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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Model Development for Thermal-Hydrology Simulations of a Full-Scale Heater Experiment in Opalinus Clay

Disposal of commercial spent nuclear fuel in a geologic repository is studied. In situ heater experiments in underground research laboratories provide a realistic representation of subsurface behavior under disposal conditions. Here, this study describes process model development and modeling analysis for a full-scale heater experiment in opalinus clay host rock. The results of thermal-hydrology simulation, solving coupled nonisothermal multiphase flow, and comparison with experimental data are presented. The modeling results closely match the experimental data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Bayesian Treed Multivariate Gaussian Process with Adaptive Design: Application to a Carbon Capture Unit

Computer experiments (numerical simulations) are widely used in scientific research to study and predict the behavior of complex systems, which usually have responses consisting of a set of distinct outputs. The computational cost of the simulations at high resolution are often expensive and become impractical for parametric studies at different input values. To overcome these difficulties we develop a Bayesian treed multivariate Gaussian process (BTMGP) as an extension of the Bayesian treed Gaussian process (BTGP) in order to model and evaluate a multivariate process. A suitable choice of covariance function and the prior distributions facilitates the different Markov chain Monte Carlo (MCMC) movements. We utilize this model to sequentially sample the input space for the most informative values, taking into account model uncertainty and expertise gained. A simulation study demonstrates the use of the proposed method and compares it with alternative approaches. We apply the sequential sampling technique and BTMGP to model the multiphase flow in a full scale regenerator of a carbon capture unit. The application presented in this paper is an important tool for research into carbon dioxide emissions from thermal power plants.

Konomi, Bledar A.↗
DOE OSTI2016

Influence of phase connectivity on the relationship among capillary pressure, fluid saturation, and interfacial area in two-fluid-phase porous medium systems

Here, multiphase flow in porous medium systems is typically modeled using continuum mechanical representations at the macroscale in terms of averaged quantities. These models require closure relations to produce solvable forms. One of these required closure relations is an expression relating fluid pressures, fluid saturations, and, in some cases, the interfacial area between the fluid phases, and the Euler characteristic. An unresolved question is whether the inclusion of these additional morphological and topological measures can lead to a non-hysteretic closure relation compared to the hysteretic forms that are used in traditional models, which typically do not include interfacial areas, or the Euler characteristic. We develop a lattice-Boltzmann (LB) simulation approach to investigate the equilibrium states of a two-fluid-phase porous medium system, which include disconnected now- wetting phase features. The proposed approach is applied to a synthetic medium consisting of 1,964 spheres arranged in a random, non-overlapping, close-packed manner, yielding a total of 42,908 different equilibrium points. This information is evaluated using a generalized additive modeling approach to determine if a unique function from this family exists, which can explain the data. The variance of various model estimates is computed, and we conclude that, except for the limiting behavior close to a single fluid regime, capillary pressure can be expressed as a deterministic and non-hysteretic function of fluid saturation, interfacial area between the fluid phases, and the Euler characteristic. This work is unique in the methods employed, the size of the data set, the resolution in space and time, the true equilibrium nature of the data, the parameterizations investigated, and the broad set of functions examined. The conclusion of essentially non-hysteretic behavior provides support for an evolving class of two-fluid-phase flow in porous medium systems models.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Numerical calculation of the particle–fluid–particle stress in random arrays of fixed particles

Based on the nearest particle statistics [Zhang, J. Fluid Mech. 910, A16 (2021)], the phase interaction force in a multiphase flow is decomposed into a particle–mean-field force and the divergence of the particle–fluid–particle (PFP) stress. The PFP stress is proportional to the correlation product of the distance from a particle to its nearest neighbor and the force on the particle conditionally averaged on the nearest-neighbor location. In this work, a functional form of the stress is obtained corrected to the first order of the ratio between the interparticle distance and the macroscopic length scale of the flow. Particle-resolved numerical simulations are used to calculate the PFP stress in random arrays of fixed particles and to explore the physics represented by the stress. The numerical results show that the PFP stress is attractive along the direction of the flow and is repulsive in the directions perpendicular to the flow. In the flow regime simulated, this PFP stress can be considered as a macroscopic representation of the drafting–kissing–tumbling mechanism. Finally, the Reynolds stress for the fluid phase is also calculated and compared with the PFP stress.

42 ENGINEERING↗
DOE OSTI2023

Point-particle drag, lift, and torque closure models using machine learning: Hierarchical approach and interpretability

Developing deterministic neighborhood-informed point-particle closure models using machine learning has garnered interest recently from the dispersed multiphase flow community. The robustness of neural models for this complex multibody problem is hindered by the availability of particle-resolved data. Here, the present work addresses this unavoidable limitation of data paucity by implementing two strategies: (1) by using a rotation and reflection equivariant neural network and (2) by pursuing a physics-based hierarchical machine learning approach. The resulting machine-learned models are observed to achieve a maximum accuracy of 85% and 96% in the prediction of neighbor-induced force and torque fluctuations, respectively, for a wide range of Reynolds number and volume fraction conditions considered. Furthermore, we pursue force and torque network architectures that provide universal prediction spanning a wide range of Reynolds number (0.25 ≤ Re ≤250) and particle volume fraction (0 ≤ φ ≤0.4). The hierarchical nature of the approach enables improved prediction of quantities such as streamwise torque, by going beyond binary interactions to include trinary interactions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Controlling Viscous Fingering Using Time-Dependent Strategies

Control and stabilization of viscous fingering of immiscible fluids impacts a wide variety of pressure-driven multiphase flows. Here, we report theoretical and experimental results on time-dependent control strategy by manipulating the gap thickness b(t) in a lifting Hele-Shaw cell in the power-law form b(t) = b 1 t 1/7 . Experimental results show good quantitative agreement with the predictions of linear stability analysis. Furthermore, by choosing the value of a single time-independent control parameter we can either totally suppress the viscous fingering instability or maintain a series of non-splitting viscous fingers during the fluid displacement process. Besides the gap thickness of a Hele-Shaw cell, in principle, time-dependent control strategies can also be placed on the injection rate, viscosity of the displaced fluid, and interfacial tensions between the two fluids.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Cross-Plane Acquisitions in Electrical Capacitance Volume Tomography

Electrical capacitance tomography (ECT) is a widely used imaging modality to image two-dimensional cross sections of multiphase flows. Recent developments in electrical capacitance volume tomography (ECVT) have made it possible to directly obtain volumetric images from measured data. An ECVT is instrumental for obtaining accurate phase hold up information and velocity information that are needed for the optimization of certain flow processes. However, compared to ECT, the high correlation between the measurements in ECVT exacerbates the ill-conditioning of the associated image reconstruction problem. Previous studies have suggested that neglecting mutual capacitance data between the ECVT electrodes located at cross-planes that are well separated along the sensor axis can be done without significantly affecting the reconstructed image. In addition, this may help constrain the ill-conditioning of the reconstruction problem. Here, we examine in detail and quantify the effect of reduced cross-plane acquisition strategies for optimizing the image reconstruction and constraining the ill-conditioning of typical ECVT settings.

20 FOSSIL-FUELED POWER PLANTS↗
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 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 OSTI2017

Evenly spaced columns in the Bishop Tuff (California, USA) as relicts of hydrothermal cooling

A few square kilometers of the Bishop Tuff in eastern California (USA) have evenly spaced columns that are more resistant to erosion than the surrounding tuff owing to the precipitation of mordenite, a low-temperature (100-130 °C) zeolite. We hypothesize that the columns are a result of instabilities at the liquid water and steam interface as cold water seeped into the still-cooling Bishop Tuff. We use two methods to quantitatively assess this hypothesis. First, scaling shows which hydrodynamic instabilities exist in the system. Second, to account for the effects of multiphase flow, latent heat, and the finite amplitude and temporal evolution of these instabilities we use two-dimensional numerical models of liquid water infiltrating hot tuff. These tests highlight several features of boiling hydrothermal systems. (1) The geometry of at least some convection appears to be broadly captured by linear stability theory that neglects reactive transport, heterogeneity of the host rock, and the finite amplitude of instabilities. (2) Slopes > 10% set the wavelength of convection, meaning that these columns formed somewhere with relatively gentle topography. (3) For permeabilities of > 10 -13 m 2 , the wavelength of the instability changes through time, slowing infiltration, while for permeabilities < 10 -15 m 2 , cooling is dominated by conduction. The spacing and stability of columns increase with higher vertical permeability and decrease with higher horizontal permeability. These columns are a rare window into hydrothermal processes that may be widespread.

58 GEOSCIENCES↗
DOE OSTI2020

(Invited) Virtual Analysis of Gas-Diffusion-Electrode CO 2 Electrolyzers

The electrochemical reduction of CO 2 (CO 2 R) to value-added products is an attractive technology for tackling the rising atmospheric CO 2 levels and storing intermittent renewable energy into chemical bonds. Fundamental understanding of CO 2 R has progressed significantly in recent years and is critical in the development of CO 2 R to liquid-fuel electrolyzers, where gas-diffusion electrodes (GDEs) have been shown to be key enabling architectures. Various designs have been proposed and studied in the literature to enhance overall selectivity, rates, and maximize the conversion of CO 2 , the latter of which is only now being recognized as a critical issue. In this respect, there is a need to explore the governing phenomena inherent in these architectures to enable optimization. Mathematical modeling is ideally suited to tackle and explore these multiphysics interactions and provide virtual design analysis. In this talk, we discuss modeling methodologies and physics inherent in these devices and present our recent modeling of GDEs for CO 2 reduction. We specifically examine the impacts of multiphase flow and related phenomena on overall cell performance. We then explore the performance and limitations of various cell designs guided by simulation results and examine potential methods for improving water management and tuning catalyst selectivity including the use of different anion-exchange and bipolar membranes. Finally, we discuss the disparities in local environments between aqueous and GDE devices and propose strategies to reduce the gap in knowledge between the two systems.

42 ENGINEERING↗
DOE OSTI2020

Virtual Analysis of Gas-Diffusion-Electrode CO 2 Electrolyzers

The electrochemical reduction of CO 2 (CO2R) to value-added products is an attractive technology for tackling the rising atmospheric CO 2 levels and storing intermittent renewable energy into chemical bonds. Fundamental understanding of CO 2 R has progressed significantly in recent years and is critical in the development of CO 2 R to liquid-fuel electrolyzers, where gas-diffusion electrodes (GDEs) have been shown to be key enabling architectures. Various designs have been proposed and studied in the literature to enhance overall selectivity, rates, and maximize the conversion of CO 2 , the latter of which is only now being recognized as a critical issue. In this respect, there is a need to explore the governing phenomena inherent in these architectures to enable optimization. Mathematical modeling is ideally suited to tackle and explore these multiphysics interactions and provide virtual design analysis. In this talk, we discuss modeling methodologies and physics inherent in these devices and present our recent modeling of GDEs for CO 2 reduction. Here, we specifically examine the impacts of multiphase flow and related phenomena on overall cell performance. We then explore the performance and limitations of various cell designs guided by simulation results and examine potential methods for improving water management and tuning catalyst selectivity including the use of different anion-exchange and bipolar membranes. Finally, we discuss the disparities in local environments between aqueous and GDE devices and propose strategies to reduce the gap in knowledge between the two systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Numerical Investigation of Vertical Plunging Jet Using a Hybrid Multifluid–VOF Multiphase CFD Solver

A novel hybrid multiphase flow solver has been used to conduct simulations of a vertical plunging liquid jet. This solver combines a multifluid methodology with selective interface sharpening to enable simulation of both the initial jet impingement and the long-time entrained bubble plume phenomena. Models are implemented for variable bubble size capturing and dynamic switching of interface sharpened regions to capture transitions between the initially fully segregated flow types into the dispersed bubbly flow regime. It was found that the solver was able to capture the salient features of the flow phenomena under study and areas for quantitative improvement have been explored and identified. In particular, a population balance approach is employed and detailed calibration of the underlying models with experimental data is required to enable quantitative prediction of bubble size and distribution to capture the transition between segregated and dispersed flow types with greater fidelity.

42 ENGINEERING↗
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 OSTI2023

PFLOTRAN 5

PFLOTRAN leverages massively parallel, high performance computing to simulate large-scale non-isothermal multiphase flow, multicomponent reactive transport and electrical resistivity tomography (ERT) problems in the subsurface environment. Researchers have employed PFLOTRAN to simulate these Earth system processes on leadership class supercomputers for over two decades. The code is designed to predict the future estate of environmental systems and better inform stakeholders in the regulatory decision making process (e.g., fate of contaminants, long-term stewardship for nuclear waste, impact of climate change, etc.). A diverse team of scientists oversees PFLOTRAN development and maintenance under an open-source licensing agreement and manages contributions from an international community of researchers.

Hammond, Glenn↗
DOE OSTI2022

Codes for sub-resolution modeling of the apparent mass loss in quantitative broadband X-ray radiography

SAND2022-3463 O This code is intended to accompany the journal manuscript, “Sub-resolution modeling of the apparent mass loss in quantitative broadband X-ray radiography." The manuscript covers in detail how to improve the quantitative mass distribution measurements made for optical diagnostics of multiphase flows. The code contains four separate script files in MATLAB format to support the objective of improving mass measurements. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Rahman, Naveed↗
DOE OSTI2021

AMReX: Block-structured adaptive mesh refinement for multiphysics applications

Block-structured adaptive mesh refinement (AMR) provides the basis for the temporal and spatial discretization strategy for a number of Exascale Computing Project applications in the areas of accelerator design, additive manufacturing, astrophysics, combustion, cosmology, multiphase flow, and wind plant modeling. AMReX is a software framework that provides a unified infrastructure with the functionality needed for these and other AMR applications to be able to effectively and efficiently utilize machines from laptops to exascale architectures. AMR reduces the computational cost and memory footprint compared to a uniform mesh while preserving accurate descriptions of different physical processes in complex multiphysics algorithms. AMReX supports algorithms that solve systems of partial differential equations in simple or complex geometries and those that use particles and/or particle–mesh operations to represent component physical processes. In this article, we will discuss the core elements of the AMReX framework such as data containers and iterators as well as several specialized operations to meet the needs of the application projects. In addition, we will highlight the strategy that the AMReX team is pursuing to achieve highly performant code across a range of accelerator-based architectures for a variety of different applications.

Zhang, Weiqun↗
DOE OSTI2022

NUMERICAL INVESTIGATION OF AIR DEHUMIDIFICATION THROUGH WATER DROPLETS DIELECTROPHORESIS

This paper numerically investigated the dehumidification of moist airflow in a converging rectangular duct with electrically enhanced condensation. The aim was to develop a condensation model that predicted water vapor condensation by employing the dielectrophoresis principle. The charged droplets were injected into the computational domain to produce a gradient electric field. The dielectrophoretic interaction between the droplets and vapor molecules of the humid air resulted in local water vapor condensation at the vapor and liquid droplet interface. This phenomenon is described in detail in this paper. A hybrid Eulerian-Lagrangian solver SprayFoam was developed based on OpenFOAM® to simulate condensation in two-phase gas-liquid mixtures. The following developments were made based on the standard compressible multiphase flow solver SprayFoam in OpenFOAM®: (1) Eulerian solver for gas phase (2) multi-component species transport, and (3) Lagrangian solver for gas-droplet two-phase flows and sub-models for liquid droplets. The newly developed numerical model was experimentally validated with data from a series of tests conducted in the authors' laboratory. The results showed that the simulations followed the same trends as the data, and the model predicted the condensation due to the electro-spray injection process in the air stream. The injected water droplets, which are electrically charged to their Rayleigh limits, increased in size while wiping out the humidity from the air. The simulations indicated that the dehumidification was about 1% for 0.5 cubic feet per minute airflow rate. Scaling up to larger flows is a future follow-up work.

Yel Mahi, *Maliha↗