Abstract:

Miscible fluid mixtures, like pouring honey into water, Coca Cola into strong wine, are common phenomena in our daily life. While two miscible fluids are mixed together, their appearances in terms of colors and shapes will change due to their mixing interaction. The interaction between the mixture components could be regarded as a combination of the diffusing process and demixing process. If the former dominates the interaction, it is miscible; otherwise, it is immiscible. The complex microscopic interplay between the mixture components makes the simulation highly challenging. So far, there have been some dedicated research in computer graphics dealing with immiscible mixtures, but few works have been done focusing on miscible mixtures. In this paper, for the first time, we introduce a two-fluid lattice Boltzmann method (LBM), called TFLBM, applied to miscible binary mixtures. Different from other similar methods, the viscous and diffusing properties of the fluid in our work are considered separately, so that the physical insight is exposed more clearly and rationally. In addition, the operation of LBM is mostly a linear local computation, and graphics processing unit (GPU) has been utilized to achieve real-time simulation.  

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Abstract

Fluid dynamics presents many computational challenges, particularly in the area of complex fluids, where microscopic/mesoscopic details of the fluid components are important in addition to the bulk properties such as the viscosity. One useful method for studying such systems is based on the lattice Boltzmann equation (LBE) for the incompressible Navier-Stokes equations (for a review see e.g., ref. 1). The LBE provides a natural way for the microscopic details—e.g., composition, liquid crystal ordering, and so on—to be coupled to the fluid flow. In addition, by relaxing the constraint of exact incompressibility the LBE allows the fluid pressure to be computed locally, and thus is extremely well suited to parallel computation. 

Abstract:

Many complex natural systems studied in the geosciences are characterized by simple local-scale interactions that result in complex emergent behavior. Simulations of these systems, often implemented in parallel using standard central processing unit (CPU) clusters, may be better suited to parallel processing environments with large numbers of simple processors. Such an environment is found in graphics processing units (GPUs) on graphics cards.

This paper discusses GPU implementations of three example applications from computational fluid dynamics, seismic wave propagation, and rock magnetism. These candidate applications involve important numerical modeling techniques, widely employed in physical system simulations, that are themselves examples of distinct computing classes identified as fundamental to scientific and engineering computing. The presented numerical methods (and respective computing classes they belong to) are: (1) a lattice-Boltzmann code for geofluid dynamics (structured grid class); (2) a spectral-finite-element code for seismic wave propagation simulations (sparse linear algebra class); and (3) a least-squares minimization code for interpreting magnetic force microscopy data (dense linear algebra class). Significant performance increases (between 10× and 30× in most cases) are seen in all three applications, demonstrating the power of GPU implementations for these types of simulations and, more generally, their associated computing classes. 

Paper available through ACM.

Abstract:  

We present different kernels based on Lattice-Boltzmann methods for the solution of the two-dimensional Shallow Water and Navier-Stokes equations on fully structured lattices. The functionality ranges from simple scenarios like open-channel flows with planar beds to simulations with complex scene geometries like solid obstacles and non-planar bed topography with drystates and even interaction of the fluid with floating objects. The kernels are integrated into a hardware-oriented collection of libraries targeting multiple fundamentally different parallel hardware architectures like commodity multicore CPUs, the Cell BE, NVIDIA GPUs and clusters. We provide an algorithmic study which compares the different solvers in terms of performance and numerical accuracy in view of their capabilities and their specific implementation and optimisation on the different architectures. We show that an eightfold speedup over optimised multithreaded CPU code can be obtained with the GPU using basic methods and that even very complex flow phenomena can be simulated with significant speedups without loss of accuracy. 

Abstract:

Investigating how fluids flow inside the complicated geometries of porous rocks is an important problem in the petroleum industry. The lattice Boltzmann method (LBM) can be used to calculate porous rocks’ permeability. In this paper, we show how to implement this method efficiently on modern GPUs. Both a sequential CPU implementation and a parallelized GPU implementation is developed. Both implementations were tested using three porous data sets with known permeabilities. Our work shows that it is possible to calculate the permeability of porous rocks of simulations sizes up to 3683, which fit into the 4 GB memory of the NVIDIA Quadro FX 5800 card. Our single floating-point precision simulation resulted in respectbale 0.95-1.59 MLUPS whereas our GPU implentation achieved remarkable 180+ MLUPS for several lattices in the 1603 to 3683 range allowing calculations that would take hours on the CPU to be done in minutes on the GPU. Techniques for reducing round-off errors are also discussed and implemented.