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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Modeling gas migration through clay-based buffer material using coupled multiphase fluid flow and geomechanics with stress-dependent gas permeability

A model for gas migration through clay-based buffer material is developed for modeling gas generation and migration associated with deep geologic nuclear waste disposal. The model is based on a multiphase fluid flow and geomechanics simulator that is adapted to consider enhanced gas flow when gas pressure is high enough to approach the confining stress magnitude. A key feature in the model is a direct coupling between gas permeability and stress, through a non-linear stress-dependent permeability function. The model was first tested and calibrated by modelling two different laboratory gas migration tests on Wyoming (MX-80) bentonite samples. The calibrated model was then applied to model gas migration through a bentonite buffer of a large-scale gas injection test (Lasgit) conducted at the Äspö Hard Rock Laboratory in Sweden. Observed preferential gas migration along interfaces (between compacted blocks and along the canister surface) required explicit representation of such interfaces in the model. The model with the stress-dependent gas permeability accurately captured observed experimental responses in terms of gas breakthrough time, peak gas pressure, and cumulative gas flow rates. The calibrated model was finally applied to simulate migration of hydrogen gas generated within a breached nuclear waste canister over 10,000 years, involving migration of much larger gas volumes. For the considered gas generation rate and host rock properties, the generated gas could migrate through the bentonite buffer and released into the surrounding host rock at a maximum gas pressure somewhat higher than the initial total stress, though a significant amount of hydrogen remained within the buffer. This modelling sets the stage for further detailed analysis of the impact of hydrogen gas generation on the long-term performance of nuclear waste repositories.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
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 OSTI2022

A robust deep learning workflow to predict multiphase flow behavior during geological C O 2 sequestration injection and Post-Injection periods

Simulation of multiphase flow in porous media is essential to manage the geologic CO 2 sequestration (GCS) process, and physics-based simulation approaches usually take prohibitively high computational cost due to the nonlinearity of the coupled physics. This paper contributes to the development and evaluation of a deep learning workflow that accurately and efficiently predicts the temporal-spatial evolution of pressure and CO 2 plumes during injection and post-injection periods of GCS operations. Based on a Fourier Neural Operator, the deep learning workflow takes input variables or features including rock properties, well operational controls and time steps, and predicts the state variables of pressure and CO 2 saturation. To further improve the predictive fidelity, separate deep learning models are trained for CO 2 injection and post-injection periods due to the difference in primary driving force of fluid flow and transport during these two phases. We also explore different combinations of features to predict the state variables. We use a realistic example of CO 2 injection and storage in a 3D heterogeneous saline aquifer, and apply the deep learning workflow that is trained from physics-based simulation data and emulate the physics process. Through this numerical experiment, we demonstrate that using two separate deep learning models to distinguish post-injection from injection period generates the most accurate prediction of pressure, and a single deep learning model of the whole GCS process including the cumulative injection volume of CO 2 as a deep learning feature, leads to the most accurate prediction of CO 2 saturation. For the post-injection period, it is key to use cumulative CO 2 injection volume to inform the deep learning models about the total carbon storage when predicting either pressure or saturation. The deep learning workflow not only provides high predictive fidelity across temporal and spatial scales, but also offers a speedup of 250 times compared to full physics reservoir simulation, and thus will be a significant predictive tool for engineers to manage the long-term process of 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↗
DOE OSTI2021

Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments

In this paper, we developed a machine learning-based Bayesian approach to inversely quantify and reduce the uncertainties of multiphase computational fluid dynamics (MCFD) simulations for bubbly flows. The proposed approach is supported by high-resolution two-phase flow measurements, including those by double-sensor conductivity probes, high-speed imaging, and particle image velocimetry. Local distributions of key physical quantities of interest (QoIs), including the void fraction and phasic velocities, are obtained to support the Bayesian inference. In the process, the epistemic uncertainties of the closure relations are inversely quantified while the aleatory uncertainties from stochastic fluctuations of the system are evaluated based on experimental uncertainty analysis. The combined uncertainties are then propagated through the MCFD solver to obtain uncertainties of the QoIs, based on which probability-boxes are constructed for validation. The proposed approach relies on three machine learning methods: feedforward neural networks and principal component analysis for surrogate modeling, and Gaussian processes for model form uncertainty modeling. The whole process is implemented within the framework of an open-source deep learning library PyTorch with graphics processing unit (GPU) acceleration, thus ensuring the efficiency of the computation. The results demonstrate that with the support of high-resolution data, the uncertainties of MCFD simulations can be significantly reduced. The proposed approach has the potential for other applications that involve numerical models with empirical parameters.

42 ENGINEERING↗
DOE OSTI2024

Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs

The molten sand that is a mixture of calcia, magnesia, alumina and silicate, known as CMAS, is characterized by its high viscosity, density and surface tension. The unique properties of CMAS make it a challenging material to deal with in high-temperature applications, requiring innovative solutions and materials to prevent its buildup and damage to critical equipment. Here, we use multiphase many-body dissipative particle dynamics simulations to study the wetting dynamics of highly viscous molten CMAS droplets. The simulations are performed in three dimensions, with varying initial droplet sizes and equilibrium contact angles. We propose a parametric ordinary differential equation (ODE) that captures the spreading radius behaviour of the CMAS droplets. The ODE parameters are then identified based on the physics-informed neural network (PINN) framework. Subsequently, the closed-form dependency of parameter values found by the PINN on the initial radii and contact angles are given using symbolic regression. Finally, we employ Bayesian PINNs (B-PINNs) to assess and quantify the uncertainty associated with the discovered parameters. In brief, this study provides insight into spreading dynamics of CMAS droplets by fusing simple parametric ODE modelling and state-of-the-art machine-learning techniques.

Mechanics↗
DOE OSTI2025

The role of the droplet interface in controlling the multiphase oxidation of thiosulfate by ozone

Predicting reaction kinetics in aqueous microdroplets, including aerosols and cloud droplets, is challenging due to the probability that the underlying reaction mechanism can occur both at the surface and in the interior of the droplet. Additionally, few studies directly measure the surface activities of doubly charged anions, despite their prevalence in the atmosphere. Here, deep-UV second harmonic generation spectroscopy is used to probe surface affinities of the doubly charged anions thiosulfate, sulfate, and sulfite, key species in the thiosulfate ozonation reaction mechanism. Thiosulfate has an appreciable surface affinity with a measured Gibbs free energy of adsorption of -7.3 ± 2.5 kJ mol -1 in neutral solution, while sulfate and sulfite exhibit negligible surface propensity. The Gibbs free energy is combined with data from liquid flat jet ambient pressure X-ray photoelectron spectroscopy to constrain the concentration of thiosulfate at the surface in our model. Stochastic kinetic simulations leveraging these novel measurements show that the primary reaction between thiosulfate and ozone occurs at the interface and in the bulk, with the contribution of the interface decreasing from ~65% at pH 5 to ~45% at pH 13. Additionally, sulfate, the major product of thiosulfate ozonation and an important species in atmospheric processes, can be produced by two different pathways at pH 5, one with a contribution from the interface of >70% and the other occurring predominantly in the bulk (>98%). The observations in this work have implications for mining wastewater remediation, atmospheric chemistry, and understanding other complex reaction mechanisms in multiphase environments. Future interfacial or microdroplet/aerosol chemistry studies should carefully consider the role of both surface and bulk chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Automated Fitting of a Semi-empirical Multiphase Equation of State for Carbon

The equation of state (EOS) of carbon is important in high explosive, geophysical, and inertial confinement fusion applications. Within the semi-empirical Sesame framework, the EOS of each phase is represented by a sum of cold, vibrational, and thermal electronic Helmholtz free energy contributions. Each phase has ~5-10 independent parameters that are adjusted to reproduce single-phase data (e.g., thermal expansion, isothermal compression) as well as experimentally- and computationally- derived phase boundaries. Manual calibration of the full multiphase EOS is arduous. We present our progress in development of automated EOS parameterization based on minimization of an objective function. Here, this function encodes deviation of model EOS results from experimental/computational benchmarks. Optimization is implemented as a combination of global (particle swarm) and local optimization techniques.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Multiphase phase-field approach for solid–solid phase transformations via propagating interfacial phase in HMX

In this study, a thermodynamically consistent multiphase phase-field theory has been formulated to describe temperature-induced solid–solid phase transformations via an interfacial phase. This theory satisfies all thermodynamic equilibrium and stability conditions. It allows us to formulate the analytical solutions of the interface profile, energy, width, and velocity for each of the propagating solid–melt and solid–solid interfaces. The formation of the intermediate melt during solid–solid phase transformation has been studied hundreds of degrees below melting temperature in the HMX energetic crystal. The effect of the penalizing term in the phase-field model and two nanoscale material parameters on the appearance and degree of disordering of intermediate melt has been analyzed for the non-equilibrium interface. The developed approach is applicable to phase transitions in geological, pharmaceutical, ferroelectric, colloidal, and superhard materials where phase transformations occur via the intermediate phase.

Roy, Arunabha M. (ORCID:000000034790726X)↗
DOE OSTI2024

Calibration and validation of the foundation for a multiphase strength model for tin

In this work, the Common Model of Multi-phase Strength and Equation of State (CMMP) model was applied to tin. Specifically, calibrations of the strength-specific elements of the CMMP foundation were developed with a combination of experiments and theory, and then the model was validated experimentally. The first element of the foundation is a multi-phase analytic treatment of the melt temperature and the shear modulus for the solid phases. These models were parameterized for each phase based on ab initio calculations using the software VASP (Vienna Ab initio Simulations Package) based on density functional theory. The shear modulus model for the ambient phase was validated with ultrasonic sound speed measurements as a function of pressure and temperature. The second element of the foundation is a viscoplastic strength model for the phase, upon which strength for inaccessible higher-pressure phases can be scaled as necessary. The stress–strain response of tin was measured at strain rates of 10 -3 to 3 x 10 3 s -1 and temperatures ranging from 87 to 373 K. The Preston–Tonks–Wallace (PTW) strength model was fit to that data using Bayesian model calibration. For validation, six forward and two reverse Taylor impact experiments were performed at different velocities to measure large plastic deformation of tin at strain rates up to ⁠10 5 s -1 . The PTW model accurately predicted the deformed shapes of the cylinders, with modest discrepancies attributed to the inability of PTW to capture the effects of twinning and dynamic recrystallization. Some material in the simulations of higher velocity Taylor cylinders reached the melting temperature, thus testing the multiphase model because of the presence of a second phase, the liquid. In simulations using a traditional modeling approach, the abrupt reduction of strength upon melt resulted in poor predictions of the deformed shape and non-physical temperatures. With CMMP, the most deformed material points evolved gradually to a mixed solid–liquid but never a fully liquid state, never fully lost strength, stayed at the melt temperature as the latent heat of fusion was absorbed, and predicted the deformed shape well.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Calculating shock Hugoniot and isentropes using multiphase equation of state tables and application to shock and release of diamond ablators in inertial confinement fusion implosions

Advances in shock and ramp compression techniques now allow experimental access to unprecedented extreme conditions of pressure and temperature, providing a means to test theoretical models. Here, we describe a simple methodology to compute multi-phase shock Hugoniot and isentropes using multiphase equation of state tables. We treat explicitly the phase coexistence along the phase boundary to reveal the evolution of the sample as it undergoes the phase transformation in adiabatic conditions. We illustrate the method by calculating the predicted shock and shock-and-release behavior of diamond at conditions relevant for the initial stage of inertial confinement fusion implosions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Multiphase magnetism in Yb 2 Ti 2 O 7

We use neutron scattering to show that ferromagnetism and antiferromagnetism coexist in the low T state of the pyrochlore quantum magnet Yb 2 Ti 2 O 7 . While magnetic Bragg peaks evidence long-range static ferromagnetic order, inelastic scattering shows that short-range correlated antiferromagnetism is also present. Small-angle neutron scattering provides direct evidence for mesoscale magnetic structure that we associate with metastable antiferromagnetism. Classical Monte Carlo simulations based on exchange interactions inferred from ⟨ 111 ⟩ -oriented high-field spin wave measurements confirm that antiferromagnetism is metastable within the otherwise ferromagnetic ground state. The apparent lack of coherent spin wave excitations and strong sensitivity to quenched disorder characterizing Yb 2 Ti 2 O 7 is a consequence of this multiphase magnetism.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Multiphasic size-dependent growth dynamics of nanoparticle ensembles

Colloidal nanoparticles are of great interest in modern science and industry. However, the thermodynamic mechanism and dynamics of nanoparticle growth have yet to be understood. Addressing these issues, we tracked hundreds of in-situ growth trajectories of a nanoparticle ensemble using liquid-phase TEM and found that the nanoparticle growth, including coalescence, exhibits nanoparticle size-dependent multiphasic dynamics, unexplainable by current theories. Motivated by this finding, we developed a model and theory for an ensemble of growing nanoparticles, providing a unified, quantitative understanding of the time-dependent mean and fluctuation of nanoparticle size and size-dependent growth rate profiles across various nanoparticle systems and experimental conditions. Our work reveals that the chemical potential in a small nanoparticle strongly deviates from the Gibbs-Thomson equation, shedding light on how it governs the size-dependent growth dynamics of nanoparticles.

Kim, Ji-Hyun↗
DOE OSTI2023

Uncertainty Quantification for Multiphase Computational Fluid Dynamics Closure Relations with a Physics-Informed Bayesian Approach

Multiphase Computational Fluid Dynamics (MCFD) based on the two-fluid model is considered a promising tool to model complex two-phase flow systems. MCFD simulation can predict local flow features without resolving interfacial information. As a result, the MCFD solver relies on closure relations to describe the interaction between the two phases. Those empirical or semi-mechanistic closure relations constitute a major source of uncertainty for MCFD predictions. In this paper, we leverage a physics-informed uncertainty quantification (UQ) approach to inversely quantify the closure relations’ model form uncertainty in a physically consistent manner. This proposed approach considers the model form uncertainty terms as stochastic fields that are additive to the closure relation outputs. Combining dimensionality reduction and Gaussian processes, the posterior distribution of the stochastic fields can be effectively quantified within the Bayesian framework with the support of experimental measurements. As this UQ approach is fully integrated into the MCFD solving process, the physical constraints of the system can be naturally preserved in the UQ results. Here, in a case study of adiabatic bubbly flow, we demonstrate that this UQ approach can quantify the model form uncertainty of the MCFD interfacial force closure relations, thus effectively improving the simulation results with relatively sparse data support.

42 ENGINEERING↗
DOE OSTI2025

Multiphase Species Transport Modeling for Molten Salt Reactors in the System Analysis Module: Generation, Decay, Deposition, and Extraction of Insoluble Fission Products

With the increase of interests in the design and deployment of advanced reactor systems, a desire for simulation tools supporting system analysis of reactor operation and safety is rising. Molten salt reactors (MSRs), one of the advanced reactor systems, utilize liquid fused salt fuel as both coolant and fuel. During operation, MSR generates insoluble fission products, including noble metals and gases. The buildup of these species in fuel salt presents safety concerns as they may deposit on surfaces of critical components and produce excessive decay heat, causing the failure of system components. Timely removal of these noble metals and gases would ensure the safe operation of the reactor system. The dynamic nature of salt fuel system, involving the generation, decay, deposition, and extraction of noble metals and gases, calls for robust species transport models to facilitate system analysis and monitoring, and design of efficient species removal components. This paper concentrates on the development of a computational framework for species transport, consisting of multiphase transport model formulation, mass transfer between phases, numerical implementation in MOOSE environment, verification through Method of Manufacture Solutions (MMS) and validation against experimental data from the Molten Salt Reactor Experiment (MSRE). Integrating this framework into the System Analysis Module (SAM) code further enhances SAM’s capabilities for advanced reactor analysis in the future.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Brewster angle-cavity ringdown spectroscopy for low temperature plasma measurements in multiphases

Here we report on the development of a Brewster angle-cavity ringdown spectroscopy (BA-CRDS) system for low temperature plasma diagnostics. The system can measure gas species in solutions, with a detection limit (minimum detectable absorbance) of 9.1 × 10 -5 , which is equivalent to a detection limit of 0.04 parts per billion for measuring OH radicals in water at 308 nm. With higher reflectivity ringdown mirrors and improved design of a Brewster angle cell, the detection limit can potentially be up to 10 -6 or lower. In this exploratory study, the absorption cross sections of HgBr 2 and H 2 O 2 in the aqueous phase at 256 nm are measured to be (1.8 ± 0.1) × 10 -18 cm 2 and (5.2 ± 0.5) × 10 -20 cm 2 , respectively. Furthermore, temporal profiles of absorbance from distilled water, HgBr 2 , and H 2 O 2 solutions when interacting with a helium atmospheric plasma jet are individually characterized at different plasma powers, gas flow rates, and/or solute concentrations. The observed linear temporal profiles of absorbance from the plasma-interacted water suggest formation of H 2 O 2 from plasma-generated OH radicals, while the nonlinear temporal profiles from the plasma-treated HgBr 2 solutions reveal possible removal of HgBr 2 by OH radicals. Our results demonstrate that the new BA-CRDS system is a powerful tool for quantification of reactive plasma species in multiphases or other complex settings.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Multiphase outflows in post-starburst E+A galaxies - II. A direct connection between the neutral and ionized outflow phases

ABSTRACT Post-starburst E+A galaxies are systems that hosted a powerful starburst that was quenched abruptly. Simulations suggest that these systems provide the missing link between major merger ULIRGs and red and dead ellipticals, where AGN feedback is responsible for the expulsion or destruction of the molecular gas. However, many details remain unresolved and little is known about AGN-driven winds in this short-lived phase. We present spatially resolved IFU spectroscopy with MUSE/VLT of SDSS J124754.95-033738.6, a post-starburst E+A galaxy with a recent starburst that started 70 Myr ago and ended 30 Myr ago, with a peak SFR of $\sim 150\, \mathrm{M_{\odot }\,yr^{ -1}}$. We detect disturbed gas throughout the entire field of view, suggesting triggering by a minor merger. We detect fast-moving multiphased gas clouds, embedded in a double-cone face-on outflow, which are traced by ionized emission lines and neutral NaID emission and absorption lines. We find remarkable similarities between the kinematics, spatial extents, and line luminosities of the ionized and neutral gas phases, and propose a model in which they are part of the same outflowing clouds, which are exposed to both stellar and AGN radiation. Our photoionization model provides consistent ionized line ratios, NaID absorption optical depths and EWs, and dust reddening. Using the model, we estimate, for the first time, the neutral-to-ionized gas mass ratio (about 20), the sodium neutral fraction, and the size of the outflowing clouds. This is one of the best ever observed direct connections between the neutral and ionized outflow phases in AGN.

Baron, Dalya↗
DOE OSTI2023

SILCC – VII. Gas kinematics and multiphase outflows of the simulated ISM at high gas surface densities

ABSTRACT We present magnetohydrodynamic (MHD) simulations of the star-forming multiphase interstellar medium (ISM) in stratified galactic patches with gas surface densities Σgas = 10, 30, 50, and 100 $\mathrm{M_\odot \, pc^{-2}}$. The silcc project simulation framework accounts for non-equilibrium thermal and chemical processes in the warm and cold ISM. The sink-based star formation and feedback model includes stellar winds, hydrogen-ionizing UV radiation, core-collapse supernovae, and cosmic ray (CR) injection and diffusion. The simulations follow the observed relation between Σgas and the star formation rate surface density ΣSFR. CRs qualitatively change the outflow phase structure. Without CRs, the outflows transition from a two-phase (warm and hot at 1 kpc) to a single-phase (hot at 2 kpc) structure. With CRs, the outflow always has three phases (cold, warm, and hot), dominated in mass by the warm phase. The impact of CRs on mass loading decreases for higher Σgas and the mass loading factors of the CR-supported outflows are of order unity independent of ΣSFR. Similar to observations, vertical velocity dispersions of the warm ionized medium (WIM) and the cold neutral medium (CNM) correlate with the star formation rate as $\sigma _\mathrm{z} \propto \Sigma _\mathrm{SFR}^a$, with a ∼ 0.20. In the absence of stellar feedback, we find no correlation. The velocity dispersion of the WIM is a factor ∼2.2 higher than that of the CNM, in agreement with local observations. For $\Sigma _\mathrm{SFR} \gtrsim 1.5 \times 10^{-2}\, \mathrm{M}_\odot \, \mathrm{yr}^{-1}\, \mathrm{kpc}^{-2}$ the WIM motions become supersonic.

Astronomy & Astrophysics↗