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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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The goal is to use available data from Health Physics Research Reactor (HPRR) operation to create a benchmark report for inclusion in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook, or SINBAD, as a criticality accident alarm system (CAAS) shielding benchmark.
This is a slide deck intended for use at the upcoming IAEA workshop "EVT2002488 Consultancy Meeting on Industrial Applications of Nucleonic Control Systems based on Neutrons from sealed sources and generators". This slide deck provides a survey of U.S. commercial neutron generator technology, including manufacturers, and a few examples of use in academia, and some prior research at INL. All data and figures in this presentation are either publicly available (note citations) or are INL materials that have been previously release through STISM for public presentations or papers.
This research uses legacy seismic data to characterize the subsurface seismic structured, including permafrost, offshore Oliktok Point, Alaska.
Time-series, PV performance field data from 2019-2020 for the bifacial tracking research field at NREL. Dataset also contains meterological data for site.
Information and communication technologies have been widely used in smart grid for efficient operation. However, these technologies are vulnerable to malicious cyber attacks, which may lead to severe reliability and economic issues. Recently, a variety of data-driven anomaly detection approaches have been explored to detect potential cyber attacks in smart grids. In this paper, we researched on the electricity market data aiming to identify anomalies from the locational marginal prices (LMPs) and provide a new indicator for potential cyber attacks in power grids. Specifically, a novel data-driven probabilistic anomaly detection framework is proposed for electricity market, which consists of three major components: long short-term memory (LSTM) based deterministic electricity price forecasting, probabilistic electricity price forecasting and anomaly detection. This framework is tested on a model-based electricity market simulator under two types of cyber attacks, i.e., load redistribution attack (LRA) and price responsive attack (PRA). Numerical results on the simulated LMPs show that the proposed framework is capable of detecting data anomalies over these attacks.
As the path towards Urban Air Mobility (UAM) continues to take shape, there are outstanding technical challenges to achieving safe and effective air transportation operations under this new paradigm. To inform and guide technology development for UAM, NASA is investigating the current state-of-the-art in key technology areas including traffic management, detect-and-avoid, and autonomy. In support of this effort, a new perception testbed was developed at NASA Ames Research Center to collect data from an array of sensing systems representative of those that could be found on a future UAM vehicle. This testbed, featuring a Light-Detection-and-Ranging (LIDAR) instrument, a long-wave infrared sensor, and a visible spectrum camera was deployed for a multiday test campaign in the Fog Chamber at Sandia National Laboratories (SNL), in Albuquerque, New Mexico. During the test campaign, fog conditions were created for tests with targets including a human, a resolution chart, and a small unmanned aerial vehicle (sUAV). Here, this paper describes in detail, the developed perception testbed, the experimental setup in the fog chamber, the resulting data, and presents an initial result from analysis of the data with the evaluation of methods to increase contrast through filtering techniques.
This data set includes modeled (AgroIBIS-VSF) output of crop yield for miscanthus and a corn/soybean rotation in .nc format. Input includes the precipitation scenarios (.csv) used to generate the runs, and supplemental observed yield monitor data (.csv) from the research area of interest.
The harmonization of testing protocols for proton exchange membrane (PEM) electrolyzers is essential for ensuring accurate and reliable performance assessments and accelerating the development of hydrogen production technologies. This protocol provides a structured approach to PEM electrolyzer setup and testing, incorporating key considerations for test station design and single-cell characterization techniques. Polarization curves and electrochemical impedance spectroscopy (EIS) are detailed, along with best practices from academic and industry research groups to enhance data accuracy and comparability. By addressing material variability and harmonizing testing methodologies, this framework enables more precise evaluations of membrane electrode assemblies and electrolyzer components. Harmonized protocols not only streamline development efforts but also foster collaboration across institutions, ultimately supporting the commercialization of hydrogen solutions through improved stack efficiency and durability.
The Third Wind Forecast Improvement Project (WFIP3) is a multi-institutional field campaign designed to advance the understanding and prediction of the offshore atmospheric boundary layer along the US east coast. Extending from February 2024 through August 2025, WFIP3 combines long-term coastal and offshore measurements with targeted modeling and forecasting efforts. This data paper presents the WFIP3 event log, a curated record of 578 d of meteorological phenomena and field observations that complements the campaign's extensive high-frequency datasets. The event log provides both manually documented daily weather discussions and automatically derived indicators of atmospheric processes – including low-level jets, wind ramps, extreme wind veer, and weak wind conditions – based on observations from scanning lidars deployed at three coastal and offshore sites. The dataset offers structured metadata, standardized time and site identifiers, and consistent terminology to facilitate its integration with WFIP3's observational and modeling data products. The log supports diverse applications, from model evaluation and forecast verification to the selection of case studies on offshore boundary-layer dynamics. The WFIP3 event log is publicly available through the US Department of Energy's Wind Data Hub, providing the research community with a transparent and enduring contextual reference for the interpretation and use of WFIP3 measurements.
Critical minerals are essential to the future of clean energy, especially energy storage, electric vehicles, and advanced electronics. In this paper, we argue that process systems engineering (PSE) paradigms provide essential frameworks for enhancing the sustainability and efficiency of critical mineral processing pathways. As a concrete example, we review challenges and opportu-nities across material-to-infrastructure scales for process intensification (PI) with membranes. Within critical mineral processing, there is a need to reduce environmental impact, especially con-cerning chemical reagent usage. Feed concentrations and product demand variability require flex-ible, intensified processes. Further, unique feedstocks require unique processes (i.e., no one-size-fits-all recycling or refining system exists). Membrane materials span a vast design space that allows significant optimization. Therefore, there is a need to rapidly identify the best opportunities for membrane implementation, thus informing materials optimization with process and infrastructure scale performance targets. Finally, scale-up must be accelerated and de-risked across the materials-to-process levels to fully realize the opportunity presented by membranes, thereby fostering the development of a circular economy for critical minerals. Tackling these challenges requires integrating efforts across diverse disciplines. We advocate for a holistic molecular-to-systems perspective for fully realizing PI with membranes to address sustainability challenges in critical mineral processing. The opportunities for PI with membranes are excellent applications for emerging research in machine learning, data science, automation, and optimization.
In order to facilitate bootstrap photometric calibrations of early Rubin Observatory data we have created an all sky reference catalog called The Monster. This reference catalog uses a rank-ordered set of other reference catalogs to generate synthetic ugrizy-band fluxes that can be used calibrate images processed with the LSST science pipelines. This document describes the methodology used to create The Monster, documents the input external reference catalogs, and performs basic data validation of the first version of The Monster.
This document defines the guidelines, principles, and formats for user-facing tutorials that demonstrate how to use the Rubin Science Platform (RSP) to analyze data from the Legacy Survey of Space and Time (LSST). All Rubin staff and the broader science community should use these guidelines when contributing to the sets of Jupyter Notebook or documentation-based tutorials maintained by the Rubin Community Science team (CST).
This document outlines Rubin Observatory's plans for a dedicated \emph{Early Science Program} to enable high-impact science prior to the first annual data release of the Legacy Survey of Space and Time (LSST). Components of the Early Science Program include releasing science-grade commissioning data products via a series of ``Data Previews,'' ramping up of the transient alert stream during commissioning, implementing a program of incremental template generation to augment alert production in the early phases of the survey, and the first LSST Data Release, DR1, based on the first 6 months of data from the LSST. A detailed breakdown of which data products can be expected when is provided. The Rubin Operations team is working closely with the science community to optimize the Early Science Program for the time-domain and solar system science achievable in the first year of operations. This is a living document; both it and the Early Science Program will continue to evolve over the course of commissioning and pre-operations in response to the state of the as-built system and to community guidance.
We present the photometric redshifts (photo-z) inferred using algorithms implemented in the Redshift Assessment Infrastructure Layers (RAIL) for the NSF-DOE Vera C. Rubin Observatory Data Preview 2 (DP2). We produce a compilation of reference redshift catalog using spectroscopic, grism and many band photometric redshift dataset hosted on the LIneA Photo-z Server. We curate training and testing set for assessing the scientific and technical performance of Rubin photo-z. The algorithm applied to the object catalog are FlexZBoost, BPZ, kNN, GPz, DNF and TPz; with a combination of 6-band and 4-band photo-z depending on availability of u and y photometry. The redshift point estimates and uncertainty estimation in tabular format through the Large Survey DataBase (LSDB).
Biomimetic model organisms could be useful surrogates for live animals in many applications if the models have sufficient biofidelity. One such application is for use in field and laboratory tests of fish mortality associated with passage through hydropower turbines. Laboratory trials suggest that blade strikes are especially injurious and often causes mortality when fish are struck by thinner blades moving at higher velocities. Dose-response relationships have been created from these data, but the exact relationship between fish mortality and the actual forces enacted on fish during simulated blade strike testing remains unknown. Here, we describe the methods used to create a prototype biomimetic model fish composed of ballistic gelatin and covered with a surrogate skin to better approximate the biomechanical properties of a fish body. Frozen fish were scanned with high-fidelity laser scanners, and a 3D-printed, reusable mold was created from which to cast our gelatin model. Computed tomography scan data, imaged directly or taken from online data repositories, were also successfully used to create CAD models for use in additive manufacturing of molds. One 3-axis accelerometer was embedded into the gelatin to compare accelerometer data to dose-response data from previous laboratory research on live fish. The resulting model ( i.e. , Gelfish) had a statistically indistinguishable tissue durometer to that of real fish tissue and preliminary blade strike impact testing suggested its overall flexibility was similar to that of live fish. Gelfish was designed with biofidelity as its guiding principle and our results suggest initial experimentation was successful. Future research will include replication of initial Gelfish test results, quantitative measurement of model flexibility relative to real fish, and inclusion of surrogate skeletal structures to enhance biofidelity. Use of more sophisticated sensors would also better quantify the physical forces of blade strike impact and help determine how said forces correlate with rates of mortality observed during tests on live fish.
An international team of over 1,000 scientists and engineers from more than 30 countries is building the most advanced neutrino experiment in the world, which could change our understanding of the universe. Groundbreaking for this revolutionary endeavor—hosted by the U.S. Department of Energy’s Fermilab with contributions from across the U.S. and around the globe—took place in July of 2017. A large prototype detector successfully recorded data at the European research center CERN from 2018 to 2020.
An international team of over 1,000 scientists and engineers from more than 30 countries is building the most advanced neutrino experiment in the world, which could change our understanding of the universe. Groundbreaking for this revolutionary endeavor—hosted by the U.S. Department of Energy’s Fermilab with contributions from across the U.S. and around the globe—took place in July of 2017. A large prototype detector successfully recorded data at the European research center CERN from 2018 to 2020.
The objective of NREL's Algal Biofuel Techno-Economic Analysis (TEA) project is to provide process modeling and analysis to support Algae Program activities, utilizing TEA models to relate key process parameters with overall economics for cultivation, processing, and conversion of algal biomass to fuels and coproducts. By quantifying economic implications of key process metrics, TEA models highlight the technical requirements to achieve future program cost goals as well as enabling a means to track progress towards these goals. This project provides high impact and relevance though generation of critical cost data tied to funded research, with our analyses subsequently exercised by BETO to guide program plans, FOA priorities, and other directives. This includes costs for both algal biomass production and downstream conversion, most notably to support BETO's fuel cost targets below $2.5/GGE by 2030. To mitigate a key risk/challenge in constraining our work to academic analyses rooted only in future projections, our work also seeks to provide near-term value to today's algae industry through frequent industry engagement, while maintaining close ties with other BETO collaborators. This project has made numerous accomplishments since the 2019 peer review, including a continued focus on opportunities for value-added products, with related analyses for new pathway opportunities to achieve BETO cost goals and notable State-of-Technology (SOT) improvements over prior cost benchmarks.