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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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To assist design of adequate protective clothing, mathematical model of man's thermoregulatory system has been developed so that body thermal responses under immersed conditions can be predicted accurately. Experimental data encompassed wide range of water temperatures, protective clothing, breathing-gas mixtures, and durations of immersion.
The generation of ULF waves by ground-based magnetic and electric dipoles is studied with a simplified model consisting of three adjoining homogeneous regions representing the groud, the vacuum (free space) region, and the ionosphere. The system is assumed to be immersed in a homogeneous magnetic field with an arbitrary tilt angle. By the use of Fourier techniques and the method of stationary phase, analytic expressions are obtained for the field strength of the compressional Alfven waves in the ionosphere. Expressions are also obtained for the strength of the torsional Alfven wave in the ionosphere and the ULF magnetic field at ground level. Numerical results are obtained for the compressional Alfven-wave field strength in the ionosphere with a nonvertical geomagnetic field and for the ULF magnetic field at ground level for a vertical geomagnetic field.
For decades, the Goddard Earth Sciences Data and Information Services Center (GES DISC) has archived and distributed enormous volumes of NASA Earth science data (accompanied with many developed tools and services) to various research/applications communities and the general public. Being “immersed” in the Big Data era, we have inevitably faced the challenges of our continually increasing archived data in both volume and variety, as well as enhanced user needs and demands. In recent years, we have actively analyzed different types of user metrics, such as operational distribution metrics (recording numbers of distinct users and downloaded data files, size of distributed data volume): user publication metrics (mining info from our Giovanni users’ publications): and Bugzilla metrics (collecting info from user questions or feedback from user assistance tickets). Such metrics have helped us achieve a better understanding of user needs, demands, characteristics, and behaviors, which has then helped us improve our user services. Now we will present a “Sequel” of integrated analysis of multiple metrics at the GES DISC by introducing and adding one new kind of metrics acquired via utilizing our recently implemented Google Analytic 360 suite. Several “newer” reports, e.g., “What web site features and links are the most popular (and least)?” and “What are the top 25 dataset Keyword searches?” retrieved from this new metrics set will be presented, along with the aforementioned “traditional” metrics results.
A 3D microstructure resolving electrochemical transport and interfacial chemistry solver. Electrode microstructure plays an important role in determining the performance of an electrochemical system, e.g. lithium ion battery. EchemAMR is a microstructure scale model that solves the governing equations for ion transport, electrical current continuity, interfacial chemistry and structural mechanics. Complex microstructure geometries from imaging can be directly imported into EchemAMR. A volume fraction based description of the geometry on Cartesian grid with an immersed interface formulation enables simplified meshing and large-scale simulations with millions of degrees of freedom. EchemAMR has been tested against systems with analytic solutions for numerical convergence and highly resolved lithium ion battery microstructures. EchemAMR demonstrates excellent mass conversation and efficient scaling on heterogenous High-Performance Computing (HPC) with central and graphics processing units.
A biothermal model with a physically-controlled subsystem and a dynamically-controlled subsystem is developed to simulate the thermoregulatory system of man under immersed conditions. The model is consistent with experimental data for seminude subjects immersed to neck in cool to temperate water and for 'wet-suited' subjects immersed to neck in cold water. Equations are derived for predicting body temperatures under various dive conditions.
A method for calculating the surface brightness distribution on a plane-parallel reflection nebula of uniform density illuminated by a star located either in front of, behind, or arbitrarily inside the scattering medium is proposed. The Monte Carlo technique is used to find solutions to the radiative transfer problem. The scattering properties of the nebular particles are parameterized by the albedo for single scattering and a three-parameter analytic phase function. Calculations are then presented for the surface brightness distribution across the face of such nebulae with (1) a foreground star and (2) immersed stars. The calculations include the full effects of multiple scattering, are independent of a particular assumed grain material or size distribution, and are applicable to any wavelength region for which observations can be obtained.
As gas-solid heterogeneous catalytic reactions are molecular in nature, a full mechanistic understanding of the process requires atomic scale characterization under realistic operating conditions. While atomic resolution imaging has become a routine in modern high-vacuum (scanning) transmission electron microscopy ((S)TEM), both image quality and resolution nominally degrade when reaction gases are introduced. In this work, we systematically assess the effects of different gases at various pressures on the quality and resolution of images obtained at room temperature in the annular dark field STEM imaging mode using a differentially pumped (DP) gas cell. This imaging mode is largely free from inelastic scattering effects induced by the presence of gases and retains good imaging properties over a wide range of gas mass/pressures. We demonstrate the application of the ESTEM with atomic resolution images of a complex oxide alkane oxidation catalyst MoVNbTeOx (M1) immersed in light and heavy gas environments.
In this paper, we present a hybrid discrete bubble-lattice Boltzmann–discrete element modelling framework for simulating gas-charged sediments, especially in the seabed. A discrete bubble model proposed in chemical engineering is adapted in the coupled discrete element/lattice Boltzmann method to model the migration of gas bubbles in saturated sediments involving interactions between gas bubbles and fluid/solid phases. Surface tension is introduced into the discrete bubble model in this work, so that it can handle the complex gas–fluid–solid interface. The lattice Boltzmann and discrete element methods are, respectively, employed to simulate fluid flows and mechanical behaviours of sediments. A velocity interpolation-based immerse boundary method is utilised to resolve the coupling between the fluid flow and the solid/gas phase. The proposed technique is preliminarily validated using simulations of bubble migration in fluids, which is followed by high-resolution investigations of the transport of a gas bubble in seabed sediments. Lastly, it is demonstrated that this hybrid method can reproduce, to a certain degree, the characters of bubbles moving in seabed sediment tests.
Computer program has been developed as analytical tool to predict severity of effects of exhaust of rocket engines on adjacent spacecraft surfaces. Program computes forces, moments, pressures, and heating rates on surfaces immersed in or subjected to exhaust plume environments. Predictions will be useful in design of systems where such problems are anticipated.
Highlights: •An efficient path-integral formulation of the Bose polaron problem is proposed. •The spectrum of an impurity moving in 1D Bose gas is given by analytic formula. •The effective-mass approximation reproduces well the exact Bose polaron spectrum. -- Abstract: The full momentum dependence of spectrum of a point-like impurity immersed in a dilute one-dimensional Bose gas is calculated on the mean-field level. In particular we elaborate, to the finite-momentum Bose polaron, the path-integral approach whose semi-classical approximation leads to the conventional mean-field treatment of the problem while quantum corrections can be easily accounted by standard loop expansion techniques. The extracted low-energy parameters of impurity spectrum, namely, the binding energy and the effective mass of particle, are shown to be in qualitative agreement with the results of quantum Monte Carlo simulations.
Visualizing 3D molecular structures is crucial to understanding and predicting their chemical behavior. However, static 2D hand-drawn skeletal structures remain the preferred method of chemical communication. Here, we combine cutting-edge technologies in augmented reality (AR), machine learning, and computational chemistry to develop MolAR, an open-source mobile application for visualizing molecules in AR directly from their hand-drawn chemical structures. Users can also visualize any molecule or protein directly from its name or protein data bank ID and compute chemical properties in real time via quantum chemistry cloud computing. MolAR provides an easily accessible platform for the scientific community to visualize and interact with 3D molecular structures in an immersive and engaging way.
Electrical resistivity tomography (ERT) has been widely used in environmental applications to study processes associated with subsurface contaminants and contaminant remediation. Anthropogenic alterations in subsurface electrical conductivity associated with contamination often originate from highly industrialized areas with significant amounts of buried metallic infrastructure. The deleterious influence of such infrastructure on imaging results generally limits the utility of ERT where it might otherwise prove useful for subsurface investigation and monitoring. In this manuscript we present a method of accurately modeling the effects of buried conductive infrastructure within the forward modeling algorithm, thereby removing them from the inversion results. The method is implemented in parallel using immersed interface boundary conditions, whereby the global solution is reconstructed from a series of well-conditioned partial solutions. Forward modeling accuracy is demonstrated by comparison with analytic solutions. Synthetic imaging examples are used to investigate imaging capabilities within a subsurface containing electrically conductive buried tanks, transfer piping, and well casing, using both well casings and vertical electrode arrays as current sources and potential measurement electrodes. Results show that, although accurate infrastructure modeling removes the dominating influence of buried metallic features, the presence of metallic infrastructure degrades imaging resolution compared to standard ERT imaging. However, accurate imaging results may be obtained if electrodes are appropriately located.
An analysis of experimental nozzle, exhaust plume, and exhaust plume impingement data is presented. The data were obtained for subscale solid propellant motors with propellant Al loadings of 2, 10 and 15% exhausting to simulated altitudes of 50,000, 100,000 and 112,000 ft. Analytical predictions were made using a fully coupled two-phase method of characteristics numerical solution and a technique for defining thermal and pressure environments experienced by bodies immersed in two-phase exhaust plumes.
Molten-salt reactors (MSRs) represent a promising solution for next-generation nuclear energy, offering advantages in safety, fuel efficiency, and waste minimization. However, their liquid-fueled design presents unique challenges for spent fuel management, making post-shutdown waste characterization essential for developing effective strategies. Despite this need, there is a notable absence of visualization platforms specifically tailored to the unique characteristics and analytical requirements of MSR waste management. Existing tools in the nuclear industry are primarily designed for reactor operations or generic data exploration and lack both integration with MSR-specific multiphysics frameworks and the ability to simultaneously visualize time-dependent thermal fields, chemical composition evolution, and radiation distribution patterns. To address these limitations, this paper presents an immersive virtual reality (VR) visualization platform that processes and displays high-fidelity multiphysics simulation output from the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework in real-time, using Unity. The platform visualizes MSR waste characteristics such as nuclide decay, salt cooling, and corrosion by using Exodus II output data and running on a VR headset. It includes a user-friendly interface with features such as visibility toggling, cross-sectional slicing, and time-series animation for exploring simulation data. These capabilities support experimental design, stakeholder engagement, and public communication by making complex reactor behavior more accessible and understandable. By enhancing spatial reasoning and reducing cognitive load, this immersive environment fosters more effective communication and decision-making in MSR waste management.
The energy systems are generating data at a scale and complexity that increasingly outpaces our ability to analyze it. This talk argues that rigorous, large-scale visualization is a critical tool for meeting that challenge. Drawing on recent work in the Computational Science Center at the National Laboratory of the Rockies, we trace a progression of visualization research culminating in an immersive digital twin of a real campus power grid. Along the way, we examine why visualization matters at all, where widely-used techniques quietly fail, and what becomes analytically possible when you match the display environment to the scale of the problem.
Human Factors Engineering support was provided for the 30% design review of the late Space Station Freedom Payload Control Area (PCA). The PCA was to be the payload operations control room, analogous to the Spacelab Payload Operations Control Center (POCC). This effort began with a systematic collection and refinement of the relevant requirements driving the spatial layout of the consoles and PCA. This information was used as input for specialized human factors analytical tools and techniques in the design and design analysis activities. Design concepts and configuration options were developed and reviewed using sketches, 2-D Computer-Aided Design (CAD) drawings, and immersive Virtual Reality (VR) mockups.
National Laboratory of the Rockies (NLR), in collaboration with the Dubai Electricity and Water Authority (DEWA) and Infra-X, has undertaken the Energy Visualization Analysis Project. The aim of this project is to enhance analytical and 3D visualization capabilities for distribution network planning and renewable energy integration. As modern grid continues to evolve with large-scale solar PV deployment and emerging distributed energy resources (DERs), the ability to effectively analyze, visualize, and communicate complex grid behaviors has become increasingly critical. The project focuses on developing empirical use cases based on real distribution feeder data and engineering workflows, ensuring the outcomes are directly aligned with operational environment. Through time-series power flow simulations and nodal hosting capacity analysis, the study quantifies the impacts of high PV penetration on voltage and thermal limits within representative 11 kV feeders. These analyses identify specific nodes and conditions where DER integration challenges arise. Furthermore, a Battery Energy Storage System (BESS) optimization algorithm was applied to determine the optimal size and placement of storage systems that can mitigate network constraints and enhance hosting capacity. The comparative results between base-case and BESS-augmented scenarios clearly demonstrate improvements in network stability and load management efficiency. In parallel, the NLR team developed an immersive 3D visualization framework, enabling interactive exploration of grid simulations using commodity head-mounted display (HMD) systems. This framework transforms conventional 2D simulation data into spatially intuitive visual environments - allowing engineers to analyze feeder conditions, PV hosting potential, and BESS effects in real time. This report represents the first foundational phase in establishing a visualization-driven analytical ecosystem. It provides a methodological foundation for data integration, visualization architecture, and simulation-based decision support, paving the way for large-scale adoption of immersive visualization across DEWA's Smart Grid Initiative, R&D activities, and future network resilience studies.
In this work, we study the spontaneous spreading of water droplets immersed in oil and report an unexpectedly slow kinetic regime not described by previous spreading models. We can quantitatively describe the observed regime crossover and spreading rate in the late kinetic regime with an analytical model considering the presence of periodic metastable states induced by nanoscale topographic features (characteristic area ~4 nm 2 , height ~1 nm) observed via atomic force microscopy. The analytical model proposed in this work reveals that certain combinations of droplet volume and nanoscale topographic parameters can significantly hinder or promote wetting processes such as spreading, wicking, and imbibition.