Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “input data”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

WaterTAP3 Model Input Data for NAWI's Eight Source Water Baseline Analyses

This folder contains the input data for the WaterTAP3 model that was used for the eight NAWI (National Alliance for Water Innovation) source water baselines studies published in the Environmental Science and Technology special issue: Technology Baselines and Innovation Priorities for Water Treatment and Supply. There are also eight other separate DAMS submissions, one per source water, that include the model results for the published studies. In this data submission, all model inputs across the eight baselines are included. The data structure and content are described in a README.txt file. For more details on how to use the data in WaterTAP3 please refer to the model documentation and GitHub site found at "WaterTAP3 Github" linked in the submission resources.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2024

NEWS Integrated Analysis (NEWS-IA) Dataset: Budgets and Input Data

This documentation provides a summary of the underlying physical and mathematical descriptions of the global energy and water cycle budgets used to produce the NASA Energy and Water Cycle Study (NEWS) Integrated Analysis. In addition to discussion of the budget equations, a summary is provided concerning NEWS regions, input data sources, and data fields found in the NEWS-IA input data file.

J Brent Roberts↗
DOE OSTI2021

The AME 2020 atomic mass evaluation (I). Evaluation of input data, and adjustment procedures*

Abstract This is the first of two articles (Part I and Part II) that presents the results of the new atomic mass evaluation, AME2020. It includes complete information on the experimental input data that were used to derive the tables of recommended values which are given in Part II. This article describes the evaluation philosophy and procedures that were implemented in the selection of specific nuclear reaction, decay and mass-spectrometric data which were used in a least-squares fit adjustment in order to determine the recommended mass values and their uncertainties. All input data, including both the accepted and rejected ones, are tabulated and compared with the adjusted values obtained from the least-squares fit analysis. Differences with the previous AME2016 evaluation are discussed and specific examples are presented for several nuclides that may be of interest to AME users.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

GRIDCERF - Geospatial Raster Input Data for Capacity Expansion Regional Feasibility

The Geospatial Raster Input Data for Capacity Expansion Regional Feasibility (GRIDCERF) data package is a high-resolution product to evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States. GRIDCERF offers hundreds of individual suitability layers for use with both renewable and non-renewable power plant technology configurations in a harmonized format that can be easily ingested by geospatially-enabled modeling software. It also provides pre-compiled technology-specific suitability layers and allows for user customization to robustly address science objectives when evaluating varying future conditions. GRIDCERF data can be directly used with the CERF (Capacity Expansion Regional Feasibility) model to site power plants at a 1km resolution. GRIDCERF includes composite technology siting suitability raster layers for the following utility scale technology configurations. Note that, in addition to technology sub-types shown below, various cooling types are also included (recirculating, pond, once-through, recirculating-seawater, dry-hybrid, or dry) for various technologies. Biomass Conventional (with or without CCS) IGCC (with or without CCS) Coal Conventional (with or without CCS) IGCC (with or without CCS) Natural Gas Combined-cycle (CC) (with or without CCS) Turbine Geothermal Enhanced Geothermal Systems (EGS) - Class 1 through Class 5 resource potential Nuclear Gen 2 Light Water Reactor (LWR) Gen 3 Small Modular Reactor (SMR) Gen 3 AP1000 Refined Liquids Combined-cycle (CC) (with or without CCS) Turbine Solar Photovoltaic (PV) - for capacity factors in the range of 6-18% Utility-scale Concentrating Solar Power (CSP) - for capacity factors in the range of 24-46% Tower Wind (Onshore) - for capacity factors in the range of 5-50% 80m hub height 100m hub height 120m hub height 140m hub height Wind (Offshore) - for capacity factors in the range of 25-60% 100m hub height 140m hub height 160m hub height

capacity expansion↗
DOE OSTI2026

GRIDCERF - Geospatial Raster Input Data for Capacity Expansion Regional Feasibility

The Geospatial Raster Input Data for Capacity Expansion Regional Feasibility (GRIDCERF) data package is a high-resolution product to evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States. GRIDCERF offers hundreds of individual suitability layers for use with both renewable and non-renewable power plant technology configurations in a harmonized format that can be easily ingested by geospatially-enabled modeling software. It also provides pre-compiled technology-specific suitability layers and allows for user customization to robustly address science objectives when evaluating varying future conditions. GRIDCERF data can be directly used with the CERF (Capacity Expansion Regional Feasibility) model to site power plants at a 1km resolution. GRIDCERF includes composite technology siting suitability raster layers for the following utility scale technology configurations. Note that, in addition to technology sub-types shown below, various cooling types are also included (recirculating, pond, once-through, recirculating-seawater, dry-hybrid, or dry) for various technologies. Biomass Conventional (with or without CCS) IGCC (with or without CCS) Coal Conventional (with or without CCS) IGCC (with or without CCS) Natural Gas Combined-cycle (CC) (with or without CCS) Turbine Geothermal Enhanced Geothermal Systems (EGS) - Class 1 through Class 5 resource potential Nuclear Gen 2 Light Water Reactor (LWR) Gen 3 Small Modular Reactor (SMR) Gen 3 AP1000 Refined Liquids Combined-cycle (CC) (with or without CCS) Turbine Solar Photovoltaic (PV) - for capacity factors in the range of 6-18% Utility-scale Concentrating Solar Power (CSP) - for capacity factors in the range of 24-46% Tower Wind (Onshore) - for capacity factors in the range of 5-50% 80m hub height 100m hub height 120m hub height 140m hub height Wind (Offshore) - for capacity factors in the range of 25-60% 100m hub height 140m hub height 160m hub height

capacity expansion↗
NASA NTRS1967

Master control data handling program uses automatic data input

General purpose digital computer program is applicable for use with analysis programs that require basic data and calculated parameters as input. It is designed to automate input data preparation for flight control computer programs, but it is general enough to permit application in other areas.

Alliston, W.↗
NASA NTRS1983

A computer program for obtaining airplane configuration plots from digital Datcom input data

A computer program is described which reads the input file for the Stability and Control Digital Datcom program and generates plots from the aircraft configuration data. These plots can be used to verify the geometric input data to the Digital Datcom program. The program described interfaces with utilities available for plotting aircraft configurations by creating a file from the Digital Datcom input data.

Roy, M. L.↗
NASA NTRS1995

Calculation of surface and top of atmosphere radiative fluxes from physical quantities based on ISCCP data sets. 1: Method and sensitivity to input data uncertainties

The largest uncertainty in upwelling shortwave (SW) fluxes (approximately equal 10-15 W/m(exp 2), regional daily mean) is caused by uncertainties in land surface albedo, whereas the largest uncertainty in downwelling SW at the surface (approximately equal 5-10 W/m(exp 2), regional daily mean) is related to cloud detection errors. The uncertainty of upwelling longwave (LW) fluxes (approximately 10-20 W/m(exp 2), regional daily mean) depends on the accuracy of the surface temperature for the surface LW fluxes and the atmospheric temperature for the top of atmosphere LW fluxes. The dominant source of uncertainty is downwelling LW fluxes at the surface (approximately equal 10-15 W/m(exp 2)) is uncertainty in atmospheric temperature and, secondarily, atmospheric humidity; clouds play little role except in the polar regions. The uncertainties of the individual flux components and the total net fluxes are largest over land (15-20 W/m(exp 2)) because of uncertainties in surface albedo (especially its spectral dependence) and surface temperature and emissivity (including its spectral dependence). Clouds are the most important modulator of the SW fluxes, but over land areas, uncertainties in net SW at the surface depend almost as much on uncertainties in surface albedo. Although atmospheric and surface temperature variations cause larger LW flux variations, the most notable feature of the net LW fluxes is the changing relative importance of clouds and water vapor with latitude. Uncertainty in individual flux values is dominated by sampling effects because of large natrual variations, but uncertainty in monthly mean fluxes is dominated by bias errors in the input quantities.

Zhang, Y.-C.↗
DOE OSTI2021

Sentinel-1 Input Data for PSInSAR Analysis

Files used to perform the Persistent Scatterer InSAR analysis with SARPROZ. The data is sourced from ESAs Sentinel-1 project and covers Brady Hot Springs and Desert Peak geothermal areas. The original titles are included for the Sentinel-1 data. The naming guide is included as a link in this submission. The data contains SAR (Radar) data from the Sentinel 1A satellite between July 2017 and December 2019. The data is necessary to replicate our results, and can be used for further PSInSAR, DInSAR and other modern interferometric analyses to determine line of sight, vertical and, possibly, East-West horizontal displacement. These displacement analysis show the soil deformation in time (including average displacement velocity), which can be used to indicate subsoil phenomena: temperature changes, subsidence, uplift, and inform other analyses like seismicity and porosity.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models

Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in situ surface platforms, and numerical weather prediction models. Among the employed instruments are microwave radiometers (MWRs), infrared spectrometers (IRSs), radio acoustic sounding systems (RASSs), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud-base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWR and/or IRS instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the above-mentioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (a.g.l.) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1976

A generalization of MSK-type signaling based upon input data symbol pulse shaping

Minimum-shift-keying (MSK), which is a special case of continuous phase frequency-shift-keying (CPFSK) with frequency deviation ratio equal to 0.5, is known to be spectrally equivalent to a form of offset quadrature phase-shift-keying (OQPSK) in which the symbol pulse shape is a half-cycle sinusoid rather than the usual rectangular form. Appropriate shaping of the input data symbols allows one to generate an entire class of constant envelope, MSK-type signals, whose spectral properties are in some applications more desirable than those of MSK or OQPSK. The present study derives and presents a set of conditions on the input pulse shaping which in turn describes the class of envelope shapes allowable. The autocorrelation function and power spectral density of this class of signals are then derived, and specific examples are given to illustrate the desirable spectral properties. Such properties are important considerations in system design where interchannel and intersymbol interference degradations must be kept to a minimum.

Simon, M. K.↗
NASA NTRS1996

Development and Implementation of Software for Visualizing and Editing Multidimensional Flight Simulation Input Data

In a real-time or batch mode simulation that is designed to model aircraft dynamics over a wide range of flight conditions, a table look- up scheme is implemented to determine the forces and moments on the vehicle based upon the values of parameters such as angle of attack, altitude, Mach number, and control surface deflections. Simulation Aerodynamic Variable Interface (SAVI) is a graphical user interface to the flight simulation input data, designed to operate on workstations that support X Windows. The purpose of the application is to provide two and three dimensional visualization of the data, to allow an intuitive sense of the data set. SAVI also allows the user to manipulate the data, either to conduct an interactive study of the influence of changes on the vehicle dynamics, or to make revisions to data set based on new information such as flight test. This paper discusses the reasons for developing the application, provides an overview of its capabilities, and outlines the software architecture and operating environment.

Whelan, Todd Michael↗
NASA NTRS2015

Can Simulation Credibility Be Improved Using Sensitivity Analysis to Understand Input Data Effects on Model Outcome?

Model and simulation (MS) credibility is defined as, the quality to elicit belief or trust in MS results. NASA-STD-7009 [1] delineates eight components (Verification, Validation, Input Pedigree, Results Uncertainty, Results Robustness, Use History, MS Management, People Qualifications) that address quantifying model credibility, and provides guidance to the model developers, analysts, and end users for assessing the MS credibility. Of the eight characteristics, input pedigree, or the quality of the data used to develop model input parameters, governing functions, or initial conditions, can vary significantly. These data quality differences have varying consequences across the range of MS application. NASA-STD-7009 requires that the lowest input data quality be used to represent the entire set of input data when scoring the input pedigree credibility of the model. This requirement provides a conservative assessment of model inputs, and maximizes the communication of the potential level of risk of using model outputs. Unfortunately, in practice, this may result in overly pessimistic communication of the MS output, undermining the credibility of simulation predictions to decision makers. This presentation proposes an alternative assessment mechanism, utilizing results parameter robustness, also known as model input sensitivity, to improve the credibility scoring process for specific simulations.

Credibility↗
NASA NTRS2016

The Model Intercomparison Project on the Climatic Response to Volcanic Forcing (VolMIP): Experimental Design and Forcing Input Data for CMIP6

The enhancement of the stratospheric aerosol layer by volcanic eruptions induces a complex set of responses causing global and regional climate effects on a broad range of timescales. Uncertainties exist regarding the climatic response to strong volcanic forcing identified in coupled climate simulations that contributed to the fifth phase of the Coupled Model Intercomparison Project (CMIP5). In order to better understand the sources of these model diversities, the Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP) has defined a coordinated set of idealized volcanic perturbation experiments to be carried out in alignment with the CMIP6 protocol. VolMIP provides a common stratospheric aerosol data set for each experiment to minimize differences in the applied volcanic forcing. It defines a set of initial conditions to assess how internal climate variability contributes to determining the response. VolMIP will assess to what extent volcanically forced responses of the coupled ocean-atmosphere system are robustly simulated by state-of-the-art coupled climate models and identify the causes that limit robust simulated behavior, especially differences in the treatment of physical processes. This paper illustrates the design of the idealized volcanic perturbation experiments in the VolMIP protocol and describes the common aerosol forcing input data sets to be used.

Volcanoes↗