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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Formal Provenance Representation of the Data and Information Supporting the National Climate Assessment

The Global Change Information System (GCIS) provides a framework for the formal representation of structured metadata about data and information about global change. The pilot deployment of the system supports the National Climate Assessment (NCA), a major report of the U.S. Global Change Research Program (USGCRP). A consumer of that report can use the system to browse and explore that supporting information. Additionally, capturing that information into a structured data model and presenting it in standard formats through well defined open inter- faces, including query interfaces suitable for data mining and linking with other databases, the information becomes valuable for other analytic uses as well.

Provenance↗
NASA NTRS2018

Development and Documentation of kL-Based Linear, Nonlinear, and Full Reynolds Stress Turbulence Models

The development and implementation of kL-based Reynolds Average Navier-Stokes (RANS) turbulence models are reported herein. The kL is based on Abdol-Hamid's closure and Menter's modi cation to Rotta's two-equation model. Rotta shows that a reliable transport equation can be formed from the turbulent length scale L, and the turbulent kinetic energy k. Rotta's kL equation is well suited for term-by-term modeling and displays useful features compared to other scale formulation. One of the important di erences is the inclusion of higher order velocity derivatives in the source terms of the scale equation. This can enhance the ability of RANS solvers to simulate unsteady ows in URANS mode. The present report documents the formulation of three model levels of turbulence models as implemented in the CFD code FUN3D. Methodology and calibration examples are shown in detail. The levels are the linear k-kL and the two-equation algebraic Reynolds stress model (ARSM) as well as the full Reynolds Stress Model (RSM). Attached, separated and corner ow cases are documented and compared with experimental, theoretical and other turbulence model data. The results show generally very good comparisons with canonical and experimental data. The results from this formulation are similar or better than results using the SST two- equation turbulence model. ARSM shows great promise with similar level of computational resources as general two equation turbulence models.

Abdol-Hamid, Khaled S.↗
NASA NTRS2020

Neptune Global Reference Atmospheric Model (Neptune-GRAM): User Guide

This Technical Memorandum (TM) presents the Neptune Global Reference Atmospheric Model (Neptune-GRAM) and its updated features. Neptune-GRAM is an engineering-oriented atmospheric model that estimates mean values and statistical variations of atmospheric properties for Neptune. This TM summarizes the atmospheric data model in Neptune-GRAM and provides a guide for the user to obtain, set up, and run the code in various configurations. Additional details regarding the Neptune-GRAM input and output files and how to interpret Neptune-GRAM results are also provided.

H L Justh↗
NASA NTRS2021

Uranus Global Reference Atmospheric Model (Uranus-GRAM): User Guide

This Technical Memorandum (TM) presents the Uranus Global Reference Atmospheric Model (Uranus-GRAM) and the updated features of the GRAMs. Uranus-GRAM is an engineering-oriented atmospheric model that estimates mean values and statistical variations of atmospheric properties for Uranus. This TM summarizes the atmospheric data model in Uranus-GRAM and provides a guide for the user to obtain, set up, and run the code in various configurations. Additional details regarding the Uranus-GRAM input and output files and how to interpret Uranus-GRAM results are also provided.

Uranus Global Reference Atmospheric Model↗
NASA NTRS2021

Earth Global Reference Atmospheric Model (Earth-GRAM): User Guide

This Technical Memorandum (TM) presents the Earth Global Reference Atmospheric Model (Earth-GRAM) and the updated features of the GRAMs. Earth-GRAM is an engineering-oriented atmospheric model that estimates mean values and statistical variations of atmospheric properties for Earth. This TM summarizes the atmospheric data model in Earth-GRAM and provides a guide for the user to obtain, set up, and run the code in various configurations. Additional details regarding the Earth-GRAM input and output files and how to interpret Earth-GRAM results are also provided.

Atmospheric Models↗
NASA NTRS2021

Venus Global Reference Atmospheric Model (Venus-GRAM): User Guide

This Technical Memorandum (TM) presents the Venus Global Reference Atmospheric Model (Venus-GRAM) and the updated features of the GRAMs. Venus-GRAM is an engineering-oriented atmospheric model that estimates mean values and statistical variations of atmospheric properties for Venus. This TM summarizes the atmospheric data model in Venus-GRAM and provides a guide for the user to obtain, set up, and run the code in various configurations. Additional details regarding the Venus-GRAM input and output files and how to interpret Venus-GRAM results are also provided.

atmospheric models↗
NASA NTRS2021

Mars Global Reference Atmospheric Model (Mars-GRAM): User Guide

This Technical Memorandum (TM) presents the Mars Global Reference Atmospheric Model (Mars-GRAM) and the updated features of the GRAMs. Mars-GRAM is an engineering-oriented atmospheric model that estimates mean values and statistical variations of atmospheric properties for Mars. This TM summarizes the atmospheric data model in Mars-GRAM and provides a guide for the user to obtain, set up, and run the code in various configurations. Additional details regarding the Mars-GRAM input and output files and how to interpret Mars-GRAM results are also provided.

atmospheric models↗
NASA NTRS2024

Mars Global Reference Atmospheric Model (Mars-GRAM) 2024: User Guide

This Technical Memorandum (TM) presents the Mars Global Reference Atmospheric Model (Mars-GRAM) 2024 and its updated features. Mars-GRAM is an engineering-oriented atmospheric model that estimates mean values and statistical variations of atmospheric properties for Mars. This TM summarizes the atmospheric data model in Mars-GRAM and provides a guide for the user to obtain, set up, and run the code in various configurations. Additional details regarding the Mars-GRAM input and output files and how to interpret Mars-GRAM results are also provided.

atmospheric density↗
NASA NTRS1991

Multiaxis thrust vectoring using axisymmetric nozzles and postexit vanes on an F/A-18 configuration vehicle

A ground-based investigation was conducted on an operational system of multiaxis thrust vectoring using postexit vanes around an axisymmetric nozzle. This thrust vectoring system will be tested on the NASA F/A-18 High Alpha Research Vehicle (HARV) aircraft. The system provides thrust vectoring capability in both pitch and yaw. Ground based data were gathered from two separate tests at NASA Langley Research Center. The first was a static test in the 16-foot Transonic Tunnel Cold-Jet Facility with a 14.25 percent scale model of the axisymmetric nozzle and the postexit vanes. The second test was conducted in the 30 by 60 foot wind tunnel with a 16 percent F/A-18 complete configuration model. Data from the two sets are being used to develop models of jet plume deflection and thrust loss as a function of vane deflection. In addition, an aerodynamic interaction model based on plume deflection angles will be developed. Results from the scale model nozzle test showed that increased vane deflection caused exhaust plume turning. Aerodynamic interaction effects consisted primarily of favorable interaction of moments and unfavorable interaction of forces caused by the vectored jet plume.

Bowers, Albion H.↗
NASA NTRS1983

Modeling correlation with flight data

The molecular contamination flow model (space 2 program) predicts the induced environment of the space shuttle orbiter/payload on orbit as well as the induced gas flow between the orbiter and payload surfaces. This computer program relates the input parameters which characterize the time dependent status of the orbiter/payload to the required output parameters. An application of the model to data from space transportation system flights 1, 2, and 3 shows that the model correlates well for direct flow and for return flux (STS-2/H2O). Correlation for return flux from other molecular sources/species appears to be within expectations but more analysis is required. The space 2 model seems to be an adequate predictive tool.

Ehlers, H. K. F.↗
NASA NTRSDate not supplied

Prediction-Based Approaches for Generation of Noise-Power-Distance Data with Application to Urban Air Mobility Vehicles

In contrast to most commercial air traffic today, vehicles serving the urban air mobility (UAM) market are anticipated to operate within communities and be close to the public at large. The approved model for assessing environmental impact of air traffic actions in the United States, the Federal Aviation Administration (FAA) Aviation Environmental Design Tool (AEDT), does not directly support analysis of such operations due to a combined lack of UAM aircraft flight performance model data and aircraft noise data. This paper addresses the latter by offering two prediction-based approaches for generation of noise-power-distance (NPD) data for use within AEDT. One utilizes AEDT’s fixed-wing aircraft modeling approach and the other utilizes the rotary-wing aircraft modeling approach.

noise-power-distance↗
DOE OSTI2024

Thinking Bayesian for plasma physicists

Bayesian statistics offers a powerful technique for plasma physicists to infer knowledge from the heterogeneous data types encountered. To explain this power, a simple example, Gaussian Process Regression, and the application of Bayesian statistics to inverse problems are explained. The likelihood is the key distribution because it contains the data model, or theoretic predictions, of the desired quantities. By using prior knowledge, the distribution of the inferred quantities of interest based on the data given can be inferred. Because it is a distribution of inferred quantities given the data and not a single prediction, uncertainty quantification is a natural consequence of Bayesian statistics. The benefits of machine learning in developing surrogate models for solving inverse problems are discussed, as well as progress in quantitatively understanding the errors that such a model introduces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Evaluating proxy influence in assimilated paleoclimate reconstructions – Testing the exchangeability of two ensembles of spatial processes

Climate field reconstructions (CFR) attempt to estimate spatiotemporal fields of climate variables in the past using climate proxies such as tree rings, ice cores, and corals. Data Assimilation (DA) methods are a recent and promising new means of deriving CFRs that optimally fuse climate proxies with climate model output. Despite the growing application of DA-based CFRs, little is understood about how much the assimilated proxies change the statistical properties of the climate model data. To address this question, we propose a robust and computationally efficient method, based on functional data depth, to evaluate differences in the distributions of two spatiotemporal processes. In this study, we apply our test to study global and regional proxy influence in DA-based CFRs by comparing the background and analysis states, which are treated as two samples of spatiotemporal fields. We find that the analysis states are significantly altered from the climate-model-based background states due to the assimilation of proxies. Moreover, the difference between the analysis and background states increases with the number of proxies, even in regions far beyond proxy collection sites. Our approach allows us to characterize the added value of proxies, indicating where and when the analysis states are distinct from the background states.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1982

Cost effectiveness of conventional versus LANDSAT use data for hydrologic modeling

Six case studies were analyzed to investigate the cost effectiveness of using land use data obtained from LANDSAT as opposed to conventionally obtained data. A procedure was developed to determine the relative effectiveness of the two alternative means of acquiring data for hydrological modelling. The cost of conventionally acquired data ranged between $3,000 and $16,000 for the six test basins. Information based on LANDSAT imagery cost between $2,000 and $5,000. Results of the effectiveness analysis shows the differences between the two methods are insignificant. From the cost comparison and the act that each method, conventional and LANDSAT, is shown to be equally effective in developing land use data for hydrologic studies, the cost effectiveness of the conventional or LANDSAT method is found to be a function of basin size for the six test watersheds analyzed. The LANDSAT approach is cost effective for areas containing more than 10 square miles.

George, T. S.↗
NASA NTRS1991

Sea ice - atmosphere interaction: Application of multispectral satellite data in polar surface energy flux estimates

In the past six months, work has continued on energy flux sensitivity studies, ice surface temperature retrievals, corrections to Advanced Very High Resolution Radiometer (AVHRR) thermal infrared data, modelling of cloud fraction retrievals, and radiation climatologies. We tentatively conclude that the SSM/I may not provide accurate enough estimates of ice concentration and type to improve our shorter term energy flux estimates. SSM/I derived parameters may still be applicable in longer term climatological flux characterizations. We hold promise for a system coupling observation to a ice deformation model. Such a model may provide information on ice distribution which can be used in energy flux calculations. Considerable variation was found in modelled energy flux estimates when bulk transfer coefficients are modulated by lead fetch. It is still unclear what the optimum formulation is and this will be the subject of further work. Data sets for ice surface temperature retrievals were assembled and preliminary data analysis was started. Finally, construction of a conceptual framework for further modelling of the Arctic radiation flux climatology was started.

Steffen, Konrad↗
DOE OSTI2020

Inferred inflow forecast horizons guiding reservoir release decisions across the United States

Abstract. Medium- to long-range forecasts often guide reservoir release decisions to support water management objectives, including mitigating flood and drought risks. While there is a burgeoning field of science targeted at improving forecast products and associated decision support models, data describing how and when forecasts are applied in practice remain undeveloped. This lack of knowledge may prevent hydrological modelers from developing accurate reservoir release schemes for large-scale, distributed hydrology models that are increasingly used to assess the vulnerabilities of large regions to hydrological stress. We address this issue by estimating seasonally varying, regulated inflow forecast horizons used in the operations of more than 300 dams throughout the conterminous United States (CONUS). For each dam, we take actual forward observed inflows (perfect foresight) as a proxy for forecasted flows available to the operator and then identify for each week of the year the forward horizon that best explains the release decisions taken. Resulting “horizon curves” specify for each dam the inferred inflow forecast horizon as a function of the week of the water year. These curves are analyzed for strength of evidence for contribution of medium- to long-range forecasts in decision making. We use random forest classification to estimate that approximately 80 % of large dams and reservoirs in the US (1553±50 out of 1927 dams with at least 10 Mm3 storage capacity) adopt medium- to long-range inflow forecasts to inform release decisions during at least part of the water year. Long-range forecast horizons (more than 6 weeks ahead) are detected in the operations of reservoirs located in high-elevation regions of the western US, where snowpack information likely guides the release. A simulation exercise conducted on four key western US reservoirs indicates that forecast-informed models of reservoir operations may outperform models that neglect the horizon curve – including during flood and drought conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

kL-based Linear and Nonlinear Two-Equation Turbulence Models

The development and implementation of kL-based Reynolds Average Navier-Stokes (RANS) two-equation turbulence models are reported herein. The kL is based on Abdol-Hamid's closure and Menter's modification to Rotta's two-equation model. Rotta shows that a reliable transport equation can be formed from the turbulent length scale L, and the turbulent kinetic energy k. Rotta's kL equation is well suited for term-by-term modeling and displays useful features compared to other scale formulation. One of the important differences is the inclusion of higher order velocity derivatives in the source terms of the scale equation. This can enhance the ability of RANS solvers to simulate unsteady flows in URANS mode. The present report documents the formulation of two model levels of turbulence models as implemented in the computational fluid dynamics FUN3D code. The levels are the two-equation linear k-kL and the two-equation algebraic Reynolds stress model (ARSM). Free shear, separated and corner flow cases are documented and compared with experimental, and other turbulence model data. The results show generally very good comparisons with experimental data. The results from this formulation are similar or better than results using the SST two-equation turbulence model. ARSM shows great promise with similar level of computational resources as basic two-equation turbulence models.

Abdol-Hamid, K. S.↗
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↗