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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

First High-Throughput Evaluation of Dark Matter Detector Materials

In this work we perform the first high-throughput search and evaluation of materials that can serve as excellent low-mass dark matter detectors. Using properties of close to 1000 materials from the Materials Project database, we project the sensitivity in dark matter parameter space for experiments constructed from each material, including both absorption and scattering processes between dark matter and electrons. Using the anisotropic materials in the dataset, we further compute the level of daily modulation in interaction rates and the resulting directional sensitivities, highlighting materials with prospects to detect the dark matter wind. Our methods provide the basic tools for the data-driven design of dark matter detectors, and our findings lay the groundwork for the next generation of highly optimized direct searches for dark matter as light as the keV scale. This represents a major step in the application of results from condensed matter physics to dark matter search design.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

U.S. Solar Siting Regulation and Zoning Ordinances (2025)

A machine readable collection of documented solar siting ordinances at the state and local (e.g., county, township) level throughout the United States. The data were compiled using the Infrastructure Continuous Ordinance Mapping for Planning and Siting Systems (INFRA-COMPASS) tool, which leverages Large Language Models (LLMs) to automate the collection of local codes and ordinances applicable to energy infrastructure. URLs for the ordinance source documents are included in the Solar Ordinances spreadsheet. The GeoPackage file included below contains the jurisdiction shapes for each ordinance. Note that the GeoPackage file is formatted for ingestion by NLR's reVX setbacks tool and therefore does not contain any of the state-level regulations. NOTE: This data was collected with the help of generative AI. The Large Language Models used for this effort make mistakes. Always validate the data for critical use cases. This data is an update to a previously developed database of wind ordinances found in OEDI Submission 5734: see the "U.S. Solar Siting Regulation and Zoning Ordinances 2022" link below. INFRA-COMPASS version used for collection: v0.11.3 LLMs used for collection: GPT-4.1, GPT-4.1 mini, GPT-4.1 nano

14 SOLAR ENERGY↗
DOE OSTI2024

Antarctic lake viromes reveal potential virus associated influences on nutrient cycling in ice-covered lakes

The McMurdo Dry Valleys (MDVs) of Antarctica are a mosaic of extreme habitats which are dominated by microbial life. The MDVs include glacial melt holes, streams, lakes, and soils, which are interconnected through the transfer of energy and flux of inorganic and organic material via wind and hydrology. For the first time, we provide new data on the viral community structure and function in the MDVs through metagenomics of the planktonic and benthic mat communities of Lakes Bonney and Fryxell. Viral taxonomic diversity was compared across lakes and ecological function was investigated by characterizing auxiliary metabolic genes (AMGs) and predicting viral hosts. Our data suggest that viral communities differed between the lakes and among sites: these differences were connected to microbial host communities. AMGs were associated with the potential augmentation of multiple biogeochemical processes in host, most notably with phosphorus acquisition, organic nitrogen acquisition, sulfur oxidation, and photosynthesis. Viral genome abundances containing AMGs differed between the lakes and microbial mats, indicating site specialization. Using procrustes analysis, we also identified significant coupling between viral and bacterial communities (p = 0.001). Finally, host predictions indicate viral host preference among the assembled viromes. Collectively, our data show that: (i) viruses are uniquely distributed through the McMurdo Dry Valley lakes, (ii) their AMGs can contribute to overcoming host nutrient limitation and, (iii) viral and bacterial MDV communities are tightly coupled.

Microbiology↗
DOE OSTI2023

MiGRIDS

MiGRIDS is a software that models islanded microgrid power systems with different controls and components. For example, using load and resource data from a microgrid, you could model it with additional wind turbines, battery etc. You could also try out different dispatch schemes to see which one worked best. MiGRIDS is designed to help optimize the size and dispatch of grid components in a microgrid. While a grid connect feature is expected to be added in the future, islanded operation is the focus. Note that this is a basic implementation and more features and functionality (such as a GUI) are coming! MiGRIDS runs time-step energy balance simulations for different grid components and controls. In smaller microgrid environments, dispatch decisions are being made on the order of seconds. In order to fully capture their effect, this tool lets you run simulations on the order of seconds. The end result is a more realistic representation of what can be achieved by integrating different components and control strategies in a grid.

Morgan, Tawna↗
DOE OSTI2022

Time Sequence Machine Learning-Based Data Intrusion Detection for Smart Voltage Source Converter-Enabled Power Grid

Smart inverters of distributed energy resources can enable cloud computing, condition monitoring, result visualization, remote control, and peer-to-peer energy trading in advanced power systems. However, the advent of data injection attacks in the communication architecture can alter measurement characteristics of power grids and have devastating consequences. In this article, we propose a time sequence machine learning-based anomaly detection methodology for detecting cyber intrusion into control signal setpoints and dc voltage signal measurement bias of the voltage source converter (VSC) in wind generators. We first investigated the effects of four types of denial of service, tampering signal, and stealthy-type data intrusion attacks on smart VSCs and overall wind farms. We then proposed a novel time sequence machine learning-based intrusion detection framework that can be implemented to detect different cyberattacks in the VSCs. The performance of the proposed framework has been compared with that of autoencoder and clustering-based intrusion detection framework. The proposed framework was validated by using the IEEE 39 bus power system in the presence of four wind farms in different locations. Using several metrics for intrusion detection performance, we validated the effectiveness of the proposed framework.

42 ENGINEERING↗
DOE OSTI2026

Observations of Offshore Low‐Level Jets Off the U.S. East Coast Reveal Systematic Biases in ERA5 and HRRR

Low-level jets (LLJs)—wind speed maxima typically occurring a few hundred meters above the surface—are common off the U.S. East Coast and influence many atmospheric processes with societal importance, including cloud formation, aviation safety, and search-and-rescue. However, their vertical structure and frequency remain poorly quantified due to limited offshore observations. This study presents new scanning Doppler LiDAR and infrared spectroradiometer data from the 2024 summer deployment of an offshore barge during the Wind Forecast Improvement Project 3. These coupled wind and temperature profiles provide unprecedented resolution to assess LLJ behavior and model performance. LLJs occurred in over 21% of observed profiles, with a weak diurnal preference for nighttime and early morning hours and maximum winds typically near 300 m. Both ERA5 and High-Resolution Rapid Refresh analysis underestimate jet wind speeds and misrepresent the boundary layer thermal structure. These results highlight persistent model biases and the critical need for high-resolution offshore observations.

17 WIND ENERGY↗
DOE OSTI2026

- Analysis of a scaled wind turbine on a floating semisubmersible in an ocean basin

The Offshore Code Comparison, Collaboration, Continued, with Correlation (OC5) is an international research project run under the International Energy Agency (IEA) Wind Task 30. The project is focused on validating the tools used design offshore wind systems. OC5 consists of four phases. • Phase 2: Analysis of a scaled wind turbine on a floating semisubmersible in an ocean basin This website provides the data used for validation in these four phases, as well as simulation results from multiple participants.

17 WIND ENERGY↗
DOE OSTI2022

The DataHawk2 uncrewed aircraft system for atmospheric research

Abstract. The DataHawk2 (DH2) is a small, fixed-wing, uncrewed aircraft system, or UAS, developed at the University of Colorado (CU) primarily for taking detailed thermodynamic measurements of the atmospheric boundary layer. The DH2 weighs 1.7 kg and has a wingspan of 1.3 m, with a flight endurance of approximately 60 min, depending on configuration. In the DH2's most modern form, the aircraft carries a Vaisala RSS-421 sensor for pressure, temperature, and relative humidity measurements, two CU-developed infrared temperature sensors, and a CU-developed fine-wire array, in addition to sensors required to support autopilot function (pitot tube with pressure sensor, GPS receiver, inertial measurement unit), from which wind speed and direction can also be estimated. This paper presents a description of the DH2, including information on its design and development work, and puts the DH2 into context with respect to other contemporary UASs. Data from recent field work (MOSAiC, the Multidisciplinary drifting Observatory for the Study of Arctic Climate) is presented and compared with radiosondes deployed during that campaign to provide an overview of sensor and system performance. These data show good agreement across pressure, temperature, and relative humidity as well as across wind speed and direction. Additional examples of measurements provided by the DH2 are given from a variety of previous campaigns in locations ranging from the continental United States to Japan and northern Alaska. Finally, a look toward future system improvements and upcoming research campaign participation is given.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Modeling and observations of North Atlantic cyclones: Implications for U.S. Offshore wind energy

To meet the Biden-Harris administration's goal of deploying 30 GW of offshore wind power by 2030 and 110 GW by 2050, expansion of wind energy into U.S. territorial waters prone to tropical cyclones (TCs) and extratropical cyclones (ETCs) is essential. This requires a deeper understanding of cyclone-related risks and the development of robust, resilient offshore wind energy systems. Here, this paper provides a comprehensive review of state-of-the-science measurement and modeling capabilities for studying TCs and ETCs, and their impacts across various spatial and temporal scales. We explore measurement capabilities for environments influenced by TCs and ETCs, including near-surface and vertical profiles of critical variables that characterize these cyclones. The capabilities and limitations of Earth system and mesoscale models are assessed for their effectiveness in capturing atmosphere–ocean–wave interactions that influence TC/ETC-induced risks under a changing climate. Additionally, we discuss microscale modeling capabilities designed to bridge scale gaps from the weather scale (a few kilometers) to the turbine scale (dozens to a few meters). We also review machine learning (ML)-based, data-driven models for simulating TC/ETC events at both weather and wind turbine scales. Special attention is given to extreme metocean conditions like extreme wind gusts, rapid wind direction changes, and high waves, which pose threats to offshore wind energy infrastructure. Finally, the paper outlines the research challenges and future directions needed to enhance the resilience and design of next-generation offshore wind turbines against extreme weather conditions.

17 WIND ENERGY↗
DOE OSTI2022

Intersections of Disadvantaged Communities and Renewable Energy Potential: Data Set and Analysis to Inform Equitable Investment Prioritization in the United States

Renewable energy development can bolster local economies through job creation, local tax revenues, and reduced energy costs; however, communities most in need of economic development and employment opportunities often see lower levels of renewable energy deployment. We sought to identify areas where disadvantaged community indicators and high generation potential from cost-effective renewable energy opportunities intersect and deployment could lead to economic development and job creation. This presentation will highlight several of our findings. This research and the associated county-level data set are intended to inform national- and state-level energy-related assistance programs, economic development efforts, and infrastructure programs seeking to prioritize investments in disadvantaged communities.

community energy planning↗
DOE OSTI2022

Invertible Neural Networks for Aerodynamic Design of Wind Turbine Blades

The state-of-the-practice methods for aerodynamic design of wind turbine blads use linearized blade element momentum theory (BEM) to optimize the twist and chord profiles from a pre-selected set of 2D airfoil shapes. In this work, we apply invertible neural network (INN) tools to enable the rapid inverse aerodynamic design of wind turbine blades including component airfoils. The INN is trained on data obtained through the use of robust automated mesh generation and the HAMSTR computational fluid dynamics solver with advanced turbulence and transition models validated for turbine applications. Our design technique is a significant improvement over the state-of-the-practice linearized blade element momentum (BEM) techniques in capturing 3D nonlinear aerodynamic effects that are critical for optimal design of the rotors. This is made possible by developing sparse, invertible neural networks (INNs) for inverse design and optimization that realize a 100x cost reduction compared to adjoint-based computational fluid dynamics (CFD) approaches, while enabling increased robustness of the final design. We demonstrate the INN tool for design of a section of the NREL 5-MW blade. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of wind turbine blades.

aerodynamic design↗
DOE OSTI2023

A new coupling of a GPU-resident large-eddy simulation code with a multiphysics wind turbine simulation tool

The development of new wind farm control strategies can benefit from combined analysis of flow dynamics in the farm and the behavior of individual turbines within one simulation environment. In this work, we present such an environment by developing a new coupling between the large-eddy simulation (LES) code GRASP and the multiphysics wind turbine simulation tool OpenFAST via an actuator line model (ALM). In addition, the implementation of the recently proposed filtered actuator line model (FALM) within the coupling is described. The new ALM implementation is cross-verified with results from four other commonly used research LES codes. The results for the blade loads and the near wake obtained with the new coupling are consistent with the other codes. Deviations are observed in the far wake. The results further indicate that the FALM is able to reduce the lift and power overprediction from which the traditional ALM suffers on coarse LES grids. This new simulation environment paves the way for future wind farm simulations under realistic weather conditions by leveraging GRASP's ability to impose data from large-scale meteorological models as boundary conditions.

17 WIND ENERGY↗
DOE OSTI2023

Extrapolation of the Rainflow-Counted Load Ranges for Fatigue Assessment of the Wind Turbine's Blades

Wind turbine design standards recommend the use of statistical modeling coupled with extrapolation of the short-term load data to long-term periods for fatigue reliability assessment. However, statistical error and computational expense can limit the accuracy of such approaches. In the case of wind turbine blades, the errors are more significant because of the high material fatigue exponent that makes the damage estimations more sensitive to variations. In addition, due to different excitation sources, the flapwise load range histogram is not unimodal, and thus its statistical modeling is complex. In the present work, we provide three methods for statistical modeling of the flapwise bending moment ranges including a novel approach based on frequency-based separation of the modes. The first two methods are simplified approaches for modeling the most crucial load ranges using unimodal distributions and the third method involves multimodal distribution fitting. The research is based on 3600 10-minute aeroelastic simulations of DTU 10MW case study wind turbine from which a benchmark damage equivalent load (DEL) is calculated. The DEL calculated by each of the three proposed methods is compared to this reference. The results show that the conventional approach based on using 6 seeds as well as using mixture models fitted on the limited data lead to under-conservative results with errors up to 23%. On the other hand, the simplified unimodal approaches provided in this work can provide conservative estimations of the fatigue damage with mean values 5% and 12% higher than the benchmark. However, the variability of the DEL estimates is higher when using unimodal extrapolation of the load ranges, and the data can be conservative by 17.5%. The proposed unimodal fits suggested for modeling and extrapolation of the blade's load ranges provide less errors relatively and most importantly conservative DEL estimations while maintaining computational efficiency.

blade fatigue↗
DOE OSTI2025

Baltimore Social-Environmental Collaborative (BSEC) Doppler Lidar & Derived Products

This repository contains all processed Doppler‐lidar outputs from the PSU lidar deployed for the Baltimore Social‐Environmental Collaborative (BSEC) project. Vertical Stare Scans (fixed‐beam, vertical profiling): 1 Hz backscatter intensity (m⁻¹ sr⁻¹), signal‐to‐noise ratio (unitless), and Doppler vertical‐velocity (m s⁻¹) on ~30 m range gates, stored as CF-compliant NetCDF. Wind Profiles (horizontal‐wind retrieval): daily NetCDF outputs of retrieved horizontal wind speed (m s⁻¹) and direction (degrees), computed from the angled‐scan returns. Profile Statistics (summary statistics on the vertical velocity): 15 min windows (default) of mean, variance, skewness, kurtosis, high-frequency variance, etc., as a function of height; saved as CF-compliant NetCDF files. Boundary Layer Height (BLH) (fuzzy-logic output): 15 min BLH estimates (m), with lower/upper fuzzy bounds (m) and a quality flag (0–4) indicating data status (e.g., no data, good, below range, ran out of signal, cloud-topped). Cloud Base Height (Haar-gradient detection): 15 min estimates of cloud-base height (m) with a cloud-detection quality flag (0–3: none, low, moderate, high). All five product streams are organized by year and date under their own top-level folders (01_Vertical_Stare_Scans/ through 05_Cloud_Height/). Each folder contains a data_ /YYYY/ subdirectory with daily CF-compliant NetCDF outputs (96 windows per day at 15 min intervals). Global attributes in each file include creation history, version (2.0.0), institution, and source. Instrument & MeasurementsThe PSU Doppler Lidar samples aerosol backscatter (m⁻¹ sr⁻¹), signal-to-noise ratio, and radial velocity at ~1 Hz. Vertical stare scans point the beam straight up; after collecting angled scans through multiple elevation angles, the "Wind Profiles" product contains the fully retrieved horizontal wind speed and direction. Data were collected continuously at ~30 m range resolution, with a typical height ceiling of ~12 km. How to Use Open any NetCDF with Python's xarray, MATLAB, or similar CF-compliant tools. Stare scans and angled-scan retrievals (Wind Profiles) are CF-compliant daily NetCDF files. Profile-Statistics, BLH, and Cloud Height files are daily 15 min summaries (96 time steps per file). Inspect the included variables (e.g., vertical_velocity_variance, wind_speed, BLH, cloud_base_height) for your analyses. Use the quality flags (BLH_flag, cloud_flag) to filter out poor-quality retrievals. For more information or questions about processing methods, please contact:Nicholas E. Prince ⟨nec5299@psu.edu⟩Penn State Department of Meteorology & Atmospheric Science

Air Quality↗
DOE OSTI2021

Towards modelling ghostly damped Ly α s

ABSTRACT We use simple models of the spatial structure of the quasar broad-line region (BLR) to investigate the properties of so-called ghostly damped Ly α (DLA) systems detected in Sloan Digital Sky Survey (SDSS) data. These absorbers are characterized by the presence of strong metal lines but no H i Ly α trough is seen in the quasar spectrum indicating that, although the region emitting the quasar continuum is covered by an absorbing cloud, the BLR is only partially covered. One of the models has a spherical geometry, another one is the combination of two wind flows, whereas the third model is a Keplerian disc. The models can reproduce the typical shape of the quasar Ly α emission and different ghostly configurations. We show that the DLA H i column density can be recovered precisely independently of the BLR model used. The size of the absorbing cloud and its distance to the centre of the AGN are correlated. However, it may be possible to disentangle the two using an independent estimate of the radius from the determination of the particle density. Comparison of the model outputs with SDSS data shows that the wind and disc models are more versatile than the spherical one and can be more easily adapted to the observations. For all the systems, we derive log N(H i)(cm−2) > 20.5. With higher quality data, it may be possible to distinguish between the models.

Laloux, Brivael↗