed generative model is optimized for fluids by a novel loss function that guarantees divergence-free velocity fields at all times. In addition, we demonstrate that we can handle complex parameterizations in reduced spaces, and advance simulations in time by integrating in the latent space with a second network. Our method models a wide variety of fluid behaviors, thus enabling applications such as fast construction of simulations, interpolation of fluids with different parameters, time re-sampling, latent space simulations, and compression of fluid simulation data. Reconstructed velocity fields are generated up to 700x faster than re-simulating the data with the underlying CPU solver, while achieving compression rates of up to 1300x.Comment: Computer Graphics Forum (Proceedings of EUROGRAPHICS 2019), additional materials: http://www.byungsoo.me/project/deep-fluids

Numerical Prediction of Jet Noise Using Compressible Lattice Boltzmann Method

Computational fluid dynamics (CFD) encompasses a variety of numerical methods. Some depend on macroscopic model representatives, which are solved by finite volume, finite element or finite difference method, while others rely on a microscopic description. The lattice Boltzmann method (LBM) is considered a mesoscopic particle method, with its scale lying between macroscopic and microscopic. LBM works well when solving incompressible flow problems, but limitations arise when solving compressible flows, particularly at high Mach numbers. In the present research, this limitation will be overcome by using higher-order Taylor series expansion of the Maxwell equilibrium distribution function and Kataoka and Tsutahara (KT) models for compressible flows. The multiple relaxation times (MRT) approach associated with the collision term of the lattice Boltzmann equation (LBE) will be adopted to enhance the numerical stability of the code, while the large eddy simulation (LES) scale model will be implemented in LBM to simulate compressible jet flows at high subsonic speeds pertinent to jet noise problems. Three-dimensional simulation is performed using 19- and 15-lattice velocity with D3Q19 and D3Q15 models, respectively. In addition, compressible LBM is applied to simulate both heated and unheated jets to show the ability of the nonadiabatic fifth-order equilibrium distribution function in solving nonadiabatic compressible flows. The near-field flow physics and noise simulations are performed using a compressible lattice Boltzmann method. The results from the LMB simulation are used in the Kirchhoff surface integral approach to predict far-field jet noise. Finally, because of the ability of lattice Boltzmann in parallel computing and to improve the computation efficiency of LBM on the numerical simulations of turbulent flows, compute unified device architecture (CUDA) is used to implement LBM in the graphics processing unit (GPU), creating the hybrid code LBM-MRT-LES by utilizing the Kirchhoff integral method, a powerful tool for simulating aeroacoustics problems

Parallel implementation of a cellular automata in a hybrid CPU/GPU environment

Cellular Automata (CA) simulations can be used to model multiple systems, in fields like biology, physics and mathematics. In this work, a possible framework to execute a popular CA in hybrid CPU and GPUs (Graphics Processing Units) environments is presented. The inherently parallel nature of CA and the parallelism offered by GPUs makes their combination attractive. Benchmarks are conducted in several hardware scenarios. The use of MPI /OMP is explored for CPUs, together with the use of MPI in GPU clusters. Speed-ups up to 20 x are found when comparing GPU implementations to the serial CPU version of the code.WPDP- XIII Workshop procesamiento distribuido y paraleloRed de Universidades con Carreras en Informática (RedUNCI

Lattice Boltzmann Liquid Simulations on Graphics Hardware

Fluid simulation is widely used in the visual effects industry. The high level of detail required to produce realistic visual effects requires significant computation. Usually, expensive computer clusters are used in order to reduce the time required. However, general purpose Graphics Processing Unit (GPU) computing has potential as a relatively inexpensive way to reduce these simulation times. In recent years, GPUs have been used to achieve enormous speedups via their massively parallel architectures. Within the field of fluid simulation, the Lattice Boltzmann Method (LBM) stands out as a candidate for GPU execution because its grid-based structure is a natural fit for GPU parallelism. This thesis describes the design and implementation of a GPU-based free-surface LBM fluid simulation. Broadly, our approach is to ensure that the steps that perform most of the work in the LBM (the stream and collide steps) make efficient use of GPU resources. We achieve this by removing complexity from the core stream and collide steps and handling interactions with obstacles and tracking of the fluid interface in separate GPU kernels. To determine the efficiency of our design, we perform separate, detailed analyses of the performance of the kernels associated with the stream and collide steps of the LBM. We demonstrate that these kernels make efficient use of GPU resources and achieve speedups of 29.6 and 223.7, respectively. Our analysis of the overall performance of all kernels shows that significant time is spent performing obstacle adjustment and interface movement as a result of limitations associated with GPU memory accesses. Lastly, we compare our GPU LBM implementation with a single-core CPU LBM implementation. Our results show speedups of up to 81.6 with no significant differences in output from the simulations on both platforms. We conclude that order of magnitude speedups are possible using GPUs to perform free-surface LBM fluid simulations, and that GPUs can, therefore, significantly reduce the cost of performing high-detail fluid simulations for visual effects