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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 721 records · Page 40
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DOE OSTI2024

Capturing thin structures in VOF simulations with two-plane reconstruction

A novel interface reconstruction strategy for volume of fluid (VOF) methods is introduced that represents the liquid-gas interface as two planes that co-exist within a single computational cell. In comparison to the piecewise linear interface calculation (PLIC), this new algorithm greatly improves the accuracy of the reconstruction, in particular when dealing with thin structures such as films. The placement of the two planes requires the solution of a non-linear optimization problem in six dimensions, which has the potential to be overly expensive. Further, an efficient solution to this optimization problem is presented here that exploits two key ideas: an algorithm for extracting multiple plane orientations from transported surface data, and an efficient and mass-conserving distance-finding algorithm that accounts for two planes with arbitrary orientation. Additionally, a simple and robust strategy is presented to accurately represent the surface tension forces produced at the interface of subgrid-thickness films. The performance of this new VOF reconstruction is demonstrated on several test cases that illustrate the capability to handle arbitrarily thin films.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
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 OSTI2023

Massively-parallel Lagrangian particle code and applications

Massively-parallel, distributed-memory algorithms for the Lagrangian particle hydrodynamic method (Samulyak et al., 2018) have been developed, verified, and implemented. The key component of parallel algorithms is a particle management module that includes a parallel construction of octree databases, dynamic adaptation and refinement of octrees, and particle migration between parallel subdomains. The particle management module is based on the p4est (parallel forest of k-trees) library. The massively-parallel Lagrangian particle code has been applied to a variety of fundamental science and applied problems. A summary of Lagrangian particle code applications to the injection of impurities into thermonuclear fusion devices and to the simulation of supersonic hydrogen jets in support of laser-plasma wakefield acceleration research has also been presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A coupled polygonal DEM-LBM technique based on an immersed boundary method and energy-conserving contact algorithm

Here we present a framework of coupling polygonal discrete elements and the lattice Boltzmann method using a direct forcing immersed boundary scheme. In this technique, an energy-conserving contact algorithm is utilized to handle the interactions between convex and concave polygonal particles. The surface of a polygon is represented by discrete boundary points which includes vertices of polygonal particles and/or points interpolated from vertices. The fluid-particle coupling is obtained through the interactions of the boundary points and the imaginary fluid particles using a direct-forcing immersed boundary method. Validations of the proposed technique are made by single particle and multiple arbitrarily-shaped particle sedimentation tests, and the effect of particle shape is illustrated using a drafting-kissing-tumbling benchmark.

42 ENGINEERING↗
DOE OSTI2015

The Nanoscale Basis of CO 2 Trapping for Geologic Storage

Carbon capture and storage (CCS) is likely to be a critical technology to achieve large reductions in global carbon emissions over the next century. Research on the subsurface storage of CO 2 is aimed at reducing uncertainties in the efficacy of CO 2 storage in sedimentary rock formations. Three key parameters that have a nanoscale basis and that contribute uncertainty to predictions of CO 2 trapping are the vertical permeability k v of seals, the residual CO 2 saturation S g,r in reservoir rocks, and the reactive surface area a r of silicate minerals. This review summarizes recent progress and identifies outstanding research needs in these areas. Available data suggest that the permeability of shale and mudstone seals is heavily dependent on clay fraction and can be extremely low even in the presence of fractures. Investigations of residual CO 2 trapping indicate that CO 2 -induced alteration in the wettability of mineral surfaces may significantly influence S g,r . Ultimately, the rate and extent of CO 2 conversion to mineral phases are uncertain due to a poor understanding of the kinetics of slow reactions between minerals and fluids. Finally, rapidly improving characterization techniques using X-rays and neutrons, and computing capability for simulating chemical interactions, provide promise for important advances.

58 GEOSCIENCES↗
DOE OSTI2017

Predictive Model for Particle Residence Time Distributions in Riser Reactors. Part 1: Model Development and Validation

Here in this computational study, we model the mixing of biomass pyrolysis vapor with solid catalyst in circulating riser reactors with a focus on the determination of solid catalyst residence time distributions (RTDs). A comprehensive set of 2D and 3D simulations were conducted for a pilot-scale riser using the Eulerian-Eulerian two-fluid modeling framework with and without sub-grid-scale models for the gas-solids interaction. A validation test case was also simulated and compared to experiments, showing agreement in the pressure gradient and RTD mean and spread. For simulation cases, it was found that for accurate RTD prediction, the Johnson and Jackson partial slip solids boundary condition was required for all models and a sub-grid model is useful so that ultra high resolutions grids that are very computationally intensive are not required. Finally, we discovered a 2/3 scaling relation for the RTD mean and spread when comparing resolved 2D simulations to validated unresolved 3D sub-grid-scale model simulations.

09 BIOMASS FUELS↗
DOE OSTI2019

CO 2 Wettability of Sandstones: Addressing Conflicting Capillary Behaviors

Understanding the capillary and wetting behavior of supercritical CO 2 (scCO 2 ) and brine in reservoir rocks is essential for reliable predictions of geologic carbon storage, as it strongly impacts CO 2 migration and residual trapping in the reservoir. The wetting state of such systems can be assessed through laboratory measurements of the capillary pressure characteristic curve. Yet, while some studies reported consistent scaling with strongly water wet systems, some others observed deviations from hydrophilic conditions. We present core-flooding drainage capillary pressure measurements using scCO 2 /water and N 2 /water on a Berea sandstone, untreated, then fired, and then exposed to scCO 2 for 28 days. The aim is to explore the impact of firing and longer exposure to scCO 2 , two potential sources for variability in experimental observations, on capillarity and wettability. The results show excellent agreement among all the core-flooding capillary pressure data, suggesting no change in wetting state due to firing or longer exposure.

58 GEOSCIENCES↗
DOE OSTI2019

Nitrogen-Driven Chromatographic Separation During Gas Injection Into Hydrate-Bearing Sediments

Hydrates are solid phases composed of water cages enclosing gas molecules that may host large quantities of recoverable natural gas and may serve to sequester CO 2 on geological time scales. Most hydrate studies focus on hydrates containing a single gas component, such as CH 4 or CO 2 . Yet, there are several settings in which multiple components form hydrate mixtures, or mixed hydrates, including a subsurface injection technique that claims to simultaneously recover CH 4 and sequester CO 2 called “guest molecule exchange.” Here, we combine multicomponent phase behavior for hydrate-forming systems with a multiphase fluid flow simulator to understand the evolution of hydrate and nonhydrate phases during subsurface injection. We simulate various scenarios for systems composed of H 2 O, CH 4 , CO 2 , and N 2 . Our study probes the impact of injection composition, initial reservoir composition, and transport of each component through the model domain. We observe chromatographic separation from the combined effect of compositional partitioning in each phase, variable flow speed of each phase, and compositional dependence of phase stabilities. We show that N 2 drives chromatographic separation to create a CH 4 -free zone and a CO 2 -free zone that are connected by a continuous N 2 -dominated vapor phase. While our results are theoretical and should be validated experimentally, they imply that guest molecule exchange acts as two sequential processes rather than as a simultaneous process. Furthermore, they show that injections into reservoirs with and without free water have vastly different behaviors, which has implications for the interpretation of the guest molecule exchange field test and various laboratory studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Impact of Confining Stress on Capillary Pressure Behavior During Drainage Through Rough Fractures

In this work we study, numerically, the behavior of capillary pressure $(P_c)$ during slow immiscible displacement in a rough fracture as a function of the degree of fracture aperture heterogeneity that results from two distinct mechanisms: normal confining stress and fracture surface correlation. We generate synthetic self-affine rough fractures at different correlation scales, solve the elastic contact problem to model the effect of confining stress, and simulate slow immiscible displacement of a wetting fluid by a nonwetting one using a modified invasion percolation model that accounts for in-plane curvature of the fluid-fluid interface. Our modeling results indicate that the power spectral density, $S(f)$, of $P_c$, can be used to qualitatively characterize fracture aperture heterogeneity. We show that the distribution of forward avalanche sizes follows a power law $N_f (S_f)$ ∝ $S^{-a}_f$ , with exponent $α$ = 2, in agreement with previously reported values for porous media and equal to the expected theoretical exponent for a self-organized criticality process.

58 GEOSCIENCES↗
DOE OSTI2022

Coupled Hydromechanical Modeling of Induced Seismicity From CO 2 Injection in the Illinois Basin

Injection of CO 2 for geologic carbon sequestration into deep sedimentary formations involves fluid pressure increases that engage hydromechanical processes that can cause seismicity by activation of existing faults. In this work, we use a coupled multiphase fluid flow and geomechanical simulator to model spatiotemporal fluid pressure and stress changes in order to study the poroelastic effect of CO 2 injection on faults in crystalline basement rock below the injection zone. The seismicity rate along features interpreted to be basement faults is modeled using Dieterich's rate-and-state earthquake nucleation model. The methodology is applied to microseismicity detected during CO 2 injection into the Mount Simon formation during the Illinois Basin—Decatur Project. The modeling accurately captures an observed reduction in seismicity rate when the injection in the second well was into a slightly shallower zone above the base of the Mount Simon formation. Moreover, the modeling shows that it is important to consider poroelastic stress changes, in addition to fluid pressure changes for accurately modeling of the observed seismicity rate.

58 GEOSCIENCES↗
DOE OSTI2015

Insight into the epitaxial growth of high optical quality GaAs{sub 1–x}Bi{sub x}

The ternary alloy GaAs{sub 1–x}Bi{sub x} is a potentially important material for infrared light emitting devices, but its use has been limited by poor optical quality. We report on the synthesis of GaAs{sub 1–x}Bi{sub x} epi-layers that exhibit narrow, band edge photoluminescence similar to other ternary GaAs based alloys, e.g., In{sub y}Ga{sub 1–y}As. The measured spectral linewidths are as low as 14 meV and 37 meV at low temperature (6 K) and room temperature, respectively, and are less than half of previously reported values. The improved optical quality is attributed to the use of incident UV irradiation of the epitaxial surface and the presence of a partial surface coverage of bismuth in a surfactant layer during epitaxy. Comparisons of samples grown under illuminated and dark conditions provide insight into possible surface processes that may be altered by the incident UV light. The improved optical quality now opens up possibilities for the practical use of GaAs{sub 1–x}Bi{sub x} in optoelectronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Influence of baroclinic vorticity production on unsteady drag coefficient in shock–particle interaction

The influence of baroclinic vorticity production on the unsteady drag coefficient in shock–particle interaction is numerically studied in this work. Numerical simulations are performed for shock–particle interaction utilizing a high–resolution axisymmetric solver for the Euler equations that allows for multi-material interface and shock propagation in both the particle and surrounding medium. We consider an aluminum particle in nitromethane and allow for particle deformation. We compute the vorticity production and unsteady drag coefficient as a function of time to explain the complex physical mechanisms that occur during shock–particle interaction. We observe baroclinic vorticity production as the shock propagates over the particle and find that the vorticity is primarily generated at the surface of the particle. After the passage of the shock over the particle, the generated vortex traverses downstream, thus creating a sharpened particle edge and low pressure on the downstream side of the particle, followed by the trapping of the vortex at the particle edge. These mechanisms lead to the generation of a quasi-steady drag force even after the passage of the shock, thus suggesting the importance of baroclinic vorticity production to the unsteady drag coefficient. Finally, we compute the unsteady drag coefficient for various shock Mach numbers and particle ellipticities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

A combined numerical and experimental study to elucidate primary breakup dynamics in liquid metal droplet-on-demand printing

Droplet-on-demand liquid metal jetting is emerging as a powerful technology for the additive manufacturing of metallic parts. The success of this method hinges on overcoming several technological challenges. Additionally, the principal one among these challenges is the controlled repeatable ejection of single uniform droplets. Due to the high density and surface tension of liquid metals, the droplet ejection process occurs near the minimal extremes of the printability phase diagram, defined by acceptable ranges for the Weber (We) and Ohnesorge (Oh) numbers. In this work, we experimentally demonstrate the satellite-free ejection of pneumatically actuated molten tin droplets in this extreme corner of printability and use a combination of high-speed video analysis and volume-of-fluid modeling to elucidate the droplet dynamics. While the simulations at low Oh and We can correctly describe several aspects of the breakup process, such as an increasing tail and pinch-point near the nozzle, no single parameter set can completely capture the droplet shape at breakup. Instead, the experimental droplet dynamics appear to include features from both high and low Oh breakup. This disagreement is ascribed to the incomplete description of the droplet ejection process including wetting and exit effects near the nozzle opening and surface effects such as transient cooling and oxide formation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Surfactants and cloud droplet activation: A systematic extension of Köhler theory based on analysis of droplet stability

The activation of aerosol particles to form cloud droplets, a necessary first step in cloud formation, controls much of the impact that aerosols have on clouds and climate. Recently, there has been a surge of interest in extending the Köhler theory of cloud droplet activation to include surface active (typically organic) as well as water-soluble (typically inorganic) aerosol components, but a systematic framework for doing this has yet to be developed. Here, we apply a droplet stability analysis to this end. Ideal and Szyszkowski–Langmuir surfactant models are analyzed to demonstrate the new approach, but the underlying theoretical framework is fundamental and model free. A key finding is that superficial densities at the cloud activation threshold (Köhler maximum) are significantly sub-monolayer, with fractional coverage ranging from 69% to 85% for the organic compounds and mixtures studied. The result, significant for model inventories of cloud condensation nuclei, is a weakening of the surfactant effect relative to expectations based on bulk sample measurements. Analytical results are obtained for the loci of Köhler maxima and applied to aerosol mixtures containing an arbitrary number of water-soluble and surfactant components.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Operator learning for predicting multiscale bubble growth dynamics

We report simulating and predicting multiscale problems that couple multiple physics and dynamics across many orders of spatiotemporal scales is a great challenge that has not been investigated systematically by deep neural networks (DNNs). Herein, we develop a framework based on operator regression, the so-called deep operator network (DeepONet), with the long-term objective to simplify multiscale modeling by avoiding the fragile and time-consuming “hand-shaking” interface algorithms for stitching together heterogeneous descriptions of multiscale phenomena. To this end, as a first step, we investigate if a DeepONet can learn the dynamics of different scale regimes, one at the deterministic macroscale and the other at the stochastic microscale regime with inherent thermal fluctuations. Specifically, we test the effectiveness and accuracy of the DeepONet in predicting multirate bubble growth dynamics, which is described by a Rayleigh–Plesset (R–P) equation at the macroscale and modeled as a stochastic nucleation and cavitation process at the microscale by dissipative particle dynamics (DPD). First, we generate data using the R–P equation for multirate bubble growth dynamics caused by randomly time-varying liquid pressures drawn from Gaussian random fields (GRFs). Our results show that properly trained DeepONets can accurately predict the macroscale bubble growth dynamics and can outperform long short-term memory networks. We also demonstrate that the DeepONet can extrapolate accurately outside the input distribution using only very few new measurements. Subsequently, we train the DeepONet with DPD data corresponding to stochastic bubble growth dynamics. Although the DPD data are noisy and we only collect sparse data points on the trajectories, the trained DeepONet model is able to predict accurately the mean bubble dynamics for time-varying GRF pressures. Taken together, our findings demonstrate that DeepONets can be employed to unify the macroscale and microscale models of the multirate bubble growth problem, hence providing new insight into the role of operator regression via DNNs in tackling realistic multiscale problems and in simplifying modeling with heterogeneous descriptions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Investigation of turbulent inflow specification in Euler–Lagrange simulations of mid-field spray

The process of atomization of a liquid jet by a parallel high-speed gas stream results in a spray, whose downstream development is of considerable interest to several applications. The round jet spray can be spatially divided into (i) a near-field (near-nozzle) region of liquid atomization and (ii) a downstream mid-field region of fully-dispersed droplets. In order to accurately model mid-field droplet dispersion, this work aims at developing a rigorous and robust injection model for Euler–Lagrange spray simulations. Results from experiments are used to obtain the relevant droplet number density, size distribution, and mean and standard deviation velocity distributions of the injection model, systematically in a step-by-step process. Two-phase large eddy simulations are performed by stochastically generating the Lagrangian droplets at the inlet of the mid-field region. Number flux, diameter distribution, mean velocity, and other time-averaged statistics at several downstream locations are shown to agree well with the corresponding experimental data.

42 ENGINEERING↗
DOE OSTI2021

Corona splashing triggered by a loose monolayer of particles

In nature, high-speed raindrops often impact and spread on particulate surfaces (e.g., soil, plant leaves with spores or pollen). We study the dynamics of droplet impact on a loosely packed monolayer of particles by combining experimental and mathematical approaches. We find that the presence of mobile particles lowers the critical impact velocity at which the droplet exhibits corona splashing, as the particle area fraction is systematically increased. We rationalize this experimental observation by considering the jamming of frictional particles at the spreading rim. Elucidating the splashing transition of the drop on a particulate bed can lead to a better understanding of soil loss and erosion from falling raindrops

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗