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All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

SSC Engineering Analysis

A package for the automation of the Engineering Analysis (EA) process at the Stennis Space Center has been customized. It provides the ability to assign and track analysis tasks electronically, and electronically route a task for approval. It now provides a mechanism to keep these analyses under configuration management. It also allows the analysis to be stored and linked to the engineering data that is needed to perform the analysis (drawings, etc.). PTC s (Parametric Technology Corp o ration) Windchill product was customized to allow the EA to be created, routed, and maintained under configuration management. Using Infoengine Tasks, JSP (JavaServer Pages), Javascript, a user interface was created within the Windchill product that allows users to create EAs. Not only does this interface allow users to create and track EAs, but it plugs directly into the out-ofthe- box ability to associate these analyses with other relevant engineering data such as drawings. Also, using the Windchill workflow tool, the Design and Data Management System (DDMS) team created an electronic routing process based on the manual/informal approval process. The team also added the ability for users to notify and track notifications to individuals about the EA. Prior to the Engineering Analysis creation, there was no electronic way of creating and tracking these analyses. There was also a feature that was added that would allow users to track/log e-mail notifications of the EA.

Ryan, Harry↗
NASA NTRS2009

Lunar Regolith Characterization for Simulant Design and Evaluation using Figure of Merit Algorithms

NASA's Marshall Space Flight Center (MSFC), in conjunction with the United States Geological Survey (USGS) and aided by personnel from the Astromaterials Research and Exploration Science group at Johnson Space Center (ARES-JSC), is implementing a new data acquisition strategy to support the development and evaluation of lunar regolith simulants. The first analyses of lunar regolith samples by the simulant group were carried out in early 2008 on samples from Apollo 16 core 64001/64002. The results of these analyses are combined with data compiled from the literature to generate a reference composition and particle size distribution (PSD)) for lunar highlands regolith. In this paper we present the specifics of particle type composition and PSD for this reference composition. Furthermore. we use Figure-of-Merit (FoM) routines to measure the characteristics of a number of lunar regolith simulants against this reference composition. The lunar highlands regolith reference composition and the FoM results are presented to guide simulant producers and simulant users in their research and development processes.

Schrader, Christian M.↗
NASA NTRS2016

[EC5-Space Suit Assembly Team- Internship]

There were three main projects in this internship. The first pertained to the Bearing Dust Cycle Test, in particular automating the test to allow for easier administration. The second concerned modifying the communication system setup in the Z2 suit, where speakers and mics were adjusted to allow for more space in the helmet. And finally, the last project concerned the tensile strength testing of fabrics deemed as candidates for space suit materials and desired to be sent off for radiation testing. The major duties here are split up between the major projects detailed above. For the Bearing Dust Cycle Test, the first objective was to find a way to automate administration of the test, as the previous version was long and tedious to perform. In order to do this, it was necessary to introduce additional electronics and perform programming to control the automation. Once this was done, it would be necessary to update documents concerning the test setup, procedure, and potential hazards. Finally, I was tasked with running tests using the new system to confirm system performance. For the Z2 communication system modifications, it was necessary to investigate alternative speakers and microphones which may have better performance than those currently used in the suit. Further, new speaker and microphone positions needed to be identified to keep them out of the way of the suit user. Once this was done, appropriate hardware (such as speaker or microphone cases and holders) could be prototyped and fabricated. For the suit material strength testing, the first task was to gather and document various test fabrics to identify the best suit material candidates. Then, it was needed to prepare samples for testing to establish baseline measurements and specify a testing procedure. Once the data was fully collected, additional test samples would be prepared and sent off-site to undergo irradiation before being tested again to observe changes in strength performance. For the Bearing Dust Cycle Test, automation was achieved through use of a servo motor and code written in LabVIEW. With this a small electrical servo controller was constructed and added to the system. For the Z2 communication modifications speaker cases were developed and printed, and new speakers and mics were selected. This allowed us to move the speakers and mics to locations to remain out of the suit users way. For the suit material strength testing, five material candidates were identified and test samples were created. These samples underwent testing, and baseline test results were gathered, though these results are currently being investigated for accuracy. The main process efficiency developed during the course of this internship comes from automation of the Bearing Dust Cycle Test. In particular, many hours of human involvement and precise operation are replaced with a simple motor setup. Thus it is no longer required to man the test, saving valuable employee time. This internship has confirmed a few things for me, namely that I both want to work as an engineer for an aerospace firm and that in particular I want to work for the Johnson Space Center. I am also confirmed in my desire to work with electronics, though I was surprised to enjoy prototyping 3D CAD design as much as I did. Therefore, I will make an effort to build my skills in this area so that I can continue to design mechanical models. In fact, I found the process of hands-on prototyping to be perhaps the most fun aspect of my time working here. This internship has also furthered my excitement for continual education, and I will hopefully be pursuing a masters in my field in the near future.

Maicke, Andrew↗
NASA NTRS1996

Space Station Furnace Facility Management Information System (SSFF-MIS) Development

This report summarizes the chronology, results, and lessons learned from the development of the SSFF-MIS. This system has been nearly two years in development and has yielded some valuable insights into specialized MIS development. General: In December of 1994, the Camber Corporation and Science Applications International Corporation (SAIC) were contracted to design, develop, and implement a MIS for Marshall Space Flight Center's Space Station Furnace Facility Project. The system was to be accessible from both EBM-Compatible PC and Macintosh platforms. The system was required to contain data manually entered into the MIS as well as data imported from other MSFC sources. Electronic interfaces were established for each data source and retrieval was to be performed at prescribed time intervals. The SOW requirement that predominantly drove the development software selection was the dual-platform (IBM-PC and Macintosh) requirement. The requirement that the system would be maintained by Government personnel influenced the selection of Commercial Off-the-shelf software because of its inherent stability and readily available documentation and support. Microsoft FoxPro Professional 2.6 for Windows and Macintosh was selected as the development tool. This is a software development tool that has been in use for many years. It is stable and powerful. Microsoft has since released the replacement for this product, Microsoft Visual FoxPro, but at the time of this development, it was only available on the Windows platform. The initial contract included included the requirement for capabilities relating to the Work- and Organizational Breakdown Structures, cost (plan and actuals), workforce (plan and actuals), critical path scheduling, trend analysis, procurements and contracts, interface to manufacturing, Safety and Mission Assurance, risk analysis, and technical performance indicators. It also required full documentation of the system and training of users. During the course of the contract, the requirements for Safety and Mission Assurance interface, risk analysis, and technical performance indicators were deleted. Additional capabilities were added as reflected in the Contract Chronology below. Modification 4 added the requirement for Support Contractor manpower data, the ability to manually input data not imported from non-nal sources, a general 'health' indicator screen, and remote usage. Mod 6 included the ability to change the level of planning of Civil Service Manpower at any time and the ability to manually enter Op Codes in the manufacturing data where such codes were not provided by the EMPACS database. Modification 9 included a number of changes to report contents and formats. Modification 11 required the preparation of a detailed System Design Document.

Meade, Robert M.↗
NASA NTRSDate not supplied

Global 3D Data Visualization and Analysis Platform with Advanced Machine Learning Capabilities in Support of Lunar Exploration

Introduction: The science goals for NASA’s Artemis program include: a) Understanding the character and origin of lunar polar volatiles, b) Conducting experimental science in the lunar environment and c) Investigating and mitigating exploration risks. The permanently shadowed regions (PSRs) on the Lunar south pole are expected to host large quantities of water-ice and volatiles that are important for sustainable Lunar exploration. There are several missions such as onboard Korea Pathfinder Lunar Orbiter (KPLO: Korean name Danuri) with onboard ShadowCam camera, Astrobotic Peregrine Mission One [4], and other efforts underway to obtain high resolution topographic, minerals, volatiles and other information on the moon. We envision a need in immediate future for platforms to integrate these data sets, provide rendering and visualization capabilities in the context of a 3D Lunar globe for easier information access and analysis. NASA's Celestial Mapping System (CMS) is developed to address the need for 3D tools for planetary science investigations, mission planning, in-situ operations, in a 3D-first design constructed around a unified view of a planetary globe. At present CMS provides many critical functionalities that include: 1) equipment planning and optimized placement on Lunar surface 2) line-of-sight (LOS) analysis 3) powerful measurement tools based on 3D terrain with realistic 3D models to represent rovers, astronauts and equipment 4) visualization of derived mapping products (e.g. resource maps), and 5) a data engine for hosting new observations that are not available in other contemporary lunar data tools. Planetary Data Ingestion: CMS can consume and analyze data from locally hosted and external third party sources. It is compatible with Open Geospatial Consortium (OGC) data and file standards and currently integrates datasets from the Astrogeology Science Center of USGS. This includes global and local data acquired from NASA (LRO, Clementine, Lunar Orbiter) and JAXA (SELENE/Kaguya), with capability of integrating more datasets. In addition, users can specify other WMS-hosted data endpoints, which CMS can then query and stream data from automatically. To set-up an automated process for ingestion and accurate rendering, visualization and analysis of external 3rd party planetary datasets within CMS, we initiated the process of ingesting unique dataset of super-enhanced images of the permanently shadowed regions (PSRs) at the lunar poles which were produced by the Hyper-effective nOise Removal U-net Software (HORUS) tool. This tool was developed to enhance the extremely low-light images of the interior of PSRs and provide the ability to see within these regions at and discern surface features (i.e. boulders and craters) down to 3 meters in size. We focused on the Nobile region on the Lunar south pole, selected site for VIPER mission and stitched several images to create a high-resolution map within one of the PSR of Nobile crater. Figure 1 shows the dark PSR zone form the original NAC layer of LRO as the base layer (left image) and the illuminated areas within that crater (center) which was created by ingesting and merging several of HORUS generated images. At present we employ a semi-automated process to ensure spatial accuracy and merger of several overlapping zones. However, we are in the process of completely automating this process by employing AI based techniques that would rank, sort, and stack the images based on their information density. The georectification of the images would employ selected features. Analysis on Ingested Planetary Datasets: Once an external planetary data-set is successfully ingested, georectified and merged seamlessly as a data-layer; CMS’ numerous analysis tools can be used on this data. A Line of Sight (LOS) tool has been developed for CMS which analyzes terrain profiles and obstructions to determine visibility for remote observers. Figure 1 (right image) shows the viewshed analysis on the same PSR in the Nobile region. The yellow pin shows the observer location outside the PSR. The yellow area shows the visible part of PSR. The obstructed area with no visibility for the observer is shown in red. The Measurements tool allows the user to take area and distance measurements of features on the terrain using various shapes. Measurement type can be specified in a number of ways: Line, Path, Polygon, Circle, Ellipse, Square, Rectangle or Freehand. Once the shape is specified, elevation information can then be extracted along each of these shapes. Figure 2 (left) shows the measurements performed on a crater n illuminated PSR in Nobile region. The equipment placement tool allows the user to place a 3D equipment model at a desired location and analyze its coverage area. The equipment placement tool is coupled with LOS to determine the coverage. Figure 2 (right) shows an equipment placed on the Lunar terrain and it’s coverage area. The red rays are blocked sight lines and the green rays are non-obstructed sight lines with the cyan lines showing the point of intersection with the terrain. More details are provided in the video demonstrations in Reference 5. Overcoming Polar Distortions: 3D geospatial applications exhibit significant distortions in polar imagery due to several reasons: 1) distortions in the source imagery, 2) incompatible tessellation algorithms at the poles, and 3) map projections. We are leveraging new tessellation algorithms and reprojecting data using projections that are better suited for Lunar poles. The goal is to seamlessly switch to polar projections while maintaining 3D view and navigation.

Maps↗
NASA NTRS1986

PROFILE user's guide

User information for program PROFILE, an aerodynamics design utility for refining, plotting, and tabulating airfoil profiles is provided. The theory and implementation details for two of the more complex options are also presented. These are the REFINE option, for smoothing curvature in selected regions while retaining or seeking some specified thickness ratio, and the OPTIMIZE option, which seeks a specified curvature distribution. REFINE uses linear techniques to manipulate ordinates via the central difference approximation to second derivatives, while OPTIMIZE works directly with curvature using nonlinear least squares techniques. Use of programs QPLOT and BPLOT is also described, since all of the plots provided by PROFILE (airfoil coordinates, curvature distributions) are achieved via the general purpose QPLOT utility. BPLOT illustrates (again, via QPLOT) the shape functions used by two of PROFILE's options. The programs were designed and implemented for the Applied Aerodynamics Branch at NASA Ames Research Center, Moffett Field, California, and written in FORTRAN and run on a VAX-11/780 under VMS.

Collins, L.↗
NASA NTRS1992

Spatial region filtering in IRAF/PROS

In order to analyze x ray data, it is nearly always necessary to extract source and background events from a data set. Typically, this is done by defining geometric spatial regions of the data set to describe the source and background. For example, one might wish to extract source events from a circular or elliptical region centered at a particular pixel, and background events from a circular or elliptical annulus whose inner radius matches the source region. At the same time, it might be necessary to exclude one or more nearby sources from the source or background region in question. Thus, it might be necessary to define a pie-shaped region or even an entirely irregularly-shaped region to exclude. A spatial filtering scheme called REGIONS was implemented in IRAF/PROS to support these and other types of spatial region extraction. It allows users to create a spatial mask by specifying one or more ASCII geometric shape descriptors (box, circle, ellipse, pie, point, annulus, and polygon) as regions to be included or excluded in the mask. In addition, two or more shapes can be combined using Boolean algebra to create an infinite variety of sophisticated regions. Each geometric shape has a specific set of parameters that describe that shape. For example, a circle is described by a center and a radius, while a box is described by a center, length, width, and rotation angle. These quantities can be specified in units of pixels or, in cases where the target image contains world coordinate system information, they can be described in units such as RA and Dec. Users can create region mask files by feeding an ASCII region descriptor to the IRAF/PROS plcreate task. Temporary masks can also be created from ASCII region descriptors by individual applications that call the routines in the region creation library. This library implements a yacc-based region parser that compiles the ASCII descriptors into 'software CPU' instructions which are then executed to create the mask. The mask created from these region descriptors is a standard IRAF PLIO mask. It can be combined with other PLIO masks (e.g., exposure masks) to provide complete spatial filtering capabilities. The capabilities of the region filtering scheme are described. It also discusses the design philosophy guiding our work, as well as our plans for the future.

Mandel, Eric↗
NASA NTRS1996

F-15B/Flight Test Fixture 2: A Test Bed for Flight Research

NASA Dryden Flight Research Center has developed a second-generation flight test fixture for use as a generic test bed for aerodynamic and fluid mechanics research. The Flight Test Fixture 2 (FTF-2) is a low-aspect-ratio vertical fin-like shape that is mounted on the centerline of the F-I5B lower fuselage. The fixture is designed for flight research at Mach numbers to a maximum of 2.0. The FTF-2 is a composite structure with a modular configuration and removable components for functional flexibility. This report documents the flow environment of the fixture, such as surface pressure distributions and boundary-layer profiles, throughout a matrix of conditions within the F-15B/FTF-2 flight envelope. Environmental conditions within the fixture are presented to assist in the design and testing of future avionics and instrumentation. The intent of this document is to serve as a user's guide and assist in the development of future flight experiments that use the FTF-2 as a test bed. Additional information enclosed in the appendices has been included to assist with more detailed analyses, if required.

David M Richwine↗
NASA NTRS2001

Photochemical Phenomenology Model for the New Millennium

The "Photochemical Phenomenology Model for the New Millennium" project tackles the issue of reengineering and extension of validated physics-based modeling capabilities ("legacy" computer codes) to application-oriented software for use in science and science-support activities. While the design and architecture layouts are in terms of general particle distributions involved in scattering, impact, and reactive interactions, initial Photochemical Phenomenology Modeling Tool (PPMT) implementations are aimed at construction and evaluation of photochemical transport models with rapid execution for use in remote sensing data analysis activities in distributed systems. Current focus is on the Composite Infrared Spectrometer (CIRS) data acquired during the CASSINI flyby of Jupiter. Overall, the project has stayed on the development track outlined in the Year 1 annual report and most Year 2 goals have been met. The issues that have required the most attention are: implementation of the core photochemistry algorithms; implementation of a functional Java Graphical User Interface; completion of a functional CORBA Component Model framework; and assessment of performance issues. Specific accomplishments and the difficulties encountered are summarized in this report. Work to be carried out in the next year center on: completion of testing of the initial operational implementation; its application to analysis of the CASSINI/CIRS Jovian flyby data; extension of the PPMT to incorporate additional phenomenology algorithms; and delivery of a mature operational implementation.

Bishop, James↗
NASA NTRSDate not supplied

Global 3D Data Visualization and Analysis Platform With Advanced Machine Learning Capabilities in Support of Lunar Exploration

Introduction: The science goals for NASA’s Artemis program include: a) Understanding the character and origin of lunar polar volatiles, b) Conducting experimental science in the lunar environment and c) Investigating and mitigating exploration risks [1]. The permanently shadowed regions (PSRs) on the Lunar south pole are expected to host large quantities of water-ice and volatiles that are important for sustainable Lunar exploration [2]. There are several missions such as onboard Korea Pathfinder Lunar Orbiter (KPLO: Korean name Danuri) with onboard ShadowCam camera [3], Astrobotic Peregrine Mission One [4], and other efforts underway to obtain high resolution topographic, minerals, volatiles and other information on the moon. We envision a need in immediate future for platforms to integrate these data sets, provide rendering and visualization capabilities in the context of a 3D Lunar globe for easier information access and analysis. NASA's Celestial Mapping System (CMS) [5] is developed to address the need for 3D tools for planetary science investigations, mission planning, in-situ operations, in a 3D-first design constructed around a unified view of a planetary globe. At present CMS provides many critical functionalities that include: 1) equipment planning and optimized placement on Lunar surface 2) line-of-sight (LOS) analysis 3) powerful measurement tools based on 3D terrain with realistic 3D models to represent rovers, astronauts and equipment 4) visualization of derived mapping products (e.g. resource maps), and 5) a data engine for hosting new observations that are not available in other contemporary lunar data tools [5, 7]. Planetary Data Ingestion: CMS can consume and analyze data from locally hosted and external third party sources. It is compatible with Open Geospatial Consortium (OGC) data and file standards and currently integrates datasets from the Astrogeology Science Center of USGS. This includes global and local data acquired from NASA (LRO, Clementine, Lunar Orbiter) and JAXA (SELENE/Kaguya), with capability of integrating more datasets. In addition, users can specify other WMS-hosted data endpoints, which CMS can then query and stream data from automatically. To set-up an automated process for ingestion and accurate rendering, visualization and analysis of external 3rd party planetary datasets within CMS, we initiated the process of ingesting unique dataset of super-enhanced images of the permanently shadowed regions (PSRs) at the lunar poles which were produced by the Hyper-effective nOise Removal U-net Software (HORUS) tool [8]. This tool was developed to enhance the extremely low-light images of the interior of PSRs and provide the ability to see within these regions at and discern surface features (i.e. boulders and craters) down to 3 meters in size. We focused on the Nobile region on the Lunar south pole, selected site for VIPER mission and stitched several images to create a high-resolution map within one of the PSR of Nobile crater. Figure 1 shows the dark PSR zone form the original NAC layer of LRO as the base layer (left image) and the illuminated areas within that crater (center) which was created by ingesting and merging several of HORUS generated images. At present we employ a semi-automated process to ensure spatial accuracy and merger of several overlapping zones. However, we are in the process of completely automating this process by employing AI based techniques that would rank, sort, and stack the images based on their information density. The georectification of the images would employ selected features. Analysis on Ingested Planetary Datasets: Once an external planetary data-set is successfully ingested, georectified and merged seamlessly as a data-layer; CMS’ numerous analysis tools can be used on this data. A Line of Sight (LOS) tool has been developed for CMS which analyzes terrain profiles and obstructions to determine visibility for remote observers [5,6]. Figure 1 (right image) shows the viewshed analysis on the same PSR in the Nobile region. The yellow pin shows the observer location outside the PSR. The yellow area shows the visible part of PSR. The obstructed area with no visibility for the observer is shown in red. The Measurements tool allows the user to take area and distance measurements of features on the terrain using various shapes. Measurement type can be specified in a number of ways: Line, Path, Polygon, Circle, Ellipse, Square, Rectangle or Freehand. Once the shape is specified, elevation information can then be extracted along each of these shapes. Figure 2 (left) shows the measurements performed on a crater n illuminated PSR in Nobile region. The equipment placement tool allows the user to place a 3D equipment model at a desired location and analyze its coverage area. The equipment placement tool is coupled with LOS to determine the coverage. Figure 2 (right) shows an equipment placed on the Lunar terrain and it’s coverage area. The red rays are blocked sight lines and the green rays are non-obstructed sight lines with the cyan lines showing the point of intersection with the terrain. More details are provided in the video demonstrations in Reference 5. Overcoming Polar Distortions: 3D geospatial applications exhibit significant distortions in polar imagery due to several reasons: 1) distortions in the source imagery, 2) incompatible tessellation algorithms at the poles, and 3) map projections. We are leveraging new tessellation algorithms and reprojecting data using projections that are better suited for Lunar poles. The goal is to seamlessly switch to polar projections while maintaining 3D view and navigation.

Maps↗
NASA NTRS1995

File-System Workload on a Scientific Multiprocessor

Many scientific applications have intense computational and I/O requirements. Although multiprocessors have permitted astounding increases in computational performance, the formidable I/O needs of these applications cannot be met by current multiprocessors a their I/O subsystems. To prevent I/O subsystems from forever bottlenecking multiprocessors and limiting the range of feasible applications, new I/O subsystems must be designed. The successful design of computer systems (both hardware and software) depends on a thorough understanding of their intended use. A system designer optimizes the policies and mechanisms for the cases expected to most common in the user's workload. In the case of multiprocessor file systems, however, designers have been forced to build file systems based only on speculation about how they would be used, extrapolating from file-system characterizations of general-purpose workloads on uniprocessor and distributed systems or scientific workloads on vector supercomputers (see sidebar on related work). To help these system designers, in June 1993 we began the Charisma Project, so named because the project sought to characterize 1/0 in scientific multiprocessor applications from a variety of production parallel computing platforms and sites. The Charisma project is unique in recording individual read and write requests-in live, multiprogramming, parallel workloads (rather than from selected or nonparallel applications). In this article, we present the first results from the project: a characterization of the file-system workload an iPSC/860 multiprocessor running production, parallel scientific applications at NASA's Ames Research Center.

Kotz, David↗
NASA NTRS2016

Stewardship of NASA's Earth Science Data and Ensuring Long-Term Active Archives

Program, NASA has followed an open data policy, with non-discriminatory access to data with no period of exclusive access. NASA has well-established processes for assigning and or accepting datasets into one of 12 Distributed Active Archive Centers (DAACs) that are parts of EOSDIS. EOSDIS has been evolving through several information technology cycles, adapting to hardware and software changes in the commercial sector. NASA is responsible for maintaining Earth science data as long as users are interested in using them for research and applications, which is well beyond the life of the data gathering missions. For science data to remain useful over long periods of time, steps must be taken to preserve: (1) Data bits with no corruption, (2) Discoverability and access, (3) Readability, (4) Understandability, (5) Usability' and (6). Reproducibility of results. NASAs Earth Science data and Information System (ESDIS) Project, along with the 12 EOSDIS Distributed Active Archive Centers (DAACs), has made significant progress in each of these areas over the last decade, and continues to evolve its active archive capabilities. Particular attention is being paid in recent years to ensure that the datasets are published in an easily accessible and citable manner through a unified metadata model, a common metadata repository (CMR), a coherent view through the earthdata.gov website, and assignment of Digital Object Identifiers (DOI) with well-designed landing product information pages.

Data Management↗
NASA NTRSDate not supplied

Transitioning TEMPO Data for Air Quality Management Applications at the NASA SPoRT Center

The TEMPO mission has been observing air pollutants every hour during the daytime across its Field of Regard (FoR) covering greater North America since First Light on August 2, 2023. The highly anticipated public release of TEMPO data occurred on May 20, 2024, consisting of level 2 and level 3 trace gas data products of nitrogen dioxide, formaldehyde, and ozone. The NASA SPoRT Center is developing value-added products and tools to support the TEMPO mission and Early Adopters program with special attention on stakeholder from air agencies. One component of our work is focused on evaluating the TEMPO products over stakeholder target areas using Pandora and surface monitor observations to characterize the uncertainties and develop best practices for processing and analyzing TEMPO data. Methods for oversampling TEMPO data to 1 km resolution are being applied over the target areas to resolve fine-scale emission sources and pollutant gradients. Machine learning techniques using TEMPO, surface monitor, and model data to estimate surface-level nitrogen dioxide concentrations are being developed over the target areas. A SPoRT viewer for TEMPO has been launched for providing visualizations of the TEMPO products and an ArcGIS dashboard is being designed for enabling air agency stakeholders to efficiently analyze TEMPO data. Training materials including user guides are being developed to ensure the effective and sustained use of TEMPO data at our stakeholder agencies. One major goal of our TEMPO initiatives at SPoRT is to better enable the inclusion of TEMPO data in air quality management applications such as exceptional event demonstrations through our close engagement with stakeholders. This talk will provide an update on our SPoRT activities and showcase use cases of TEMPO data for monitoring different emission sources including wildland fire smoke.

Air Quality↗
NASA NTRS2014

A Guide to Axial-Flow Turbine Off-Design Computer Program AXOD2

A Users Guide for the axial flow turbine off-design computer program AXOD2 is composed in this paper. This Users Guide is supplementary to the original Users Manual of AXOD. Three notable contributions of AXOD2 to its predecessor AXOD, both in the context of the Guide or in the functionality of the code, are described and discussed in length. These are: 1) a rational representation of the mathematical principles applied, with concise descriptions of the formulas implemented in the actual coding. Their physical implications are addressed; 2) the creation and documentation of an Addendum Listing of input namelist-parameters unique to AXOD2, that differ from or are in addition to the original input-namelists given in the Manual of AXOD. Their usages are discussed; and 3) the institution of proper stoppages of the code execution, encoding termination messaging and error messages of the execution to AXOD2. These measures are to safe-guard the integrity of the code execution, such that a failure mode encountered during a case-study would not plunge the code execution into indefinite loop, or cause a blow-out of the program execution. Details on these are discussed and illustrated in this paper. Moreover, this computer program has since been reconstructed substantially. Standard FORTRAN Langue was instituted, and the code was formatted in Double Precision (REAL*8). As the result, the code is now suited for use in a local Desktop Computer Environment, is perfectly portable to any Operating System, and can be executed by any FORTRAN compiler equivalent to a FORTRAN 9095 compiler. AXOD2 will be available through NASA Glenn Research Center (GRC) Software Repository.

Gas Turbine Engines↗
NASA NTRS1998

An Operational Concept for Flying FMS Trajectories in Center and TRACON Airspace

Current Flight Management Systems (FMS) do a good job of constructing and flying an optimal trajectory for a single aircraft. Unfortunately, flight crews are often unable to fly these FMS routes during arrivals at busy airports. The Center TRACON Automation System (CTAS) has been designed to aid Center and TRACON (Terminal Radar Approach Control) controllers in assigning runways, sequencing and vectoring all classes of aircraft. CTAS bases its advisories on trajectory predictions for arriving aircraft using algorithms very similar to those in airborne FMS systems. This paper presents near and far term operational concepts for how a ground ATM (air traffic management) automation system like CTAS could work more effectively with the airborne automation in FMS equipped aircraft. The concepts for a more compatible air-ground system include: 1) a common route databases for both CTAS and FMS; 2) datalink to downlink information on aircraft weight, final approach speed and trajectory intent and to uplink wind information; 3) new FMS functions to allow flight crews to easily update their FMS trajectory to match the trajectory suggested by the ground automation with voice clearances, and 4) in the far term, datalink to downlink user preferred trajectories and to uplink trajectory clearances in the terminal area. The paper analyses some of the human factors issues that may result in allowing aircraft to fly FMS routes during enroute descent and in the terminal area. A series of linked human in the loop flight deck and air traffic control simulations and a field test with the NASA 757 are being conducted at NASA's Ames and Langley Research Centers to address these issues and to evaluate the operational feasibility of these approaches to more efficient flight and increased airport throughput.

Palmer, Everett↗
NASA NTRS2012

Key Decision Record Creation and Approval Module

Retaining good key decision records is critical to ensuring the success of a project or operation. Having adequately documented decisions with supporting documents and rationale can greatly reduce the amount of rework or reinvention over a project's, vehicle's, or facility's lifecycle. Stennis Space Center developed and uses a software tool that automates the Key Decision Record (KDR) process for its engineering and test projects. It provides the ability for a user to log key decisions that are made during the course of a project. By customizing Parametric Technology Corporation's (PTC) Windchill product, the team was able to log all information about a decision, and electronically route that information for approval. Customizing the Windchill product allowed the team to directly connect these decisions to the engineering data that it might affect and notify data owners of the decision. The user interface was created in JSP and Javascript, within the OOTB (Out of the Box) Windchill product, allowing users to create KDRs. Not only does this interface allow users to create and track KDRs, but it also plugs directly into the OOTB ability to associate these decision records with other relevant engineering data such as drawings, designs, models, requirements, or specifications

Hebert, Barrt↗
NASA NTRS2018

Building a QC Database of Meteorological Data from NASA KSC and the United States Air Force's Eastern Range

The National Aeronautics and Space Administration's (NASA) Marshall Space Flight Center (MSFC) Natural Environments Branch (EV44) provides atmospheric databases and analysis in support of space vehicle design and day-of-launch operations for NASA and commercial launch vehicle programs launching from the NASA Kennedy Space Center (KSC), co-located on the United States Air Force's Eastern Range (ER) at the Cape Canaveral Air Force Station. The ER complex is one of the most heavily instrumented sites in the United States with over 31 towers measuring various atmospheric parameters on a continuous basis. An inherent challenge with large datasets consists of ensuring erroneous data are removed from databases, and thus excluded from launch vehicle design analyses. EV44 has put forth great effort in developing quality control (QC) procedures for individual meteorological instruments, however no standard QC procedures for all databases currently exists resulting in QC databases that have inconsistencies in variables, development methodologies, and periods of record. The goal of this activity is to use the previous efforts to develop a standardized set of QC procedures from which to build meteorological databases from KSC and the ER, while maintaining open communication with end users from the launch community to develop ways to improve, adapt and grow the QC database. Details of the QC procedures will be described. As the rate of launches increases with additional launch vehicle programs, It is becoming more important that weather databases are continually updated and checked for data quality before use in launch vehicle design and certification analyses.

Brenton, J. C.↗
NASA NTRS2019

The Weather Analysis Display (WAND) Tool: Developing a Meteorological Data Display Tool for Situational Awareness During Day-Of-Launch of Space Launch Vehicles Using Python

Atmospheric conditions are an important driver in the design and operation of space launch vehicles. The Profile Envision and Splicing Tool (PRESTO) was developed by NASA’s Marshall Space Flight Center (MSFC) Natural Environments Branch (NE) to generate vertically complete atmospheric profiles from various data sources at NASA’s Kennedy Space Center (KSC), co-located on the United States Air Force (USAF) Eastern Range (ER), for NASA’s Space Launch System (SLS) day-of-launch (DOL) loads and trajectory analysis. PRESTO was designed solely to generate a vertically complete atmospheric profile (Orcutt et al., 2017). However, NE has also been tasked to provide a quality assessment of meteorological data examined on DOL, which goes beyond PRESTO’s utility. Thus, NE developed the Weather Analysis Display (WAND) to visualize data from all available observation systems in conjunction with climatological databases. WAND can display data from various sources in multiple ways, including Skew-T Log-P plots, time-height cross sections, and time series. WAND was developed in Python 3 taking advantage of common packages, such as NumPy for data handling, SciPy for mathematical functions, Matplotlib for data visualization, and Tkinter for the execution of the Graphical User Interface (GUI).

Orcutt, John M.↗