Multi-scale simulation of multiphase multi-component flow in porous media using the Lattice Boltzmann Method

This thesis consists of work mainly performed within the Qatar Carbonates and Carbon Storage Research Centre (QCCSRC) project, focusing on the prediction of flow and transport properties in porous media. The direct pore scale simulation of complex fluid flow on reservoir rocks is the main topic of this work. A simulation package based on the lattice Boltzmann method has been developed to study single and multiphase flow as well as thermal and solute dispersion in porous media. The simulator has been extensively validated by comparing simulation results to reference solutions. Various numerical experiments have been performed to study the single/multiphase/solute dispersion flow in reservoir rocks. The simulator successfully predicts various transport properties including single phase and relative permeability, capillary pressure, initial-residual saturation, residual cluster size distribution and dispersion coefficient. The prediction has been compared to available experimental data and was generally found to be in good agreement. The simulator is also ready for exploring the two-phase dynamic problem with coupled and nonlinear physical processes including the effect of wettability, surface tension and hysteresis. To improve the efficiency of the lattice Boltzmann simulations, an optimised collision model and corresponding parallel operation are proposed and implemented. A sparse storage scheme which significantly reduces the memory requirement has been designed and implemented for complex porous media. Due to the application of these optimisation schemes, it is possible to perform simulations on large scale samples (Size >1024x512x512). The optimised code shows very good and promising performance, and nearly ideal scalability was achieved.Open Acces

Lattice-Boltzmann Modelling of Immiscible Fluid Displacement in Geologic Porous Media

Over the past two decades, multicomponent lattice-Boltzmann (LB) modelling has become a popular numerical technique to study the porous medium systems. For this technique to become a mature platform at a production level and to solve realistic problem that can be readily incorporated in the digital core analysis services for the oil and gas industries, there are still some challenges to resolve. This thesis intends to resolve some of issues confronted by the LB community. The first part of the thesis investigates the impact of the fundamental trade-off between image resolution and field of view on LB modelling. This is of practical value since 3D images of geological samples rarely have both sufficient resolution to capture fine structure and sufficient field of view to capture a full representative elementary volume of the medium. To optimise the simulations, it is important to know the minimum number of grid points that LB methods require to deliver physically meaningful results, and allow for the sources of measurement uncertainty to be appropriately balanced. We choose two commonly used multicomponent LB models, Shan-Chen and Rothman-Keller models, and study the behaviour of these two models when the phase interfacial radius of curvature and the feature size of the medium approach the discrete unit size of the computational grid. Both simple, small-scale test geometries and real porous media are considered. Models' behaviour in the extreme discrete limit is classified ranging from gradual loss of accuracy to catastrophic numerical breakdown. Based on this study, we provide guidance for experimental data collection and how to apply the LB methods to accurately resolve physics of interest for two-fluid flow in porous media. Resolution effects are particularly relevant to the study of low-porosity systems, including fractured materials, when the typical pore width may only be a few voxels across. The second part of the thesis explores the two-fluid displacement mechanism, especially the Haines jump dynamics and associated snap-off during drainage, by using a novel flux boundary condition, which is numerically more stable, and can more realistically replicate experiments given a prescribed capillary number. Irreversible events such as Haines jump in multiphase flow is what ultimately determines the hysteric behaviour of the porous medium systems. The high temporal resolution of LB methods makes it a suitable candidate to capture the dynamics of fast events (e.g. Haines jump in millisecond). We study the impacts of both the geometries of porous medium using persistent homology and the dynamic factors of fluids (i.e. viscosity ratio and capillary number) on the occurrence and frequency of snap-off events during drainage

Scaling soft matter physics to thousands of graphics processing units in parallel

We describe a multi-graphics processing unit (GPU) implementation of the Ludwig application, which specialises in simulating a variety of complex fluids via lattice Boltzmann fluid dynamics coupled to additional physics describing complex fluid constituents. We describe our methodology in augmenting the original central processing unit (CPU) version with GPU functionality in a maintainable fashion. We present several optimisations that maximise performance on the GPU architecture through tuning for the GPU memory hierarchy. We describe how we implement particles within the fluid in such a way to avoid a major diversion of the CPU and GPU codebases, whilst minimising data transfer at each time step. We detail our halo-exchange communication phase for the code, which exploits overlapping to allow efficient parallel scaling to many GPUs. We present results showing that the application demonstrates excellent scaling to at least 8192 GPUs in parallel, the largest system tested at the time of writing. The GPU version (on NVIDIA K20X GPUs) is around 3.5-5 times faster that the CPU version (on fully utilised AMD Opteron 6274 16-core CPUs), comparing equal numbers of CPUs and GPUs

Analyzing and Modeling the Performance of the HemeLB Lattice-Boltzmann Simulation Environment

We investigate the performance of the HemeLB lattice-Boltzmann simulator for cerebrovascular blood flow, aimed at providing timely and clinically relevant assistance to neurosurgeons. HemeLB is optimised for sparse geometries, supports interactive use, and scales well to 32,768 cores for problems with ~81 million lattice sites. We obtain a maximum performance of 29.5 billion site updates per second, with only an 11% slowdown for highly sparse problems (5% fluid fraction). We present steering and visualisation performance measurements and provide a model which allows users to predict the performance, thereby determining how to run simulations with maximum accuracy within time constraints.Comment: Accepted by the Journal of Computational Science. 33 pages, 16 figures, 7 table

Accuracy and performance of the lattice Boltzmann method with 64-bit, 32-bit, and customized 16-bit number formats

Fluid dynamics simulations with the lattice Boltzmann method (LBM) are very memory-intensive. Alongside reduction in memory footprint, significant performance benefits can be achieved by using FP32 (single) precision compared to FP64 (double) precision, especially on GPUs. Here, we evaluate the possibility to use even FP16 and Posit16 (half) precision for storing fluid populations, while still carrying arithmetic operations in FP32. For this, we first show that the commonly occurring number range in the LBM is a lot smaller than the FP16 number range. Based on this observation, we develop novel 16-bit formats - based on a modified IEEE-754 and on a modified Posit standard - that are specifically tailored to the needs of the LBM. We then carry out an in-depth characterization of LBM accuracy for six different test systems with increasing complexity: Poiseuille flow, Taylor-Green vortices, Karman vortex streets, lid-driven cavity, a microcapsule in shear flow (utilizing the immersed-boundary method) and finally the impact of a raindrop (based on a Volume-of-Fluid approach). We find that the difference in accuracy between FP64 and FP32 is negligible in almost all cases, and that for a large number of cases even 16-bit is sufficient. Finally, we provide a detailed performance analysis of all precision levels on a large number of hardware microarchitectures and show that significant speedup is achieved with mixed FP32/16-bit.Comment: 30 pages, 20 figures, 4 tables, 2 code listing

Classical and reactive molecular dynamics: Principles and applications in combustion and energy systems

Molecular dynamics (MD) has evolved into a ubiquitous, versatile and powerful computational method for fundamental research in science branches such as biology, chemistry, biomedicine and physics over the past 60 years. Powered by rapidly advanced supercomputing technologies in recent decades, MD has entered the engineering domain as a first-principle predictive method for material properties, physicochemical processes, and even as a design tool. Such developments have far-reaching consequences, and are covered for the first time in the present paper, with a focus on MD for combustion and energy systems encompassing topics like gas/liquid/solid fuel oxidation, pyrolysis, catalytic combustion, heterogeneous combustion, electrochemistry, nanoparticle synthesis, heat transfer, phase change, and fluid mechanics. First, the theoretical framework of the MD methodology is described systemically, covering both classical and reactive MD. The emphasis is on the development of the reactive force field (ReaxFF) MD, which enables chemical reactions to be simulated within the MD framework, utilizing quantum chemistry calculations and/or experimental data for the force field training. Second, details of the numerical methods, boundary conditions, post-processing and computational costs of MD simulations are provided. This is followed by a critical review of selected applications of classical and reactive MD methods in combustion and energy systems. It is demonstrated that the ReaxFF MD has been successfully deployed to gain fundamental insights into pyrolysis and/or oxidation of gas/liquid/solid fuels, revealing detailed energy changes and chemical pathways. Moreover, the complex physico-chemical dynamic processes in catalytic reactions, soot formation, and flame synthesis of nanoparticles are made plainly visible from an atomistic perspective. Flow, heat transfer and phase change phenomena are also scrutinized by MD simulations. Unprecedented details of nanoscale processes such as droplet collision, fuel droplet evaporation, and CO2 capture and storage under subcritical and supercritical conditions are examined at the atomic level. Finally, the outlook for atomistic simulations of combustion and energy systems is discussed in the context of emerging computing platforms, machine learning and multiscale modelling