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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 829 records · Page 46

CFD Validation Study of a Hypersonic Cone-Slice-Flap Variable Geometry Configuration

Model validation is the process of determining the degree of accuracy between physical reality and the model. The result of model validation can either be used to improve the model through calibration or quantify the model-form uncertainty. This work focuses on providing the model-form uncertainty through an area metric for a hypersonic cone-slice-flap variable geometry configuration given uncertainty in both the simulation and experimental data. The research here compares two different turbulence models for the simulations. For a variable geometry, performing uncertainty quantification to capture the model-form uncertainty on every configuration is computationally challenging. This work lays out a procedure that can give an accurate representation of the model-form uncertainty using a small number of high-fidelity runs and many low-fidelity runs on multiple configurations. Running this comparison provides a quantifiable measurement for the accuracy of each turbulence model for this type of design. The high-fidelity CFD solver used was VULCAN-CFD and the low-fidelity results came from Cart3D. The experimental data came from the 20-Inch Mach 6 Tunnel located at NASA Langley Research Center. The present work showed that the using both the Spalart and Allamaras and Menter Shear-Stress Transport turbulence models overpredicted the drag and lift coefficient, while underpredicting the pitching moment coefficient. The model-form uncertainty estimate resulted in up to a 13.6% change in the total uncertainty for the drag coefficient, up to a 57.4% change in total uncertainty for the lift coefficient, and up to a 100% change in total uncertainty for the pitching moment coefficient.

Laura M. White↗

Community Input on the Need, Scope, and Development Roadmap of a Proposed National Virtual Biosecurity for Bioenergy Crops Center (NVBBCC)

Brookhaven National Laboratory (BNL) was awarded a pilot project in FY22 under the U.S. Department of Energy (DOE) Office of Science Biopreparedness Research Virtual Environment (BRaVE) initiative, to define research priorities, needs, and requirements for a national virtual center devoted to the biosecurity of bioenergy crops. The mission of the proposed center, referred to as the National Virtual Biosecurity for Bioenergy Crop Center (NVBBCC), would be to provide the scientific basis and tools to detect, characterize, model, and mitigate biothreats to bioenergy crops. This function will be essential to ensure the projected increased US reliance over the next few decades on key plant-based energy products, such as biojet fuel. The NVBBCC is envisioned as a distributed, virtual center with multiple national laboratories at its core to maximize the use of existing unique facilities and expertise across the DOE complex. A major goal of the pilot project was to develop a roadmap for establishing NVBBCC through a series of meetings to gather community input. A total of about 150 individuals, drawn from DOE laboratories, the USDA, academia, NIH, DHS and the private sector participated in six planning meetings held in FY23. Four of the meetings were focused on specific research topics (disease detection, dispersion and disease propagation, biomolecular characterization of plant-pathogen interaction, and mitigation strategies). These four meetings were followed by a meeting that focused on computational needs to support collaborative, data-intensive research within a distributed center as well as workforce development. A final meeting focused on establishing and maintaining preparedness within NVBBCC to respond to an emerging disease within bioenergy crops and how it would collaborate and coordinate with USDA and DHS.

09 BIOMASS FUELS↗

The Adhesion Strength of Impact Ice Measured Using a Modified Lap Joint Test

Numerous methodologies have been utilized to measure the adhesion strength of impact ice, and the data reported in the literature varies significantly from test to test. In order to initiate an investigation to determine the cause of this disparity, a new test methodology has been developed and utilized in the Icing Research Tunnel at the NASA Glenn Research Center. Data was obtained while varying the temperature, test section velocity, and cloud droplet mean volumetric diameter. The first data set acquired using this new test method is presented. New trends demonstrate the effect of annealing ice samples, with temperature being a key variable. Observations during the test and analysis of the results suggest the presence of large residual stresses in the samples.

Work, Andrew H., Jr.↗

Data Assimilation in the Ocean and in the Atmosphere: What Should be Next?

As part of the International Symposium on Assimilation of Observation in Meteorology and Oceanography, a panel discussion was held on the evening of 15 March 1995. The purpose of this panel discussion was focus on what the next major areas of research in data assimilation should be. The panelists had five minutes each for short presentations (Kalman filters, representers, etc.) and this was followed by an open discussion. This preprocessing will require a good understanding of the fine-scale phenomena. Least square methods such as Kalman filters and variational schemes are inefficient estimators of non-Gaussian field such as chemical traces. Regardless of the modeling technique employed (Lagrangian methods seem best), a least squares assimilation scheme will smear fine structure. The estimator of maximum likelihood must be sought, by examination of tracer probability distributions.

Kalnay, Eugenia↗

The CanBikeCO Full Pilot: Long-Term Results and Analysis From an E-Bike Program in Colorado, USA

Personal micromobility devices like bicycles, e-bikes, and scooters are low- or zero-energy alternatives to single-occupancy vehicles. However, a lack of data has led to a dearth of data-driven research on personally owned e-bike usage. We present longitudinal findings from the CanBikeCO program, focused on e-bike adoption and use across demographics, trip characteristics, and geographies in the state of Colorado. CanBikeCO recorded travel survey data from low-income individuals provided with personal e-bikes by the Colorado Energy Office in six communities across Colorado from July 2021 to December 2022. The data were collected using a custom instance of the National Renewable Energy Laboratory OpenPATH platform, which combines passive data collection with semantic information such as trip mode and purpose labels. To our knowledge, there are no prior travel survey data on personally owned e-bikes with this range and scope. Insights from this unique dataset include: (i) work trips were 17% more likely than average trips to be taken on an e-bike, (ii) e-bikes were most often reported to replace cars (34% of e-bike trips) and other personal micromobility devices (22%), and (iii) participants favored walking for trips less than 1 mile, e-bikes for trips of 1-3 miles, and e-bikes, cars, or shared rides for trips of 3-20 miles. The data used to generate these results have been made available in the Transportation Secure Data Center. We find e-bike use is appealing across age groups and may be related to characteristics of land use, urban form, occupation, income, and car ownership. We conclude for this population that the energy demand added by e-bike use (induced demand and replacing non-motorized modes) is outweighed by the reduction in energy demand from replacement of single-occupancy vehicle trips with e-bike trips. Our findings suggest considerable potential for energy savings from personal e-bike ownership.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Data and information system requirements for Global Change Research

Efforts to develop local information systems for supporting interdisciplinary Global Change Research are described. A prototype system, the Interdisciplinary Science Data and Information System (IDS-DIS), designed to interface the larger archives centers of EOS-DIS is presented. Particular attention is given to a data query information management system (IMS), which has been used to tabulate information of Landsat data worldwide. The use of these data in a modeling analysis of deforestation and carbon dioxide emissions is demonstrated. The development of distributed local information systems is considered to be complementary to the development of central data archives. Global Change Research under the EOS program is likely to result in proliferation of data centers. It is concluded that a distributed system is a feasible and natural way to manage data and information for global change research.

Skole, David L.↗

Air Quality Analysis and Tools For Policy

South Asia is home to some of the most significant air quality challenges in the world. More than 99% of the region’s population is exposed to unhealthy fine particulate matter (PM2.5) concentrations, as defined by the World Health Organization. Recent policy initiatives in South Asia show that air quality policy is a priority, given the health impacts of air pollution. At the same time, capacity and data to assess and address air pollution remains limited in many areas. The main objective of this report is to outline a set of publicly-available tools that can be used to inform policies to improve air quality in South Asia. The report primarily focuses on tools such as emission inventories and integrated modeling systems with country-level resolution and illustrates their air quality assessment capabilities through examples and brief technical summaries. The report focuses on: (a) the Community Emissions Data System (CEDS) that provides historical emissions of key pollutants for all countries in South Asia, and (b) profiles an analysis of Indian air pollution and policy scenarios, using several integrated modeling tools. Both of these examples draw on recent research and data that outlines the scale of the challenge as well as the policy options for reducing air pollution in the region. The report concludes with key recommendations for regional stakeholders working towards mitigating air pollution in South Asia.

54 ENVIRONMENTAL SCIENCES↗

A Statistical Analysis of Impact Ice Adhesion Strength Data Acquired with a Modified Lap Joint Test

Numerous methodologies have been utilized to measure the adhesion strength of impact ice, and the data reported in the literature varies significantly from method to method. In order to initiate an investigation to determine the cause of this disparity, a lap-joint shear test methodology that was recently developed was utilized in the Icing Research Tunnel at the NASA Glenn Research Center. Data was obtained while varying the temperature, test section velocity, liquid water content, and cloud droplet median volumetric diameter, among other parameters, over five campaigns. A new data set acquired using this new test method is presented. The results are analyzed with standard statistical methodologies and demonstrate a strong correlation between the apparent adhesion strength of ice and temperature, annealing time, and heating time. Observations during the test and analysis of the results suggest the presence of large residual stresses in the samples, which is in agreement with prior work. Due to the nature of the test methodology, and all known ice adhesion test methodologies, the measured, or apparent, adhesion strength is geometry dependent, a fact emphasized by the results presented here. The data was organized both cumulatively and independently by result code. Trends in the data are discussed in the context of causal physical mechanisms. The data shows the apparent adhesion strength was not linear with temperature, liquid water content, annealing time, and other variables. Short-term heating of the samples was shown to have negligible effect, and the apparent adhesion strength had a flat trend with velocity.

Ice Adhesion↗

Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer

Natural language processing (NLP), which aims to convert human language into expressions that can be analyzed by computers, is one of the most rapidly developing and widely used technologies in the field of artificial intelligence. Natural language processing algorithms convert unstructured free text data into structured data that can be extracted and analyzed at scale. In medicine, this unlocking of the rich, expressive data within clinical free text in electronic medical records will help untap the full potential of big data for research and clinical purposes. Recent major NLP algorithmic advances have significantly improved the performance of these algorithms, leading to a surge in academic and industry interest in developing tools to automate information extraction and phenotyping from clinical texts. Thus, these technologies are poised to transform medical research and alter clinical practices in the future. Radiation oncology stands to benefit from NLP algorithms if they are appropriately developed and deployed, as they may enable advances such as automated inclusion of radiation therapy details into cancer registries, discovery of novel insights about cancer care, and improved patient data curation and presentation at the point of care. However, challenges remain before the full value of NLP is realized, such as the plethora of jargon specific to radiation oncology, nonstandard nomenclature, a lack of publicly available labeled data for model development, and interoperability limitations between radiation oncology data silos. Successful development and implementation of high quality and high value NLP models for radiation oncology will require close collaboration between computer scientists and the radiation oncology community. Here, we present a primer on artificial intelligence algorithms in general and NLP algorithms in particular; provide guidance on how to assess the performance of such algorithms; review prior research on NLP algorithms for oncology; and describe future avenues for NLP in radiation oncology research and clinics.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Observing ocean-atmosphere exchanges with space-borne sensors, appendix C

At present, space-borne sensors cannot give accurate measurements of net longwave radiation and sensible heat flux at ocean surface. The variability of ocean surface net insolation was successfully estimated from observations using geostationary satellites. Global coverages of the low frequency variation of major atmosphere-ocean exchanges, momentum, latent heat, and insolation are within reach. Data from research and defense satellites should be made available in useful form to the user community. High quality in-situ measurements are required to calibrate space-borne sensors and numerical models are needed to integrate satellite and in-situ data as well as interpolate both in space and time. Techniques that can resolve boundary layer parameters should be developed.

Liu, W. T.↗

Temperature, Humidity, and Time-Lapse Video Data from the East River Watershed, Water Years 2024 and 2025

This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across the East River basin from 3 October 2023 to 8 August 2025. Instruments were deployed at 19 sites as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. Time-lapse cameras were deployed at three sites. Imagery from sites AP BONUS and AP5 (Avery Picnic) provides insight into large-scale seasonal snow cover variability. Imagery from site EL2 (Emerald Lake) shows smaller-scale snow patterns across a nearby meadow. Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into short videos stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.

54 ENVIRONMENTAL SCIENCES↗

Earth Survey Applications Division

Accomplishments of research and data analysis conducted to study physical parameters and processes inside the Earth and on the Earth's surface, to define techniques and systems for remotely sensing the processes and measuring the parameters of scientific and applications interest, and the transfer of promising operational applications techniques to the user community of Earth resources monitors, managers, and decision makers are described. Research areas covered include: geobotany, magnetic field modeling, crustal studies, crustal dynamics, sea surface topography, land resources, remote sensing of vegetation and soils, and hydrological sciences. Major accomplishments include: production of global maps of magnetic anomalies using Magsat data; computation of the global mean sea surface using GEOS-3 and Seasat altimetry data; delineation of the effects of topography on the interpretation of remotely-sensed data; application of snowmelt runoff models to water resources management; and mapping of snow depth over wheat growing areas using Nimbus microwave data.

Carpenter, L.↗

SLR Data: from Station to User

The ILRS provides laser ranging data and products on an operational basis to geodesy analysts as well as a broader scientific community. ILRS is one of four services within the International Association for Geodesy (IAG) supporting space geodesy. IAG established these services to facilitate international cooperation and scientific research: Networks, Data centers, Analysis groups. Services perform successful operations through cooperation of many international organizations, leveraging their respective resources to all levels of service functionality.

Noll, Carey↗

Real-Time Data from the Orbital Acceleration Research Experiment (OARE)

The objective of the Orbital Acceleration Research Experiment (OARE) is to measure, with high accuracy, the low-frequency, low-magnitude acceleration levels onboard the space shuttle. The shuttle experiences acceleration from atmospheric drag, gravity gradient forces, shuttle rotations, crew activities, water/waste dumps, and shuttle attitude thrusters. The OARE instrument has successfully flown on five past shuttle missions and is scheduled for five upcoming microgravity science missions. The data collected by OARE will be utilized by microgravity scientists to better predict and analyze the influence and effects of the shuttle's on-orbit microgravity environment on experiments in materials, combustion, and fluids research.

Source record↗

Passive microwave remote sensing for sea ice research

Techniques for gathering data by remote sensors on satellites utilized for sea ice research are summarized. Measurement of brightness temperatures by a passive microwave imager converted to maps of total sea ice concentration and to the areal fractions covered by first year and multiyear ice are described. Several ancillary observations, especially by means of automatic data buoys and submarines equipped with upward looking sonars, are needed to improve the validation and interpretation of satellite data. The design and performance characteristics of the Navy's Special Sensor Microwave Imager, expected to be in orbit in late 1985, are described. It is recommended that data from that instrument be processed to a form suitable for research applications and archived in a readily accessible form. The sea ice data products required for research purposes are described and recommendations for their archival and distribution to the scientific community are presented.

Source record↗

A multiband radiometer and data acquisition system for remote sensing field research

Specifications are described for a recently developed prototype multispectral data acquisition system which consists of multiband radiometer with 8 bands between 0.4 and 12.5 micrometers and a data recording module to record data from the radometer and ancillary sources. The systems is adaptable to helicopter, truck, or tripod platforms, as well as hand-held operation. The general characteristics are: (1) comparatively inexpensive to acquire, maintain and operate; (2) simple to operate and calibrate; (3) complete with data hardware and software; and (4) well documented for use by researchers. The instrument system is to be commercially available and can be utilized by many researchers to obtain large numbers of accurate, calibrated spectral measurements. It can be a key element in improving and advancing the capability for field research in remote sensing.

Bauer, M. E.↗