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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Determination of the thermodynamic correction factor of fluids confined in nano-metric slit pores from molecular simulation

The multi-component diffusive mass transport is generally quantified by means of the Maxwell-Stefan diffusion coefficients when using molecular simulations. These coefficients can be related to the Fick diffusion coefficients using the thermodynamic correction factor matrix, which requires to run several simulations to estimate all the elements of the matrix. In a recent work, Schnell et al. [“Thermodynamics of small systems embedded in a reservoir: A detailed analysis of finite size effects,” Mol. Phys. 110, 1069–1079 (2012)] developed an approach to determine the full matrix of thermodynamic factors from a single simulation in bulk. This approach relies on finite size effects of small systems on the density fluctuations. We present here an extension of their work for inhomogeneous Lennard Jones fluids confined in slit pores. We first verified this extension by cross validating the results obtained from this approach with the results obtained from the simulated adsorption isotherms, which allows to determine the thermodynamic factor in porous medium. We then studied the effects of the pore width (from 1 to 15 molecular sizes), of the solid-fluid interaction potential (Lennard Jones 9-3, hard wall potential) and of the reduced fluid density (from 0.1 to 0.7 at a reduced temperature T* = 2) on the thermodynamic factor. The deviation of the thermodynamic factor compared to its equivalent bulk value decreases when increasing the pore width and becomes insignificant for reduced pore width above 15. We also found that the thermodynamic factor is sensitive to the magnitude of the fluid-fluid and solid-fluid interactions, which softens or exacerbates the density fluctuations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Toward electron temperature profiles in hot-dense plasmas from x-ray spectral ensembles

High repetition rate laser systems enable new strategies for diagnosing plasma behavior with large datasets. Here, we define an ensemble technique that relies on randomized targeting of x-ray tracer micro-stripes. On each shot, a high-intensity laser pulse is focused on a solid target with Ti tracer stripes embedded in an Al foil, randomly targeting a micro-stripe, a portion of a stripe, or a gap between stripes. High-resolution, time-integrated x-ray spectrometers capture line emission from the portion of the micro-stripe that is heated to sufficiently high electron temperatures. Accumulation of many such cases is used to construct ensemble distributions of x-ray line intensities that encompass all relative offsets of the laser focus to the micro-stripe centers. Synthetic intensity distributions are likewise generated using collisional-radiative modeling. Bayesian fitting of modeled to measured intensity distributions establishes the most likely radial temperature profiles, enabling comparison to hydrodynamic models and calling into question the cylindrical symmetry of these micro-stripe-embedded systems. Ensemble techniques have significant potential for high-energy-density plasma diagnostics, especially with the advent of high repetition rate experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

The structure and dark halo core properties of dwarf spheroidal galaxies

The structure and dark matter halo core properties of dwarf spheroidal galaxies (dSphs) are investigated. A double-isothermal (DIS) model of an isothermal, non-self-gravitating stellar system embedded in an isothermal dark halo core provides an excellent fit to the various observed stellar surface density distributions. The stellar core scale length a{sub *} is sensitive to the central dark matter density ρ{sub 0,d}. The maximum stellar radius traces the dark halo core radius r{sub c,d}. The concentration c{sub *} of the stellar system, determined by a King profile fit, depends on the ratio of the stellar-to-dark-matter velocity dispersion σ{sub ∗}/σ{sub d}. Simple empirical relationships are derived that allow us to calculate the dark halo core parameters ρ{sub 0,d}, r{sub c,d}, and σ{sub d} given the observable stellar quantities σ{sub *}, a{sub *}, and c{sub *}. The DIS model is applied to the Milky Way’s dSphs. All dSphs closely follow the same universal dark halo scaling relations ρ{sub 0,d}×r{sub c,d}=75{sub −45}{sup +85} M{sub ⊙} pc{sup −2} that characterize the cores of more massive galaxies over a large range in masses. The dark halo core mass is a strong function of core radius, M{sub c,d}∼r{sub c,d}{sup 2}. Inside a fixed radius of ∼400 pc the total dark matter mass is, however, roughly constant with M{sub d}=2.6±1.4×10{sup 7} M{sub ⊙}, although outliers are expected. The dark halo core densities of the Galaxy’s dSphs are very high, with ρ{sub 0,d} ≈ 0.2 M{sub ⊙} pc{sup −3}. dSphs should therefore be tidally undisturbed. Evidence for tidal effects might then provide a serious challenge for the CDM scenario.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Leveraging SETS resilience capabilities for safe-to-fail infrastructure under climate change

As the rehabilitation of infrastructure is outpaced by changes in the profile, frequency, and intensity of extreme weather events, infrastructure’s service disruptions and failures become increasingly likely. Safe-to-fail approaches for infrastructure planning and design improve the capacity of cities to adapt for uncertain climate futures by identifying social, ecological, and technological systems (SETS) capabilities to prepare for potential failure scenarios. In this work, we argue for transforming infrastructure planning and design to effectively utilize safe-to-fail approaches by navigating the opportunities and trade-offs of SETS resilience capabilities. From a technological vantage point, traditional infrastructure planning approaches account for social and ecological domains as external design conditions rather than embedded system characteristics. Safe-to-fail approaches directly challenge the isolation of the technological domain by necessitating a recognition that SETS domains are interconnected and interdependent in infrastructure systems, as such risks and system capabilities for resilience must be managed cohesively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

An introduction to neuromorphic computing and its potential impact for unattended ground sensors

Neuromorphic computers are hardware systems that mimic the brain’s computational process phenomenology. This is in contrast to neural network accelerators, such as the Google TPU or the Intel Neural Compute Stick, which seek to accelerate the fundamental computation and data flows of neural network models used in the field of machine learning. Neuromorphic computers emulate the integrate and fire neuron dynamics of the brain to achieve a spiking communication architecture for computation. While neural networks are brain-inspired, they drastically oversimplify the brain’s computation model. Neuromorphic architectures are closer to the true computation model of the brain (albeit, still simplified). Neuromorphic computing models herald a 1000x power improvement over conventional CPU architectures. Sandia National Labs is a major contributor to the research community on neuromorphic systems by performing design analysis, evaluation, and algorithm development for neuromorphic computers. Space-based remote sensing development has been a focused target of funding for exploratory research into neuromorphic systems for their potential advantage in that program area; SNL has led some of these efforts. Recently, neuromorphic application evaluation has reached the NA-22 program area. This same exploratory research and algorithm development should penetrate the unattended ground sensor space for SNL’s mission partners and program areas. Neuromorphic computing paradigms offer a distinct advantage for the SWaP-constrained embedded systems of our diverse sponsor-driven program areas.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Photon detection system for ProtoDUNE dual phase

The Deep Underground Neutrino Experiment (DUNE) is a 40-kton underground liquid argon time-projection-chamber (LAr TPC) detector, for long-baseline neutrino oscillation studies, and for neutrino astrophysics and nucleon decay searches. Photon detector systems embedded within the LAr TPC add precise timing capabilities for non-beam events. The ProtoDUNE dual phase detector will consist of a 6×6×6 m3 liquid argon time-projection chamber placed at CERN and the light readout will be formed by 8-inch cryogenic photomultipliers from Hamamatsu. The characterization of the 36 photomultipliers, the base design, and the light calibration system are described. In addition, preliminary results from a 3×1×1 m3 LAr double phase detector operating at CERN are presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

U.S. electric power sector transitions required to achieve 80% reductions in economy-wide greenhouse gas emissions: Results based on a state-level model of the U.S. energy system

The United States has articulated a deep decarbonization strategy for achieving a reduction in economy-wide greenhouse gas (GHG) emissions of 80% below 2005 levels by 2050. Achieving such deep emissions reductions will entail a major transformation of the energy system and of the electric power sector in particular. , This study uses a detailed state-level model of the U.S. energy system embedded within a global integrated assessment model (GCAM-USA) to demonstrate pathways for the evolution of the U.S. electric power sector that achieve 80% economy-wide reductions in GHG emissions by 2050. The pathways presented in this report are based on feedback received during a workshop of experts organized by the U.S. Department of Energy’s Office of Energy Policy and Systems Analysis. Our analysis demonstrates that achieving deep decarbonization by 2050 will require substantial decarbonization of the electric power sector resulting in an increase in the deployment of zero-carbon and low-carbon technologies such as renewables and carbon capture utilization and storage. The present results also show that the degree to which the electric power sector will need to decarbonize and low-carbon technologies will need to deploy depends on the nature of technological advances in the energy sector, the ability of end-use sectors to electrify and level of electricity demand.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Enhanced Preparation for Intelligent Cybermanufacturing Systems (EPICS)

Opportunities exist for realizing transformative advances in productivity and reductions in energy footprint through ubiquitous sensing in manufacturing environments. Enhanced Preparation for Intelligent Cybermanufacturing Systems (EPICS) is a 21-month (4 academic semesters, plus one summer) experience for graduate students that focuses on scaling the knowledge, understanding and leadership skills in the cyber manufacturing area. Masters students (8/year, 32 total) complete 2-year projects on industrially-driven project topics, rotating to internships in summer semester to work on scoping and implementation at project partners. Students complete academic training in embedded systems, process modeling, data science, and cloud-based systems design. Their projects are targeted toward sensor retrofit, process monitoring, root cause analysis, and sensor fusion.

Advanced Manufacturing↗
DOE OSTI2023

Introducing the embedded random phase approximation: H 2 dissociative adsorption on Cu(111) as an exemplar

The random phase approximation (RPA) as a means of treating electron correlation recently has been shown to outperform standard density functional theory (DFT) approximations in a variety of cases. However, the computational cost of the RPA is substantially more than DFT, especially when aiming to study extended surfaces. Properly accounting for sufficient surface ensemble size, Brillouin zone sampling, and vacuum separation of periodic images in standard periodic-planewave-based DFT code raises the cost to achieve converged results. Here, we show that sub-system embedding schemes enable use of the RPA for modeling heterogeneous reactions at reduced computational cost. Further, we explore two different embedded RPA (emb-RPA) approaches, periodic emb-RPA and cluster emb-RPA. We use the (experimentally and theoretically) well-studied H 2 dissociative adsorption on Cu(111) as our exemplar, and first perform full periodic RPA calculations as a benchmark. The full RPA results match well the semi-empirical barrier fit to experimental observables and others derived from high-level computations, e.g., from recent embedded n-electron valence second order perturbation theory [Zhao et al., J. Chem. Theory Comput. 16(11), 7078–7088 (2020)] and quantum Monte Carlo [Doblhoff-Dier et al., J. Chem. Theory Comput. 13(7), 3208–3219 (2017)] simulations. Among the two emb-RPA approaches tested, the cluster emb-RPA accurately reproduces the energy profile (maximum error of 50 meV along the reaction pathway) while reducing the computational cost by approximately two orders of magnitude. We therefore expect that the embedded cluster approach will enable wider RPA implementation in heterogeneous catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Electric field controlled spin interference in a system with Rashba spin-orbit coupling

There have been intense research efforts over the last years focused on understanding the Rashba spin-orbit coupling effect from the perspective of possible spintronics applications. An important component of this line of research is aimed at control and manipulation of electron’s spin degrees of freedom in semiconductor quantum dot devices. A promising way to achieve this goal is to make use of the tunable Rashba effect that relies on the spin-orbit interaction in a two-dimensional electron system embedded in a host semiconducting material that lacks inversion-symmetry. This way, the Rashba spin-orbit coupling effect may potentially lead to fabrication of a new generation of spintronic devices where control of spin, thus magnetic properties, is achieved via an electric field and not a magnetic field. In this work we investigate theoretically the electron’s spin interference and accumulation process in a Rashba spin-orbit coupled system consisting of a pair of two-dimensional semiconductor quantum dots connected to each other via two conducting semi-circular channels. The strength of the confinement energy on the quantum dots is tuned by gate potentials that allow “leakage” of electrons from one dot to another. While going through the conducting channels, the electrons are spin-orbit coupled to a microscopically generated electric field applied perpendicular to the two-dimensional system. We show that interference of spin wave functions of electrons travelling through the two channels gives rise to interference/conductance patterns that lead to the observation of the geometric Berry’s phase. Achieving a predictable and measurable observation of Berry’s phase allows one to control the spin dynamics of the electrons. It is demonstrated that this system allows use of a microscopically generated electric field to control Berry’s phase, thus, enables one to tune the spin-dependent interference pattern and spintronic properties with no need for injection of spin-polarized electrons.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Results of Speciation Determination Measurements of Insulating Sulfur Hexafluoride Gas

Under the direction of the NNSA NA-231 Mo-99 Program, SRNL has provided technical assistance to U.S. companies under cooperative agreements. One such area of technical assistance has been for the recommendation for contamination management methods in the eventuality that tritium contaminates the insulating gas of a high-voltage ion source used in the accelerator-based process developed by SHINE Medical Technologies to produce Mo-99. The SHINE accelerator process uses tritium as the target of a deuterium ion beam, where the ion beam is produced within a high-voltage ion source insulated with sulfur hexafluoride (SF 6 ), and is separated from the tritium target by an extensive pump train. Normal operating conditions preclude the conditions necessary for contamination of the insulating gas, however certain atypical conditions could result in a tritium contamination event. Accelerator based processes for medical isotope production represent an anomalous tritium contamination challenge due to the presence of SF 6 . SF 6 decomposes through normal use as an insulator gas, and the exact speciation of the decomposition byproducts depends on a variety of factors. This creates a scenario where the anticipation of species present in the insulating gas mixture is difficult. In turn, the fate of tritium within such a chemical system is unclear. This document discusses the results of measurements made in-situ of the SF 6 insulating gas in use within a SHINE Medical Technology’s high-voltage ion source. This was done in an effort to determine the chemical speciation of the gas, allowing for the recommendation of management strategies in the event that tritium contaminates the insulating gas. The results of the measurements reported in this work suggest that tritium contamination in the insulating gas may result in two chemical forms of tritium in the system, tritiated water and molecular tritium. These two forms of tritium would be captured by two methods. Tritiated water may be captured and removed from the SF 6 by an adsorbent canister already present within the pressure vessel that houses the insulating gas while in use. It may also be separated from the SF 6 during routine purification of the SF 6 , which is performed by SHINE personnel routinely. The purification cart will perform all non-embedded systems cleanup necessary to remediate tritiated species in the system. The purification cart passes the insulating gas through several filters, followed by the liquefication of the SF 6 . The filters will separate tritiated water from the SF 6 at this stage. It is expected that molecular tritium will accumulate in the gas-phase during the liquification step, which is separated and stored. Tritium may be recovered from this mixture through more conventional hydrogen purification methods such as a palladium diffuser.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

CS651 Computer Systems Security Foundations 3d Imagination Cyber Security Management Plan

3d Imagination is a new company that bases its business on selling and improving 3d open source related hardware. The devices that they sell include 3d imagers, 3d printers, pick and place machines and laser etchers. They have a fast company intranet for ease in sharing, storing and printing large, complex 3d designs. They have an employee set that requires a variety of operating systems including Windows, Mac and a variety of Linux both for running business services as well as design and test machines. There are a wide variety of private networks for testing transfer rates to and from the 3d devices, without interference with other network tra c. They do video conferencing conferencing with customers and other designers. One of their machines is based on the project found at delta.firepick.org(Krassenstein, 2014; Biggs, 2014), which in future, will perform most of those functions. Their devices all include embedded systems, that may have full blown operating systems. Most of their systems are designed to have swappable parts, so when a new technology is born, it can be quickly adopted by people with 3d Imagination hardware. This company is producing a fair number of systems and components, however to get the funding they need to mass produce quality parts, so they are preparing for an IPO to raise the funds they need. They would like to have a cyber-security audit performed so they can give their investors con dence that they are protecting their data, customers information and printers in a proactive manner.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Machine Learning in Power System Operations: Training Data

Reliability and stability of the electric grid today has depended upon operations of the grid which include the protective relay. Today, the electricity sector faces new challenges with the shift of generation resource characteristics away from the traditional “big iron” generation to inverter-based resources (IBR) which shift the physics and assumption used in grid operation and protection. These changing conditions represent new challenges for protective relays (identification of faults) and increased challenges for protection engineers (correct settings and configuration, reduction of mis-operations), both issues recognized in research and industry. Finding new approaches to reduce mis-operations in relaying and new approaches to fault identification is critical to grid operations. Using today’s modern technology of embedded systems, edge computing, machine learning (ML), and communications we can help address challenges and augment and improve on existing power system operations methodologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

The formation and dynamical evolution of young star clusters

Recent observations have revealed a variety of young star clusters, including embedded systems, young massive clusters, and associations. We study the formation and dynamical evolution of these clusters using a combination of simulations and theoretical models. Our simulations start with a turbulent molecular cloud that collapses under its own gravity. The stars are assumed to form in the densest regions in the collapsing cloud after an initial free-fall time of the molecular cloud. The dynamical evolution of these stellar distributions is continued by means of direct N-body simulations. The molecular clouds typical of the Milky Way Galaxy tend to form embedded clusters that evolve to resemble open clusters. The associations were initially considerably more clumpy, but they lost their irregularity in about a dynamical timescale, due to the relaxation process. The densest molecular clouds, which are absent in the Milky Way but are typical in starburst galaxies, form massive, young star clusters. They indeed are rare in the Milky Way. Our models indicate a distinct evolutionary path from molecular clouds to open clusters and associations or to massive star clusters. The mass–radius relation for both types of evolutionary tracks excellently matches the observations. According to our calculations, the time evolution of the half-mass–radius relation for open clusters and associations follows r{sub h}/pc=2.7(t{sub age}/pc){sup 2/3}, whereas for massive star clusters r{sub h}/pc=0.34(t{sub age}/Myr){sup 2/3}. Both trends are consistent with the observed age–mass–radius relation for clusters in the Milky Way.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

DriveSense: A Noise-Resilient Framework for Driving Mode Identification

Accurate drive mode classification is essential for enhancing the reliability and predictive maintenance of heavy-duty electric trucks. This study proposes a novel fuzzy logic-based framework, DriveSense, for real-time drive mode classification, addressing key challenges such as sensor noise, transitional behaviors, and computational efficiency. The proposed approach integrates a two-stage filtering pipeline, combining adaptive outlier removal and a dynamic Kalman filter to enhance data quality. A fuzzy inference system with smoothened trapezoidal membership functions is then applied to classify driving modes into standstill, constant speed, acceleration, and deceleration while mitigating the effects of noise and edge cases. Performance evaluation using real-world and simulated drive cycles demonstrates significant improvements in classification accuracy (up to 97.8%), F1-score (up to 0.97), and robustness against noise, while reducing false positives. Comparative analysis against baseline models, demonstrates DriveSense’s superior accuracy and generalizability across diverse driving patterns. The framework’s lightweight and interpretable fuzzy inference engine operates with low computational latency, ensuring compatibility with real-time embedded systems typical of heavy-duty electric trucks. Moreover, DriveSense models transitional behaviors through overlapping fuzzy sets and adaptive borderline classification logic, enabling smooth identification of subtle shifts such as rolling stops or gradual deceleration. These results highlight DriveSense’s potential to enhance predictive maintenance strategies, reduce downtime, and support scalable, fleet-wide diagnostics.

Kumar, Praveen [Oak Ridge National Laboratory (ORN↗
DOE OSTI2024

Method for Coupled Electromagnetic and Circuit Simulations to Evaluate Surge Arrester Performance in Protecting Equipment Against E1 HEMP

Surge arrester behavioral modeling for realistic systems embedded in an E1 high-altitude electromagnetic pulse environment inherently encompasses three interconnected complications: (1) the need to account for signal propagation across two domains, electromagnetics and electrical; (2) the need to include both linear and nonlinear circuit components in the analysis; and (3) the need to understand that the over-current and over-voltage mitigation performance is dependent not only on the properties of the surge arrester and protected load but also on the topology of the overall electrical network. This study presents a framework to address these challenges in a systematic manner to consider the effectiveness of protective measures for a common class of equipment in power generation facilities. Full-wave simulations were carried out to derive circuit-domain (i.e., lumped element–based) equivalent models for the excitation waveform and the physical components of the system. Then, these equivalent models were imported to a circuit solver and combined with a high-frequency surge arrester model to evaluate mitigation performance. The methodology outlined is general enough such that it can be applied for other electromagnetic interference problems that involve E2/E3 HEMP or microwave emissions.

42 ENGINEERING↗
DOE OSTI2014

Fluorescence from a quantum dot and metallic nanosphere hybrid system

We present energy absorption and interference in a quantum dot-metallic nanosphere system embedded on a dielectric substrate. A control field is applied to induce dipole moments in the nanosphere and the quantum dot, and a probe field is applied to monitor absorption. Dipole moments in the quantum dot or the metal nanosphere are induced, both by the external fields and by each other's dipole fields. Thus, in addition to direct polarization, the metal nanosphere and the quantum dot will sense one another via the dipole-dipole interaction. The density matrix method was used to show that the absorption spectrum can be split from one peak to two peaks by the control field, and this can also be done by placing the metal sphere close to the quantum dot. When the two are extremely close together, a self-interaction in the quantum dot produces an asymmetry in the absorption peaks. In addition, the fluorescence efficiency can be quenched by the addition of a metal nanosphere. This hybrid system could be used to create ultra-fast switching and sensing nanodevices.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

lumicap v0.1

Automated HDR luminance imaging system designed for daylighting research and building science. It controls a fisheye-lens camera to capture time-lapse bracket sequences, merges them into calibrated HDR images, and runs a full post-processing pipeline — all unattended. Features: - Scheduled LDR bracket capture via gphoto2 - HDR merging with vignetting, ND filter, and fisheye projection corrections - Illuminance and luminance meter integration (Konica Minolta T-10A, LS-100/150) - Daylight glare probability (DGP) and solar position computation - Automated false-color rendering, JPEG thumbnails, and daily time-lapse video - CSV data logging per timestep Uses: - Long-term monitoring of daylight conditions in buildings - Glare analysis for occupant comfort research - Solar irradiance and sky luminance studies Advantages: - End-to-end automation — capture, calibration, analysis, and archiving run without manual intervention - Built on the proven Radiance toolchain, ensuring photometrically accurate HDR output - Hardware-agnostic meter support via serial auto-detection - Lightweight — no GUI overhead, deployable on a headless Raspberry Pi or similar embedded system

Wang, Taoning [Lawrence Berkeley National Laborato↗