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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

LANDSAT-4 image data quality analysis

Seven heterogeneous areas within the entire Des Moines, Iowa test site were selected to define candidate spectral training classes using a clustering algorithm. In addition to the 91 cluster nonsupervised classes, three supervised training classes were defined. The original candidate training classes were reduced to 42 spectrally separable training classes. The minimum and average transformed divergence values for the 42 spectral classes and for the best subsets of Y TM spectral bands are shown in a table. The best spectral band for any combination of 1 through 7 bands is the first middle IR band. The next best band is the near IR, followed by the red band and than the thermal IR. The best combination of four bands includes one from each of the four regions of the spectrum (visible, near IR, middle IR, and thermal IR).

Anuta, P. E.↗
NASA NTRS1983

LANDSAT-4 image data quality analysis

Seven heterogeneous areas within the Des Moines, Iowa area test site were selected to define candidate spectral training classes using a clustering algorithm. In addition to the 91 cluster (nonsupervised) classes, three supervised training classes were defined and subsequently included in the training statistics file. The identity of all 94 candidate classes were determined using available reference data. Through analysis of the interclass separabilities, the original 94 candidate training classes were reduced to 42 spectrally separable final classes. The minimum average transformed divergence values for the 42 spectral classes and for the best subsets of TM spectral bands are shown in a table.

Anuta, P. E.↗
NASA NTRS2003

Automating CapCom: Pragmatic Operations and Technology Research for Human Exploration of Mars

During the Apollo program, NASA and the scientific community used terrestrial analog sites for understanding planetary features and for training astronauts to be scientists. More recently, computer scientists and human factors specialists have followed geologists and biologists into the field, learning how science is actually done on expeditions in extreme environments. Research stations have been constructed by the Mars Society in the Arctic and American southwest, providing facilities for hundreds of researchers to investigate how small crews might live and work on Mars. Combining these interests-science, operations, and technology-in Mars analog field expeditions provides tremendous synergy and authenticity to speculations about Mars missions. By relating historical analyses of Apollo and field science, engineers are creating experimental prototypes that provide significant new capabilities, such as a computer system that automates some of the functions of Apollo s CapCom. Thus, analog studies have created a community of practice-a new collaboration between scientists and engineers-so that technology begins with real human needs and works incrementally towards the challenges of the human exploration of Mars.

Clancey, William J.↗
NASA NTRS2007

PISCES: A "Stepping Stone" to International Space Exploration and Development

The Pacific International Space Center for Exploration Systems (PISCES) was initiated by the Japan/US Science, Technology and Space Application Programs (JUSTSAP) to advance research and education in space exploration technology and systems working closely with the State of Hawaii. Hawaii has a heritage with space exploration including the training of Apollo astronauts and testing of lunar rover systems in some of the most realistic terrestrial sites available. The high altitude dry environment with greater solar insolation, and the dry lunar regolith-like volcanic ash and cratered terrain make Hawaiian sites ideal to support, international space exploration technology development, demonstration, education and training. This paper will summarize development and roles of PISCES in lunar surface analogs, simulations, technology demonstrations, research and training for space exploration technology and systems.

Howell, Joe T.↗
NASA NTRS1999

Support for Astronaut's View of Mexican/ Central American Fires and on-Line Earth Observations Training Manual

A small project to compile remote sensing and in-site data to review the processes leading to the May 1998 Mexican/Central American fires was undertaken. A web page based on this project was assembled. The second project initiated involved an interactive and on-line program that will replace the paper version of the Earth Observations Preflight Training Manual. Technical support was provided to Prof. Marvin Glasser as needed.

Kaminski, Charles F., Jr.↗
DOE OSTI2020

Post-Closure Inspection Report for the Tonopah Test Range and Nevada Test and Training Range, Nevada, For Calendar Year 2019, Revision 0

This report provides the results of the annual post-closure use restriction (UR) inspections at the closed corrective action sites (CASs) located on the Tonopah Test Range (TTR) and Nevada Test and Training Range (NTTR) that are accessed through the TTR/NTTR main gate. This report covers post-closure UR inspections for calendar year 2019, and includes visual inspections and repair activities completed at the following corrective action units (CAUs): • CAU 400, Bomblet Pit and Five Points Landfill (TTR) • CAU 407, Roller Coaster RadSafe Area (TTR) • CAU 424, Area 3 Landfill Complexes (TTR) • CAU 453, Area 9 UXO Landfill (TTR) • CAU 487, Thunderwell Site (TTR)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Public – Private Partnership to Promote Efficient Manufacturing and Workforce Development (IAC Final Technical Report)

Through delivering ninety (90) energy assessments to small- and medium-sized manufacturing facilities in and around Tennessee, significant understandings were gained through providing detailed energy assessment reports to manufacturers and training seventy-seven (77) engineering students in which thirty-five (35) certified IAC students were recognized by DOE. The robust involvement of Tennessee Valley Authority (TVA), local utility and Tennessee Tech’s Cybersecurity Education, Research, and Outreach Center (CEROC) in majority of assessments made the technical operation very effective where leaded to 40.5% implementation rate of energy recommendations. Other benefits of the award to the public are a dynamic modeling tool capable of analyzing existing cooling towers for energy use optimization was developed and presented to DOE IAC, providing technical assistance to non-participating manufacturers in the form of workshop and training, publishing fifteen (15) peer-reviewed conference and journal papers as well as a book chapter in the area of industrial energy efficiency field, assisting the TVA Magnolia Combined Cycle Power Plant to become the First 50001 Ready Certified Power Plant in the U.S. through briefing them on ISO 50001 and 50001 Ready as well as providing an energy assessment to the plant, assisted the new IAC center at Clemson University with on-site and remote support, and provided assessment and training in unique areas of water, waste water, cyber security and smart manufacturing to our clients, students and community.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Non-Resource Conservation and Recovery Act Corrective Action Unit (CAU) Post-Closure Inspection Report, Nevada National Security Site, Nevada For Calendar Year 2024

This report includes visual inspection results, descriptions of maintenance and repair activities, and recommendations for calendar year 2024 for use restrictions at corrective action sites located on the Nevada National Security Site (NNSS) and on the Nevada Test and Training Range that are accessed through the NNSS Main Gate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

National Park Air Quality Index Dataset

The National Park Air Quality Index dataset (NPS-AQI) consists of webcam images taken from the National Park Service's publicly available air quality web cameras and associated measurements for air pollutants, AQI, and meteorological data obtained via the publicly available NPS Gaseous Pollutant Monitoring Program. The full dataset is a collection of 146,822 images paired with air quality measurements. The specific measurements reported are: ozone ppm, 8-hour running average ozone ppm, so2 ppm, AQI (derived from ozone), temperature, and humidity. The images are 1500X1000 pixel PNG files arranged into folders by NPS site and named according to the time and date the image was taken. There are three CSV files (representing "training", "validation", and "testing" images splits) containing image names and associated NPS site names, air pollutant measurements, and meteorlogical data.

Svinth, Christian N↗
NASA NTRS1979

Nationwide forestry applications program. Ten-Ecosystem Study (TES) site 8, Grays Harbor County, Washington

The author has identified the following significant results. Level 2 forest features (softwood, hardwood, clear-cut, and water) can be classified with an overall accuracy of 71.6 percent plus or minus 6.7 percent at the 90 percent confidence level for the particular data and conditions existing at the time of the study. Signatures derived from training fields taken from only 10 percent of the site are not sufficient to adequately classify the site. The level 3 softwood age group classification appears reasonable, although no statistical evaluation was performed.

Prill, J. C.↗
DOE OSTI2024

Activities at Pacific Northwest National Laboratory to Advance Nuclear Radiation Science among Underrepresented STEM Students

The Partnership for Radiation Studies (PaRS) consortium is a collaboration between two minority-serving institutions and two U.S. national laboratories. The five-year consortium is funded by the U.S. Department of Energy’s (DOE) National Nuclear Security Administration (NNSA), and it aims enhance workforce of underrepresented students in STEM disciplines. PaRS’ long-term goal is to train students for their prospective hiring in DOE laboratories and sites. As one of the two integral national laboratory partners, the Pacific Northwest National Laboratory (PNNL) is playing an integral role in achieving this goal via pertinent lectures, seminars, hands-on trainings, and outreach activities. This paper summarizes PNNL’s key activities and initiatives to train the next generation of underrepresented students in STEM disciplines.

Minority Serving Institutions Partnership Program,↗
NASA NTRS1995

The effects of Crew Resource Management (CRM) training in airline maintenance: Results following three year's experience

An airline maintenance department undertook a CRM training program to change its safety and operating culture. In 2 1/2 years this airline trained 2200 management staff and salaried professionals. Participants completed attitude surveys immediately before and after the training, as well as two months, six months, and one year afterward. On-site interviews were conducted to test and confirm the survey results. Comparing managers' attitudes immediately after their training with their pretraining attitudes showed significant improvement for three attitudes. A fourth attitude, assertiveness, improved significantly above the pretraining levels two months after training. The expected effect of the training on all four attitude scales did not change significantly thereafter. Participants' self-reported behaviors and interview comments confirmed their shift from passive to more active behaviors over time. Safety, efficiency, and dependability performance were measured before the onset of the training and for some 30 months afterward. Associations with subsequent performance were strongest with positive attitudes about sharing command (participation), assertiveness, and stress management when those attitudes were measured 2 and 12 months after the training. The two month follow-up survey results were especially strong and indicate that active behaviors learned from the CRM training consolidate and strengthen in the months immediately following training.

Taylor, J. C.↗
DOE OSTI2022

Dataset for 'Ombadi, M. & Varadharajan, C. (2022). Urbanization and aridity mediate distinct salinity response to floods in rivers and streams across the Contiguous United States, Water Research'

This package contains data sets and code used to obtain the results in Ombadi, M., & Varadharajan, C. (2022). Urbanization and aridity mediate distinct salinity response to floods in rivers and streams across the Contiguous United States. Water Research, 118664. The folder "data" contains 259 .csv files, each of which has daily time series of concurrent streamflow (Q) and specific conductance (SC) for each of the sites used in this study originally downloaded from the USGS National Water Information System (NWIS; USGS, 2016). The number of data points in each of the files is at least 3650 (i.e. 10 years of daily measurements). The folder "RF_single_data" contains 259 .csv files, each of which include data used to train and test the Random Forest models at individual sites for predicting SC during days of floods. The folder "RF_regional_data" contains 3 .csv files, each of which include scaled data compiled from all sites within each climate zone (arid, temperate and wet). "metadata.csv" contains the physical properties of the 259 catchments corresponding to the sites used in this study; this data was extracted from GAGES-II dataset (Falcone et al., 2010). "RF_implementation.ipynb" is a Jupyter notebook with the code needed to implement the analysis using Random Forest models either for individual sites or for the regional models (for each climate zone). The code utilizes the data in the two folders: "RF_single_data" and "RF_regional_data" and the metadata.csv file.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning

Stream temperature (Ts) is an important water quality parameter that affects ecosystem health and human water use for beneficial purposes. Accurate Ts predictions at different spatial and temporal scales can inform water management decisions that account for the effects of changing climate and extreme events. In particular, widespread predictions of Ts in unmonitored stream reaches can enable decision makers to be responsive to changes caused by unforeseen disturbances. In this study, we demonstrate the use of classical machine learning (ML) models, support vector regression and gradient boosted trees (XGBoost), for monthly Ts predictions in 78 pristine and human-impacted catchments of the Mid-Atlantic and Pacific Northwest hydrologic regions spanning different geologies, climate, and land use. The ML models were trained using long-term monitoring data from 1980–2020 for three scenarios: (1) temporal predictions at a single site, (2) temporal predictions for multiple sites within a region, and (3) spatiotemporal predictions in unmonitored basins (PUB). In the first two scenarios, the ML models predicted Ts with median root mean squared errors (RMSE) of 0.69–0.84 °C and 0.92–1.02 °C across different model types for the temporal predictions at single and multiple sites respectively. For the PUB scenario, we used a bootstrap aggregation approach using models trained with different subsets of data, for which an ensemble XGBoost implementation outperformed all other modeling configurations (median RMSE 0.62 °C).The ML models improved median monthly Ts estimates compared to baseline statistical multi-linear regression models by 15–48% depending on the site and scenario. Air temperature was found to be the primary driver of monthly Ts for all sites, with secondary influence of month of the year (seasonality) and solar radiation, while discharge was a significant predictor at only 10 sites. The predictive performance of the ML models was robust to configuration changes in model setup and inputs, but was influenced by the distance to the nearest dam with RMSE <1 °C at sites situated greater than 16 and 44 km from a dam for the temporal single site and regional scenarios, and over 1.4 km from a dam for the PUB scenario. Our results show that classical ML models with solely meteorological inputs can be used for spatial and temporal predictions of monthly Ts in pristine and managed basins with reasonable (<1 °C) accuracy for most locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Best estimate of the planetary boundary layer height from multiple remote sensing measurements

Remote sensing measurements have been widely used to estimate the planetary boundary layer height (PBLHT). Each remote sensing approach offers unique strengths and faces different limitations. In this study, we use machine learning (ML) methods to produce a best-estimate PBLHT (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory. Three ML models – random forest (RF) classifier, RF regressor, and light gradient-boosting machine (LightGBM) – were trained on a dataset from 2017 to 2023 that included radiosonde, various remote sensing PBLHT estimates, and atmospheric meteorological conditions. Evaluations indicated that PBLHT-BE-ML from all three models improved alignment with the PBLHT derived from radiosonde data (PBLHT-SONDE), with LightGBM demonstrating the highest accuracy under both stable and unstable boundary layer conditions. Feature analysis revealed that the most influential input features at the SGP site were the PBLHT estimates derived from (a) potential temperature profiles retrieved using Raman lidar (RL) and atmospheric emitted radiance interferometer (AERI) measurements (PBLHT-THERMO), (b) vertical velocity variance profiles from Doppler lidar (PBLHT-DL), and (c) aerosol backscatter profiles from micropulse lidar (PBLHT-MPL). The trained models were then used to predict PBLHT-BE-ML at a temporal resolution of 10 min, effectively capturing the diurnal evolution of PBLHT and its significant seasonal variations, with the largest diurnal variation observed over summer at the SGP site. We applied these trained models to data from the ARM Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field campaign (EPC), where the PBLHT-BE-ML, particularly with the LightGBM model, demonstrated improved accuracy against PBLHT-SONDE. Analyses of model performance at both the SGP and EPC sites suggest that expanding the training dataset to include various surface types, such as ocean and ice-covered areas, could further enhance ML model performance for PBLHT estimation across varied geographic regions.

Zhang, Damao [Pacific Northwest National Laborator↗
DOE OSTI2026

Data from: Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N₂O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N₂O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N2O fluxes from US cropland. Trained and validated on approximately 12,000 N2O chamber measurements at 17 U.S. Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N2O at both training (R² = 0.84, RMSE = 16.4 g N ha⁻¹ d⁻¹) and held-out testing sites (R² = 0.84, RMSE = 6.2 g N ha⁻¹ d⁻¹). Analyses identified six dominant N₂O drivers: soil organic carbon (SOC), NH₄⁺, NO₃⁻, water-filled pore space (WFPS), soil temperature, and biomass production. Wet, warm soils produced large N₂O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N₂O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

agricultural sciences↗
NASA NTRS2002

Virtually Out of This World!

Ames Research Center granted Reality Capture Technologies (RCT), Inc., a license to further develop NASA's Mars Map software platform. The company incorporated NASA#s innovation into software that uses the Virtual Plant Model (VPM)(TM) to structure, modify, and implement the construction sites of industrial facilities, as well as develop, validate, and train operators on procedures. The VPM orchestrates the exchange of information between engineering, production, and business transaction systems. This enables users to simulate, control, and optimize work processes while increasing the reliability of critical business decisions. Engineers can complete the construction process and test various aspects of it in virtual reality before building the actual structure. With virtual access to and simulation of the construction site, project personnel can manage, access control, and respond to changes on complex constructions more effectively. Engineers can also create operating procedures, training, and documentation. Virtual Plant Model(TM) is a trademark of Reality Capture Technologies, Inc.

Source record↗
DOE OSTI2021

Improving Prediction of Surface Solar Irradiance Variability by Integrating Observed Cloud Characteristics and Machine Learning

A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.

Riihimaki, Laura↗