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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Extending Shared Socioeconomic Pathways to Future Water Supply In-frastructure Scenarios: A Case Study of San Antonio, TX

Datasets supporting findings and visualization behind Okoye and McManamay (2025) Extending Shared Socioeconomic Pathways to Future Water Supply Infrastructure Scenarios: A Case Study of San Antonio, TX. Environmental Research Communications, DOI: 10.57931/2563186 These datasets contains the results of a site selection analysis for municipal water supply planning across multiple Shared Socioeconomic Pathways (SSPs 1–5) and hard scenario classification of water systems in San Antonio, TX. It includes data at the resolution of individual surface water supply sources (COMIDs) and integrates a wide range of hydrologic, socioeconomic, infrastructural, and scenario-based planning variables. Please refer to the README file provided in Files for more details. Descriptions of the datasets are provided below. Dataset(s) Descriptions: Dataset_SSP1_SSP4.xlsx - Contains data used for site selection optimization under SSP1 to SSP4. This dataset was generated based on multi-indicator computations (e.g., WAI, WQI, ERI, WTC, WIC), scenario demand projections, and resource and spatial constraints, excluding new reservoir values. Dataset_SSP5.xlsx - Used for site optimization under SSP5. Unlike Dataset_SSP1_SSP4, this dataset includes new reservoir features with updated calculations of WAI, WTC, and WIC to reflect the added infrastructure and supply potential. hard_classification.xlsx - Provides the scenario classification output for each site. Includes both the initial scenario classification based on Euclidean Distance and adjusted classifications based on 30% change reduction BAU.zip - Zipped folder of .shp files showing spatially optimized water supply sites per SSP under the Business-As-Usual (BAU) water demand strategy LowGW.zip - Zipped folder of .shp files showing optimized site selections under the Low Groundwater strategy

geospatial↗
NASA NTRS1979

Linear feature selection with applications

Several ways in which feature selection techniques were used in LACIE are discussed. In all cases, the methods require some a priori information and assumptions; in most, the classification procedure (Bayes optimal) was chosen in advance. The transformations used for dimensionality reduction are linear, that is, the variables in feature space are always linear combinations of the original measurements. Several numerically tractable criteria developed for LACIE, which provide information about the probability of misclassification, are discussed. Recent results on linear feature selection techniques are included. Their use in LACIE is discussed. Related open questions are mentioned.

Decell, H. P., Jr.↗
NASA NTRS2005

Aerodynamics of an Axisymmetric Missile Concept Having Cruciform Strakes and In-Line Tail Fins From Mach 0.60 to 4.63

An experimental study has been performed to develop a large force and moment aerodynamic data set on a slender axisymmetric missile configuration having cruciform strakes and in-line control tail fins. The data include six-component balance measurements of the configuration aerodynamics and three-component measurements on all four tail fins. The test variables include angle of attack, roll angle, Mach number, model buildup, strake length, nose size, and tail fin deflection angles to provide pitch, yaw, and roll control. Test Mach numbers ranged from 0.60 to 4.63. The entire data set is presented on a CD-ROM that is attached to this paper. The CD-ROM also includes extensive plots of both the six-component configuration data and the three-component tail fin data. Selected samples of these plots are presented in this paper to illustrate the features of the data and to investigate the effects of the test variables.

Jerry M Allen↗
NASA NTRS2005

Aerodynamics of an Axisymmetric Missile Concept Having Cruciform Strakes and In-Line Tail Fins From Mach 0.60 to 4.63, Supplement

An experimental study has been performed to develop a large force and moment aerodynamic data set on a slender axisymmetric missile configuration having cruciform strakes and in-line control tail fins. The data include six-component balance measurements of the configuration aerodynamics and three-component measurements on all four tail fins. The test variables include angle of attack, roll angle, Mach number, model buildup, strake length, nose size, and tail fin deflection angles to provide pitch, yaw, and roll control. Test Mach numbers ranged from 0.60 to 4.63. The entire data set is presented on a CD-ROM that is attached to this paper. The CD-ROM also includes extensive plots of both the six-component configuration data and the three-component tail fin data. Selected samples of these plots are presented in this paper to illustrate the features of the data and to investigate the effects of the test variables.

Jerry M Allen↗
NASA NTRS1983

Variable anodic thermal control coating

A process for providing a thermal control solar stable surface coating for aluminum surfaces adapted to be exposed to solar radiation wherein selected values within the range of 0.10 to 0.72 thermal emittance (epsilon sub tau) and 0.2 to 0.4 solar absorptance (alpha subs) are reproducibly obtained by anodizing the surface area in a chromic acid solution for a selected period of time. The rate voltage and time, along with the parameters of initial epsilon sub tau and alpha subs, temperature of the chromic acid solution, acid concentration of the solution and the material anodized determines the final values of epsilon/tau sub and alpha sub S. 9 Claims, 5 Drawing Figures.

Gilliland, C. S.↗
NASA NTRS1987

Probabilistic Structural Analysis Methods (PSAM) for select space propulsion system structural components

The objective is the development of several modular structural analysis packages capable of predicting the probabilistic response distribution for key structural variables such as maximum stress, natural frequencies, transient response, etc. The structural analysis packages are to include stochastic modeling of loads, material properties, geometry (tolerances), and boundary conditions. The solution is to be in terms of the cumulative probability of exceedance distribution (CDF) and confidence bounds. Two methods of probability modeling are to be included as well as three types of structural models - probabilistic finite-element method (PFEM); probabilistic approximate analysis methods (PAAM); and probabilistic boundary element methods (PBEM). The purpose in doing probabilistic structural analysis is to provide the designer with a more realistic ability to assess the importance of uncertainty in the response of a high performance structure. Probabilistic Structural Analysis Method (PSAM) tools will estimate structural safety and reliability, while providing the engineer with information on the confidence that should be given to the predicted behavior. Perhaps most critically, the PSAM results will directly provide information on the sensitivity of the design response to those variables which are seen to be uncertain.

Cruse, T. A.↗
NASA NTRS2014

Application of Spectral Analysis Techniques in the Intercomparison of Aerosol Data. Part II: Using Maximum Covariance Analysis to Effectively Compare Spatiotemporal Variability of Satellite and AERONET Measured Aerosol Optical Depth

Moderate Resolution Imaging SpectroRadiometer (MODIS) and Multi-angle Imaging Spectroradiomater (MISR) provide regular aerosol observations with global coverage. It is essential to examine the coherency between space- and ground-measured aerosol parameters in representing aerosol spatial and temporal variability, especially in the climate forcing and model validation context. In this paper, we introduce Maximum Covariance Analysis (MCA), also known as Singular Value Decomposition analysis as an effective way to compare correlated aerosol spatial and temporal patterns between satellite measurements and AERONET data. This technique not only successfully extracts the variability of major aerosol regimes but also allows the simultaneous examination of the aerosol variability both spatially and temporally. More importantly, it well accommodates the sparsely distributed AERONET data, for which other spectral decomposition methods, such as Principal Component Analysis, do not yield satisfactory results. The comparison shows overall good agreement between MODIS/MISR and AERONET AOD variability. The correlations between the first three modes of MCA results for both MODIS/AERONET and MISR/ AERONET are above 0.8 for the full data set and above 0.75 for the AOD anomaly data. The correlations between MODIS and MISR modes are also quite high (greater than 0.9). We also examine the extent of spatial agreement between satellite and AERONET AOD data at the selected stations. Some sites with disagreements in the MCA results, such as Kanpur, also have low spatial coherency. This should be associated partly with high AOD spatial variability and partly with uncertainties in satellite retrievals due to the seasonally varying aerosol types and surface properties.

aerosols↗
DOE OSTI2021

Metal–Organic Frameworks and Metal–Organic Gels for Oxygen Electrocatalysis: Structural and Compositional Considerations

Increasing demand for sustainable and clean energy is calling for the next-generation energy conversion and storage technologies such as fuel cells, water electrolyzers, CO 2 /N 2 reduction electrolyzers, metal–air batteries, etc. All these electrochemical processes involve oxygen electrocatalysis. Boosting the intrinsic activity and the active-site density through rational design of metal–organic frameworks (MOFs) and metal–organic gels (MOGs) as precursors represents a new approach toward improving oxygen electrocatalysis efficiency. MOFs/MOGs afford a broad selection of combinations between metal nodes and organic linkers and are known to produce electrocatalysts with high surface areas, variable porosity, and excellent activity after pyrolysis. Some recent studies on MOFs/MOGs for oxygen electrocatalysis and their new perspectives in synthesis, characterization, and performance are discussed in this review. New insights on the structural and compositional design in MOF/MOG-derived oxygen electrocatalysts are summarized. Critical challenges and future research directions are also outlined.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Early experiences on the OLCF Frontier system with AthenaPK and Parthenon–Hydro

The Oak Ridge Leadership Computing Facility (OLCF) has been preparing the nation's first exascale system, Frontier, for production and end users. Frontier is based on HPE Cray's new EX architecture and Slingshot interconnect and features 74 cabinets of optimized 3rd Gen AMD EPYC CPUs for HPC and AI and AMD Instinct 250X accelerators. As a part of this preparation, “real-world” user codes have been selected to help assess the functionality, performance, and usability of the system. This article describes early experiences using the system in collaboration with the Hamburg Observatory for two selected codes, which have since been adopted in the OLCF test harness. Experiences discussed include efforts to resolve performance variability and per-cycle slowdowns. Results are shown for a performance portable astrophysical magnetohydronamics code, AthenaPK, and a mini-application stressing the core functionality of a performance portable block-structured adaptive mesh refinement framework, Parthenon-Hydro. Here, these results show good scaling characteristics to the full system. At the largest scale, the Parthenon-Hydro miniapp reaches a total of $1.7$ $\times$ $10^{13}$ zone-cycles/s on 9216 nodes (73,728 logical GPUs) at ≈92% weak scaling parallel efficiency (starting from a single node using a second-order, finite-volume method).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Estimating carrying capacity for juvenile salmon using quantile random forest models

Abstract Establishing robust methods and metrics to evaluate habitat quality is critical for the recovery of endangered Pacific salmonids ( Oncorhynchus spp.). A variety of modeling approaches are used for status and trend monitoring of anadromous species throughout the Pacific Northwest, USA, but current methods may fail to capture the complex relationship between fish and habitat and are often limited in predictive power beyond specific watersheds. Further, the focus on species distribution and abundance is not easily manipulated to predict carrying capacity and traditional stock‐recruitment analyses are reliant on long‐term data which are not always available. In this study, we developed a quantile random forest model to provide estimates of habitat carrying capacity for Chinook salmon ( O. tshawytscha ) parr during the summer months, at both the site and watershed scale. Quantile random forest models allow for the consideration of noisy data, correlated variables, and non‐linear relationships: common features in fish–habitat datasets. We leveraged Columbia Habitat Monitoring Program data to select habitat co‐variates and predict capacity at those sites. We also identified a set of globally available attributes to extrapolate capacity estimate predictions throughout wadeable streams within the Columbia River basin. Total capacity estimates for watersheds closely matched estimates from alternative fish productivity models. Carrying capacity estimates based on quantile random forest models, like those presented here, provide managers a framework to guide the identification, prioritization, and development of habitat rehabilitation actions to recover salmon populations.

See, Kevin E.↗
DOE OSTI2019

Linear downscaling from MODIS to landsat: connecting landscape composition with ecosystem functions

The open and free access to Landsat and MODIS products have greatly promoted scientific investigations on spatiotemporal change in land mosaics and ecosystem functions at landscape to regional scales. Unfortunately, there is a major mismatch in spatial resolution between MODIS products at coarser resolution (≥ 250 m) and landscape structure based on classified Landsat scenes at finer resolution (30 m). Based on practical needs for downscaling popular MODIS products at 500 m resolution to match classified land cover at Landsat 30 m resolution, we proposed an innovative modelling approach so that landscape structure and ecosystem functions can be directly studied for their interconnections. As a proof-of-concept of our downscaling approach, we selected the watershed of the Kalamazoo River in southwestern Michigan, USA as the testbed. MODIS products for three fundamental variables of ecosystem function are downscaled to ensure the approach can be extrapolated to multiple functional measurements. They are blue-sky albedo (0–1), evapotranspiration (ET, mm), and gross primary production (GPP, Mg C ha -1 year -1 ). An object-oriented classification of Landsat images in 2011 was processed to generate a land cover map for landscape structure. The downscaling model was tested for the five Level IV ecoregions within the watershed. We achieved satisfactory downscaling models for albedo, ET, and GPP for all five ecoregions. The adjusted R2 was > 0.995 for albedo, 0.915–0.997 for ET, and 0.902–0.962 for GPP. The estimated albedo, ET, and GPP values appear different in the region. The estimated albedo was the lowest for water (0.076–0.107) and the highest for cropland (0.166–0.172). Estimated ET was the highest for the built-up cover type (525.6–687.1 mm) and the lowest for forest (209.7–459.7 mm). The estimated GPP was the highest for the build-up cover type (8.65–9.85 Mg C ha -1 year -1 ) and the lowest for forest. Estimated values for albedo, ET, and GPP appear reasonable for their ranges in the Kalamazoo River region and are consistent with values reported in the literature. Despite these promising results, the downscaling approach relies on strong assumptions and can carry substantial uncertainty. Finally, it is only valid at a spatial scale where similar climate, soil, and landforms exist (i.e., values in isolated patches of the same cover type are similar). Plausibly, the uncertainties associated with each estimation, as well as the model residuals, can be explored for other pattern-process relationships within the landscape.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Sputtered p-Type Cu x Zn 1– x S Back Contact to CdTe Solar Cells

As thin-film cadmium telluride (CdTe) solar cells gain prominence, one particular challenge is optimizing contacts and their interfaces to transfer charge without losses in efficiency. Back contact recombination is still significant and will prevent CdTe solar technology from reaching its full potential in device efficiency, and transparent back contacts have not been developed for bifacial solar technology or multijunction solar cells. To address these challenges, here we investigate sputtered Cu x Zn 1– x S as a p-type semi-transparent back contact material to thin-film polycrystalline CdTe solar cells at Cu concentrations x = 0.30, 0.45, and 0.60. This material is selected for its high hole conductivity (160–2120 S cm –1 ), wide optical band gap (2.25–2.75 eV), and variable ionization potential (approximately 6–7 eV) that can be aligned to that of CdTe. We report that without device optimization, CdTe solar cells with these Cu x Zn 1– x S back contacts perform as well as control cells with standard ZnTe:Cu back contacts. We observe no reduction in external quantum efficiency, low contact barrier heights of approximately 0.3 eV, and carrier lifetimes on par with those of baseline CdTe. These cells are relatively stable over one year in air, with V OC and efficiency of the x = 0.30 cell decreasing by only 1 and 3%, respectively. Using scanning electron microscopy and scanning transmission electron microscopy to investigate the Cu x Zn 1– x S/CdTe interface, we demonstrate that the Cu x Zn 1– x S layer segregates into a bilayer of Cu-Te-S and Zn-Cd-S, and thermodynamic reaction calculations support these findings. Despite its bilayer formation, the back contact still functions well. This investigation explains some of the physical mechanisms governing the device stack, inspires future work to understand interfacial chemistry and charge transfer, and elicits optimization to achieve higher-efficiency CdTe cells.

14 SOLAR ENERGY↗
DOE OSTI2026

An Optimized Parameterization of Sub‐Grid Scale Advection for Convection Permitting Models

Convection‐permitting models (CPMs) explicitly resolve deep convection yet under‐resolve the organized lateral exchanges among drafts and their environment that control entrainment/detrainment, precipitation efficiency, and mesoscale structure. In this work, we introduce the Optimized Advection Scheme (OAS), which introduces a small rotation of the Cartesian frame of reference for the horizontal winds relative to other variables used in advection that induces cross‐gradient transport to mimic under‐resolved convective mixing. The rotation angle is selected to minimize the Kullback–Leibler divergence between the simulated and satellite observed precipitation intensity distributions, yielding a physically consistent perturbation that is computationally inexpensive and portable. Optimized Advection Scheme is implemented in WRF and evaluated over Amazon (April 2014). It shifts precipitation–precipitable‐water joint distributions toward lighter rain, reduces overly intense rates, and improves mesoscale convective system (MCS) lifetime and propagation. Mechanistically, the added cross‐gradient transport promotes convective detrainment and environmental mixing, which cools and moistens the mid‐troposphere, weakens downward momentum transport, alleviates excessive downwelling shortwave biases, and warms the surface temperature. The optimized rotation angle yields comparable improvements at 4‐km and 1‐km grid spacing, demonstrating resolution‐independent benefits across the CPM gray zone. By targeting the dynamical root of under‐mixed convective circulations, rather than tuning model microphysics or closures, OAS delivers robust, scale‐aware improvements in precipitation statistics, cloud vertical structure, and characteristics of MCS (MCSs), offering a practical pathway to more reliable CPM simulations for weather and climate applications.

CPM↗
DOE OSTI2023

Fully synthetic platform to rapidly generate tetravalent bispecific nanobody–based immunoglobulins

Nanobodies bind a target antigen with a kinetic profile similar to a conventional antibody, but exist as a single heavy chain domain that can be readily multimerized to engage antigen via multiple interactions. Presently, most nanobodies are produced by immunizing camelids; however, platforms for animal-free production are growing in popularity. Here, we describe the development of a fully synthetic nanobody library based on an engineered human V H 3-23 variable gene and a multispecific antibody-like format designed for biparatopic target engagement. To validate our library, we selected nanobodies against the SARS-CoV-2 receptor–binding domain and employed an on-yeast epitope binning strategy to rapidly map the specificities of the selected nanobodies. We then generated antibody-like molecules by replacing the V H and V L domains of a conventional antibody with two different nanobodies, designed as a molecular clamp to engage the receptor-binding domain biparatopically. The resulting bispecific tetra-nanobody immunoglobulins neutralized diverse SARS-CoV-2 variants with potencies similar to antibodies isolated from convalescent donors. Subsequent biochemical analyses confirmed the accuracy of the on-yeast epitope binning and structures of both individual nanobodies, and a tetra-nanobody immunoglobulin revealed that the intended mode of interaction had been achieved. This overall workflow is applicable to nearly any protein target and provides a blueprint for a modular workflow for the development of multispecific molecules.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Economy and Emissions Impacts from Solazyme Fuel in UPS Delivery Vehicles

To improve understanding of the potential fuel economy and emissions impacts from switching a fleet of vehicles from conventional petroleum diesel to synthetic renewable diesel, the National Renewable Energy Laboratory (NREL) conducted fuel economy and emissions analyses at NREL's Renewable Fuels and Lubricants (ReFUEL) Laboratory. Representative test cycles were developed based on real-world data from six package delivery vehicles and six class 8 day-cab tractors operated by UPS in the Dallas, Texas, area. A three-week in-field data collection period yielded 170 days of real-world vehicle operations data that NREL used to select representative standard drive cycles for testing. Fuel economy and emissions tests at the ReFUEL Laboratory showed that, in general, when switching from conventional diesel to renewable diesel observed changes in tailpipe carbon dioxide (CO2), fuel consumption, and fuel economy are primarily driven by changes in fuel properties such as the hydrogen-to-carbon ratio, density, and lower heating value (LHV). The vehicles tested with the renewable diesel showed a consistent 4.2% reduction in tailpipe CO2 emissions, but a 3.5%-4.8% reduction in fuel economy compared with local pump diesel. This is consistent with the 4.2% lower volumetric LHV of the sourced renewable diesel compared to the pump diesel. The UPS package car tested on renewable diesel also demonstrated a 4.1% oxides of nitrogen (NOx) reduction. NOx emissions from the UPS selective-catalyst-reduction-equipped tractor were an order of magnitude lower than the package car but showed relatively higher variability in results from cycle to cycle.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

A Comparative Study of Machine Learning Algorithms for Industry-Specific Freight Generation Model

According to Bureau of Transportation Statistics, the U.S. transportation system handled 14,329 million ton-miles of freight per day in 2020. Understanding the generation of these freight shipments is crucial for transportation researchers, planners, and policymakers to design and plan for a more efficient and connected freight transportation system. Traditionally, the freight generation modeling has been based on Ordinary Least Square (OLS) regression, although more advanced Machine Learning (ML) algorithms have been evaluated and proven to have excellent performance in various transportation applications in recent years. Furthermore, one modeling approach applied for one industry might not always be applicable for another as their freight generation logics can be quite different. The objective of this study is to apply and evaluate alternative ML algorithms in the estimation of freight generation for each of 45 industry types. Seven alternative ML algorithms, along with the base OLS regression, were evaluated and compared. In addition, the study considered different combinations of variables in both the original and logarithmic form as well as hyperparameters of those ML algorithms in the model selection for each industry type. The results showed statistically significant improvements in the root mean square error reduction by the alternative ML algorithms over the OLS for over 80% of cases. The study suggests utilizing the alternative ML algorithms can reduce the root mean square error by about 30%, depending on industry types.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1978

Nationwide forestry applications program. Ten-Ecosystem Study (TES) site 6, Fort Yukon, Alaska

The author has identified the following significant results. Analysis of the processing results has led to the following conclusions: (1) LANDSAT imagery was a reliable resource for the stratification of level 2 forest features (softwood, hardwood, tundra, and water). These features can be classified with an accuracy of 72.4 percent + or - 5.9 percent at the 90 percent confidence level. (2) Training fields selected for signature development from only 10 percent of the area did not adequately and efficiently cover the class variability for the entire area. (3) Derived regression transformations were ineffective in recovering the loss of level 1 forest proportions and level 2 softwood and hardwood proportions.

Ward, J. F.↗