A High Performance Lattice Boltzmann Solver with Applications to Multiphase Flow in Porous Media

Multiphase flow is significant to many industrial processes such as the geologic storage of CO2 and oil recovery. Microscale simulation of flow in complex geological formations such as saline aquifers or oilfields is a complex and challenging task. The main goal of our study is to overcome high computational demand of multiphase flow simulations by using high performance computing. To model multiphase flow in porous media, we used a multiphase flow lattice Boltzmann (LB) method, which is recognized as an alternative to the classical computational fluid dynamics (CFD) methods. The developed LB model used an extended Color-Gradient approach with improved numerical stability, and it can be used to compute multiphase flow simulations with low capillary number and high viscosity ratios. To optimize computational efficiency, we apply the LB model to a parallel scheme written in C++ using the Message Passing Interface (MPI). Highly parallel runs of these simulations were performed using the HPC system at the Texas Advanced Computing Center at the University of Texas at Austin. We herein introduce the capability of our tool for multiphase flow simulation in porous media and present its application to CO2 sequestration in geological formations. The model has been applied to the simulation of CO2 and brine in sandstone rocks, by employing three-dimensional micro-CT images of rock samples. Injection of supercritical CO2 into the brine-saturated rock samples is simulated and complex displacement patterns under various reservoir conditions are identified.Texas Advanced Computing Center (TACC

Learned multiphysics inversion with differentiable programming and machine learning

We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, our software is designed to be both readable and scalable. This allows researchers to easily formulate their problems in an abstract fashion while exploiting the latest developments in high-performance computing. We illustrate and demonstrate our design principles and their benefits by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which aside from coupling of wave physics and multiphase flow, involves machine learning

HIGH PERFORMANCE COMPUTING: CLEAN COAL GASIFIER DESIGNS USING HYBRID PARALLELIZATION

One of the targets for coal gasification in the near future is capturing 90% of the carbon with less than a 10% increase in cost of electricity. Aggressive goals like this will require innovative gasifier designs to reach the market place quickly, with less risk, and in an economically viable way. Researchers at the National Energy Technology Laboratory (NETL) are collaborating with industry, academia, and other national labs on multiphase computational models like the legacy code MFIX (Multiphase Flow with Interphase eXchange) which can help design, operate, and scale-up clean coal gasifiers to meet the challenges or a carbon constrained world. In fact, NETL has hosted a series of multiphase workshops which has produced a multiphase flow science technology roadmap to achieve the goal “that by 2015 multiphase science based computer simulations play a significant role in the design, operation, and troubleshooting of multiphase flow devices in fossil fuel processing plants”. In this study, we present our experience of porting MFIX, an open source multiphase computational fluid dynamic model, to a high performance computing platform and how the resulting high fidelity simulations are impacting the design of clean coal gasifiers of tomorrow. Inherent to these gasifiers is the various time and length scales which require very high spatial resolution, large number of iterations with small time-steps to resolve and predict the spatiotemporal variations in gas and solids volume fractions, velocities, temperatures with any associated phase change and chemical reactions. These requirements resulted in perhaps the largest known simulations of gas-solids reacting flows, providing detailed information about the gas-solids flow structure, pressure, temperature and species distribution in the gasifier. From a computational science perspective, we found that global communication has to be reduced to achieve scalability to 1000s of cores and hybrid parallelization can yield substantial improvement in time-to-solution when utilizing thousands of multi-core processors

Numerical Simulations of Shock and Rarefaction Waves Interacting With Interfaces in Compressible Multiphase Flows

Developing a highly accurate numerical framework to study multiphase mixing in high speed flows containing shear layers, shocks, and strong accelerations is critical to many scientific and engineering endeavors. These flows occur across a wide range of scales: from tiny bubbles in human tissue to massive stars collapsing. The lack of understanding of these flows has impeded the success of many engineering applications, our comprehension of astrophysical and planetary formation processes, and the development of biomedical technologies. Controlling mixing between different fluids is central to achieving fusion energy, where mixing is undesirable, and supersonic combustion, where enhanced mixing is important. Iron, found throughout the universe and a necessary component for life, is dispersed through the mixing processes of a dying star. Non-invasive treatments using ultrasound to induce bubble collapse in tissue are being developed to destroy tumors or deliver genes to specific cells. Laboratory experiments of these flows are challenging because the initial conditions and material properties are difficult to control, modern diagnostics are unable to resolve the flow dynamics and conditions, and experiments of these flows are expensive. Numerical simulations can circumvent these difficulties and, therefore, have become a necessary component of any scientific challenge. Advances in the three fields of numerical methods, high performance computing, and multiphase flow modeling are presented: (i) novel numerical methods to capture accurately the multiphase nature of the problem; (ii) modern high performance computing paradigms to resolve the disparate time and length scales of the physical processes; (iii) new insights and models of the dynamics of multiphase flows, including mixing through hydrodynamic instabilities. These studies have direct applications to engineering and biomedical fields such as fuel injection problems, plasma deposition, cancer treatments, and turbomachinery.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133458/1/marchdf_1.pd

NEPTUNE_CFD High Parallel Computing Performances for Particle-Laden Reactive Flows

This paper presents high performance computing of NEPTUNE_CFD V1.07@Tlse. NEPTUNE_CFD is an unstructured parallelized code (MPI) using unsteady Eulerian multi-fluid approach for dilute and dense particle-laden reactive flows. Three-dimensional numerical simulations of two test cases have been carried out. The first one, a uniform granular shear flow exhibits an excellent scalability of NEPTUNE_CFD up to 1024 cores, and demonstrates the good agreement between the parallel simulation results and the analytical solutions. Strong scaling and weak scaling benchmarks have been performed. The second test case, a realistic dense fluidized bed shows the code computing performances on an industrial geometry