Over the last two decades, lattice Boltzmann methods have become an increasingly popular tool to compute the flow in complex geometries such as porous media. In addition to single phase simulations allowing, for example, a precise quantification of the permeability of a porous sample, a number of extensions to the lattice Boltzmann method are available which allow to study multiphase and multicomponent flows on a pore scale level. In this article we give an extensive overview on a number of these diffuse interface models and discuss their advantages and disadvantages. Furthermore, we shortly report on multiphase flows containing solid particles, as well as implementation details and optimization issues.

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Multiphase lattice Boltzmann simulations for porous media applications : a review

Multiphase lattice Boltzmann simulations for porous media applications -- a review

The optimal location and sizing of distributed generation is a suitable option for improving the operation of electric systems. This paper proposes a parallel implementation of the Population-Based Incremental Learning (PBIL) algorithm to locate distributed generators (DGs), and the use of Particle Swarm Optimization (PSO) to define the size those devices. The resulting method is a master-slave hybrid approach based on both the parallel PBIL (PPBIL) algorithm and the PSO, which reduces the computation time in comparison with other techniques commonly used to address this problem. Moreover, the new hybrid method also reduces the active power losses and improves the nodal voltage profiles. In order to verify the performance of the new method, test systems with 33 and 69 buses are implemented in Matlab, using Matpower, for evaluating multiple cases. Finally, the proposed method is contrasted with the Loss Sensitivity Factor (LSF), a Genetic Algorithm (GA) and a Parallel Monte-Carlo algorithm. The results demonstrate that the proposed PPBIL-PSO method provides the best balance between processing time, voltage profiles and reduction of power losses.

Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

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

A novel heterogeneous algorithm to simulate multiphase flow in porous media on multicore CPU–GPU systems

A Lattice-Boltzmann solver for 3D fluid simulation on GPU

Given a two-dimensional finite element mesh and a point in the plane, we were interested in finding the element to which the point belongs.
In this work four techniques are described that allow to reduce the order of the search algorithm. They are: the uniform grid technique, the quasi-uniform grid technique, the quad-tree technique, and a combination between the first and third ones.
It was found that, for a wide variety of meshes, the last two methods are computationally cheaper both in CPU time and memory requirements.

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Algoritmos Eficientes para la Búsqueda del Elemento de una Red que Contiene un Punto Dado

Se implemento en una Unidad de Procesamiento Grafico (GPU) un modelo de Lattice Boltzmann (LBM) para resolucion de las ecuaciones de Navier-Stokes en 3 dimensiones, utilizando el lenguaje de GPU Computed Unified Device Architecture (CUDA) de NVIDIA. Se realizaron simulaciones de flujo en una cavidad cubica con velocidad constante en la capa superior con diferentes tamanos de grillas y numeros de Reynolds comparando los resultados con los encontrados en la literatura mostrando gran coherencia. Al tratarse de un problema acotado por el ancho de banda de memoria, se optimizo el paso de propagacion utilizando memoria compartida y accesos alineados a memoria global alcanzando el 35% del ancho de banda nominal de la placa. El speedup logrado con respecto a una implementacion similar optimizada para CPU es de dos ordenes de magnitud.

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Enzo Alberto Dari

Papers

A Lattice-Boltzmann solver for 3D fluid simulation on GPU

Algoritmos Eficientes para la Búsqueda del Elemento de una Red que Contiene un Punto Dado

Uso de GPUS para la Simulación de Fluidos en 3D con el Método de Lattice Boltzmann

Lattice-boltzmann Navier-stokes Simulation on Graphic Processing Units