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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,081 records · Page 60

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Load-Packaged AC/DC and DC/DC Power Electronics Converter Performance Data

This data set contains experimentally characterized performance data from load-packaged alternating current to direct current (AC/DC) and direct current to direct current (DC/DC) power electronics converters associated with lighting devices and miscellaneous electrical loads typically found in commercial buildings in the United States. The data set contains input power, output power, efficiency, and harmonic spectrum data for 58 AC/DC converters and 35 DC/DC converters.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1987

Integration of topographic data with synthetic aperture radar data for determining forest properties in mountainous terrain

The elevation gradients affecting tropical forest stand characteristics are presently studied in light of multipolarization airborne SAR data. A 'rubber sheeting' computer code was used to georeference the SAR data sets to the digital elevation data. The TOPO code from NASA's NSTL generated the terrain slope and aspect angle data from the terrain elevation data set; computed local incidence angles were used to delete those data areas that were shadowed, and to produce local incidence angle data that were not shadowed. The results obtained demonstrate that the SAR data are related to the elevation gradient.

Wu, Shih-Tseng↗

DOE OSTI2026

ComStock Measure Documentation: Variable-Speed Pumps

Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy's ResStock and ComStock models, this work produces national data sets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the commercial building stock across the United States. The "baseline" model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass adoption impact on the baseline building stock. "Measures" refers to various "what-if" scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public data sets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario - variable speed pumps - and briefly introduces key results. The full public data set can be accessed on the ComStock data lake or via the Data Viewer at comstock.nlr.gov. The public data set enables users to create custom aggregations of results for their use case (e.g., filter to a specific county or building type).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

ComStock Measure Documentation: High-Efficiency Rooftop Unit

Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy's ResStock and ComStock models, this work produces national data sets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the commercial building stock across the United States. The "baseline" model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass adoption impact on the baseline building stock. "Measures" refers to various "what-if" scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public data sets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario - high-efficiency rooftop unit (RTU) - and briefly introduces key results. The full public data set can be accessed on the Comstock data lake or via the Data Viewer at comstock.nlr.gov. The public data set enables users to create custom aggregations of results for their use case (e.g., filter to a specific county or building type).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2000

BOREAS HYD-9 Tipping Bucket Rain Gauge Data

The BOREAS HYD-9 team collected several data sets containing precipitation and strearnflow measurements over the BOREAS study areas. This data set contains the measurements from the tipping bucket rain gauges at the BOREAS NSA and SSA. These measurements were submitted in 15-minute and 1-hour intervals. Only the 15-minute interval data set was loaded into the data base tables. Data were collected from the tipping bucket gauges from mid-April until mid-October in 1994, 1995, and 1996. The data are available in tabular ASCII files. The data files are available on a CD-ROM (see document number 20010000884) or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Kouwen, Nick↗

NASA NTRS2012

Multi-Beam Approach for Accelerating Alignment and Calibration of HyspIRI-Like Imaging Spectrometers

A paper describes an optical stimulus that produces more consistent results, and can be automated for unattended, routine generation of data analysis products needed by the integration and testing team assembling a high-fidelity imaging spectrometer system. One key attribute of the system is an arrangement of pick-off mirrors that provides multiple input beams (five in this implementation) to simultaneously provide stimulus light to several field angles along the field of view of the sensor under test, allowing one data set to contain all the information that previously required five data sets to be separately collected. This stimulus can also be fed by quickly reconfigured sources that ultimately provide three data set types that would previously be collected separately using three different setups: Spectral Response Function (SRF), Cross-track Response Function (CRF), and Along-track Response Function (ARF), respectively. This method also lends itself to expansion of the number of field points if less interpolation across the field of view is desirable. An absolute minimum of three is required at the beginning stages of imaging spectrometer alignment.

Eastwood, Michael L.↗

NASA NTRS2001

Evaluation of Stratospheric Transport in New 3D Models Using the Global Modeling Initiative Grading Criteria

The Global Modeling Initiative (GMI) Team developed objective criteria for model evaluation in order to identify the best representation of the stratosphere. This work created a method to quantitatively and objectively discriminate between different models. In the original GMI study, 3 different meteorological data sets were used to run an offline chemistry and transport model (CTM). Observationally-based grading criteria were derived and applied to these simulations and various aspects of stratospheric transport were evaluated; grades were assigned. Here we report on the application of the GMI evaluation criteria to CTM simulations integrated with a new assimilated wind data set and a new general circulation model (GCM) wind data set. The Finite Volume Community Climate Model (FV-CCM) is a new GCM developed at Goddard which uses the NCAR CCM physics and the Lin and Rood advection scheme. The FV-Data Assimilation System (FV-DAS) is a new data assimilation system which uses the FV-CCM as its core model. One year CTM simulations of 2.5 degrees longitude by 2 degrees latitude resolution were run for each wind data set. We present the evaluation of temperature and annual transport cycles in the lower and middle stratosphere in the two new CTM simulations. We include an evaluation of high latitude transport which was not part of the original GMI criteria. Grades for the new simulations will be compared with those assigned during the original GMT evaluations and areas of improvement will be identified.

Strahan, Susan E.↗

DOE OSTI2024

Snow Distribution Patterns Revisited: A Physics-Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub-Arctic

Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year-to-year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in-situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics-based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in-situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics-based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in-situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time-series estimations of snow depth were observed. These results can be applied to any physics-based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Robust and optimal alignment of high-dimensional data using maximum likelihood estimation through a random sample consensus framework

Abstract Correcting spatial orientations of groups of high-dimensional data sets such that they are all in a consistent coordinate system is often a time-consuming and error-prone process. Automation of this process can be accomplished by using Generalized Procrustes Analysis to estimate the relative orientations among a population of high-dimensional data sets. A least squares Procrustes solution is applied through a maximum likelihood estimation and random sample consensus framework for robustness. The likelihood model is comprised of a mixture distribution where inliers are modeled using t -distribution and outliers from a uniform distribution. Applications will focus on a synthetic data set that emulates triaxial acceleration data and also real shock data from a population of triaxial accelerometers. Outliers represent either non-rigid body responses, environmental noise, and/or sensor and data acquisition issues. The intended application for the methodology is to robustly automate the rotation of populations of experimentally collected triaxial accelerometer data sets to a single global coordinate system.

LOSAC↗

NASA NTRS1995

Latitudinal variation of helicity of photospheric magnetic fields

Using a 1988-1994 data set of original photospheric vector magnetograms as well as published data, we have studied the average magnetic helicity of 69 diverse active regions, adopting the linear force-free field parameter alpha as a measure. This average value was determined by minimizing the differences between the computed constant-alpha force-free and observed horizontal magnetic fields. The average magnetic helicity shows a sign difference at the 2 sigma level in opposite hemispheres. In our data set, 76% of the active regions in the northern hemisphere have negative helicity, and 69% in the southern hemisphere, positive. Although the data show considerable variation from one active region to the next, the data set as a whole suggest that the magnitude of the average helicity increases with solar latitude, starting at zero near the equator, reaches a maximum near 15 deg - 25 deg in both hemispheres, and drops back toward smaller values avove 35 deg - 40 deg. Qualitative comparison with published models shows that such latitudinal variation of the average magnetic helicity may result from either turbulent convective motions or differential rotation, although our studies of rotating sunspots lead us to favor the former.

Pevtsov, Alexei A.↗

DOE OSTI2020

Hydrogeologic Model Based on Reduced Complexity

This data set provides the basic hydrogeologic model used for development of the TOUGH2 reservoir modeling results for the Kimberlina site. The hydrogeologic model is based on the static geologic model provided as a separate data set but has some simplifications regarding the geologic layers. Some geologic layers have been merged for the hydrogeologic model because the hydrogeologic properties were very similar. The data set contains several files. The word file explains the structure of the data files. The pdf contains a table with the hydrogeologic properties. The data set also includes 15 thickness files for 15 geological layers (including 3 Vedder sand layers and 3 Vedder shale layers), three elevations files (with reference elevation at ground surface/model top at 9000 m), and one fault trace file for seven faults. Most users may only need 2-3 faults close to the injection well (also see the figure). The arrangement of these layer thickness and elevations files can be see from the three layer files (for Tecplot). To develop a grid structure, start with the Vedder top elevation (REFVedderTop.dat) and add/subtract the thicknesses of layers above/below.

3D Groundwater Model↗

NASA NTRS2012

An Automated Algorithm to Screen Massive Training Samples for a Global Impervious Surface Classification

An algorithm is developed to automatically screen the outliers from massive training samples for Global Land Survey - Imperviousness Mapping Project (GLS-IMP). GLS-IMP is to produce a global 30 m spatial resolution impervious cover data set for years 2000 and 2010 based on the Landsat Global Land Survey (GLS) data set. This unprecedented high resolution impervious cover data set is not only significant to the urbanization studies but also desired by the global carbon, hydrology, and energy balance researches. A supervised classification method, regression tree, is applied in this project. A set of accurate training samples is the key to the supervised classifications. Here we developed the global scale training samples from 1 m or so resolution fine resolution satellite data (Quickbird and Worldview2), and then aggregate the fine resolution impervious cover map to 30 m resolution. In order to improve the classification accuracy, the training samples should be screened before used to train the regression tree. It is impossible to manually screen 30 m resolution training samples collected globally. For example, in Europe only, there are 174 training sites. The size of the sites ranges from 4.5 km by 4.5 km to 8.1 km by 3.6 km. The amount training samples are over six millions. Therefore, we develop this automated statistic based algorithm to screen the training samples in two levels: site and scene level. At the site level, all the training samples are divided to 10 groups according to the percentage of the impervious surface within a sample pixel. The samples following in each 10% forms one group. For each group, both univariate and multivariate outliers are detected and removed. Then the screen process escalates to the scene level. A similar screen process but with a looser threshold is applied on the scene level considering the possible variance due to the site difference. We do not perform the screen process across the scenes because the scenes might vary due to the phenology, solar-view geometry, and atmospheric condition etc. factors but not actual landcover difference. Finally, we will compare the classification results from screened and unscreened training samples to assess the improvement achieved by cleaning up the training samples. Keywords:

Tan, Bin↗

NASA NTRS1982

Total ozone-solar activity relationship

Approximately six years of global ozone monthly mean data from the Nimbus 4 BUV instrument are compared with monthly values of solar activity using 10.7-cm flux, F(10.7), as a parameter. Several techniques are explored in calculating the correlation between the two data sets, and all are found to yield relatively high correlations, ranging from R = 0.68 for 'raw' monthly means to 0.94 using a six-month running mean for each data set. It is shown, however, that the bulk of the correlation derives from the long-term decreasing trends in both data sets. When the long-term trends are removed, a cross-correlation analysis produces a maximum with no phase shift or with the ozone variations leading the solar variations by one month, thereby reducing the likelihood of a cause and effect relationship on time scales of this order. In view of the current uncertainty in the long-term stability of the BUV instrument and the resulting uncertainty in any long-term trend derived from its data, it is considered unrealistic to draw firm conclusions about a solar cycle influence on total ozone from this satellite data set.

Reber, C. A.↗

NASA NTRS1994

Parallelization and visual analysis of multidimensional fields: Application to ozone production, destruction, and transport in three dimensions

Atmospheric modeling is a grand challenge problem for several reasons, including its inordinate computational requirements and its generation of large amounts of data concurrent with its use of very large data sets derived from measurement instruments like satellites. In addition, atmospheric models are typically run several times, on new data sets or to reprocess existing data sets, to investigate or reinvestigate specific chemical or physical processes occurring in the earth's atmosphere, to understand model fidelity with respect to observational data, or simply to experiment with specific model parameters or components.

Schwan, Karsten↗

DOE OSTI2020

A Bayesian approach to evaluation of soil biogeochemical models

Abstract. To make predictions about the carbon cycling consequences of rising global surface temperatures, Earth system scientists rely on mathematical soil biogeochemical models (SBMs). However, it is not clear which models have better predictive accuracy, and a rigorous quantitative approach for comparing and validating the predictions has yet to be established. In this study, we present a Bayesian approach to SBM comparison that can be incorporated into a statistical model selection framework. We compared the fits of linear and nonlinear SBMs to soil respiration data compiled in a recent meta-analysis of soil warming field experiments. Fit quality was quantified using Bayesian goodness-of-fit metrics, including the widely applicable information criterion (WAIC) and leave-one-out cross validation (LOO). We found that the linear model generally outperformed the nonlinear model at fitting the meta-analysis data set. Both WAIC and LOO computed higher overfitting risk and effective numbers of parameters for the nonlinear model compared to the linear model, conditional on the data set. Goodness of fit for both models generally improved when they were initialized with lower and more realistic steady-state soil organic carbon densities. Still, testing whether linear models offer definitively superior predictive performance over nonlinear models on a global scale will require comparisons with additional site-specific data sets of suitable size and dimensionality. Such comparisons can build upon the approach defined in this study to make more rigorous statistical determinations about model accuracy while leveraging emerging data sets, such as those from long-term ecological research experiments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Light-Duty Vehicle Trip Classification Using One-Class Novelty Detection and Exhaustive Feature Extraction

Travel mode classification within travel survey data sets, especially light-duty vehicle (LDV) trips, is foundational, though nontrivial, to emerging mobility systems, travel behavior analysis, and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels. The detection approaches are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the dataset requirements. This work proposes an LDV trip detection model as a supplement to current travel mode detection methods, for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine-learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique - one-class support vector machines (OCSVMs) - and a novel exhaustive feature extraction (EFE) technique on continuous time series data (i.e., Global Positioning System [GPS] speed profiles) for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of preexisting in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size and feature selection, which will benefit applications limited by highly imbalanced data. The paper also discusses performance comparison with regular machine-learning approaches, the model's robustness, and the potential to extend the proposed model to multi-modal trip prediction.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Apparatus and method for determining state of change (SOC) and state of health (SOH) of electrical cells

Systems and methods for prediction of state of charge (SOH), state of health (SOC) and other characteristics of batteries using acoustic signals, includes determining acoustic data at two or more states of charge and determining a reduced acoustic data set representative of the acoustic data at the two or more states of charge. The reduced acoustic data set includes time of flight (TOF) shift, total signal amplitude, or other data points related to the states of charge. Machine learning models use at least the reduced acoustic dataset in conjunction with non-acoustic data such as voltage and temperature for predicting the characteristics of any other independent battery.

25 ENERGY STORAGE↗

DOE OSTI2023

Apparatus and method for determining state of change (SOC) and state of health (SOH) of electrical cells

Steingart, Daniel Artemis↗