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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI1988

Nuclear Safety [Vol. 29, No. 3, July-September 1988]

Nuclear Safety is a review journal that covers significant developments in the field of nuclear safety. Its scope includes the analysis and control of hazards associated with nuclear energy, operations involving fissionable materials, and the products of nuclear fission and their effects on the environment. Primary emphasis is on safety in reactor design, construction, and operation; however, the safety aspects of the entire fuel cycle, including fuel fabrication, spent-fuel processing, nuclear waste disposal, handling of radioisotopes, and environmental effects of these operations, are also treated. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 259 Fifteenth Water Reactor Safety Information Meeting by E. G. Silver; CONTROL AND INSTRUMENTATION: 284 Reliability Technology to Improve and/or Maintain Emergency Diesel Generator Performance by S. Karimian and J. H. Taylor, 293 A Noise Diagnostics System for Operator Advice by G. Hessel, P. Liewers, P. Schumann, W. Schmitt, and F.-P. Weiss; PLANT SAFETY FEATURES: 307 A Passive Containment System for Advanced Light-Water Reactors by O. B. Falls, Jr., and F. W. Kleimola; ENVIRONMENTAL EFFECTS: 318 Data Base Construction for a Computerized Radiological Risk Investigation System by L. M. Hively, J. E. Nyquist, J. L. Bledsoe, and A. L. Sjoreen, 326 Erratum to "Radiation Hormesis and Nuclear Safety," Vol. 29, No. 1; OPERATING EXPERIENCES: 327 Operational Safety Experience and Passive Safety Testing at the Fast Flux Text Facility by Q. L. Baird, J. L. Rathbun, D. D. Stepnewski, R. L. Stover, and A. E. Waltar, 344 Backfilling of Independent Residual Heat Removal Systems in West Germany and Switzerland by G. Eckert and Y. Salomon, 353 Reactor Shutdown Experience Compiled by J. W. Cletcher, 356 Selected Safety-Related Events Compiled by G. A. Murphy, 363 Operating U.S. Power Reactors Compiled by E. G. Silver; RECENT DEVELOPMENTS: 384 General Administrative Activities Compiled by E. G. Silver, 390 Reports, Standards, and Safety Guides by D. S. Queener, 395 Status of Power-Reactor Projects Undergoing Licensing Review Compiled by E. G. Silver, 400 Proposed Rule Changes as of Mar. 31,1988; ANNOUNCEMENTS: 283 International ENS/ANS Conference on Thermal Reactor Safety "NUCSAFE 88", 317 Northwestern University Short Course on Radiation Safety, 407 Second International Seminar on Small- and Medium-Sized Nuclear Reactors, 407 International Workshop on New Developments in Occupational Dose Control and ALARA Implementation at Nuclear Power Plants and Similar Facilities, 408 Fourth International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-4), 404 The Authors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Solar Photovoltaic (PV) Module Facts and Trends

Unprecedented growth of solar PV has led to growing concerns about PV module toxicity and potential environmental and human health impacts. This factsheet provides objective, science-based data to help address these concerns and empower communities with the resources they need to make solar PV decisions.

14 SOLAR ENERGY↗
DOE OSTI2026

Cosmological constraints from the DESI DR1 joint power spectrum and bispectrum analysis

We derive cosmological parameter constraints from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) galaxy clustering data, based on a joint full-shape analysis of the power spectrum multipoles and the bispectrum monopole using the ShapeFit framework. This is the follow-up of our previous work, in which we obtained for the first time constraints on the ShapeFit parameters using the bispectrum of DESI DR1. Here we present the first ShapeFit cosmological inference results using the bispectrum of DESI DR1. We recover values for the matter density parameter and Hubble constant of respectively $Ω_m=0.310\pm0.012$ and $H_0=[68.92\pm0.97]\,\mathrm{km\, s^{-1} Mpc^{-1}}$, consistent with previous results from the full DESI DR1 dataset that did not use the bispectrum signal. The inclusion of the bispectrum significantly tightens the constraints on the amplitude of fluctuations, reducing the error-bars in $\ln(A_s\times10^{10})$ by approximately 20%, compared to using the power spectrum alone. We also explore extended cosmological models by performing fits for the evolving dark energy equation of state $w_0w_a$, and the sum of neutrino masses $\sum m_ν$. In these cases, we obtain constraints slightly larger than the ones from previous works from the DESI collaboration, due to not combining the full-shape results with other probes in all tracers. We find no strong evidence of deviations from standard $Λ$CDM, with the dark energy equation-of-state remaining within 2$σ$ from a cosmological constant $Λ$, and the neutrino mass being consistent with the normal hierarchy, $\sum m_ν<0.1\,[eV]$ at 95% confidence limit. These constraints are broadly consistent with other DESI DR1 analyses, thus validating the robustness of the ShapeFit compression approach and the inclusion of the bispectrum for cosmological inference.

Novell-Masot, S. [ICC, Barcelona U.; Geneva U., De↗
DOE OSTI2026

Cosmological constraints from the DESI DR1 joint power spectrum and bispectrum analysis

We derive cosmological parameter constraints from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) galaxy clustering data, based on a joint full-shape analysis of the power spectrum multipoles and the bispectrum monopole using the ShapeFit framework. This is the follow-up of our previous work, in which we obtained for the first time constraints on the ShapeFit parameters using the bispectrum of DESI DR1. Here we present the first ShapeFit cosmological inference results using the bispectrum of DESI DR1. We recover values for the matter density parameter and Hubble constant of respectively $Ω_m=0.310\pm0.012$ and $H_0=[68.92\pm0.97]\,\mathrm{km\, s^{-1} Mpc^{-1}}$, consistent with previous results from the full DESI DR1 dataset that did not use the bispectrum signal. The inclusion of the bispectrum significantly tightens the constraints on the amplitude of fluctuations, reducing the error-bars in $\ln(A_s\times10^{10})$ by approximately 20%, compared to using the power spectrum alone. We also explore extended cosmological models by performing fits for the evolving dark energy equation of state $w_0w_a$, and the sum of neutrino masses $\sum m_ν$. In these cases, we obtain constraints slightly larger than the ones from previous works from the DESI collaboration, due to not combining the full-shape results with other probes in all tracers. We find no strong evidence of deviations from standard $Λ$CDM, with the dark energy equation-of-state remaining within 2$σ$ from a cosmological constant $Λ$, and the neutrino mass being consistent with the normal hierarchy, $\sum m_ν<0.1\,[eV]$ at 95% confidence limit. These constraints are broadly consistent with other DESI DR1 analyses, thus validating the robustness of the ShapeFit compression approach and the inclusion of the bispectrum for cosmological inference.

Novell-Masot, S. [ICC, Barcelona U.; Geneva U., De↗
DOE OSTI2025

Dani Sleight Intern Poster

The NRDS Portal is a login-based data storage solution and science data gateway for researchers to centralize and analyze data before publication. Users can upload, edit, review, and approve their own datasets within the site to eventually be published for public use on the main NRDS site. More development was needed to extend NRDS Portal with new Artificial Intelligence features.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Nuclear Data Adjustment for Nonlinear Applications in the OECD/NEA WPNCS SG14 Benchmark—A Bayesian Inverse UQ-Based Approach for Data Assimilation

The Organisation for Economic Co-operation and Development Working Party on Nuclear Criticality Safety has proposed a benchmark exercise to assess the performance of current nuclear data adjustment techniques applied to nonlinear applications and experiments with low correlation to applications. This work introduces Bayesian inverse uncertainty quantification (IUQ) employing scientific machine learning surrogate models as a method for nuclear data adjustments in this benchmark, and compares IUQ to the more traditional methods of generalized linear least squares (GLLS) and Monte Carlo Bayes (MOCABA). Posterior predictions from IUQ showed agreement with GLLS and MOCABA for linear applications. Here, when comparing GLLS, MOCABA, and IUQ posterior predictions to computed model responses using adjusted parameters, we observe that the GLLS predictions failed to replicate the computed response distributions for nonlinear applications, while MOCABA showed near agreement, and IUQ used the computed model responses directly. We also discuss observations on why experiments with low correlation to applications can be informative to nuclear data adjustments and identify some properties useful in selecting experiments for inclusion in nuclear data adjustment. Performance in this benchmark indicates potential for Bayesian IUQ in nuclear data adjustments.

Bayesian calibration↗
DOE OSTI2025

Nuclear Data Adjustment for Nonlinear Applications in the OECD/NEA WPNCS SG14 Benchmark -- A Bayesian Inverse UQ-based Approach for Data Assimilation

The Organization for Economic Cooperation and Development (OECD) Working Party on Nuclear Criticality Safety (WPNCS) proposed a benchmark exercise to assess the performance of current nuclear data adjustment techniques applied to nonlinear applications and experiments with low correlation to applications. This work introduces Bayesian Inverse Uncertainty Quantification (IUQ) as a method for nuclear data adjustments in this benchmark, and compares IUQ to the more traditional methods of Generalized Linear Least Squares (GLLS) and Monte Carlo Bayes (MOCABA). Posterior predictions from IUQ showed agreement with GLLS and MOCABA for linear applications. When comparing GLLS, MOCABA, and IUQ posterior predictions to computed model responses using adjusted parameters, we observe that GLLS predictions fail to replicate computed response distributions for nonlinear applications, while MOCABA shows near agreement, and IUQ uses computed model responses directly. We also discuss observations on why experiments with low correlation to applications can be informative to nuclear data adjustments and identify some properties useful in selecting experiments for inclusion in nuclear data adjustment. Performance in this benchmark indicates potential for Bayesian IUQ in nuclear data adjustments.

FOS: Computer and information sciences↗
DOE OSTI2026

Data for A Fluorescence-Based Transient Expression Assay for the Analysis of Upstream Open Reading Frames in Plants

Scripts for the manuscript "A fluorescence-based transient expression assay for the analysis of upstream open reading frames in plant" by Haas et al. Upstream open reading frames (uORFs) are regulatory elements present in the 5′ leaders of mRNA that can significantly impact downstream gene expression in eukaryotes. In crop engineering, editing of uORFs can provide an avenue to upregulate expression of native genes without the need to add persistent transgenic copies. Even with genome- wide methods to identify translated uORFs such as ribosome profiling, their functional characterization depends on validation through reporter gene assays and mutagenesis studies. Current screening methods for plants use luciferases or protoplasts to measure differential gene expression between wild- type and mutated transcript leaders, which requires tissue processing and/or substrate addition. Here, we present a time- and cost- efficient alternative to investigate transcript leaders by co- expression of two fluorescent proteins in Nicotiana benthamiana leaf tissue and test our assay on genes involved in photoprotection, editing of which could provide a pathway to increase CO2 assimilation during sun–shade transitions.

Gene Editing↗
DOE OSTI2024

Informing forest carbon inventories under the Paris Agreement using ground-based forest monitoring data

Human interactions with forests have shaped Earth's climate for millennia and will continue to do so as we target net-zero emission goals. Accurately characterizing these climate impacts requires making reliable forest carbon data available for forest monitoring and planning. Here, we develop a semi-automated process for submitting forest carbon measurements from the largest relevant scientific database to the International Panel on Climate Change's Emission Factor Database, which currently has sparse forest carbon data. Building this bridge from scientific research to international policy is an important step towards managing forests in a net-zero motivated future. Humans have been influencing Earth's climate via transformative impacts on forests for millennia, and forests are now recognized as critical to climate change mitigation under the Paris Agreement. The efficacy of climate change mitigation planning and reporting depends on quality data on forest carbon (C) stocks and changes. The Emission Factor Database (EFDB) of the International Panel on Climate Change (IPCC) is intended to be a definitive source for such data, but needs comprehensive and well-documented data to be so. To facilitate submission of forest C estimates from scientific studies to EFDB, we develop and document a process for semi-automated data submission from the Global Forest C database (ForC v4.0), which is the largest compilation of ground-based forest C estimates. We then assess the data currently available through ForC and provide recommendations for improving forest data collection, analysis, and reporting. As of September 2024, ForC contained ~19,286 records potentially relevant to EFDB, 1068 of which had been submitted and posted to EFDB. These represented 19% of the total EFDB records for forest land. Records were unevenly distributed across variables and geographic regions. ForC records (37%) reviewed could not be submitted because the original publication lacked required information. In the future, ground-based forest C estimates should target gaps in the record, and studies should ensure that they report all information necessary for inclusion in EFDB. Given that climate change is rapidly impacting the world's forests, timely reporting of recent estimates will be critical to accurate forest C inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Combining physics-based and data-driven models for quantitatively accurate plasma profile prediction that extrapolates well; with application to DIII-D, AUG, and ITER tokamaks

For design, scenario planning, and control, ITER and all other envisioned tokamaks rely on a variety of statistical and physics-based models to extrapolate to unseen regimes; most notably from low plasma current to high. A 'meta-learning' methodology for combining the accuracy of data-driven models with the generalizability of physics-based models is described and tested, yielding a 5–10 percent improvement in performance beyond either alone for the task of extrapolating time-dependent plasma profile prediction from low- to high- plasma current DIII-D tokamak discharges. Meanwhile, it is shown that both machine learning models extrapolated far-distribution and state-of-the-art 'physics-based' profile predictors fare worse than merely assuming plasma profiles do not change from their initial values. Finally, a variety of other mechanisms for helping data-driven models generalize—transfer learning, adding contextual information from physics simulators, and adding data from the ASDEX Upgrade tokamak—are attempted for similar extrapolation tasks but, in the methodology used in this paper, yield no significant improvement beyond simple data-driven models. Results are summarized in figures 15 and 16.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Loss Factors for Small Distributed Wind Turbines Based on Field Data in the United States

While wind energy production loss due to unavailability, environmental impacts, curtailment, and other causes has been studied and characterized at the utility-scale wind farm level, observation-based characterization of project loss is lacking for distributed wind energy, particularly for projects involving small wind turbines. Contemporary tools and research that support pre-construction distributed wind energy characterization present a wide range of default loss factors to convert gross energy estimates to net: 7-18%. We hypothesize that we can use generation observations from operational distributed wind projects to develop more accurate representations of loss. Using a density-based filtering technique on distributed wind power generation timeseries, we determine periods of typical performance and use them with regression algorithms in a measure-correlate-predict fashion to simulate what the generation would have been during periods of atypical or unreported performance. From there, the actual versus predicted generation leads to the establishment of observation-informed loss factors (median = 17%) for small, single turbine installation distributed wind projects.

17 WIND ENERGY↗
DOE OSTI2025

First Constraints on General Neutrino Interactions Based on KATRIN Data

The precision measurement of the tritium β-decay spectrum performed by the KATRIN experiment provides a unique way to search for general neutrino interactions (GNIs). All theoretically allowed GNI terms at dimension 6 involving neutrinos are incorporated into a low-energy effective field theory, and can be identified by specific signatures in the measured tritium β spectrum. In this Letter an effective description of the impact of GNIs on the β spectrum is formulated and the first constraints on the effective GNI parameters are derived based on the 4 ×10 6 electrons collected in the second measurement campaign of KATRIN in 2019. In addition, constraints on selected types of interactions are investigated, thereby exploring the potential of KATRIN to search for more specific new physics cases, including a right-handed W boson, a charged Higgs boson, or leptoquarks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

ED-cPSD: Fast Phase-Size Distribution via Sequential Erosion-Dilation

The Erosion-Dilation continuous Phase-Size Distribution, ED-cPSD, is an application for calculating continuous pore and particle-size distribution from digital reconstructions and/or image-based structural data. It is based on the erosion-dilation continuous phase-size distribution method. A continuous size distribution is a measure of the probability density of finding a particle or pore of a certain size. These distributions are of interest in any field of study involving porous media, including but not limited to electrochemistry, petroleum engineering, geology, and food science. The algorithm behind the software provides a computationally efficient way to calculate phase-size distributions for large domains. For a 3D battery electrode reconstruction with 1.3 x 10 8 voxels, the particle size distribution is derived in under 2 min on a desktop, while also retaining flexibility and computational efficiency for HPC-scale multi-threading. The software can handle structures with over 10 9 voxels. The algorithm is roughly 280 times faster than a previous version on the same task.

Characterization↗
DOE OSTI2025

The Statistical Spread of Transmission Outages on a Fast Protection Time Scale Based on Utility Data

When there is a fault, the protection system automatically removes one or more transmission lines on a fast time scale of less than one minute. The outaged lines form a pattern in the transmission network. We extract these patterns from utility outage data, determine some key statistics of these patterns, and then show how to generate new patterns consistent with these statistics. The generated patterns provide a new and easily feasible way to model the overall effect of the protection system at the scale of a large transmission system. This new data-driven generative modeling of protection is expected to contribute to simulations of disturbances in large grids so that they can better quantify the risk of blackouts. Analysis of the pattern sizes suggests an index that describes how much outages spread in the transmission network at the fast timescale.

Transmission↗
DOE OSTI2025

From Machine Learning to Machine Reasoning: A Model-based Approach to Analyze Equipment Reliability Data

In current nuclear power plants (NPPs) a large amount of condition-based data which can be used to assess and monitor component health and performance. Assessing component health from such data can be performed with a large variety of methods. While the analysis of numeric data can be performed with several methods, the extraction of information from textual data remains a challenge. Currently employed natural language processing (NLP) methods do not really provide quantitative information that might be contained in IRs. In addition, the integration of numeric and textual data to identify possible causal relationships between data elements is still an unresolved challenge. This paper presents an approach to extract information from textual (e.g., incident or maintenance reports) and numeric data that relies on model based system engineer (MBSE) models. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence while semantic analysis is designed to analyze the logic structure of a sentence. An innovative element of our approach is that semantic analysis uses MBSE models to identify links between textual elements. Similarly, numeric data is directly linked to elements of the MBSE models in order to map which functions are being monitored.

97 - MATHEMATICS AND COMPUTING↗