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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Characterizing the Effect of Capillary Heterogeneity on Multiphase Flow Pulsation in an Intermediate–Scale Beadpack Experiment using Time Series Clustering and Frequency Analysis

An intermediate-scale beadpack drainage experiment was conducted to investigate how simple layered lamination heterogeneity affects CO 2 flow. Two simple layers of capillary barriers are manually packed in the tank and slow drainage was carried out using analog fluids to mimic the capillary- and gravity-dominated CO 2 upward migration process in deep saline aquifers. Nonwetting phase saturation time series clustering analysis and frequency analysis have been conducted on the experimental data. Additionally, modified invasion percolation numerical simulations were done on a digital model of the beadpack to compare to experimental results. Results show that capillary barriers can lead to strong pulsation behavior, which in turn can cause unexpected early breaching through other barriers. The inlet pressure is found to be able to respond to saturation changes in far regions of the domain, indicating that the wetting phase can transmit pressure changes from the other phase. Although static simulations were not able to capture all the dynamic behavior observed in the experiment, Monte Carlo composite simulation results combining many different realizations can better illustrate how the nonwetting phase will behave in the heterogeneous domain. Furthermore, our results suggest the need for CO 2 storage site selection with preference given to aquifers with more capillary barriers with finer grain sizes to avoid flow pulsation and to retard plume upward migration.

58 GEOSCIENCES↗
DOE OSTI2024

Accelerating Multiphase Simulations With Denoising Diffusion Model Driven Initializations

This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics‐based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of interest. Pore‐scale simulations enhance our understanding of applications such as assessing hydrogen and storage efficiency in underground reservoirs. Nevertheless, they are computationally expensive and the presence of non‐unique solutions can require multiple simulations within a single geometry. To overcome the computational cost hurdle, we propose a method that couples generative diffusion models and physics‐based simulations. While training the data‐driven model, we simultaneously generate initial conditions and perform physics‐based simulations using these. This integrated approach enables us to receive real‐time feedback on a single compute node equipped with both CPUs and GPUs. By efficiently managing these processes within a single compute node, we can continuously monitor performance and halt training once the model meets the specified criteria. To test our model, we generate realizations in a real Berea sandstone fracture which shows that our technique is up to 4.4 times faster than commonly used flow simulation initializations.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Understanding catalysis in a multiphasic two-dimensional transition metal dichalcogenide.

Establishing processing–structure–property relationships for monolayer materials is crucial for a range of applications spanning optics, catalysis, electronics and energy. Presently, for molybdenum disulfide, a promising catalyst for artificial photosynthesis, considerable debate surrounds the structure/property relationships of its various allotropes. Here we unambiguously solve the structure of molybdenum disulfide monolayers using high-resolution transmission electron microscopy supported by density functional theory and show lithium intercalation to direct a preferential transformation of the basal plane from 2H (trigonal prismatic) to 1T' (clustered Mo). These changes alter the energetics of molybdenum disulfide interactions with hydrogen (ΔG H ), and, with respect to catalysis, the 1T' transformation renders the normally inert basal plane amenable towards hydrogen adsorption and hydrogen evolution. Furthermore, we show basal plane activation of 1T' molybdenum disulfide and a lowering of ΔG H from +1.6 eV for 2H to +0.18 eV for 1T', comparable to 2H molybdenum disulfide edges on Au(111), one of the most active hydrogen evolution catalysts known.

08 HYDROGEN↗
DOE OSTI2024

Neural network representations of multiphase Equations of State

Abstract Equations of State model relations between thermodynamic variables and are ubiquitous in scientific modelling, appearing in modern day applications ranging from Astrophysics to Climate Science. The three desired properties of a general Equation of State model are adherence to the Laws of Thermodynamics, incorporation of phase transitions, and multiscale accuracy. Analytic models that adhere to all three are hard to develop and cumbersome to work with, often resulting in sacrificing one of these elements for the sake of efficiency. In this work, two deep-learning methods are proposed that provably satisfy the first and second conditions on a large-enough region of thermodynamic variable space. The first is based on learning the generating function (thermodynamic potential) while the second is based on structure-preserving, symplectic neural networks, respectively allowing modifications near or on phase transition regions. They can be used either “from scratch” to learn a full Equation of State, or in conjunction with a pre-existing consistent model, functioning as a modification that better adheres to experimental data. We formulate the theory and provide several computational examples to justify both approaches, highlighting their advantages and shortcomings.

Science & Technology - Other Topics↗
DOE OSTI2026

Predicting multiphase flow and tracer transport for an underground chemical explosive test

Detecting radionuclide gas seepage from clandestine underground nuclear tests is central to nonproliferation explosion monitoring research. Yet, early-time (<6 day) gas transport driven by the explosive pressure wave remains poorly constrained due to scarcity of field data. We simulate multi-phase gas transport in the vadose zone using pre-shot data from a recent chemical explosion in P-Tunnel at the Nevada National Security Site, USA. Despite using a simplified 2D-radial model, predictions of tracer arrival matched observations within one order-of-magnitude. Our results show how transient blast forcing rapidly mobilizes gases from the cavity into surrounding rock – critical for optimizing sensor placement and test planning. This unique integration of field data and modeling represents a significant improvement in our ability to predict gas migration from underground explosions. More broadly, it offers insights into the coupled dynamics of pressure waves and contaminant transport in the vadose zone, with implications for monitoring and hazard assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Identifying chemically similar multiphase nanoprecipitates in compositionally complex non-equilibrium oxides via machine learning

Abstract Characterizing oxide nuclear fuels is difficult due to complex fission products, which result from time-evolving system chemistry and extreme operating environments. Here, we report a machine learning-enhanced approach that accelerates the characterization of spent nuclear fuels and improves the accuracy of identifying nanophase fission products and bubbles. We apply this approach to commercial, high-burnup, irradiated light-water reactor fuels, demonstrating relationships between fission product precipitates and gases. We also gain understanding of the fission versus decay pathways of precipitates across the radius of a fuel pellet. An algorithm is provided for quantifying the chemical segregation of the fission products with respect to the high-burnup structure, which enhances our ability to process large amounts of microscopy data, including approaching the atomistic-scale. This may provide a faster route for achieving physics-based fuel performance modeling.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Multiphase separation of copper nanowires

Here, this communication reports a new method to purify copper nanowires with nearly 100% yield from undesired copper nanoparticle side-products formed during batch processes of copper nanowire synthesis. Also, this simple separation method can yield large quantities of long, uniform, high-purity copper nanowires to meet the requirements of nanoelectronics applications as well as provide an avenue for purifying copper nanowires in the industrial scale synthesis of copper nanowires, a key step for commercialization and application of nanowires.

36 MATERIALS SCIENCE↗
DOE OSTI2012

A multiphase model for pulsed ns-laser ablation of copper in an ambient gas

Laser ablation in an ambient gas is nowadays used in a growing number of applications, such as chemical analysis and pulsed laser deposition. Despite the many applications, the technique is still poorly understood. Therefore models describing the material evolution in time during short pulse laser irradiation can be helpful to unravel the puzzle and finally result in the optimization of the related applications. In the present work, a copper target is immersed in helium, initially set at atmospheric pressure and room temperature. Calculations are performed for a Gaussian-shaped laser pulse with a wavelength of 532 nm, full width at half maximum of 6 ns, and laser fluences up to 10 J/cm{sup 2}. In order to describe the transient behaviour in and above the copper target, hydrodynamic equations are solved. An internal energy method accounting for pressure relaxation is applied for the description of the target. In the plume domain a set of conservation equations is solved, assuming local thermodynamic equilibrium. Calculated crater depths and transmission profiles are compared with experimental results and similar trends are found. Our calculations indicate that for the laser fluence regime under study, explosive boiling could play a fundamental role in the plasma formation of metals under ns-pulsed laser irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2013

A high-speed photoresist removal process using multibubble microwave plasma under a mixture of multiphase plasma environment

This paper proposes a photoresist removal process that uses multibubble microwave plasma produced in ultrapure water. A non-implanted photoresist and various kinds of ion-implanted photoresists such as B, P, and As were treated with a high ion dose of 5 × 10{sup 15} atoms/cm{sup 2} at an acceleration energy of 70 keV; this resulted in fast removal rates of more than 1 μm/min. When the distance between multibubble microwave plasma and the photoresist film was increased by a few millimeters, the photoresist removal rates drastically decreased; this suggests that short-lived radicals such as OH affect high-speed photoresist removal.

36 MATERIALS SCIENCE↗
DOE OSTI2014

An improved lattice Boltzmann scheme for multiphase fluid with multi-range interactions

Modeling of fluids with liquid to gas phase transition has become important for understanding many environmental or industrial processes. Such simulations need new techniques, because traditional solvers are often limited. The Lattice Boltzmann Model (LBM) allows simulate complex fluids, because its mesoscopic nature gives possibility to incorporate additional physics in comparison to usual methods. In this work, an improved lattice Boltzmann model for phase transition flow will be introduced. First, the state of art for Shan and Chen (SC) type of LBM will be reminded. Then, link to real thermodynamics will be established with Maxwell equal areas construction. Convergence to isothermal liquid vapor equilibrium will be shown and discussed. Inclusion of an equation of state for real fluid and better incorporation of force term is presented. Multi-range interactions have been used for SC model, but it hasn't been yet applied to real fluid with non-ideal equation of state. In this work, we evaluate this model when it is applied to real liquid-vapor equilibrium. We show that important differences are found for evaluation of gas density. In order to recover thermodynamic consistency, we use a new scheme for calculation of force term, which is a combination of multi range model and numerical weighting used by Gong and Cheng. We show the superiority of our new model by studying convergence to equilibrium values over a large temperature range. We prove that spurious velocities remaining at equilibrium are decreased.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

A DFT-based kinetic Monte Carlo simulation of multiphase oxide-metal thin film growth

Functional thin films of nanoscale metal pillars in oxide or nitride matrices known as vertically aligned nanocomposite (VAN) have gained much interest owing to their unique strain-coupled and highly anisotropic properties. So far, the deposition of these films has been explored mostly experimentally. In this work, a density functional theory (DFT)-based kinetic Monte Carlo simulation model using Bortz–Kalos–Lebowitz algorithm was developed to understand the growth of VAN films deposited by pulsed laser technique on mismatching substrates. The model has been parameterized and applied to understand the kinetics of growth thin films consisting of Au pillars in CeO2 matrix deposited on SrTiO3 substrates. The effects of pulsed laser deposition (PLD) conditions including the pulse frequency, deposition flux, and substrate temperature were explored. The simulations indicate that the Au pillar size and shape exhibit significant dependence on the PLD conditions. Namely, increasing the temperature increases the average pillar size and lowers the pillar density, and vice versa. In addition, the simulations revealed that increasing the deposition rate results in lowering the average pillar size and increasing the density. Particularly, the DFT results suggest that Au pillar size can be tuned during the initial growth of the first monolayer due to the significantly low activation barrier. Our analysis showed that the relationship between the average pillar size and pillar density is influenced by the kinetics. Furthermore, autocorrelation analysis showed that pillars self-organize in quasi-ordered patterns at certain windows of the deposition conditions, which is attributed to the complex nature of the chemical interactions in the system, the kinetics, and the deposition parameters.

Physics↗