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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Spin Berry points as crucial for ultrafast demagnetization

Laser-induced ultrafast demagnetization has puzzled researchers around the world for over two decades. Intrinsic complexity in electronic, magnetic and phononic subsystems is difficult to understand microscopically. So far, it is not possible to explain demagnetization using a single mechanism, which suggests a crucial piece of information still missing. In this paper, we return to a fundamental aspect of physics: spin and its change within each band in the entire Brillouin zone. We employ face-centered cubic (fcc) Ni as an example and use an extremely dense k mesh to map out spin changes for every band close to the Fermi level along all the high symmetry lines. To our surprise, spin angular momentum at some special k points abruptly changes from [Formula: see text] to [Formula: see text] simply by moving from one crystal momentum point to the next. This explains why intraband transitions, which the spin superdiffusion model is based upon, can induce a sharp spin moment reduction, and why electric current can change spin orientation in spintronics. These special k points, which are called spin Berry points [M. V. Berry, Proc. R. Soc. Lond. A 393 (1984) 45], are not random and appear when several bands are close to each other, so the Berry potential of spin majority states is different from that of spin minority states. Although within a single band, spin Berry points jump, when we group several neighboring bands together, they form distinctive smooth spin Berry lines. It is the band structure that disrupts those lines. Spin Berry points are crucial to laser-induced ultrafast demagnetization and spintronics.

Physics↗
DOE OSTI2026

LeWRON: Learning ElectroWeak phase tRansitiON with agentic architecture

An agent to analyze electroweak phase transition. LeWRON turns a BSM model description or a reproduction target into a structured run: symbolic setup, effective-potential artifacts, finite-temperature machinery, generated model code, a scientific report, and an interactive exploration session. It keeps both machine-readable artifacts and human-readable notes, so a run can be resumed, audited, revised, and shared.

Wang, Isaac [Fermi National Accelerator Laboratory↗
DOE OSTI2023

Property Measurements of NaCl-UCl 3 and NaCl-KCl-UCl 3 Molten Salts (Rev.1)

Thermochemical and thermophysical property values of several salt compositions of interest are needed by molten salt reactor (MSR) developers to design, license, and operate their reactors. Thermochemical and thermophysical properties being measured at Argonne include thermal transitions, phase behavior, heat capacity, density, volumetric thermal expansion of the liquid phase, thermal diffusivity, thermal conductivity, and viscosity. Several properties of eutectic compositions in the ternary NaCl-KCl-UCl 3 and binary NaCl-UCl 3 systems that may be used by MSR developers as fuel bearing salts are being measured. A 65.8 mol % NaCl–34.2 mol % UCl 3 mixture and a near-eutectic mixture of 50.9 mol % NaCl–24.4 mol % KCl–24.7 mol % UCl 3 were synthesized and the thermochemical properties of the mixtures were measured by using differential scanning calorimetry (DSC). The measured transition temperatures were compared to transition temperatures predicted using two models. A thermodynamic model of the binary NaCl-UCl 3 system was constructed using data in the Molten Salt Thermal Properties Database–Thermochemical Version 2.0 (MSTDB-TC V2.0). A ternary NaClKCl-UCl 3 system model constructed at Argonne and was described in a previous report. These comparisons can be used to validate the models. Thermophysical property values of molten salts are needed to model how salt retains and transfers heat in an MSR system. These property values are essential to the entire MSR design because molten salt is used as both the fuel and the coolant material in a salt fueled reactor. Heat capacity of the synthesized NaCl-UCl 3 and NaCl-KCl-UCl 3 salt mixtures was measured by using DSC and thermal diffusivity was measured by using laser flash analysis (LFA) at temperatures spanning the typical operating range of an MSR.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

2D Retrieval Frameworks for Hot Jupiter Phase Curves

Spectroscopic phase curves provide unique access to the three-dimensional properties of transiting exoplanet atmospheres. However, a modeling framework must be developed to deliver accurate inferences of atmospheric properties for these complex data sets. Here, we develop an approach to retrieve temperature structures and molecular abundances from phase curve spectra at any orbital phase. In the context of a representative hot Jupiter with a large day–night temperature contrast, we examine the biases in typical one-dimensional (1D) retrievals as a function of orbital phase/geometry, compared to two-dimensional (2D) models that appropriately capture the disk-integrated phase geometry. We guide our intuition by applying our new framework on a simulated Hubble Space Telescope (HST)+Spitzer phase curve data set in which the “truth” is known, followed by an application to the spectroscopic phase curve of the canonical hot Jupiter, WASP-43b. We also demonstrate the retrieval framework on simulated James Webb Space Telescope (JWST) phase curve observations. We apply our new geometric framework to a joint fit of all spectroscopic phases, assuming longitudinal molecular abundance homogeneity, resulting in an a factor of 2 improvement in abundances precision when compared to individual phase constraints. With a 1D retrieval model on simulated HST+Spitzer data, we find strongly biased molecular abundances for CH{sub 4} and CO{sub 2} at most orbital phases. With 2D, the day and night profiles retrieved from WASP-43b remain consistent throughout the orbit. JWST retrievals show that a 2D model is strongly favored at all orbital phases. Based on our new 2D retrieval implementation, we provide recommendations on when 1D models are appropriate and when more complex phase geometries involving multiple TP profiles are required to obtain an unbiased view of tidally locked planetary atmospheres.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The XRISM/Resolve View of the Fe K Region of Cyg X-3

The X-ray binary system Cygnus X-3 (4U 2030+40, V1521 Cyg) is luminous but enigmatic owing to the high intervening absorption. High-resolution X-ray spectroscopy uniquely probes the dynamics of the photoionized gas in the system. In this Letter, we report on an observation of Cyg X-3 with the XRISM/Resolve spectrometer, which provides unprecedented spectral resolution and sensitivity in the 2–10 keV band. We detect multiple kinematic and ionization components in absorption and emission whose superposition leads to complex line profiles, including strong P Cygni profiles on resonance lines. The prominent Fe xxv Heα and Fe xxvi Lyα emission complexes are clearly resolved into their characteristic fine-structure transitions. Self-consistent photoionization modeling allows us to disentangle the absorption and emission components and measure the Doppler velocity of these components as a function of binary orbital phase. We find a significantly higher velocity amplitude for the emission lines than for the absorption lines. The absorption lines generally appear blueshifted by ∼−500–600 km s −1 . We show that the wind decomposes naturally into a relatively smooth and large-scale component, perhaps associated with the background wind itself, plus a turbulent, denser structure located close to the compact object in its orbit.

Audard, Marc (ORCID:000000034721034X)↗
DOE OSTI2026

Embedded Ferroelectric Nanoclusters can drive Polarization Reversal in a Non-Ferroelectric Polar Film via the Proximity Effect

Heterogeneous nucleation from defects dominates the electric field required for polarization switching of ferroelectrics. Here, we consider the switching of a nominally non-switchable polar thin film of AlN due to the proximity effect arising from embedded ferroelectric nanoclusters of Al 1-x Sc x N. Using a Landau-Ginzburg-Devonshire thermodynamic approach and finite element modeling, we study the influence of nanocluster shape on polarization switching and domain nucleation emerging in AlN. The ferroelectric nanocluster boundary is modeled as a thin layer transitioning from Al 1-x Sc x N to AlN. We analyze the conditions under which polarization switching in the AlN film occurs at coercive fields significantly lower than its dielectric breakdown field. In the presence of spike-like Al 1-x Sc x N nanoclusters, the proximity effect enables switching of the spontaneous polarization in AlN and significantly reduces the corresponding coercive field. The internal field, which is depolarizing inside the AlN (due to its larger spontaneous polarization) and polarizing within the ferroelectric Al 1-x Sc x N nanoclusters (due to its smaller spontaneous polarization), lowers the potential barrier in the clusters and nucleates nanodomains at the Al 1-x Sc x N-AlN interface, forming localized regions of reversed polarization. Proximity effect can thus provide a pathway towards "thawing" previously "frozen" ferroelectrics through engineered nucleation for memory, actuation and optical technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A parcel-level evaluation of distributed wind opportunity in the contiguous United States

This study examines the potential for distributed wind (DW) energy across the contiguous United States, leveraging advancements in the National Renewable Energy Laboratory's distributed wind model, dWind. The novel modeling approach described here utilizes a high-resolution dataset and analyzes over 150 million parcels, a significant improvement from prior methods that extrapolated results from a smaller random sample. This achievement is enabled through key model performance improvements, such as transitioning to multiprocessing, which reduces runtime by 97 %. This optimized, high-resolution approach allows the inspection of technology deployment potential and impact on a variety of scales tailored to individual properties and regions. The results here align with prior work showing substantial opportunity for energy generation using DW technologies. Key findings reveal a substantial increase from prior results in estimated technical and economic potential for DW. Metrics tuned to highlight economic potential also show increased incentives supporting rural adoption. Results are spatially aggregated for usability and published via the U.S. Department of Energy Wind Data Portal and a custom scenario visualization platform, aiding policymakers, industry, and property owners in assessing DW viability across various scenarios and spatial scales.

17 WIND ENERGY↗
DOE OSTI2022

Embedding a Critical Point in a Hadron to Quark–Gluon Crossover Equation of State

It is shown how to embed a critical point in a smooth background equation of state so as to yield the critical exponents and critical amplitude ratios expected of a transition in the same universality class as the liquid–gas phase transition and the 3D Ising model. There are only two independent critical exponents; the relations α + 2β + $\gamma$ = 2 and β(δ - 1) = $\gamma$ arise automatically, as does a new relation between the two critical amplitudes. The resulting equation of state has parameters which may be inferred by hydrodynamic modeling of heavy-ion collisions in the Beam Energy Scan II at the Relativistic Heavy Ion Collider.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Supporting Bioproducts Industry Growth with a System Dynamics Decision-Support Tool

A bio-based economy requires chemical products as well as fuels to be produced from biomass. Although a variety of universities, government agencies, start-ups and established firms have engaged in bioproduct development, many projects have failed to reach the point of commercialization and commercialized bioproducts struggle to capture and maintain market share. To date, there has been no general research into the factors that contribute to bioproduct failure or success. This work presents the Bioproduct Transition Dynamics (BTD) system dynamics model, a decision-support tool that simulates the bioproduct development process from pre-piloting research through construction of the first commercial-scale plant, as well as the processes of obtaining funding from investors and government agencies. The core of the BTD is a feedback loop between bioproduct developers and funders, which relates development progress measured with indicators such as net present value to funders’ decisions to continue investing. External factors such as feedstock prices, market size and growth, and the existence of bioproduct consumers also influence a development project’s chances of receiving follow-on funding. Virtually any bioproduct can be represented with the BTD: direct replacements, performance-advantaged products, niche and commodity markets can all be modeled. The goal of the BTD project is to inform decisions made by developers in both established firms and start-ups, investors, government agencies, and other stakeholders interested in growing the nascent U.S. bioproducts industry. This talk will cover the general structure and functionality of the BTD, and present results from an analysis performed with the BTD to demonstrate its use as a decision-support tool and the insights it can provide. The goal of the analysis is to identify the most critical factors that lead to direct replacement and performance-advantaged bioproduct projects emerging successfully from the “Valley of Death”, and to determine if these factors differ between the two bioproduct types.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Monitoring Operational States of a Nuclear Reactor Using Seismoacoustic Signatures and Machine Learning

Monitoring nuclear reactors is an important safety and security task with growing requirements. We explore the possibility of using seismic and acoustic data for inferring the power level of an operating reactor. Continuous data recorded at a single seismoacoustic station that is located about 50 m away from a research reactor was visualized and analyzed. The data show a clear correlation between seismoacoustic features and reactor main operational states. We designed a workflow that includes two machine learning (ML) models to classify the reactor operational states (OFF, transition, and ON) and estimate reactor power levels (10%, 30%, 50%, 70%, and 90%). We applied and compared five ML algorithms for the reactor OFF-transition-ON and four approaches for the power level classification. We also compared the performance of ML models trained with seismic-only, acoustic-only, and both types of data. Five-fold cross validations were implemented to assure a thorough evaluation of the model performances. Additionally, the results show the extreme boosting gradient algorithm worked best for the first model, whereas random forests performed best for the second model. Combining seismic and acoustic data leads to better performance than using a single type of data. Seismic data contributed more than acoustic data for both models. We reached an accuracy of 0.98 for reactor OFF and ON. The accuracies for the transition state and power levels are less optimal with a minimum accuracy of 0.66. However, our results suggest seismic and acoustic data contain useful information about the transition state as well as power levels. Seismic and acoustic data could be integrated with other observations to improve monitoring performance.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Integrated Risk-Informed Condition Based Maintenance Capability and Automated Platform: Technical Report 1

Due to continuing global energy market trends, driven heavily by the abundant preserves of natural gas, there is an immediate need to reduce costs associated with operation and maintenance (O&M) for the current domestic nuclear power industry and for future reactor developments. This is to ensure that nuclear power generation remains an economically competitive and viable option in the energy market. O&M costs include labor-intensive preventive maintenance (PM) programs, which involve manually-performed inspection, calibration, testing, and maintenance of plant assets at periodic frequency and time-based replacement of assets, irrespective of their condition. This has resulted in an expensive, labor-centric business model to achieve high capacity factors. Fortunately, there are technologies (advanced sensors, data analytics, and risk assessment methodologies) that can enable the transition from a labor-centric business model to a technology-centric business model. The technology-centric business model will result in a significant reduction of PM activities, laying the foundation for real-time condition assessment of plant assets, reducing overall labor and part costs. To enable this transition, PKMJ Technical Services LLC is partnering with the U.S. Department of Energy’s Idaho National Laboratory (operated by the Battelle Energy Alliance, LLC) and the Public Services Enterprise Group (PSEG) Nuclear, LLC in the Integrated Risk-Informed Condition-Based Maintenance Capability and Automated Platform Project. In this report, the configuration of a digital cloud platform using Microsoft Azure is discussed, data from the PSEG Salem Nuclear Generating Station Units 1 & 2 are imported into a digital cloud platform, and the data is used for an evaluation of several key areas: cost impact analysis, risk-informed model development, and preventive maintenance strategy optimization. First, the cost impact analysis reviews which plant assets are potential good candidates for condition-based monitoring. Next, INL utilized the data in their local environment to develop the risk-informed model; which provides estimates of failure rates and probability of failures of assets based upon their past performance. The developed model is performed on assets selected from the cost impact analysis. Lastly, engineers assess the preventive maintenance strategy for the selected assets at PSEG against maintenance strategies in the nuclear industry for similar assets to potentially identify acceptable justification for the extension of current maintenance frequencies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Detecting depinning and nonequilibrium transitions with unsupervised machine learning

Using numerical simulations of a model disk system, we demonstrate that a machine learning generated order-parameter-like measure can detect depinning transitions and different dynamic flow phases in systems driven far from equilibrium. Furthermore, we specifically consider monodisperse passive disks with short range interactions undergoing a depinning phase transition when driven over quenched disorder. The machine learning derived order-parameter-like measure identifies the depinning transition as well as different dynamical regimes, such as the transition from a flowing liquid to a phase separated liquid-solid state that is not readily distinguished with traditional measures such as velocity-force curves or Voronoi tessellation. The order-parameter-like measure also shows markedly distinct behavior in the limit of high density where jamming effects occur. Our results should be general to the broad class of particle-based systems that exhibit depinning transitions and nonequilibrium phase transitions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Impact of fragment formation on shear viscosity in the nuclear liquid-gas phase transition region

Within the improved quantum molecular dynamics (ImQMD) model we follow the evolution of nuclear matter for planar Couette flow in a periodic box. We focus on the region of liquid-gas phase transition and extract the shear viscosity coefficient from the local stress tensor, directly following the viscosity definition. Here, by switching on and off the mean field and thus inducing the phase transition, we are able to observe the impact of clumping in the phase transition region on the viscosity.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Thermodynamically informed priors for uncertainty propagation in first-principles statistical mechanics

Here, this work demonstrates how first-principles statistical mechanics approaches within a Bayesian framework can quantify and propagate uncertainties to downstream thermodynamic calculations. To address the issue of Bayesian prior selection, knowledge of 0 K ground states in the material system of interest is incorporated into the prior. The effectiveness of this framework is shown by creating a phase diagram for the fcc zirconium nitride system, including confidence intervals on order-disorder transition temperatures.

Bayesian methods↗
DOE OSTI2021

Soot Predictions with a Laminar Flamelet Combustion Model in SIERRA/Fuego on a Coflow Scenario

This report describes an assessment of flamelet based soot models in a laminar ethylene coflow flame with a good selection of measurements suitable for model validation. Overall flow field and temperature predictions were in good agreement with available measurements. Soot profiles were in good agreement within the flame except for near the centerline where imperfections with the acetylene-based soot-production model are expected to be greatest. The model was challenged to predict the transition between non-sooting and sooting conditions with non-negligible soot emissions predicted even down to small flow rates or flame sizes. This suggests some possible deficiency in the soot oxidation models that might alter the amount of smoke emissions from flames, though this study cannot quantify the magnitude of the effect for large fires.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Gapless to gapless phase transitions in quantum spin chains

We investigate spin chains with bilinear-biquadratic (BLBQ) spin interactions as a function of an applied magnetic field h. At the Uimin-Lai-Sutherland (ULS) critical point we find a gapless to gapless transition revealed by the dynamical structure factor S(q,ω) as a function of h. At h=0, the envelope of the lowest-energy excitations goes soft at two points, q 1 =2π/3 and q 2 =4π/3, dubbed the phase A. With increasing field, the spectral peaks at each of the gapless points bifurcate, making in total four soft modes, and combine to form a new set of excitations that soften at a single point q=π at h c1 ≈0.94. Beyond h c1 the system enters another gapless B phase until the transition at h c2 =4 to the fully polarized phase. We compare the ULS model results with those for the Affleck-Kennedy-Lieb-Tasaki model as a representative of the gapped Haldane phase. We explain the mechanism of the gapless to gapless transition in the ULS model using its conserved charges and a spinon band picture. We also discuss the universality of central charges of the BLBQ family of models subjected to a magnetic field.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗