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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

File Specification for GEOS-CF Products

The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time forecasts of atmospheric composition at a horizontal resolution of 0.25 degrees (about 25 km). This GEOS Composition Forecast (GEOS-CF) system combines the GEOS weather analysis and forecasting system with the state-of-the-science GEOS-Chem chemistry module (Bey et al., 2001; Keller et al., 2014; Long et al., 2015) to provide detailed chemical analysis of a wide range of air pollutants including ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5).

Knowland, Katherine E.↗
NASA NTRS2022

GMAO Office Note No. 17 (Version 1.3) File Specification for GEOS-CF Products

The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time forecasts of atmospheric composition at a horizontal resolution of 0.25 degrees (about 25 km). This GEOS Composition Forecast (GEOS-CF) system combines the GEOS weather analysis and forecasting system with the state-of-the-science GEOS-Chem chemistry module (Bey et al., 2001; Keller et al., 2014; Long et al., 2015) to provide detailed chemical analysis of a wide range of air pollutants including ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Full model description and evaluation of the GEOS-CF tropospheric simulation and forecast skill is given in Keller et al. (2021). For evaluation of GEOS-CF stratospheric composition see Knowland et al. (2021).

GEOS-CF↗
NASA NTRS2008

Fpack and Funpack Utilities for FITS Image Compression and Uncompression

Fpack is a utility program for optimally compressing images in the FITS (Flexible Image Transport System) data format (see http://fits.gsfc.nasa.gov). The associated funpack program restores the compressed image file back to its original state (as long as a lossless compression algorithm is used). These programs may be run from the host operating system command line and are analogous to the gzip and gunzip utility programs except that they are optimized for FITS format images and offer a wider choice of compression algorithms. Fpack stores the compressed image using the FITS tiled image compression convention (see http://fits.gsfc.nasa.gov/fits_registry.html). Under this convention, the image is first divided into a user-configurable grid of rectangular tiles, and then each tile is individually compressed and stored in a variable-length array column in a FITS binary table. By default, fpack usually adopts a row-by-row tiling pattern. The FITS image header keywords remain uncompressed for fast access by FITS reading and writing software. The tiled image compression convention can in principle support any number of different compression algorithms. The fpack and funpack utilities call on routines in the CFITSIO library (http://hesarc.gsfc.nasa.gov/fitsio) to perform the actual compression and uncompression of the FITS images, which currently supports the GZIP, Rice, H-compress, and PLIO IRAF pixel list compression algorithms.

Pence, W.↗
NASA NTRS2000

BOREAS AFM-06 Mean Wind Profile Data

The Boreal Ecosystem-Atmosphere Study (BOREAS) Airborne Fluxes and Meteorology (AFM)-6 team from the National Oceanic and Atmospheric Administration/Environment Technology Laboratory (NOAA/ETL) operated a 915-MHz wind/Radio Acoustic Sounding System (RASS) profiler system in the Southern Study Area (SSA) near the Old Jack Pine (OJP) tower from 21 May 1994 to 20 Sep 1994. The data set provides wind profiles at 38 heights, containing the variables of wind speed; wind direction; and the u-, v-, and w-components of the total wind. The data are stored in tabular ASCII files. The mean wind profile data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Wilczak, James↗
NASA NTRS2025

Distributed Sensing Node Configuration

A vision and radar sensor integration strategy for surveillance of Advance Air Mobility Mission concept of airspace is contained in the CAD drawing in this publication developed as part of NASA's Transformational Tools and Technology project. High-density airspace operations with multiple aircraft type and potentially noncooperative aircraft require distributed sensor detection and tracking systems to monitor airspace for safe, autonomous flight operations for advanced air mobility, urban air mobility, and high density small uncrewed air systems (SUAS) flight concepts. Contained herein are drawings that are available in the stp and creo file formats of the distributed sensing nodes. Additional dovetail integration is included to enable rapid changing of embedded system. A day in the life node deployment is included in the 2023_last_flight_day.mp4. Please cite this publication if you use these drawings. This sensor configuration methodology is presented in detail in the publication reference below. Chester Dolph, Thomas Lombaerts, Corey A. Ippolito, Vahram Stepanyan, Evan Kawamura, Keerthana Kannan, George Szatkowski, Todd Ferante, Christopher Morris, Federica Vitiello, Flavia Causa, Roberto Opromolla and Giancarmine Fasano. "Distributed Sensor Fusion of Ground and Air Nodes using Vision and Radar Modalities for Tracking Multirotor Small Uncrewed Air Systems and Birds," AIAA 2024-1781. AIAA SCITECH 2024 Forum. January 2024.

Chester V Dolph↗
DOE OSTI2015

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activaties are currently ongoing for coupling RAVEN with software such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by input files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with software such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focusing on the input structure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analy- sis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncer- tainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is in- terested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under develop-ment at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explaination of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analy- sis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncer- tainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is in- terested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under develop-ment at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explaination of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

RAVEN User Manual, Revision 4

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assess- ment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1979

Processing of on-board recorded data for quick analysis of aircraft performance

A system of independent computer programs for the processing of digitized pulse code modulated (PCM) and frequency modulated (FM) data is described. Information is stored in a set of random files and accessed to produce both statistical and graphical output. The software system is designed primarily to present these reports within a twenty-four hour period for quick analysis of the helicopter's performance.

Michaud, N. H.↗
NASA NTRS2020

State of the Art Small Spacecraft Technology

When the first edition of NASA’s Small Spacecraft Technology State-of-the-art report was published in 2013, 247 CubeSats and 105 other non-CubeSat small spacecraft under 50 kilograms (kg) had been launched, and these represented less than 2% of launched mass into orbit. By 2019, small spacecraft with mass less than 180 kg made up almost 7% of all mass launched into orbit. Additionally, 63% of spacecraft under 600 kg had mass less than 180 kg and of those 47% were CubeSats (1). Since 2013, flight heritage for small spacecraft, primarily CubeSats, has nearly doubled and with dedicated smallsat launch capabilities readily available and expanding, opportunities to demonstrate new technologies and systems are expected to increase. The 2020 edition of this report captures and distills the wealth of new information available on small spacecraft systems from NASA and other publicly available sources. Overall, this report is a survey of small spacecraft technologies sourced from open literature; it does not endeavor to be an original source, and only considers literature in the public domain to identify and classify devices. Commonly used sources for data include manufacturer datasheets, press releases, conference papers, journal papers, public filings with government agencies, news articles, presentations, and Small Spacecraft Systems Virtual Institute Federated Search. Data not appropriate for public dissemination, such as proprietary, export controlled, or otherwise restricted data, are not considered. As a result, this report includes many dedicated hours of desk research performed by subject matter experts reviewing resources noted above.

Bruce Yost↗
NASA NTRS1986

An overview of the Land Analysis System (LAS)

An outline covering the history, development methodology, and major hardware/software components of the Land Analysis System (LAS) is presented. System support services including the Transportable Applications Executive (TAE), Catalog Manager, history files, and applications services are briefly described along with the general functional capabilities of the 224 available applications programs. Example interface menus are given and desired enhancements to the LAS system are listed.

Lu, Yun-Chi↗
NASA NTRS2013

Integrated Main Propulsion System Performance Reconstruction Process/Models

The Integrated Main Propulsion System (MPS) Performance Reconstruction process provides the MPS post-flight data files needed for postflight reporting to the project integration management and key customers to verify flight performance. This process/model was used as the baseline for the currently ongoing Space Launch System (SLS) work. The process utilizes several methodologies, including multiple software programs, to model integrated propulsion system performance through space shuttle ascent. It is used to evaluate integrated propulsion systems, including propellant tanks, feed systems, rocket engine, and pressurization systems performance throughout ascent based on flight pressure and temperature data. The latest revision incorporates new methods based on main engine power balance model updates to model higher mixture ratio operation at lower engine power levels.

Lopez, Eduardo↗
DOE OSTI2024

Designing Track for Electrospinning Unit and Cost-Effective Laser Scanning System

Nanofibers are produced in the Targeted Systems Department (TSD) by applying a large voltage to the nanofiber fluid and the collection apparatus known as the electrospinner. The fiber is then shot out of nozzles and collected onto the electrospinner into a nanofiber mat. The problem is that since the nozzle heads are stationary, there is non-uniform deposition of nanofiber on the collection drum leading to variation in thickness of the produced nanofiber mat. To mitigate this issue, a reciprocating mechanism is designed to move the nozzle along the drum collector along with the ability to vary its stroke length. This design was realized using Siemens NX and several parts were printed using a 3D printer, but is, as of writing, untested. Second project involved providing an alternative method to scan a large sample that had undergone certain surface deformation by beam interaction and detect out of plane deformation such as micro-scale swelling and surface roughness. Systems to carry out this scanning already exist; however, they are expensive and produce many files that need to be stitched together and are cumbersome to deal with. The solution is to create a scanning system using an already purchased scanning laser and linear stage motor. The laser and stage motor were combined to produce length results that aligned with a digital microscope but differing height results.

Ruffolo, Leopoldo↗
DOE OSTI2024

Designing a track for an electrospinning unit and cost-effective laser scanning system

Nanofibers are produced in the Targeted Systems Department (TSD) by applying a large voltage to the nanofiber fluid and the collection apparatus known as the electrospinner. The fiber is then shot out of nozzles and collected onto the electrospinner into a nanofiber mat. The problem is that since the nozzle heads are stationary, there is non-uniform deposition of nanofiber on the collection drum leading to variation in thickness of the produced nanofiber mat. To mitigate this issue, a reciprocating mechanism is designed to move the nozzle along the drum collector along with the ability to vary its stroke length. This design was realized using Siemens NX and several parts were printed using a 3D printer, but is, as of writing, untested. Second project involved providing an alternative method to scan a large sample that had undergone certain surface deformation by beam interaction and detect out of plane deformation such as micro-scale swelling and surface roughness. Systems to carry out this scanning already exist; however, they are expensive and produce many files that need to be stitched together and are cumbersome to deal with. The solution is to create a scanning system using an already purchased scanning laser and linear stage motor. The laser and stage motor were combined to produce length results that aligned with a digital microscope but differing height results.

Ruffolo, Leopoldo↗
NASA NTRS2004

Format for Interchange and Display of 3D Terrain Data

Visible Scalable Terrain (ViSTa) is a software format for production, interchange, and display of three-dimensional (3D) terrain data acquired by stereoscopic cameras of robotic vision systems. ViSTa is designed to support scalability of data, accuracy of displayed terrain images, and optimal utilization of computational resources. In a ViSTa file, an area of terrain is represented, at one or more levels of detail, by coordinates of isolated points and/or vertices of triangles derived from a texture map that, in turn, is derived from original terrain images. Unlike prior terrain-image software formats, ViSTa includes provisions to ensure accuracy of texture coordinates. Whereas many such formats are based on 2.5-dimensional terrain models and impose additional regularity constraints on data, ViSTa is based on a 3D model without regularity constraints. Whereas many prior formats require external data for specifying image-data coordinate systems, ViSTa provides for the inclusion of coordinate-system data within data files. ViSTa admits highspeed loading and display within a Java program. ViSTa is designed to minimize file sizes and maximize compressibility and to support straightforward reduction of resolution to reduce file size for Internet-based distribution.

Backes, Paul↗