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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 325 records · Page 18

All sources NASA NTRS DOE OSTI

NASA NTRS1974

Measurement and correlation of aerodynamic heating to surface corrugation stiffened structures in thick turbulent boundary layers

The flow conditions for which heating distributions were measured on corrugated surfaces and wavy walls in turbulent boundary layers are shown, along with the ratio of the displacement thickness to the roughness height versus the local edge Mach number for an equivalent smooth surface. The present data are seen to greatly extend the range of data available on corrugated surfaces in turbulent boundary layers. These data were obtained by testing fullscale corrugation roughened panels in the wall boundary layer of a supersonic and hypersonic wind tunnel. The experimental program used to obtain the data is described. The data are analyzed and correlated in terms of the pertinent flow and geometric parameters. The developed correlations are compared with the available thin boundary layer data, as well as with previously published correlation techniques.

Brandon, H. J.↗

DOE OSTI2024

Tools for Water Ingress Testing

The Safety Storage and Engineering Team, as part of the Production Support Services division (PSS-2), is tasked with ensuring the safety of containers used for handling and storage of nuclear materials. As part of this work, water ingress tests are conducted to evaluate the water-tightness of containers intended for in-glovebox use. In collaboration, the statistics group of the Computer and Computational Sciences Division (CCS-6) provided support in developing a statistically defensible approach for determining appropriate sample sizes for water ingress testing. Water ingress testing involves multiple measurements on multiple containers. Our approach uses a simple random effects model to analyze a pilot data set, implementing prediction limits to evaluate the efficacy of collecting additional data. Although this study capitalizes on available data, our approach can be used with estimates of the ratio of between and within variability and average values, often available from past testing or expert knowledge. An interactive Shiny tool was developed as a final user-friendly product for future testing. The Shiny interface is an open-source package providing a framework for building web applications. Raw data exploration and prediction interval-based sample size assessments can quickly be conducted by the engineering team without needing to interact with the underlying code.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1993

Rain-rate data base development and rain-rate climate analysis

The single-year rain-rate distribution data available within the archives of Consultative Committee for International Radio (CCIR) Study Group 5 were compiled into a data base for use in rain-rate climate modeling and for the preparation of predictions of attenuation statistics. The four year set of tip-time sequences provided by J. Goldhirsh for locations near Wallops Island were processed to compile monthly and annual distributions of rain rate and of event durations for intervals above and below preset thresholds. A four-year data set of tropical rain-rate tip-time sequences were acquired from the NASA TRMM program for 30 gauges near Darwin, Australia. They were also processed for inclusion in the CCIR data base and the expanded data base for monthly observations at the University of Oklahoma. The empirical rain-rate distributions (edfs) accepted for inclusion in the CCIR data base were used to estimate parameters for several rain-rate distribution models: the lognormal model, the Crane two-component model, and the three parameter model proposed by Moupfuma. The intent of this segment of the study is to obtain a limited set of parameters that can be mapped globally for use in rain attenuation predictions. If the form of the distribution can be established, then perhaps available climatological data can be used to estimate the parameters rather than requiring years of rain-rate observations to set the parameters. The two-component model provided the best fit to the Wallops Island data but the Moupfuma model provided the best fit to the Darwin data.

Crane, Robert K.↗

NASA NTRS1982

A preliminary study of the particulate fallout from the space shuttle (STS-1)

Prediction of acid fallout and the dry deposition of Al2O2 was the objective of this investigation. Sampling was done at the Kennedy Space Center in Florida. The models available were not appropriate and the data available was negligible. Thus, a bimodal particle distribution was assumed normalized to the few existing data points and used as a foundation for a crude zeroth order approximation for the acid fallout. In addition, a settling spectrum for the Al2O3 particles was devised as a table look-up since the graphs in the literature at first pass could not be fitted with reasonable analytic functions. Consulting services were rendered to researchers. Special emphasis was placed on improving current techniques and adding LIDAR (Light Radar). Suggestions for future studies are made.

Hudson, G. M.↗

NASA NTRS2020

Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (LDAS) and Other Assimilated Hydrological Data at NASA GES DISC

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides science support for several data sets relevant to agriculture and food security, including the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (LDAS), or FLDAS data set. The GES DISC is one of twelve NASA Earth Observing System (EOS) data centers that process, archive, document, and distribute data from Earth science missions and related projects. The GES DISC hosts a wide range of remote sensing and model data, and provides reliable and robust data access and other services to users worldwide. Beyond data archive and access, the GES DISC offers many services to visualize and analyze the data. This presentation provides a summary of the hydrological data available at the GES DISC, along with an overview of related data services. Specifically, the FLDAS data set has been adapted to work with domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing country settings. The FLDAS global monthly data have a 0.1 x 0.1 degree spatial resolution covering the period from January 1982 to present. Global FLDAS monthly anomaly and monthly climatology data are also available at the GES DISC to evaluate how current conditions compare to averages over the FLDAS 35-year period. Several case studies using the FLDAS soil moisture, evapotranspiration, rainfall, runoff, and surface temperature data will be presented.

Loeser, Carlee↗

DOE OSTI2022

Suppressing simulation bias in multi-modal data using transfer learning

Abstract Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multi-dimensional. Simulations, however, often suffer from an inherent bias. Estimation of this bias may be poorly constrained not only because of data sparsity, but also because traditional predictive models fit only one type of observed outputs, such as scalars or images, instead of all available output data modalities, which might have been acquired and simulated at great cost. To break this limitation and open up the path for multi-modal calibration, we propose to combine a novel, transfer learning technique for suppressing the bias with recent developments in deep learning, which allow building predictive models with multi-modal outputs. First, we train an initial neural network model on simulated data to learn important correlations between different output modalities and between simulation inputs and outputs. Then, the model is partially retrained, or transfer learned, to fit the experiments; a method that has never been implemented in this type of architecture. Using fewer than 10 inertial confinement fusion experiments for training, transfer learning systematically improves the simulation predictions while a simple output calibration, which we design as a baseline, makes the predictions worse. We also offer extensive cross-validation with real and carefully designed synthetic data. The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Preliminary Transfer Learning Results on Israel Data

In this preliminary report, we use publicly available data recorded in Israel to test and expand upon existing machine learning models for seismic-phase detection and arrival-time measurement. We downloaded 3-years of waveform data from Geofon, and cross referenced the waveforms to Israel bulletin picks (Schardong et al., 2021). The initial results using existing models directly generated ubiquitous false detections and that obscured detections of signals that are clearly visible in the waveforms. However, after applying transfer learning (tuning parameters in the existing ML models using one year of the Israel-network data), the results are encouraging, i.e. ML picks agree within a few tenths of a second with bulletin picks and the number of false detections is greatly reduced. The bulletin picks are a good starting point, but they cannot be considered ground-truth. To test potential improvement in picking using ML we would like to relocate the events using the ML picks to see if the events cluster more tightly at known mine locations. However, in order to constrain event locations, we need ML picks for the whole Israeli-Jordanian network, which requires waveforms that are not publicly available.

58 GEOSCIENCES↗

NASA NTRS1967

Solubility data are compiled for metals in liquid zinc

Available data is compiled on the solubilities of various metals in liquid zinc. The temperature dependence of the solubility data is expressed using the empirical straight line relationship existing between the logarithm of the solubility and the reciprocal of the absolute temperature.

Dillon, I. G.↗

DOE OSTI2020

Monte Carlo Global QCD Analyses of the Pion Parton Distribution Functions

As the lightest hadron, the pion presents itself as a dichotomy. While being the pseudo Goldstone boson associated with chiral symmetry breaking, it is simultaneously regarded as the lightest pseudoscalar meson typically composed of constituent quark and antiquark. As the chiral symmetry shares the same global symmetry as QCD, studying the pion and its internal structure is key in understanding one of the four fundamental forces of nature, namely the quantum chromodynamic strong interaction. We study the pion's parton distribution functions (PDFs), which are universal quantities that describe the structure of the pion in terms of its constituent quarks, antiquarks, and gluons. Our goal is to discover what the available data reveal for these universal quantities, namely the PDFs. Through the use of factorization theorems and perturbative QCD, we use Monte Carlo (MC) methods to extract the PDFs from the available Drell-Yan (DY) and leading neutron (LN) data. While the DY process involves two hadrons colliding, the LN electroproduction involves an electron beam incident on a target nucleon. We can constrain well the pion PDFs at large momentum fraction using DY data, and at low momentum fraction using LN data. We also use threshold resummation in the DY process to gather predictable higher order terms associated with the soft gluon radiation because their contributions to the cross section are nontrivial. In this dissertation, we parametrize the pion PDFs and fit those parameters to the available data. We make use of Bayesian inference and use MC techniques to sample the parameter space. Three sets of results are presented. We first extract the pion PDFs by fitting the DY and LN data. Then, we include transverse momentum dependent DY data in conjunction with the DY and LN data to extract the pion PDFs. Finally, we apply various methods of threshold resummation to the DY cross section and extract pion PDFs. The pion PDFs presented here are the first global QCD analyses performed as well as the first MC extracted pion PDFs, which are at the forefront of the pion PDF community.

Barry, Patrick↗

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↗

NASA NTRS2014

Constructing the "Best" Reliability Data for the Job

Modern business and technical decisions are based on the results of analyses. When considering assessments using "reliability data", the concern is how long a system will continue to operate as designed. Generally, the results are only as good as the data used. Ideally, a large set of pass/fail tests or observations to estimate the probability of failure of the item under test would produce the best data. However, this is a costly endeavor if used for every analysis and design. Developing specific data is costly and time consuming. Instead, analysts rely on available data to assess reliability. Finding data relevant to the specific use and environment for any project is difficult, if not impossible. Instead, we attempt to develop the "best" or composite analog data to support our assessments. One method used incorporates processes for reviewing existing data sources and identifying the available information based on similar equipment, then using that generic data to derive an analog composite. Dissimilarities in equipment descriptions, environment of intended use, quality and even failure modes impact the "best" data incorporated in an analog composite. Once developed, this composite analog data provides a "better" representation of the reliability of the equipment or component can be used to support early risk or reliability trade studies, or analytical models to establish the predicted reliability data points. Data that is more representative of reality and more project specific would provide more accurate analysis, and hopefully a better final decision.

DeMott, D. L.↗

NASA NTRS2014

Constructing the Best Reliability Data for the Job

Kleinhammer, R. K.↗

NASA NTRS2016

Constructing the 'Best' Reliability Data for the Job - Developing Generic Reliability Data from Alternative Sources Early in a Product's Development Phase

Reliability practitioners advocate getting reliability involved early in a product development process. However, when assigned to estimate or assess the (potential) reliability of a product or system early in the design and development phase, they are faced with lack of reasonable models or methods for useful reliability estimation. Developing specific data is costly and time consuming. Instead, analysts rely on available data to assess reliability. Finding data relevant to the specific use and environment for any project is difficult, if not impossible. Instead, analysts attempt to develop the "best" or composite analog data to support the assessments. Industries, consortia and vendors across many areas have spent decades collecting, analyzing and tabulating fielded item and component reliability performance in terms of observed failures and operational use. This data resource provides a huge compendium of information for potential use, but can also be compartmented by industry, difficult to find out about, access, or manipulate. One method used incorporates processes for reviewing these existing data sources and identifying the available information based on similar equipment, then using that generic data to derive an analog composite. Dissimilarities in equipment descriptions, environment of intended use, quality and even failure modes impact the "best" data incorporated in an analog composite. Once developed, this composite analog data provides a "better" representation of the reliability of the equipment or component. It can be used to support early risk or reliability trade studies, or analytical models to establish the predicted reliability data points. It also establishes a baseline prior that may updated based on test data or observed operational constraints and failures, i.e., using Bayesian techniques. This tutorial presents a descriptive compilation of historical data sources across numerous industries and disciplines, along with examples of contents and data characteristics. It then presents methods for combining failure information from different sources and mathematical use of this data in early reliability estimation and analyses.

Kleinhammer, Roger K.↗

NASA NTRS2011

Nowcasting Aircraft Icing Conditions in the Presence of Multilayered Clouds Using Meteorological Satellite Data

Cloud properties retrieved from satellite data are used to diagnose aircraft icing threat in single layer and multilayered ice-over-liquid clouds. The algorithms are being applied in real time to the Geostationary Operational Environmental Satellite (GOES) data over the CONUS with multilayer data available over the eastern CONUS. METEOSAT data are also used to retrieve icing conditions over western Europe. The icing algorithm s methodology and validation are discussed along with future enhancements and plans. The icing risk product is available in image and digital formats on NASA Langley s Cloud and Radiation Products web site, http://wwwangler. larc.nasa.gov.

Spangenberg, Douglas A.↗

NASA NTRS2015

Real-Time Kennedy Space Center and Cape Canaveral Air Force Station High-Resolution Model Implementation and Verification

Customer: NASA's Launch Services Program (LSP), Ground Systems Development and Operations (GSDO), and Space Launch System (SLS) programs. NASA's LSP, GSDO, SLS and other programs at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) use the daily and weekly weather forecasts issued by the 45th Weather Squadron (45 WS) as decision tools for their day-to-day and launch operations on the Eastern Range (ER). For example, to determine if they need to limit activities such as vehicle transport to the launch pad, protect people, structures or exposed launch vehicles given a threat of severe weather, or reschedule other critical operations. The 45 WS uses numerical weather prediction models as a guide for these weather forecasts, particularly the Air Force Weather Agency (AFWA) 1.67 kilometer Weather Research and Forecasting (WRF) model. Considering the 45 WS forecasters' and Launch Weather Officers' (LWO) extensive use of the AFWA model, the 45 WS proposed a task at the September 2013 Applied Meteorology Unit (AMU) Tasking Meeting requesting the AMU verify this model. Due to the lack of archived model data available from AFWA, verification is not yet possible. Instead, the AMU proposed to implement and verify the performance of an ER version of the AMU high-resolution WRF Environmental Modeling System (EMS) model (Watson 2013) in real-time. The tasking group agreed to this proposal; therefore the AMU implemented the WRF-EMS model on the second of two NASA AMU modeling clusters. The model was set up with a triple-nested grid configuration over KSC/CCAFS based on previous AMU work (Watson 2013). The outer domain (D01) has 12-kilometer grid spacing, the middle domain (D02) has 4-kilometer grid spacing, and the inner domain (D03) has 1.33-kilometer grid spacing. The model runs a 12-hour forecast every hour, D01 and D02 domain outputs are available once an hour and D03 is every 15 minutes during the forecast period. The AMU assessed the WRF-EMS 1.33-kilometer domain model performance for the 2014 warm season (May-September). Verification statistics were computed using the Model Evaluation Tools, which compared the model forecasts to observations. The mean error values were close to 0 and the root mean square error values were less than 1.8 for mean sea-level pressure (millibars), temperature (degrees Kelvin), dewpoint temperature (degrees Kelvin), and wind speed (per millisecond), all very small differences between the forecast and observations considering the normal magnitudes of the parameters. The precipitation forecast verification results showed consistent under-forecasting of the precipitation object size. This could be an artifact of calculating the statistics for each hour rather than for the entire 12-hour period. The AMU will continue to generate verification statistics for the 1.33-kilometer WRF-EMS domain as data become available in future cool and warm seasons. More data will produce more robust statistics and reveal a more accurate assessment of model performance. Once the formal task was complete, the AMU conducted additional work to better understand the wind direction results. The results were stratified diurnally and by wind speed to determine what effects the stratifications would have on the model wind direction verification statistics. The results are summarized in the addendum at the end of this report. In addition to verifying the model's performance, the AMU also made the output available in the Advanced Weather Interactive Processing System II (AWIPS II). This allows the 45 WS and AMU staff to customize the model output display on the AMU and Range Weather Operations AWIPS II client computers and conduct real-time subjective analyses. In the future, the AMU will implement an updated version of the WRF-EMS model that incorporates local data assimilation. This model will also run in real-time and be made available in AWIPS II.

Numerical Weather Prediction↗

DOE OSTI2020

High Resolution Ocean Surface Wave Hindcast (US Wave) Data

The development of this dataset was funded by the U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, Water Power Technologies Office to improve our understanding of the U.S. wave energy resource and to provide critical information for wave energy project development and wave energy converter design. This high resolution publicly available long-term wave hindcast dataset will - when complete - cover the entire U.S. Exclusive Economic Zone (EEZ). Available data includes the Hawaiian Islands, West and Atlantic Coasts, with future additions including the Alaskan coasts, Gulf of Mexico and the Freely associated States. The data can be used to investigate the historical record of wave statistics at any U.S. site. As such, the dataset could also be of value to any entity with marine operations inside the U.S. EEZ. These data are available for download without login credentials through the free and publicly accessible Open Energy Data Initiative (OEDI) data viewer which allows users to browse and download individual or groups of files.

16 TIDAL AND WAVE POWER↗

DOE OSTI2026

Hydropower potential derived from streamflow extremes for Alaska, USA

Alaska is an expansive region known for its abundant natural resources, including thousands of miles of streams and rivers. These rivers represent potential opportunities for future hydropower development that could provide reliable energy supply for local communities. There is limited long-term high temporal resolution streamflow data available for the region, making data-driven estimates of potential hydropower and its variability across the state challenging. This study provides a novel data-driven approach for hydropower capacity estimation across Alaska. We use supervised machine learning to develop a relationship between the daily and peak flow duration curves in order to augment the size of our dataset from 44 sites to 67 sites. We perform a stochastic hydropower estimation across the 67 sites and identify approximately 1000 MW of total potential hydropower capacity distributed across these sites. Our study provides the first step towards more comprehensive hydropower estimation for this critical region, highlighting the need for future work integrating high-resolution spatial data, community needs, and economic constraints in estimates of potential hydropower development in Alaska.

Hydropower↗

DOE OSTI2025

2003 Interstate 595 Vehicle Trip-Length Study

# 2003 Interstate 595 Vehicle Trip-Length Study The 2003 Vehicle Trip-Length Study focused on Interstate 595 between Davie Road and University Drive in Florida. Survey participants answered questions about their trip's origin and destination—including the type of location such as work, home, store, etc.—the on- and off-ramps used, how many people were in the car, and the type of vehicle. The survey also collected household demographic data such as annual household income, available vehicles, the number of people living in their household, and the number of workers in their household above the age of 16. It also asked if they would use proposed bus-only lanes or train service along the corridor, if available. ## Data Collection Agency The survey was conducted by and for the Florida Department of Transportation. ## Survey Methodology The survey was conducted via mail and online in March 2003. ## Survey Records, Data, and Documentation Survey records include 7,917 participants. Origin and destination locations include street addresses, nearest intersections or landmarks, city, state, zip code, and latitude/longitude.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗