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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Potential impacts of Fukushima nuclear wastewater discharge on nutrient supply and greenhouse gas emissions of food systems

Seafood plays a critical role in promoting a nutritious and climate-friendly food system but faces challenges from marine contamination that pose food safety risks. Here, to address these challenges, we developed a Marine Pollution-induced Food System Risk Assessment and Response (MP-FSRAR) model to assess the impact of consumer perceptions of marine pollution risk events on nutrient supply and greenhouse gas (GHG) emissions. Applying this model to the proposed Fukushima Nuclear Wastewater Discharge (FNWD) into the Pacific Ocean, we identified the potential variation in nutrient supply and GHG emissions resulting from changes in Chinese consumers' willingness to consume seafood under 12 scenarios. Our analysis revealed nutrient supply gaps in daily protein, minerals, vitamins, and fatty acids, ranging from 1.5 to 2.7 g/person, 2.5–4.5 mg/person, 5.6–10.3 mcg/person, and 0.08–0.14 g/person, respectively. Closing the protein gap in China would require an additional 3.2–15.8 billion kg yr -1 protein-rich foods, including aquatic, livestock, dairy, and pulses. Additional food supply would increase extra GHG emissions by 1–79 billion kg CO 2 e yr -1 , resulting in potential GHG emissions being transmitted to other countries through the trade chain. Furthermore, we found that the livestock-based food substitution pathway had 40 times higher GHG emissions than the plant-based one. Our MP-FSRAR model provides global stakeholders with new perspectives on the potential consequences of consumer perceptions of marine pollution risks and offers valuable insights for developing countermeasures to build a more resilient and low-carbon food system.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

The water use of data center workloads: A review and assessment of key determinants

The global importance of data center water use is increasing with the rapid growth of digitalization and artificial intelligence. This study analyzes the factors influencing workload-level water use, measured in liters consumed per workload, to guide water-saving strategies in data centers. Our findings reveal workload-level water use variations exceeding 10,000-fold, driven by over 1000-fold differences in water consumption per kilowatt hour of server electricity consumed and approximately 10-fold differences in server workload efficiency. Key determinants are ranked as server efficiency, electrical grid water consumption factors, server utilization, cooling system type, infrastructure efficiency, climate zone, inactive server percentage, and server refresh cycle. Notably, there is no single recipe for minimizing water use; instead, optimal outcomes depend on tailored combinations of these factors. This analysis addresses critical knowledge gaps by identifying the determinants of data center water use and exploring their achievable minima under diverse site-specific constraints.

Data centers↗
DOE OSTI2026

Crystal Growth of Quaternary RE 2 EuSi 2 S 8 ( RE = Ce–Nd, Sm, Gd, Tb) Using Flux-Assisted Boron Chalcogen Mixture (BCM) Method: Investigation of Magnetic and Luminescence Properties

A series of quaternary rare-earth containing thiosilicates with the general formula RE 2 EuSi 2 S 8 (RE = Ce–Nd, Sm, Gd, Tb) has been synthesized via the flux-assisted boron chalcogen mixture (BCM) crystal growth method. High-quality single crystals were obtained, and their crystal structures were determined by single-crystal X-ray diffraction. The RE 2 EuSi 2 S 8 series crystallizes in the trigonal system, adopting the space group R-3c. Polycrystalline samples were employed for physical property measurements, including magnetic susceptibility measurements, UV–visible diffuse reflectance, and photoluminescent response. Magnetic data of RE 2 EuSi 2 S 8 ( RE = Ce, Nd, and Gd) were collected over the 2–300 K temperature range. The samples were paramagnetic behavior with negative Weiss constants (θ W = −11.05, −10.55, and −1.35 K respectively). Their thermal stability was investigated using thermogravimetric analysis (TGA). Optical band gaps, estimated from diffuse reflectance spectra, were determined to be 2.2(1) eV for Ce 2 EuSi 2 S 8 , 1.8(1) eV for Nd 2 EuSi 2 S 8 , and 1.7(1) eV for Gd 2 EuSi 2 S 8 respectively. Finally, photoluminescence measurements were collected on Ce 2 EuSi 2 S 8 and Tb 2 EuSi 2 S 8 single crystals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Flux-Assisted Boron Chalcogen Mixture (BCM) Method for Synthesizing Mixed Chalcogenide Semiconductors (AkRE 2 Si 2 Se x S 8– x and CaRE 2 Si 2 Se 8 ) ( Ak = Ca and Sr; RE = La, Ce, Pr, Nd, and Sm): Investigation of Their Magnetic and Optical Properties

We report a detailed structural analysis of a series of ten quaternary rare-earth-containing seleno-thiosilicates AkRE 2 Si 2 Se x S 8-x and selenosilicates, CaRE 2 Si 2 Se 8 (Ak = Ca and Sr; RE = La, Ce, Pr, Nd, and Sm). Single crystals were obtained by using the flux-assisted boron chalcogen mixture (BCM) method and single crystal X-ray diffraction was used to determine their structures. All members of the AkRE 2 Si 2 Se x S 8-x and CaRE 2 Si 2 Se 8 series crystallize in the space group R$\bar{3}$c (space group number 167) of the trigonal crystal system. The single-crystal X-ray diffraction analysis revealed a strong preference for Se/S atoms to occupy one vs. the other of the two available sites. Polycrystalline samples were used for magnetic susceptibility and UV–visible diffuse reflectance measurements. Magnetic measurements show that CaCe₂Si₂Se₁.₇₃S₆.₂₇ and CaNd₂Si₂Se₂.₅S₅.₅ are paramagnetic with negative Weiss constants (θ = –60.1 and –26.2). Diffuse reflectance analysis gives optical band gaps of 2.7(1) eV (CaLa₂Si₂Se₂.₃₈S₅.₆₂), 2.2(1) eV (CaCe₂Si₂Se₁.₇₃S₆.₂₇), 2.5(1) eV (CaNd₂Si₂Se₂.₅S₅.₅), and 2.0(1) eV (CaCe₂Si₂Se₈), consistent with density functional theory calculations. By partially or fully replacing S sites with Se it was possible to achieve band gap tuning. Photoluminescence behavior was also investigated for CaCe 2 Si 2 Se 1.73 S 6.27 via irradiation with 375 nm ultraviolet light.

BCM method↗
DOE OSTI2026

OmicsMLMentor: A Web Application for Guided Machine Learning Analysis of Omics Data

Expression-based omics technologies (e.g. proteomics, metabolomics, transcriptomics, etc.) increasingly rely on supervised and unsupervised machine learning (ML) models to find key biomolecules distinguishing conditions, identify natural groupings in biological data, or generate predictions for outcomes of interest. Fitting ML models to omics data presents several challenges, including handling missing data, selecting a normalization method, choosing a valid model, and optimizing hyperparameters, all requiring statistical programming skills to address these challenges. Thus, the open-source web application SLOPE was designed to lower the barrier to ML modeling for omics data. SLOPE supports the fitting of 15 ML models (10 supervised and 5 unsupervised) tailored to omics datasets, such as proteomics, metabolomics, lipidomics, and transcriptomics. SLOPE offers several omics-specific features, including methods for handling missingness (imputation, conversion, removal), normalization tests, ranking of models based on the structure of a user’s data and user input, and optimal hyperparameter selections using cross-validation splits. By streamlining ML workflows for omics analysis, SLOPE address critical gaps in existing online web tools, facilitating a broader adoption of these models for omics research. Here, SLOPE is applied to data from a lignin exposure study to highlight the workflow for fitting both supervised and unsupervised models to data.

lipidomics↗
DOE OSTI2026

Coexisting kagome and heavy fermion flat bands in YbCr 6 Ge 6

Flat bands, electronic states with nearly dispersionless energy-momentum structure, provide fertile ground for unconventional quantum phases. Recent observations of flat bands at the Fermi level in kagome metals open the possibility of unifying topology and correlation-driven heavy-fermion physics. Here we show that topology and heavy-fermion correlations coexist in the layered kagome metal YbCr 6 Ge 6 . At high temperatures, an intrinsic kagome flat band—arising from frustrated hopping on the kagome lattice—dominates the Fermi level. Upon cooling, localized Yb 4f-states hybridize with the topological kagome flat bands, transforming this state into momentum-independent Kondo resonance states across the entire Brillouin zone. Topological analysis of the hybridization gaps reveals filling-tunable weak and strong topological Kondo-insulating regimes, and identifies a topological Dirac–Kondo semimetal. Taken together, these results identify YbCr 6 Ge 6 as a prototype of a topological heavy-fermion system and a platform where geometric frustration, strong correlations, and topology converge, with broad implications for correlated quantum matter.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Linear magnetic susceptibility of anisotropic superconductors of cuboidal shape

In this study, a simplified model of anisotropic magnetic susceptibility in the Meissner–London vortex-free state of cuboidal superconducting samples is presented. Using this model, precision measurements of the magnetic response in three perpendicular directions of a magnetic field with respect to primary crystal axes can be used to extract the components of the London penetration depth, thus enabling the evaluation of the general superfluid density tensor, which is needed in the analysis of the superconducting gap structure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

An Efficient 1-D Thermal Stratification Model for Pool-Type Sodium-Cooled Fast Reactors

Investigating thermal stratification in the upper plenum of a sodium fast reactor (SFR) is presently a technology gap in SFR safety analysis. Understanding thermal stratification will promote safe operation of the SFR before its commercial deployment. Stratified layers of liquid sodium with a large vertical temperature gradient could be established in the upper plenum of an SFR during a down-power or a loss-of-flow transient. These stratified layers are unstable and could result in uncertainties for the core safety of an SFR. In order to predict the occurrence of the thermal stratification efficiently, we developed a one-dimensional (1-D) transport model to estimate the temperature profile of the ambient fluid in the upper plenum. This model demands much less computational effort than computational fluid dynamics (CFD) codes and provides calculations with higher fidelity than historical system-level codes. Two flow conditions were considered separately in the current study depending on if in-vessel components are presented in the upper plenum. For the condition where in-vessel components, specifically the upper internal structure, are presented, we assumed that the impinging sodium was evenly dispersed in the ambient fluid within the distance between the bottom of the in-vessel component and the jet inlet surface. For the condition where no in-vessel components are presented, we assumed that the impinging sodium was evenly dispersed in the ambient fluid within the jet length, which was determined through data-driven trainings. The newly developed 1-D model showed similar performance with the CFD model in both cases. However, due to the assumption of flat profiles of the impinging jet axial dispersion rate, nonnegligible discrepancies between the 1-D prediction and the measured data were observed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Marine energy converters: Potential acoustic effects on fishes and aquatic invertebrates

The potential effects of underwater anthropogenic sound and substrate vibration from offshore renewable energy development on the behavior, fitness, and health of aquatic animals is a continuing concern with increased deployments and installation of these devices. Initial focus of related studies concerned offshore wind. However, over the past decade, marine energy devices, such as a tidal turbines and wave energy converters, have begun to emerge as additional, scalable renewable energy sources. Because marine energy converters (MECs) are not as well-known as other anthropogenic sources of potential disturbance, their general function and what is known about the sounds and substrate vibrations that they produce are introduced. Furthermore, while most previous studies focused on MECs and marine mammals, this paper considers the potential of MECs to cause acoustic disturbances affecting nearshore and tidal fishes and invertebrates. In particular, the focus is on particle motion and substrate vibration from MECs because these effects are the most likely to be detected by these animals. Finally, an analysis of major data gaps in understanding the acoustics of MECs and their potential impacts on fishes and aquatic invertebrates and recommendations for research needed over the next several years to improve understanding of these potential impacts are provided.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Digital holography for real-time non-invasive monitoring of larval fish at power plant intakes

Effective evaluation of technological and operational approaches to reduce entrainment of marine organisms at cooling water intake structures (CWIS) requires accurate organism-sensing systems. Current detection methods lead to large temporal data gaps, require tedious manual analysis, and are fatal to organisms. In this report we describe integrating deep learning with a non-lethal, non-intrusive imaging method—digital holography—to rapidly detect fish larvae. Laboratory experiments demonstrated that the instrument could successfully image fish larvae at flow rates exceeding ranges seen in CWIS. Holograms of two fish larvae species, in the presence of bubbles and detritus, were collected to build a large database for training a lightweight convolutional neural network. The model achieves 97% extraction accuracy in quantifying larvae, and distinguishing them from other particles, including detritus and bubbles, when applied to a dataset of manually classified images, exceeding previous metrics for non-lethal, accurate, and real-time detection. These results demonstrate the potential of in situ holographic imaging for monitoring endangered larval fish species at power plant intake structures, and for high-fidelity, real-time applications in monitoring aquatic ichthyoplankton.

endangered species↗
DOE OSTI2023

Commitment to Active Allyship Is Required to Address the Lack of Hispanic and Latinx Representation in the Earth and Atmospheric Sciences

In 2021, people of Hispanic and Latinx origin made up 6% of the atmospheric and Earth sciences workforce of the United States, yet they represent 20% of the population. Motivated by this disparity in Hispanic and Latinx representation in the atmospheric and Earth science workforce, this manuscript documents the lack of representation through existing limited demographic data. The analysis presents a clear gap in participation by Hispanic and Latinx people in academic settings, with a widening gap through each education and career stage. Several factors and challenges impacting the representation disparity include the lack of funding for and collaboration with Hispanic-serving institutions, limited opportunities due to immigration status, and limited support for international research collaborations. We highlight the need for actionable steps to address the lack of representation and provide targeted recommendations to federal funding agencies, educational institutions, faculty, and potential employers. While we wait for systemic cultural change from our scientific institutions, grassroots initiatives like those proudly led by the AMS Committee for Hispanic and Latinx Advancement will emerge to address the needs of the Hispanic and Latinx scientific and broader community. We briefly highlight some of those achievements. Lasting cultural change can only happen if our leaders are active allies in the creation of a more diverse, equitable, and inclusive future. Alongside our active allies we will continue to champion for change in our weather, water, and climate enterprise.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Data for The Role of Social Support on Midwestern Farmers’ Willingness to Grow Perennial Bioenergy Crops

The lack of farmers’ willingness to grow perennial bioenergy crops (PBCs) presents a critical barrier to the emergence of cellulosic biofuel production. The willingness relies on a complex network of economic, environmental, and social drivers, among which the influence of social factors (e.g., the influence of neighborhood, community, and communication) is less understood. This study addresses this knowledge gap via a survey analysis of midwestern farmers. The survey data are analyzed through ordinary least square regression and structural equation model, which together investigate the individual and interactive impacts of multiple factors on farmers’ decisions to adopt PBCs. Based on a farm-scale analysis, six statistically significant predictors of farmer willingness to grow PBCs are identified: perception of PBCs’ environment benefits, education level, willingness to take risks, familiarity with PBCs, portion of peers already growing PBCs, and support of biorefineries locating in the local community. Among these, the latter three predictors are social support variables. It is found that familiarity with the crops is the most significant predictor of willingness; familiarity is also an important intermediate variable that mediates the influence of many other predictors. In addition, peer adoption can both directly and indirectly affect willingness via its influence on familiarity. These findings suggest that it is a pressing need to improve farmers’ knowledge of PBCs to promote the adoption of such crops.

Economics↗
DOE OSTI2020

Key Nuclear Data Impacting Reactivity in Advanced Reactors

Advanced reactor concepts currently being developed throughout the industry are significantly different from light water reactor (LWR) designs with respect to geometry, materials, and operating conditions, and consequently, with respect to their reactor physics behavior. Given the limited operating experience with non-LWRs, the accurate simulation of reactor physics and the quantification of associated uncertainties are critical for ensuring that advanced reactor concepts operate within the appropriate safety margins. Nuclear data are a major source of input uncertainties in reactor physics analysis. As part of an ongoing project at Oak Ridge National Laboratory (ORNL), the effects of nuclear data uncertainties on key figures of merit associated with advanced reactor safety are being assessed for selected advanced reactor technologies. Key nuclear data relevant for reactor safety analysis for each selected advanced reactor technology were identified, and their impact on important key figures of merit was assessed. Available advanced reactor specifications were reviewed, results from studies performed at ORNL and other research institutions were consulted, and available evaluated nuclear data libraries were analyzed. This report summarizes the key nuclear data for nuclides in the fuel, as well as other significant data, including scattering and neutron capture in various materials for the moderator, coolant, and structure of the considered advanced reactors. For the considered advanced reactors that use low-enriched uranium (LEU) fuel, results from LWR studies provided insight into relevant nuclear data given the lack of available studies specifically addressing these new systems. The major nominal missing data that were identified consist of thermal scattering data and 135m Xe cross section data for molten salt reactor (MSR) analysis. The identified major gaps with respect to nuclear data uncertainties are missing uncertainties of thermal scattering data for high temperature gas-cooled reactors and moderated MSR systems, and incomplete uncertainties on angular distributions in particular for fast spectrum systems, such as sodiumcooled fast reactors, fast molten salt reactors, and heat pipe reactors. Furthermore, it was found that special attention should be paid to cross section and uncertainty differences between different evaluated nuclear data library releases, because significant differences in nuclear data that can lead to major differences in reactivity calculations were found, even for well-known nuclides.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Advancing the Predictability of Water Cycle Phenomena via the Application of AI to Model Ensemble Simulations and Observations

AI has the power to identify new pathways to extended predictability of the water cycle via its application to model ensembles together with accompanying observations. We discuss both scientific and technological aspects of this challenge, and address the parts of the MODEX approach involving “Model simulations, evaluation, analysis, and benchmarking” and “Identification of key knowledge gaps”. The widespread incorporation of AI into model analysis will significantly advance Earth system predictability and our predictive capabilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Energy Storage for Manufacturing and Industrial Decarbonization (Energy StorM)

This report summarizes the needs, challenges, and opportunities associated with carbon-free energy and energy storage for manufacturing and industrial decarbonization. Energy needs and challenges for different manufacturing and industrial sectors (e.g., cement/steel production, chemicals, materials synthesis) are identified. Key issues for industry include the need for large, continuous on-site capacity (tens to hundreds of megawatts), compatibility with existing infrastructure, cost, and safety. Energy storage technologies that can potentially address these needs, which include electrochemical, thermal, and chemical energy storage, are presented along with key challenges, gaps, and integration issues. Analysis tools to value energy storage technologies in the context of manufacturing and industrial decarbonizations are also presented. Material is drawn from the Energy Storage for Manufacturing and Industrial Decarbonization (Energy StorM) Workshop, held February 8 - 9, 2022. The objective was to identify research opportunities and needs for the U.S. Department of Energy as part of its Energy Storage Grand Challenge program.

25 ENERGY STORAGE↗
DOE OSTI2025

MURR LEU structural and thermal hydraulics analyses: Part I – Preliminary irradiation thermo-mechanical behavior

The University of Missouri Research Reactor (MURR) is expected to be converted from highly enriched uranium (HEU, ≥ 20 wt% U-235) U-Al x dispersion fuel to low-enriched uranium (LEU, < 20 wt% U-235) with U-10Mo monolithic fuel. This work introduces high-fidelity irradiation thermo–mechanical (T-M) analysis of the MURR LEU focusing on changes in coolant channel gap thickness. Three-dimensional (3D) finite element (FE) models were developed to simulate the irradiation T-M behavior of the MURR LEU element with all 23 curved fuel plates, the two side plates, and the combs. It was shown that channel gap thickness changes were influenced not only by plate thickness variations due to fuel swelling and creep but also by the radial displacement of consecutive MURR LEU plates. Modeling the fuel element assembly captured side plate displacements, which were shown to reduce radial fuel plate displacements towards the convex side. The maximum local radial displacement in the element was predicted at the end of life (EOL) as 23.7 mil (602.0 µm) on the lateral centerline of plate 23 towards the convex side. The maximum stripe-averaged reduction in channel gap thickness, particularly relevant for thermal hydraulics (TH) safety analysis, was calculated as 15.9 mil (403.9 µm) in single-side heated channel 24 (the outermost channel). These results account for the thermal resistance from the oxide build-up on cladding surfaces which was shown to be up to 0.82 mil (20.8 µm) thick. It was demonstrated that accounting for oxide layer thermal resistance led to a 10 °C higher peak fuel temperature and a 4.4 mil (111.8 µm) greater maximum local radial displacement. The impact of the calculated channel gap thickness changes on the MURR LEU TH safety analysis is evaluated in Part II.

Irradiation thermo-mechanical analysis↗
DOE OSTI2022

Quantifying policy gaps for achieving the net-zero GHG emissions target in the U.S. light-duty vehicle market through electrification

The U.S. light-duty vehicle (LDV) industry, a major greenhouse gas (GHG) emitting sector, is embracing decarbonization. Considering only electrification pathways, this study uses publicly-available tools – MA3T and VISION on vehicle market penetration, fleet accounting and life-cycle analysis to quantify the policy gaps for LDVs to achieve the net-zero GHG emissions target in nine vehicle penetration cases under two electricity mix scenarios, including the U.S. administration's decarbonization strategy – 100% clean electricity by 2035. The MA3T model is a multinomial discrete choice model for market share projection by vehicle technology, and the VISION is a vehicle stocks and GHG emissions projection model by using vehicle and travel characteristics. This study projects the impacts of technology and policy enforcement on shaping the dynamics and decarbonization of the LDV market. Additionally, achieving the expected improvement of battery technology and charging infrastructure is critical but can only reduce the 2050 GHG emissions to 48–54% of the 2020 level under the electricity renewable mix scenario. It is almost impossible to achieve a 100% battery electric vehicle stock by 2050 and the 2050 net-zero target in the LDV industry unless ban of internal combustion engine technology is implemented starting in 2035 and under the 2035 100% clean electricity scenario. These extreme conditions also sacrifice most from the consumer welfare perspective. A greater policy forcing intensity accelerates plug-in electric vehicle penetration, while with declining marginal effect and reduced consumer welfare. Among the investigated policy scenarios, the policy forcing intensity equivalent to a fuel tax of $1–2 per gasoline gallon reduces the most GHG emissions while keeping a positive consumer welfare.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗