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Results for “Multiphase Flow Simulation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

ORNL/grit

Grit is a software library designed specifically for conducting particle-based Lagrangian simulations with CPU/GPUperformance portability. This library allows researchers and engineers to perform a wide range of simulations, including multiphase flow, and more. Grit employs Message Passing Interface (MPI) for distributed memory parallelism and Kokkos programming model for on-node shared memory parallelism with performance portability across different architectures of GPUs and multi-core/manycore CPUs.

Ge, Wenjun [Oak Ridge National Laboratory (ORNL), ↗
DOE OSTI2022

CO2 Sweep Based on Geochemical, and Reservoir Characterization of the Residual Oil Zone of Hess's Seminole San Andres Unit (Final Report)

The study of the ROZ (residual oil zone) versus the MPZ (main pay zone) of the Seminole Field provided a unique insight into the nature of ROZ. This is because we had access to an order of magnitude of core available in other fields. We were also able to use an extensive petrophysical data base with one-foot sampling interval made available by the operator. We also obtained an extensive and unique data base containing the entire production history for the field at full resolution. The data set also includes a unique complete, highly-granular information on volumes of CO2 injection and CO2 production. We created a unique high-resolution model of the reservoir. This is the first such model that has been created. Using this very high-resolution data base we were able to make high resolution, multiphase fluid flow simulations. These fluid flow simulations have enabled our team to evaluate CO2 sweep in the ROZ in comparison with the MPZ.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Differentiable multiphase flow model for physics-informed machine learning in reservoir pressure management

Accurate subsurface reservoir pressure control is extremely challenging due to geological heterogeneity and multiphase fluid-flow dynamics. Predicting behavior in this setting relies on high-fidelity physics-based simulations that are computationally expensive. Yet, the uncertain, heterogeneous properties that control these flows make it necessary to perform many of these expensive simulations, which is often prohibitive. To address these challenges, we introduce a physics-informed machine learning workflow that couples a fully differentiable multiphase flow simulator, which is implemented in the DPFEHM framework with a convolutional neural network (CNN). The CNN learns to predict fluid extraction rates from heterogeneous permeability fields to enforce pressure limits at critical reservoir locations. By incorporating transient multiphase flow physics into the training process, our method enables more practical and accurate predictions for realistic injection-extraction scenarios compared to previous works. To speed up training, we pretrain the model on single-phase, steady-state simulations and then finetune it on full multiphase scenarios, which dramatically reduces the computational cost. We demonstrate that high-accuracy training can be achieved with fewer than three thousand full-physics multiphase flow simulations – compared to previous estimates requiring up to ten million. This drastic reduction in the number of simulations is achieved by leveraging transfer learning from much less expensive single phase simulations.

25 ENERGY STORAGE↗
DOE OSTI2023

Droplet-Gas Interactions in Nanoelectrospray Multiphase Flow

Electrospray (ES) is production of a charged droplet plume that transits from an electrically biased supply capillary to a counter electrode under the influence of an electric field. Drag interactions with the surrounding gas decelerate the droplets while transferring the momentum from droplets to accelerate the surrounding gas, resulting in an induced gas flow. This work seeks to characterize the structure of the resulting gas jet and identify the key mechanisms defining the flow structure. The phenomenon of gas jets produced by momentum transfer from nano-electrospray (nES) plumes is explored with schlieren visualization, thermal anemometry, and numerical simulations. Schlieren visualization experiments provide information on the flow structure in support of simulation predictions, and the hot thermistor anemometry measurements of gas velocities outside the spray demonstrate quantitatively validated simulation results. The study reveals the formation of a moderately high velocity coaxial gas jet within the nES plume and provides insight into the overall flow structure of the induced flow. The multiphase electrohydrodynamic simulations enable numerical experimentation to explore the fundamental physics of coupled droplet-gas transport and the resulting flow structure. The simulations, confirmed by the experiments, reveal gas jetting induced by nES with a narrow (sub hundred micrometers in diameter) core originating from the nES liquid-jet breakup region and a surrounding larger-in-extent zone (several hundred micrometers in diameter) of lower velocity gas flow within the electrospray plume. This behavior is due to nES ejecting a stream of droplets from the tip of a narrow liquid cone-jet, where the combined effect of many small droplets transferring momentum to a confined region of gas yields a narrow but high-velocity gas stream. Furthermore, the micro-jets produced by nES have practical utility for mass spectrometry, 3D printing and fabrication, and thermal management.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Feature Analysis, Tracking, and Data Reduction: An Application to Multiphase Reactor Simulation MFiX-Exa for In-Situ Use Case

As we enter the exascale computing regime, powerful supercomputers continue to produce much higher amounts of data than what can be stored for offline data processing. To utilize such high compute capabilities on these machines, much of the data processing needs to happen in situ, when the full high-resolution data is available at the supercomputer memory. In this article, we discuss our MFiX-Exa simulation, which models multiphase flow by tracking a very large number of particles through the simulation domain. In one of the use cases, the carbon particles interact with air to produce carbon dioxide bubbles from the reactor. These bubbles are of primary interest to the domain experts for these simulations. For this particle-based simulation, we propose a streaming technique that can be deployed in situ to efficiently identify the bubbles, track them over time, and use them to down-sample the data with minimal loss in these features.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

An Integrated Multiscale Experimental-Numerical Analysis on Reconsolidation of Salt-Clay Mixture for Disposal of Heat-Generating Waste (Final NEUP Technical Report)

The overall purpose of this research is to improve understanding of THMC coupling effect on the reconsolidation of granular (or crushed) salt-clay mixture used for seal systems of shafts and drifts in salt repositories. This proposed work is partially motivated by the recent work on the Waste Isolation Pilot Plant (WIPP) that shows the promising sealing capability of clay-salt mixture compared to crushed salt. In particular, primary emphasis is to develop a fully integrated multiscale experiment-numerical study to determine and explain what leads to the superior sealing ability of the clay-salt mixture. These research activities are designed to seek further understanding of (1) why clay additives may enhance the fluid trapping and (2) whether this flow barrier effect may prevail under different combinations of temperature, confining pressure, deviatoric stress and other foreseeable environmental factors. If successful, this enhanced flow trapping ability of the seal provides significant improvement to the seal and repository performance and therefore make the repository safer in the long-term. The experiment component includes microstructural investigation and macroscopic tests on a reconsolidated salt-clay mixture. In the microstructural study, the goal is to (1) characterize microscopic distributions of distinct phases (e.g., clay, salt crystal boundaries, trapped brine, and pore) to examine the connectivity of the pore network inside the salt-clay mixture with different amounts of clay additive and moisture content and (2) analyze multiscale imaging data to reconstruct the polycrystalline microstructures for numerical simulations. Meanwhile, macroscopic tests are performed to analyze how clay alters the failure/creep mechanisms in the salt-clay mixture. Microscopic and macroscopic experimental observations will both be used to calibrate and validate a multiscale model that explicitly simulates the capillary and multiphase flow in the connected pores and the deformation due to the presence of intra-crystalline brine at the pore scale via a new polyhedral discrete element–lattice Boltzmann method (DEM-LBM) coupling model. The pore-scale simulations are homogenized via an upscaling procedure that converts pore-scale information (e.g. force exerted on grain boundary, sliding, pressure-solution) to continuum measures (e.g. Cauchy stress, Darcy’s flow) at each integration point in the macroscopic multiphase TMHC model. This multiscale scheme will allow coupling be- tween high-fidelity simulations of brine-salt-clay interaction and the macroscopic TMHC model. The multiscale model helps the understanding of how the trapped brine inclusion affects the pressure-solution mechanism with the presence of clay and moisture. This work brings new insight into the sealing capacity of salt-clay mixture under elevated temperature over a long period of time - a key to evaluating the potential of salt-clay mixture usage for salt repositories.

42 ENGINEERING↗
DOE OSTI2022

Assessment of diffuse-interface methods for compressible multiphase fluid flows and elastic-plastic deformation in solids

This work describes three diffuse-interface methods for the simulation of immiscible, compressible multiphase fluid flows and elastic-plastic deformation in solids. The first method is the localized-artificial-diffusivity approach of Cook, Subramaniam et al., and Adler and Lele, in which artificial diffusion terms are added to the individual phase mass fraction transport equations and are coupled with the other conservation equations. The second method is the gradient-form approach that is based on the quasi-conservative method of Shukla et al., in which the diffusion and sharpening terms (together called regularization terms) are added to the individual phase volume fraction transport equations and are coupled with the other conservation equations. The third approach is the divergence-form approach that is based on the fully conservative method of Jain et al., in which the regularization terms are added to the individual phase volume fraction transport equations and are coupled with the other conservation equations. In the present study, all three diffuse-interface methods are used in conjunction with a four-equation, multicomponent mixture model, in which pressure and temperature equilibria are assumed among the various phases. The primary objective of this work is to compare these three methods in terms of their ability to: maintain constant interface thickness throughout the simulation; conserve mass, momentum, and energy; and maintain accurate interface shape for long-time integration. The second objective of this work is to consistently extend these methods to model interfaces between solid materials with strength. To assess and compare the methods, they are used to simulate a wide variety of problems, including (1) advection of an air bubble in water, (2) shock interaction with a helium bubble in air, (3) shock interaction and the collapse of an air bubble in water, and (4) Richtmyer–Meshkov instability of a copper–aluminum interface. The current work focuses on comparing these methods in the limit of relatively coarse grid resolution, which illustrates the true performance of these methods. In conclusion, this is because it is rarely practical to use hundreds of grid points to resolve a single bubble or drop in large-scale simulations of engineering interest.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

TOUGH-RFPA: Coupled thermal-hydraulic-mechanical Rock Failure Process Analysis with application to deep geothermal wells

This paper presents a coupled thermal-hydraulic-mechanical (THM) model for simulating failure processes in geological media. The coupling between stress/strain and heat transfer describing how thermal expansion affects stress is based on the theory of thermo-elasticity. Additionally, the coupling between stress/strain and fluid flow describing how pore-pressure affects stress is governed by Biot's consolidation theory. Stress/strain-dependent porosity/permeability/capillary-pressure defines the influence of stress on fluid flow and thermal fields. The THM model is implemented in the solver module that links the multiphase, multicomponent fluid flow simulator TOUGH2 with the geomechanical simulator RFPA (Rock Failure Process Analysis), which is based on the concept that heterogeneity leads to non-linearity and progressive failure behavior. Capability of TOUGH-RFPA simulator was verified by two benchmark problems related to hydraulic-mechanical and thermal-mechanical response in a hollow cylinder. Then the applicability of TOUGH-RFPA was demonstrated for modeling cooling-induced damage around a very deep geothermal well with mesoscopic heterogeneous properties, which showed complex damage evolution and fracturing during well cooling and fluid pressurization.

42 ENGINEERING↗
DOE OSTI2024

A deep learning-based workflow for fast prediction of 3D state variables in geological carbon storage: A dimension reduction approach

Deep learning (DL) models are extensively used as surrogate models for high-fidelity simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts of the spatial–temporal evolution of three-dimensional (3D) state variables in geological carbon storage (GCS). However, training these models in high-dimensional space remains computationally demanding and prone to overfitting because of limited training data. This paper presents a novel workflow to address these challenges by integrating dimension reduction (DR) methods. Here, the proposed workflow employed pre-trained DR models to extract the latent variables of geological models and state variables and utilized the multi-layer perceptron (MLP) for constructing mapping functions between the input and output variables in latent spaces. Subsequently, the pre-trained reconstruction models converted the MLP-predicted latent state variables to their original high-dimensional form. Furthermore, we proposed a novel strategy for the DR and reconstruction of 3D saturation fields to account for the unique data characteristics of sparsity, nonuniformity, and discontinuity. The proposed strategy applied PCA and inverse PCA for 2D average saturation fields and developed a DL-based 3D reconstruction model, leveraging three 2D average saturation fields as input to produce a 3D saturation field as output. The pre-training of DR and reconstruction models and training of MLP models were conducted on 84 Gulf of Mexico (GoM) simulations and evaluated on 12 testing simulations. Each simulation contained 720 monthly time steps, with the first 360 months as the injection period and the rest as the post-injection period. The proposed workflow, incorporating DR and DL models, accurately predicts the normalized 3D pressure fields, achieving mean square error (MSE) of 2.92 × 10 -7 compared to the ground truth obtained from a full-physics simulator. Furthermore, the proposed strategy outperformed PCA and convolutional autoencoder (CAE) models on 3D saturation fields, resulting in minor workflow prediction errors with an MSE of 2.93 × 10 -5 . The results suggest the proposed workflow provides sufficient predictive fidelity across temporal and spatial scales, and enables a speedup of 160 times compared to the full-physics simulator, facilitating improved decision-making and risk assessment for large-scale GCS management in real-time scenarios.

3D reconstruction model↗
DOE OSTI2021

Open source implementation of glued sphere discrete element method and nonspherical biomass fast pyrolysis simulation

Here, in this research, a glued-sphere Discrete Element Method (DEM) method was implemented in the open-source, Computational Fluid Dynamics (CFD) software MFiX. The implementation was verified using a cylinder-wall collision and then validated by simulating the packing and fluidization of non-spherical particles. The validated code was applied to simulate fast pyrolysis of non-spherical biomass particles in a bubbling fluidized bed. The glued sphere occupancy ratio was proposed to quantify the quality of shape resolution using glued sphere. Shape resolution showed significant influence on the packing height in the simulation of particle packing and an occupancy ratio of 80 % was recommended. Its influence is minor in fully fluidized bed but can be 8 times higher in fixed fluidized bed. Three tested heat transfer models predicted similar yields of elongated biomass fast pyrolysis. The solver developed in this research can be used to simulate other multiphase reacting flows involving non-spherical particles.

42 ENGINEERING↗
DOE OSTI2021

A micromechanical hydro-mechanical-damage coupled model for layered rocks considering multi-scale structures

An anisotropic Hydro-Mechanical-Damage coupled model was established in the framework of thermodynamics for saturated layered rocks, which contain arbitrarily-distributed cracks of smaller scales and orientated bedding planes of much larger scales. Here, the proposed model considers the anisotropic damage growth and frictional sliding of cracks and shear sliding and dilatancy of bedding planes, as well as the interaction between cracks and bedding planes. Numerical implementation of the nonlinear coupled model was developed based on TOUGHREACT, which is a well-established simulator for multiphase fluid flow and reactive transport analysis. The proposed model was first validated with laboratory water injection test results on rock blocks, and then used to study the effects of injection rates, in situ stresses and bedding planes on fluid injection processes at the field scale, demonstrating the importance to consider the variation of multi-scale structures for better understanding the coupled Hydro-Mechanical responses in layered rocks.

58 GEOSCIENCES↗
DOE OSTI2021

An Investigation of the Effects of Volume Fraction on Drag Coefficient of Non-Spherical Particles Using PR-DNS

Prediction of the drag coefficient is required in gas-particle multiphase flow modeling and simulation. Experimental data and correlations on the fixed-bed system of spherical particles with high volume fractions for various possible arrangements are available in the literature. However, the effect of volume fraction on the drag coefficient of non-spherical particles is not well studied. In solving the momentum equation, the volume fraction plays a vital role in determining the flow resistances. In this paper, we study the impact of volume fraction in the range of 0.069 to 0.65 on the drag coefficient using the computational fluid dynamics (CFD) simulation of air for Reynold number in the range of 10 to 10000 using particle resolved direct numerical solution (PR-DNS). Regular non-spherical particles such as a cube, tetrahedron, and spheroids are used in this study since their single particle’s drag coefficient data are available in the literature for comparison. For this work, the simulations are carried out in the Ansys Fluent using polyhedral mesh, which consumes significantly less computational time and power. The study showed the sphericity and volume fraction have significant impact on the bed pressure drop and average drag coefficient of the particles in the bed especially in high Reynolds number regime. The bed of the spheroid experiences the lowest drag being the most streamlined particle, and the particles with the edges result in a large drag coefficient due to flow separation at the discontinuity. The vector plots verify this behavior where large wake regions are observed behind the tetrahedron particle.

Mahyawansi, Pratik↗
DOE OSTI2022

Capillary Pressure to Relative Permeability: Task 7.3.4 Relative Permeability Analysis Final Report

Surface tension affects all aspects of fluid flow in porous media. Through measurements of surface tension interaction under multiphase conditions, a relative permeability relationship can be determined. Relative permeability is a numerical description of the interplay between two or more fluids and the porous media they flow through. It is a critical parameter for various tools used to characterized subsurface multiphase flow systems, such as numerical simulation for oil and gas development, carbon sequestration, and groundwater contamination remediation. Therefore, it is critical to get a good statistic distribution of relative permeability in the porous media under study. Empirical relationships for determining relative permeability from capillary pressure are already well established but do not provide the needed flexibility required to match laboratory derive relative permeability relationships. By expanding the existing methods for calculating relative permeability from capillary pressure data, it is possible to create both two and three-phase relative permeability relationships. Existing laboratory-measured relative permeability data along with mercury intrusion capillary (MICP) data coupled with interfacial tension and contact angle measurements were used to determine the efficacy of this approach to relative permeability curve creation. The relative permeability relationships determined with this method were fit to existing laboratory data to elucidate common fitting parameters that were then used to create relative permeability relationships from MICP data that does not have an associated laboratory-measured relative permeability relationship.

58 GEOSCIENCES↗
DOE OSTI2022

A particle resolved simulation approach for studying shock interactions with moving, colliding solid particles

This work applies a new combination of techniques for the fully resolved simulation of compressible, gas–particle multiphase flows. Here, the adaptive wavelet collocation method is used to dynamically, and efficiently, adapt the computational grid to localized flow features and the particles. A characteristic-based volume penalization method that imposes arbitrary Dirichlet, Neumann, or Robin-type immersed boundary conditions, is used to enforce the no-slip condition at particle surfaces. A hard-sphere collision model is applied to capture the particle–particle collisions. Proof of concept test cases are presented, showcasing the dynamic grid adaptation and fully resolved two-way coupling between the phases that is possible with this approach. Results for a shock-driven single cylinder under viscous and inviscid conditions are presented along with a demonstration of a shock interacting with a cloud of randomly distributed cylinders and spheres.

42 ENGINEERING↗
DOE OSTI2020

Data-Driven Model Reduction of Multiphase Flow in a Single-Hole Automotive Injector

Fuel injector design has a substantial influence on the performance and emissions of direct injection engines. To date, large eddy simulations coupled with a single-fluidmixture modeling approach have shown great success in evaluating the complex interplay among injector design, fuel properties, and operating conditions on the injector performance. However, this simulation approach is too computationally expensive to be used by industry routinely for injector design due to the fine temporal and spatial resolution required to resolve wall-bounded flow within the injector. The work presented in this paper highlights a potential pathway to addressing this issue. To study the influence of injector design, fuel properties, and operating conditions on injector performance, large eddy simulations were performed to model the turbulent multiphase flow development through a side-oriented singlehole diesel injector. Using Latin hypercube sampling, the design space spanning a range of fuel properties, operating conditions, and needle lifts were explored. Two techniques for dimensionality reduction, namely proper orthogonal decomposition and autoencoders, were compared to evaluate their accuracy in representing the flow in a reduced dimensional space. Finally, based on the findings from this work, recommendations are provided in using machine learning approaches within the context of emulation to construct reduced-ordermodels for internal flow development relevant to automotive applications.

42 ENGINEERING↗
DOE OSTI2022

Geologic Carbon Storage of Anthropogenic CO2 under the Colorado Plateau in Emery County, Utah

Geologic Carbon Storage (GCS) is a promising technology for storing large volumes of anthropogenic CO2 effectively and permanently. Numerical simulations are an integral part of site selection and characterization for any potential GCS site. As part of the DOE-funded CarbonSAFE Rocky Mountains Phase I project, a regional GCS analysis was undertaken to understand the efficacy of storing CO2 emissions from the power generation and heavy industry in central Utah’s favorable geology. In this study, the injection of CO2 for geologic storage was simulated in the Navajo Sandstone Formation in Emery County, Utah. Carbon dioxide was sourced from regional power generation stations and heavy industries throughout Utah, with an emphasis on emissions reduction at the Hunter Power Plant near Castle Dale, Utah. A simulation grid was extracted from the project’s geological model encompassing an area around Price, Huntington, and Castle Dale in central Utah. The Navajo Sandstone Member of the Glen Canyon Group was the target of CO2 injection with the overlying Carmel formation providing the primary seal. A suite of simulations was performed assessing the viability of this area for permanent CO2 storage. Results indicate that the area can not only store 46 million metric tons of anthropogenic CO2, meeting the project goals, but this area has the capacity to securely store at least 1.3 billion tons of CO2, suggesting the injection site and surrounding geology are suitable locations for commercial-scale GCS.

58 GEOSCIENCES↗
DOE OSTI2021

Polysiloxane coatings on microspheres based on Multiphase Flow with Interface Exchange-Discrete Element Modelling

Polysiloxane coatings on yttria stabilized zirconia (YSZ) microspheres of 500 μm were simulated in a spout fluidized bed coater using Multiphase Flow with Interface Exchange-Discrete Element Modelling (MFiX-DEM). In this work, two different coater configurations were developed to study the influence of gas velocity and its distribution on particle dynamics. The presence of the Wurster tube not only enhances the distribution but also increases the overall residence time of the particles. Investigations were also carried out with different Wurster tube positions (normal, 10% and 20% lowered from its initial position). Among these, 20% lowered Wurster tube position demonstrated the most effective coating process. The effects of gas inlet pressure on the average gas velocity and the distribution of particles were analyzed. More than 97% of the particles can be retained. The derived results, including average gas velocity, particle retention percentage, and distribution of particles with gas velocity, are being used to guide the experimental work in obtaining defect-free coatings for YSZ microspheres.

36 MATERIALS SCIENCE↗