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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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NASA NTRS2021

On-demand Command and Control of ASTERIA with Cloud-based Ground Station Services

ASTERIA (Arcsecond Space Telescope Enabling Research in Astrophysics) was a 6-unit CubeSat technology demonstration mission that deployed from the International Space Station on November 20th, 2017. After successfully completing its 90-day primary mission that demonstrated arcsecond-level line-of-sight pointing and focal plane thermal stability for exoplanet detection, it entered an extended mission performing onboard software demonstrations to mature technology both in space and on the ground. One of the technologies was a completely cloud-based ground system leveraging Amazon Web Services (AWS) Ground Station service.Announced in December 2018 and launched in May 2019, AWS Ground Station is a fully managed ground station service that aims to reduce the overhead associated with developing and maintaining ground system infrastructure throughout the mission lifecycle. AWS Ground Station makes available the suite of features required for any ground system in support of low-Earth orbit (LEO) and medium-Earth Orbit (MEO) satellite operations on-demand and without setting up or maintaining long-term contracts. Charges are incurred on a per-minute basis for antenna usage during scheduled tracks. Support is available for S-band uplink and downlink, along with X-band narrowband and wideband downlink. Missions that use the service may reserve tracks with any licensed AWS Ground Station antennas located across each service region and have direct access to any AWS services in support of mission operations.The cloud-based architecture built around the AWS Ground Station service greatly enhanced ASTERIA mission operations by enabling end-to-end pass automation, on-demand contact scheduling and contingency planning, along with more efficient data downlink through station availability and station-to-station handovers. It incorporated open-source software, particularly NASA's AMMOS Instrument Toolkit (AIT) and Open Mission Control Technologies (OpenMCT), along with the AWS application programming interfaces (API) to the Ground Station, Elastic Compute Cloud (EC2) and Simple Storage Service (S3) services. After showcasing operability in August 2019, the team continued using and improving this novel ground system architecture until the end of mission in December 2019. This paper describes the cloud-based ground system, how it was designed, tested, and evaluated with an in-orbit spacecraft, the operational capabilities that it enabled, along with lessons learned and recommendations for future missions.

Fesq, Lorraine↗
NASA NTRSDate not supplied

Mars Ascent Vehicle GNC Targeting Routines with Considerations for Flight Software Development

The Mars Ascent Vehicle (MAV) will be the first vehicle to perform an ascent from the surface of another atmospheric planetary body outside of the Earth-Moon system. Significant light-time delay requires complete autonomy of flight throughout ascent, and naturally a high level of reliability is desired in both MAV’s hardware and software subsystems. The MAV Guidance, Navigation and Controls (GNC) team and the MAV Flight Software (FSW) team have partnered together to improve the efficiency of algorithm integration onto the MAV flight processor, and to increase confidence that said integration is successful and without human error. An interface architecture is proposed for the GNC suite that allows both the guidance and navigation subsystems to provide code algorithms directly in C++, and the controls subsystem to provide MATLAB Simulink auto-coded algorithms. Several continuous integration/deployment (CI/CD) methodologies have been considered for ease of transition of algorithm code from the GNC team to the FSW team. The GNC/FSW teams also worked together to develop a cFS-friendly wrapper which abstracts the integration of the GNC algorithm code into an interface-level API that is compatible with cFS. Several iterations of vehicle GNC code have been produced between the GNC/FSW team’s partnership, and this strong interface between these two teams have allowed the GNC/FSW teams to greatly increase confidence of efficient and error-free implementation of the GNC code onto MAV for a successful flight.

Jason Everett↗
NASA NTRS2021

On-demand Command and Control of ASTERIA with Cloud-based Ground Station Services

ASTERIA (Arcsecond Space Telescope Enabling Research in Astrophysics) was a 6-unit CubeSat technology demonstration mission that deployed from the International Space Station on November 20th, 2017. After successfully completing its 90-day primary mission that demonstrated arcsecond-level line-of-sight pointing and focal plane thermal stability for exoplanet detection, it entered an extended mission performing onboard software demonstrations to mature technology both in space and on the ground. One of the technologies was a completely cloud-based ground system leveraging Amazon Web Services (AWS) Ground Station service. Announced in December 2018 and launched in May 2019, AWS Ground Station is a fully managed ground station service that aims to reduce the overhead associated with developing and maintaining ground system infrastructure throughout the mission lifecycle. AWS Ground Station makes available the suite of features required for any ground system in support of low-Earth orbit (LEO) and medium-Earth Orbit (MEO) satellite operations on-demand and without setting up or maintaining long-term contracts. Charges are incurred on a per-minute basis for antenna usage during scheduled tracks. Support is available for S-band uplink and downlink, along with X-band narrowband and wideband downlink. Missions that use the service may reserve tracks with any licensed AWS Ground Station antennas located across each service region and have direct access to any AWS services in support of mission operations. The cloud-based architecture built around the AWS Ground Station service greatly enhanced ASTERIA mission operations by enabling end-to-end pass automation, on-demand contact scheduling and contingency planning, along with more efficient data downlink through station availability and station-tostation handovers. It incorporated open-source software, particularly NASA's AMMOS Instrument Toolkit (AIT) and Open Mission Control Technologies (OpenMCT), along with the AWS application programming interfaces (API) to the Ground Station, Elastic Compute Cloud (EC2) and Simple Storage Service (S3) services. After showcasing operability in August 2019, the team continued using and improving this novel ground system architecture until the end of mission in December 2019. This paper describes the cloud-based ground system, how it was designed, tested, and evaluated with an inorbit spacecraft, the operational capabilities that it enabled, along with lessons learned and recommendations for future missions.

Fesq, Lorraine↗
NASA NTRSDate not supplied

An Integrated Recursive Framework for Arbitrarily Multiscale and Multi-fidelity Modeling

The NASA Multiscale Analysis Tool (NAMSAT) serves as a state-of-the-art, “plug and play,” massively multiscale modeling (M3) platform for hierarchical materials and structures. The development of NASMAT has focused on modularity, upgradability and maintainability, interoperability, and utility. Application Program Interfaces (APIs) have been developed to facilitate the integration NASMAT into other programs (commercial, research, and user-defined) as well as the integration of other codes into NASMAT. NASMAT is an integrated, recursive platform that seamlessly allows for an arbitrary number of scales, and a variety of modeling fidelity, in a single, non-linear analysis. Thus, the macroscopic response of a material can be directly linked to the behavior of the microstructure(s), including subscale defects, making it a suitable tool for integrated computational materials engineering (ICME) because . Examples using NASMAT for simulation of polycrystalline metals at elevated temperatures will be presented.

Multiscale Modeling↗
NASA NTRSDate not supplied

The SSRI Knowledge Base Tool: Current Tool Functionality Review and Planned Future Enhancements

NASA’s Small Satellite Reliability Initiative (SSRI), in conjunction with NASA’s Small Spacecraft Systems Virtual Institute (S3VI), has developed the SSRI Knowledge Base to improve mission confidence for small spacecraft. The SSRI Knowledge Base provides vetted, high-quality sources of information on elements that are key to successful small spacecraft missions. These resources include SSRI working group generated documents and presentations in addition to existing guides, publications, standards, software tools, websites, and books. The Knowledge Base is fully searchable, offers downloadable content when possible, and otherwise links to or references content directly from within the tool. All 58 of the planned topic pages that comprise the SSRI Knowledge Base have been recently completed and include over 450 unique resources that are now available for review. Over the past several months significant enhancements to the tool’s capabilities have been developed and implemented. These enhancements consist of the completed baseline content; development of an Application Programming Interface (API); improved user interfaces; scalable and searchable Best Practices and Lessons Learned (BPLL) lists with ratings; and custom website analytics. The SSRI Knowledge Base is a comprehensive and searchable online tool that consolidates and organizes resources, best practices, and lessons learned from previous small spacecraft missions sponsored by NASA, other government agencies, and academia. This free, publicly available tool is available to the entire SmallSat community at: NASA SSRI Knowledge Base | Explore. This presentation will discuss the motivation for and development of the SSRI Knowledge Base, demonstrate the existing tool, share how the smallsat community can get involved and outline plans for further enhancement development. The SSRI is a collaborative activity with broad participation from civil, Department of Defense, and both national and international commercial space systems providers and stakeholders. The S3VI is jointly sponsored by NASA’s Space Technology Mission Directorate and Science Mission Directorate.

Small Spacecraft↗
NASA NTRSDate not supplied

Constructing a Knowledge Graph & Applying Graph Algorithms to Draw Insights about GES-DISC Jira Tickets

In order to assess the complexities of Jira tickets created by NASA Goddard Earth Sciences Data and Information Services Center (GES-DISC), it was beneficial to create a knowledge graph. The knowledge graph receives ticket data through the Jira API. The creation of a knowledge graph will help to answer high-level questions about internal structure, knowledge gaps, and team organization within GES-DISC. To work towards this goal, the knowledge graph was constructed in adockerizedNeo4j graph database. Once the graph had been created, graph algorithms were applied to answer high-level questions, such as exploring the role of staff in relation to projects, which qualities of a ticket contribute to the formation of communities within the graph, etc. To answer these questions, centrality and community detection algorithms were applied using Cypher querying language. The analysis of the results of the algorithms indicated that, as expected, certain individuals were more connected to some projects, while others were serving as hub nodes between two or more projects. Similarly, specific keywords are more likely to increase a Jira ticket’s centrality in the graph. In terms of community detection, when tickets in a community have certain qualities, it is more probable for them to be grouped together. To best visualize which nodes had higher centrality scores or were grouped into certain communities, interactive graphs were created in Python using Plotly and Matplotlib. Ultimately, the project was successful in creating and deploying a knowledge graph to better understand the relationships between data in GES-DISC Jira tickets

Rebecca Lipton↗
NASA NTRSDate not supplied

Small Satellite Reliability Initiative (SSRI) Knowledge Base Tool: Update and Future Direction

NASA’s Small Satellite Reliability Initiative (SSRI), in conjunction with NASA’s Small Spacecraft Systems Virtual Institute (S3VI), has developed the SSRI Knowledge Base to improve mission confidence for small spacecraft. The SSRI Knowledge Base is a comprehensive and searchable online tool that consolidates and organizes resources, best practices, and lessons learned from previous small satellite missions sponsored by NASA, other government agencies, and academia. This free, publicly available tool is available to the entire SmallSat community at: NASA SSRI Knowledge Base | Explore. The SSRI Knowledge Base provides vetted, high-quality sources of information on elements that are key to successful small satellite missions. These resources include SSRI working group generated documents and presentations in addition to existing guides, publications, standards, software tools, websites, and books. The Knowledge Base is fully searchable, offers downloadable content when possible, and otherwise links to or references content directly from within the tool. All 58 of the planned topic pages that comprise the SSRI Knowledge Base have been recently completed and include over 450 unique resources that are now available for review. Over the past several months significant enhancements to the tool’s capabilities have been developed and implemented. These enhancements consist of the completed baseline content; development of an Application Programming Interface (API); improved user interfaces; scalable and searchable Best Practices and Lessons Learned (BPLL) lists with ratings; and custom website analytics. This presentation and paper will discuss the motivation for and development of the SSRI Knowledge Base, demonstrate the existing tool, and outline plans for further development. The SSRI is a collaborative activity with broad participation from civil, Department of Defense, and both national and international commercial space systems providers and stakeholders. The S3VI is jointly sponsored by NASA’s Space Technology Mission Directorate and Science Mission Directorate.

Small Spacecraft↗
NASA NTRSDate not supplied

Small Satellite Reliability Initiative (SSRI) Knowledge Base Tool: Use Case Review and Future Functionality and Content Direction

NASA’s Small Satellite Reliability Initiative (SSRI), in conjunction with NASA’s Small Spacecraft Systems Virtual Institute (S3VI), has developed the SSRI Knowledge Base to improve mission confidence for small spacecraft. The SSRI Knowledge Base is a comprehensive and searchable online tool that consolidates and organizes resources, best practices, and lessons learned from previous small satellite missions sponsored by NASA, other government agencies, and academia. This free, publicly available tool is available to the entire SmallSat community at: NASA SSRI Knowledge Base | Explore. The SSRI Knowledge Base provides vetted, high-quality sources of information on elements that are key to successful small satellite missions. These resources include SSRI working group generated documents and presentations in addition to existing guides, publications, standards, software tools, websites, and books. The Knowledge Base is fully searchable, offers downloadable content when possible, and otherwise links to or references content directly from within the tool. All 58 of the planned topic pages that comprise the SSRI Knowledge Base have been recently completed and include over 450 unique resources that are now available for review. Over the past several months significant enhancements to the tool’s capabilities have been developed and implemented. These enhancements consist of the completed baseline content; development of an Application Programming Interface (API); improved user interfaces; scalable and searchable Best Practices and Lessons Learned (BPLL) lists with ratings; and custom website analytics. This presentation and paper will discuss the motivation for and development of the SSRI Knowledge Base, review of potential use case(s), and outline plans for further development and content generation. The SSRI is a collaborative activity with broad participation from civil, Department of Defense, and both national and international commercial space systems providers and stakeholders. The S3VI is jointly sponsored by NASA’s Space Technology Mission Directorate and Science Mission Directorate.

Small Spacecraft↗
NASA NTRSDate not supplied

Data Science and the Knowledge Discovery Adventure

This talk will cover the important steps involved in the data science and knowledge discovery process: • Initial fact gathering (interview domain experts, review reports, articles, state-of-the-art) • Identify the problem (prediction, classification, statistical analysis, etc.) • Survey supporting data sources • Understand the data (numerical, categorical, text, sampling rate, data quality issues, etc.) • Selecting relevant features and sources • Acquire the data (set up agreements with the data stewards, APIs to download, etc.) • Merge data sources (temporal, spatial, common key, other ontologies...) • Feature Engineering (non linear domain knowledge or physics-based relationships) • Build data processing pipeline (may need to tap into data stream, develop parallel processing algorithm, federated learning etc.) • Build model and test (tune hyper-parameters, cross validation.) • Analyze/Validate results (do the results make sense. Does it answer the original question). • Deploy/Publish (Monitor and assess benefits)

Data science↗
NASA NTRSDate not supplied

Building access and community standards for opacity data at the onset of next-generation atmosphere observations

The characterization of a diverse set of exoplanet atmosphere observations, ranging from hot gas giants to small temperate rocky worlds, will be one of the legacies of upcoming facilities such as the James Webb Space Telescope (JWST). Our understanding and interpretation of such observations will hinge on our ability to link observations with atmospheric theoretical studies that critically rely on fundamental molecular and atomic opacities. Computing such opacities is a highly non-trivial and inaccessible process which requires several terabytes of available disk space, hours of CPU time per pressure-temperature combination, and requires users to carefully aggregate line lists data from various sources, which limits access and intercomparison of opacity data in the exoplanet community. Here we present MAESTRO (Molecules and Atoms in Exoplanet Science: Tools and Resources for Opacities) an opacity database that can be accessed by the community via a web interface and python API. MAESTRO was built with community input to create a version-controlled opacity database that is easily queryable, includes informative metadata to ensure reproducibility, and exports relevant citations for inclusion in publications. Scheduled for community release in 2022, MAESTRO will prove to be an invaluable community resource in the era of JWST and beyond.

Natasha Batalha↗
NASA NTRS2023

Two Air Quality Regimes in Total Column NO2 over the Gulf of Mexico in May 2019: Shipboard and Satellite Views

The Satellite Coastal and Oceanic Atmospheric Pollution Experiment (SCOAPE) cruise in the Gulf of Mexico was conducted in May 2019 by NASA and the Bureau of Ocean Energy Management to determine the feasibility of using satellite data to measure air quality in a region of concentrated oil and natural gas (ONG) operations. SCOAPE addressed both technological and scientific issues related to measuring NO2 columns over the Outer Continental Shelf. Featured were nitrogen dioxide (NO 2 ) instruments (Pandora, Teledyne API analyzer) at Cocodrie, LA (29.26°, -90.66°), and on the Research Vessel Point Sur operating off the Louisiana coast with measurements of ozone, carbon monoxide and volatile organic compounds (VOC). The findings: (1) All NO 2 observations revealed two atmospheric regimes over the Gulf, the first influenced by tropical air in 10-14 May, the second influenced by flow from urban areas on 15-17 May; (2) Comparisons of OMI v4 and TROPOMI v1.3 TC (total column) NO 2 data with shipboard Pandora NO 2 column observations averaged 13% agreement with the largest difference during 15-17 May (~20%). At Cocodrie, the satellite-Pandora agreement was ~5%. (3) Three new-model Pandora instruments displayed a TC NO 2 precision of 0.01 Dobson Units (~5%); (4) Regions of smaller, older natural gas operations showed high methane readings from leakage; elevated VOC were also detected. Neither satellite nor spectrometer captured the magnitude of ambient NO 2 variability near ONG platforms. Given an absence of regular air quality monitoring over the Gulf of Mexico, SCOAPE data constitute a baseline against which future observations can be compared.

Nitrogen dioxide↗
NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
NASA NTRS2023

Crew Earth Observations: New Tools to Support Your Research

The collection of astronaut photography hosted on the Gateway to Astronaut Photography of Earth (GAPE, eol.jsc.nasa.gov) forms one of the most extensive historical compilations of Earth remote sensing data sets available to researchers and the public. The GAPE database contains astronaut photography spanning all manned NASA spaceflight missions over the past 60 years and continuing to this day with operations on the International Space Station (ISS). The continuous crew presence in low Earth orbit (LEO) on the ISS for the last 22+ years and the advent of digital handheld cameras has resulted in an exponential increase in astronaut photography, growing the GAPE collection to over 4.5M photographs (Figs. 1 and 2). This increase in astronaut photography of Earth has corresponded to a significant increase in interest in the collection by the research community and the public. The Earth Science and Remote Sensing (ESRS) group at Johnson Space Center, which manages Crew Earth Observations (CEO) from the ISS, has been developing multiple new tools to improve the GAPE database so that users can more quickly find the imagery they need. The three major enhancements to GAPE are: a new API to interface with the database, a method for automatically georeferencing ISS photos (Fig. 3), and a new tool for automatically generating timelapse movies.

Kenton R Fisher↗
NASA NTRS2023

Development of Solar Energetic Particle Prediction Portal (SEP3)

Robust prediction of Solar Energetic Particle (SEP) events is among the key priorities of the space weather community. In the framework of NASA’s Early Stage Innovation Program, we develop the Solar Energetic Particle Prediction Portal (SEP3: https://sun.njit.edu/SEP3), which hosts web applications that allow the users to retrieve the database records. In particular, SEP3 lists the API examples to query each data source potentially important for the SEP prediction. The Portal has a search page for browsing the events from the most widely used catalogs (https://sun.njit.edu/SEP3/search.php) and a dedicated space to share the most recent achievements of the team. In addition, we have added a CDAW SEP catalog and a LASCO/SOHO CME catalog and introduced the possibility of displaying the properties of the connected events (parental solar flares and CMEs for SEPs) on the search page. The interactive widget has the capability to display GOES soft X-ray and proton flux time series from different satellites with the GOES flare records on top of them. The data portal has been used to evaluate the forecasts of the solar proton events based on the statistical properties of the GOES soft X-ray and proton fluxes and investigate machine-learning approaches to the SEP prediction.

SMD↗
NASA NTRS2022

Automating Surface Attitude Positioning and Pointing Operations for Mars 2020

The Surface Attitude Positioning and Pointing (SAPP) subsystem of the Mars Perseverance rover keeps track of the rover’s position and attitude on the surface of Mars. The SAPP Downlink Engineering Operations team members receive data from the rover on a daily basis. They must interpret the data to make sure the rover is staying safe and to support uplink planning. The SAPP team keeps track of the error growth in the rover’s attitude estimate due to noise in the Rover Inertial Measurement Unit’s (RIMU) gyroscopes used to propagate that attitude estimate whenever the rover is moving. Whenever this error grows to a particular threshold, SAPP is responsible for updating the onboard attitude knowledge using the RIMU’s accelerometers to estimate rover roll and pitch and sun imaging to estimate rover yaw, thereby reducing this attitude estimation error. Accurate attitude estimation is required so that the rover can successfully point its High Gain Antenna (HGA) to receive information from Earth and as a backup to the Mars orbiters used for sending data from the rover to Earth, point instruments on its Remote Sensing Mast (RSM), and support safe movement and placement of instruments by the rover’s ARM relative to the Martian surface. The Mars 2020 Engineering Operations team has been working to increase the operational efficiency of the mission and eventually move to a five-hour timeline for daily operations. In pursuit of this goal, the SAPP Engineering Operations team has automated their downlink process by developing a centralized Jupyter notebook to analyze the data received daily from the rover. The SAPP downlink Jupyter notebook automatically collects the data relevant to the SAPP subsystem and visualizes this information in plots and tables that can be easily read by downlink operators to aid them in assessing the status of the subsystem. Various Application Programming Interfaces (APIs) have been incorporated into the downlink daily notebook to automate the collection and posting of data, such as gathering and posting data products to the cloud. The SAPP team has also developed a SAPP downlink software library that includes functions to aid the notebook in processing data. In addition to assessing the SAPP subsystem on a daily basis, operators need to assess the long-term trending behavior of the subsystem over time. An automated trending process has been developed to collect information from the daily notebooks in order to plot and analyze that data in a centralized place. These daily and trending processes have expedited the SAPP downlink assessment and laid the groundwork to completely automate the SAPP downlink process so that SAPP operators are unnecessary unless something unexpected occurs. This paper will provide an overview of the functions that the SAPP subsystem carries out on a daily basis, and will then dive into the automations that have been developed for daily and trending downlink assessment. An assessment of the downlink efficiency will be provided, along with a summary of lessons learned and work to go. Finally, the authors will discuss how these types of automated spacecraft health assessments could be more broadly used within mission operations.

Zarifian, Anais↗
NASA NTRS2022

The Mars 2020 Ground Data System Architecture

The Mars 2020 Mission’s primary objective is to collect 20 geographically unique samples during its prime mission of one and a quarter Martian years, or just over 2 Earth years. Mission planners determined the project needed to develop a system that would enable the operations team to analyze engineering and science data, make science decisions, select viable rover targets at a millimeter resolution and validate an uplink bundle for a car sized rover with more complex science instruments than any previous Mars surface mission. All this had to be done within a five hour time frame. Doing this with a small team would be a challenge, but this had to be accomplished by a large team of engineers and scientists located across North America and Europe. Achieving this level of operational efficiency was unheard of in the prime mission. In addition, the mission had another set of requirements that had nothing to do with surface operations; the Mars 2020 Ground Data System (GDS) was also expected to comply with a new set of security requirements to keep up with the ever changing cybersecurity landscape. The Mars 2020 Ground Data System (GDS) is a re-architected version of the Mars Science Laboratory GDS. The primary goal was to integrate the lessons learned from previous Mars surface missions, accommodate a set of new requirements and capabilities required to ensure mission success, and comply with a new set of cybersecurity controls. The new architecture includes several unique qualities including a data lake, language-agnostic system-wide event-based operations, containerization, automated deployment, network segmentation, infrastructure-as-code, API-driven interfaces, and the first Mars surface GDS to operate primarily in the cloud. The new architecture enabled greater access to the system’s data, tighter integration with the operations team, and a higher level of traceability. The availability of the data also enabled a new set of capabilities previously not possible on surface missions. These new capabilities include an autonomous data to information, pipeline for downlink analysis, horizontal scaling of science data processing capabilities, autonomous round trip data tracking of science and engineering data, integration of flight system state into the tactical planning cycle, high fidelity targeting utilizing kinematic data, and hierarchical image and 3d meshes data representations. This paper will introduce the requirements for the Mars 2020 Mission, the heritage architecture, and the rationale for the changes to achieve the new architecture. The paper will continue to describe the fundamental changes made to the GDS architecture, how these changes enabled a more tightly integrated GDS, and the new capabilities that were enabled by the new architecture. The paper will conclude with the lessons learned from the process of rearchitecting a heritage GDS system and from the first 200 days of operations supporting over 800 users from around the world.

Lopez-Roig, Reynaldo↗
NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of trained machine-learning models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a Windows app that has been created to deploy trained machine-learning models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of machine-learning application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). Current version of the app focuses on the performance prediction of conventional turbofans. The app gets user input for a turbofan design, preprocesses the input data, and deploys trained machine-learning models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The machine-learning predictive models were built by employing supervised deep-learning algorithm to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these machine-learning models using the app shows that Aero-Engines AI is an easy-to-use and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage.

machine learning↗
NASA NTRSDate not supplied

RadLab and the Environmental Data Application Dashboard: Graphical and Programming Interfaces for Interrogation of Space Telemetry Data

Sensors on the International Space Station (ISS) and multiple spacecraft elsewhere in Earth orbit and in deep space continuously monitor and collect environmental data, transmitting this information back to Earth. These data include ionizing radiation and, on the ISS, CO2, relative humidity levels, and temperature, and are of great importance to space biology research. Ionizing radiation in particular has been established in ground-based experiments as being correlated with increased risk of carcinogenesis and cardiovascular and neurological effects. Looking ahead to future long duration crewed missions beyond low Earth orbit, the ability to study how factors including CO2 levels, light cycle, temperature modulate the response to ionizing radiation and microgravity is essential. To date, access to these data has been fragmented across space agencies, spacecraft, and databases. To address this issue, NASA’s Open Science Data Repository (osdr.nasa.gov) has developed two Web applications: the Environmental Data Application (EDA) and a radiation-specific RadLab. Each consists of an API (application programming interface) and an associated GUI (graphical user interface) that provide single points of access to the data. To date, OSDR has focused on the sensors from payloads and radiation detectors located on the ISS. The Web applications process telemetry information and associated data, such as spacecraft location and orientation, from multiple international databases. The applications’ request syntax enables users to interrogate these data by craft, sensor type, time range, radiation type (galactic cosmic rays, solar particle events, the contribution of the South Atlantic Anomaly), facilitating arbitrary comparisons of original source data at varying time resolutions. The applications provide programmatic access for use in computational pipelines and GUIs for data visualization and exploration, making these data FAIR (Findable, Accessible, Interoperable, and Reusable), complementing the biological data contained in OSDR, and providing the space science community with a valuable resource for scientific analyses.

radiation↗