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NASA NTRSDate not supplied

A Machine-Learning Approach to Assess Aircraft Engine System Performance

Artificial intelligence (AI)/machine learning, and big data are transforming the global business environment. They have become the most disruptive technologies for organizations to improve workplace efficiency and productivity. This work explored the application of machine learning-based predictive analytics that would enable aircraft engine designers to estimate engine system performance quickly during the conceptual design stage. Supervised machine-learning algorithm was employed to study patterns in an existing database of production and research turbofan engines, and built predictive analytics for use in predicting system performance of new turbofan designs. Specifically, the author developed deep-learning analytics to predict turbofan system weight, using turbofan design parameters as the input. The predictive analytics were trained and deployed in Keras, an open-source neural networks API (application program interface) written in Python, with TensorFlow (an open-source artificial AI library developed by Google) serving as the backend engine. The current engine-weight prediction results, together with those for the TSFC (thrust specific fuel consumption) and core-size predictions that were studied previously by the author, show that machine learning-based predictive analytics can be an effective, time-saving tool for aircraft engine design-space exploration during the conceptual design stage. It would enable expeditious identification of the best engine design amongst several candidates.

Michael T Tong↗
NASA NTRS2020

Washington Health & Air Quality: Quantifying Air Quality Parameters and Validating Air Pollution Sources Impacting the Health of Puget Sound Residents Through the Use of NASA and ESA Remote Sensing Data

In the Puget Sound region of Washington, high levels of air pollutants put residents’ health at risk by increasing their likelihood of developing critical respiratory conditions. This project used remotely-sensed data to investigate aerosol optical depth (AOD) from NASA satellite sensors including the Terra and Aqua MODerate Resolution Imaging Spectroradiometer (MODIS) and European Space Agency Copernicus Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI). The team visualized the most recent data in Google Earth Engine (GEE) API to display air pollution trends in Washington State, which will support the Puget Sound Clean Air Agency’s (PSCAA) decision-making processes. The team performed linear regressions using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to form a relationship between ground-level microscopic particles (PM2.5) and AOD in the Puget Sound region, validating the relationship using concentration readings taken from Environmental Protection Agency (EPA) air quality monitors. The team utilized estimated PM2.5 and other satellite data to produce a web-based tool and to evaluate the effectiveness of using such a tool for near real-time air quality monitoring within a particular region. The team found that the tool provides useful supplementary data that fills in the gaps of the PSCAA’s air monitoring network.

Health & Air Quality↗
NASA NTRSDate not supplied

Abrstract - OnePlace Redesign Spring 2020 Internship

The project I worked on was the OnePlace Redesign. In that, I converted the OnePlace home page to the Service Portal to match NASA OnePlace’s current application design standards, I was able to allow users to easily find applications in OnePlace and I was able to allow users to easily find and request OnePlace services (new app development, new knowledge base, etc.). It was a major accomplishment to be able to remodel a website that soon the entire Langley center that uses OnePlace will use. The website was made through ServiceNow’s Service Portal, and I organized the applications like never before, allowed a clear styling to applications a user does not have access to and/or are new applications, browsing of applications with clear button divisions, and allowing for different options depending if app is accessible or not, with proper error checking in place and very-well documented code. It was amazing to incorporate all this logic into one widget so that whoever wants to utilize this feature has no trouble at all to do so. The 3 most important things I learned were being able to do this work within ServiceNow, as it is such an important enterprise platform for CRM and boosting the connected workforce of NASA, incorporate built-in APIs from ServiceNow Glide Records to connect Server Scripts to Client Scripts and HTML, and be able to use agile to get another experience of managing work capacity with stories, and having proper communication with the team if every feature is done correctly and steps for continuing forward. The part that I enjoyed the most was the training I did for ServiceNow and I freedom I had to experiment to see how to structure the Service Portal. I was not tied down, and I believe this allowed for a better outcome of the product. As part of the internship, I created a very in-depth design document with the help of my team to document the project goals before it was started and technical documentation (including the design document) summarizing the final result of the product in terms of the new features added and the explanation of the logic thoroughly so that anyone can understand the project easily. The technical documentation will be published as a Knowledge Article within OnePlace Knowledge Base documentation.

Ariel Wald↗
NASA NTRS2020

Washington Health & Air Quality: Quantifying Air Quality Parameters and Validating Air Pollution Sources Impacting the Health of Puget Sound Residents Through the Use of NASA and ESA Remote Sensing Data

In the Puget Sound region of Washington, high levels of air pollutants put residents’ health at risk by increasing their likelihood of developing critical respiratory conditions. This project used remotely-sensed data to investigate aerosol optical depth (AOD) from NASA satellite sensors including the Terra and Aqua MODerate resolution Imaging Spectroradiometer (MODIS) and European Space Agency Copernicus Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI). The team visualized the most recent data in Google Earth Engine (GEE) API to display air pollution trends from Northern California to British Columbia, which will support the Puget Sound Clean Air Agency’s (PSCAA) decision-making processes. The team performed linear regressions using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to form a relationship between ground-level microscopic particles (PM2.5) and AOD in the Puget Sound region, validating the relationship using concentration readings taken from Environmental Protection Agency (EPA) air quality monitors. The team utilized estimated PM2.5 and other satellite data to produce a web-based tool and to evaluate the effectiveness of using such a tool for near real-time air quality monitoring within a particular region. The team found that the tool provides useful supplementary data that fills in the gaps of the PSCAA’s air monitoring network.

Health & Air Quality↗
NASA NTRSDate not supplied

Software Architecture and Hierarchy of the Nasa Multiscale Analysis Tool

The NASA Multiscale Analysis Tool (NASMAT) serves as a state-of-the-art, “plug and play,” software package which utilizes multiscale recursive micromechanics as a platform for massively multiscale modeling for hierarchical materials and structures subjected to thermomechanical loads on high performance computing systems. This paper is intended to give an overview of the design of NASMAT and how the design supports modularity, upgradability and maintainability, interoperability, and utility. First, the software architecture and hierarchy will be explored. Details on each of the 11 NASMAT procedures and the arrangement of NASMAT data will be presented. Finally, application program interfaces (APIs) that were developed to facilitate the communication of NASMAT with other programs will be described.

multiscale modeling↗
NASA NTRSDate not supplied

Providing Long-term Solar and Meteorological Resource Information from NASA Research through the GIS-Enabled POWER Web Services Portal

Solar and other renewable energy systems are optimized using geophysical parameters describing information about the solar resource and meteorological environments and how those environments may be changing in time. The Prediction of Worldwide Renewable Energy Resource (POWER) team at the NASA LaRC Research Center deployed its first version of the revitalized POWER geophysical parameter website that employs Esri Geographic Information System (GIS) tools. This web application provides access to both time series and climatological data sets spanning from a few days behind real-time back to the early 1980’s with a spatial resolution of 0.5 x 0.5 degree, thus extending over 30 years. The GIS tools enable, generate and store climatological averages using spatial queries and calculations in a spatial database resulting in greater accessibility to government agencies, industry and individuals. There is an API that provides data through a URL coding and can also invoked inside user software packages. Output data formats now include ASCII, CSV, geoTiff, JSON and .netCDF. Since May 2018, over 10.4 TB of data have been delivered to fulfill over 110 million data requests from 240,000 unique user IPs (https://power.larc.nasa.gov). This presentation provides an overview of this project and the current version of the new POWER web capabilities through to the end usage. Surface solar parameters are adapted from both the NASA/GEWEX (Global and Energy Water Cycle Exchange) SRB (Surface Radiation Budget) project and the CERES (Clouds and Earth’s Radiant Energy System) data products. The meteorological parameters are adapted from NASA’s MERRA-2 (Modern Era Retrospective-analysis for Research and Applications). We review uncertainty of various basic parameters using surface measurements. After the introduction, we specifically discuss various clusters of parameters for equator pointing tilted surfaces that provide for the estimation of optimal tilt angle by month and year. The discussion will emphasize a few examples of how solar and building engineers are using the data products. We will then preview the capabilities of Version 2 featuring examples of hourly solar and meteorological data products, customized user reports and expanded web mapping services from CERES data products. The web services provide a unique, expandable resource for renewable energy systems engineers to design, evaluate and optimize to environments worldwide.

Solar Resource↗
NASA NTRS2020

Machine Learning-Based Predictive Analytics for Aircraft Engine Conceptual Design

Big data and artificial intelligence/machine learning are transforming the global business environment. Data is now the most valuable asset for enterprises in every industry. Companies are using data-driven insights for competitive advantage. With that, the adoption of machine learning-based data analytics is rapidly taking hold across various industries, producing autonomous systems that support human decision-making. This work explored the application of machine learning to aircraft engine conceptual design. Supervised machine-learning algorithms for regression and classification were employed to study patterns in an existing, open-source database of production and research turbofan engines, and resulting in predictive analytics for use in predicting performance of new turbofan designs. Specifically, the author developed machine learning-based analytics to predict cruise thrust specific fuel consumption (TSFC) and core sizes of high-efficiency turbofan engines, using engine design parameters as the input. The predictive analytics were trained and deployed in Keras, an open-source neural networks application program interface (API) written in Python, with Google’s TensorFlow (an open source library for numerical computation) serving as the backend engine. The promising results of the predictive analytics show that machine-learning techniques merit further exploration for application in aircraft engine conceptual design.

deep-learning↗
NASA NTRS2020

Command and Control System Automated Testing

To support the National Aeronautics and Space Administration’s (NASA) Space Launch System (SLS) rocket and the Orion capsule, designed to take humans back to the moon in 2024, Kennedy Space Center (KSC) has developed the Spaceport Command and Control System (SCCS) to monitor and control the launch. Within SCCS, the Launch Control System (LCS) is designed to allow console engineers to control and monitor the status of the launch and flight hardware, as well as issue commands to ground control systems and launch vehicles. The messaging software of LCS is responsible for handling the various data types that can be sent between the hardware and software components of the LCS. Since this system is interacting with numerous devices, controllers, and viewports in real time, the distribution of data across the system must be fast, but also reliable and accurate. To verify the accuracy and reliability of the system, developers on the project have created a set of tests to be performed that covers all operations allowed by the system. Given the extensive Application Programming Interface(API) provided by the messaging software, these unit tests are rather time-consuming and costly (in terms of man-hours) to perform. Therefore, an automated testing framework is used to perform supplemental tests automatically when updates are made to the code base.

Rebecca McFadden↗
NASA NTRSDate not supplied

Entwine Point Tiles for 3D Visualization and Querying of ICESat-2

Point Cloud data from non-optical sensors present challenges in scientific computing in both volume of data and files, even for cloud services environments. As part of the Multi-Mission Algorithm and Analysis Platform (MAAP), a joint open science platform for global biomass modelling, we’ve developed a cloud optimized workflow for using ATL08 (ICESat-2) data as a point cloud. For MAAP, the ATL08 data product is published as Entwine Point Tiles (EPT), allowing users to visualize and query the full extent of this collection interactively without pre-downloading, or preprocessing. The EPT format is a cloud-optimized point cloud data format which re-organizes points into a cloud friendly spatially indexed data structure. MAAP uses AWS S3 to store these point clouds and serves them over OGC specified APIs, 3DTiles for visualization, and WFS for querying. This workflow allows for interactive 3D visualizations in a web browser, including notebook environments and facilitates on the fly subsetting for interactive data exploration, all of which can be applied to other similar sensors.

Alex Mandel↗
NASA NTRSDate not supplied

Radiation Data Portal: Connection of Radiation Measurements on Airplane Flights with Observations of Solar-Terrestrial Environment

The impact of solar radiation dramatically increases at high altitudes in the Earth’s atmosphere and in space. Therefore, continuous monitoring of the radiation environment is critical for the safety of aircraft and spacecraft crews and passengers. Addressing the problem requires a complex approach of integration of different data sources and enhancement of the visualization and search capabilities. The Radiation Portal Database represents an interactive web-based application for convenient search and visualization of in-flight radiation measurements and exploration of various properties related to the radiation environment. The primary element of the Radiation Portal back-end is a MySQL relational database that currently contains the radiation measurements obtained from the Automated Radiation Measurements for Aerospace Safety (ARMAS)device, and soft X-ray and proton fluxes from Geostationary Orbiting Environmental Satellite (GOES). The developed Application Programming Interface (API) and related Python routines allow a user to retrieve the database records directly and efficiently, without interaction with the web interface. As a use case of the Radiation Portal, we examine the properties of the ARMAS flights taken during the enhanced Solar Proton (SP) fluxes and compare them to the flights of similar time and location taken during SP-quiet periods.

SMD↗
NASA NTRS2020

NASA GPU Hackathon Yields Significant Code Improvements

The NASA GPU Hackathon 2020 brought together application developers and computer experts to help get important NASA applications running effectively on graphics processing unit (GPU) nodes. Nine teams of application developers participated in this virtual event, a major impetus for teams to modernize codes of interest for NASA missions to CPU nodes containing GPU accelerators, with a focus on hands-on problem solving. The photo in Figure1 shows 30 of the more than50 participants. The HECC project and NVIDIA jointly organized the event, and HECC provided five Pleiades nodes each with 4 V100 GPUs for teams to use. The virtual event, which took place over four days from September 28–October 7, 2020, used Microsoft Teams and Slack as collaboration tools. Each team consisted of three to six members from NASA Centers and supporting organizations. The teams were paired with one to two mentors from industry, government, and academia. The experience levels of the teams ranged from being GPU novices to advanced CUDA programming experts. OpenACC and the emerging Kokkos API were used in addition to CUDA for GPU programming. During the event, which focused on accelerating AeroSciences and CFD applications, most teams achieved considerable performance improvements on both GPUs and CPUs. For example, a team with no GPU experience completed a first port of a time-critical loop to a GPU. Another team of expert CUDA programmers were able to restructure their algorithm, yielding a factor of five speed-up. And another team sped up some of their CUDA kernels by a factor of 20, which directly translated into their production code. This article highlights some of the many successes resulting from the event.

HECC↗
NASA NTRSDate not supplied

Benchmarking and Performance of the NASA Multiscale Analysis Tool

The NASA Multiscale Analysis Tool (NASMAT) is as a “plug and play,” software package which utilizes multiscale recursive micromechanics as a platform for massively multiscale modeling of hierarchical materials and structures subjected to thermomechanical. This paper is intended to give an overview of the design of NASMAT and how the design supports modularity, upgradability and maintainability, interoperability, and utility. First, the software architecture and hierarchy will be explored. Details on each of the 11 NASMAT procedures and the arrangement of NASMAT data will be presented. Application program interfaces (APIs) that were developed to facilitate the communication of NASMAT with other programs will be described. The intended application for NASMAT is massively multiscale modeling on high performance computing systems. As such, results benchmarking the performance of the integration of NASMAT with the Abaqus commercial finite element method software are also presented.

Multiscale Modeling↗
NASA NTRSDate not supplied

NASA’s Global Change Master Directory (GCMD) Keyword Viewer Beta

The Global Change Master Directory (GCMD) Keyword Viewer is a web-based client that allows metadata curators and ontologists to search for and navigate the GCMD keywords in a user-friendly and human-readable tree structure. The Keyword Viewer is built on top of the Keyword Management Systems (KMS) API, which provides machine-to-machine access to the keywords.

Tyler B Stevens↗
NASA NTRS2003

Antarctic Planet Interferometer

The Antarctic Planet Interferometer (API) is a concept designed to detect and characterize extrasolar planets by exploiting the unique potential of the best accessible site on Earth for thermal infrared interferometry.

interferometer↗
NASA NTRS2018

SpaceWire as a Cube-Sat Instrument Interface

SpaceWire is used in the control and data interface for an instrument on a pair of small satellites, one of which was launched in summer 2017. The instrument SpaceWire interface is implemented in a Field Programmable Gate Array as an instantiated core controlled by a LEON3FT CPU, which is also implemented as an instantiated core. The UT699 processor in the flight computer provides the spacecraft side’s SpaceWire interface. A simple message based protocol consisting of four message types was defined, based on existing SpaceWire standards. One was for passing commands to and responses from the instrument in the form of text strings similar to those from a system console where each line of text is passed in a SpaceWire message. Another was for passing spacecraft time to the instrument. The third was for transferring files using a subset of the Remote Memory Access Protocol (RMAP). The fourth was for retrieving science data from the instrument. A set of user application programming interface (API) routines provided an abstracted interface to both the serial console (used during debug) and the SpaceWire device interface. Early instrument development and testing was done with a set of utilities that controlled a Star-Dundee USB-SpaceWire brick providing a user interface similar to a serial console terminal emulator with the addition of file and data transfers. Later in the integration and test process, these utilities were integrated with the COSMOS ground systems software used for spacecraft control, providing a seamless transition from standalone instrument tests to benchtop flat-sat test and full spacecraft level tests.

Lux, James P.↗
NASA NTRS2018

RESTful CFDP: Managing GDS Complexity with Microservices

NASA's Advanced Multi-Mission Operations System (AMMOS) is currently adding capability to support the CCSDS File Delivery Protocol (CFDP). This feature is being added as part of the AMMOS Mission Data Processing and Control System (AMPCS). In order to address the system’s increasing complexity, AMPCS has recently been re-architected to break down its monolithic applications into smaller, individually deployable microservices. The CFDP capability is the first new AMPCS feature to leverage this new architecture. The CFDP microservice provides a web-based Representational State Transfer (REST) application programming interface (API) for complete monitor and control of its operations, and this enables it to be decoupled from other AMPCS microservices. This also results in better scalability for redundancy and load balancing. AMPCS's CFDP microservice is designed to support generic CFDP operations, agnostic to AMPCS's legacy concept of Downlink Products. An optional runtime plug-in allows the CFDP microservice to simulate CFDP artifacts as Downlink Products. Applying the microservices software architecture pattern both in the latest release of AMPCS and in providing the new CFDP capability has resulted in a more flexible system with improved extensibility and maintainability. System complexity has also become more manageable.

Choi, Joshua S.↗
NASA NTRS2018

WebGeocalc and Cosmographia: Modern Tools to Access OPS SPICE Data

For more than two decades navigation and other ancillary data from most US and international planetary science missions have been packaged using "SPICE" (Spacecraft, Planet, Instrument, Camera-matrix, Events) system data files (a.k.a. SPICE kernels) and, in conjunction with SPICE Toolkit software used by scientists and engineers to compute observation geometry in various ground system tools ranging from mission planning and analysis applications to data production pipelines to science analysis tools. The traditional way for accessing SPICE data is by downloading necessary SPICE kernels to a user’s workstation, installing the SPICE Toolkit software available from NAIF, and writing an application calling APIs from the SPICE Toolkit library to compute numeric geometric parameters of interest. While this approach did and still does provide the greatest flexibility in implementing geometric computations of interest, it proved to be complicated for users with little programming abilities, required data to be always copied to the users’ workstations, and lacked any out-of-the-box visualization capabilities. To address these shortcomings NAIF developed the WebGeocalc (WGC) tool and extended the publicly available Cosmographia program to use SPICE. Employing these two new tools in mission operations enables easier access to SPICE computations and SPICE-based visualizations for a wider variety of mission personnel.

Semenov, Boris V.↗
NASA NTRSDate not supplied

Streamlining GNC Architecture Development and FSW Integration forthe Mars Ascent Vehicle

The Mars Ascent Vehicle (MAV) will be the first vehicle to perform an ascent from the surface ofanother atmospheric planetary body outside of the Earth-Moon system. Significant light-time delayrequires complete autonomy of flight throughout ascent, and naturally a high level of reliability isdesired 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 efficiencyof algorithm integration onto the MAV flight processor, and to increase confidence that said integrationis successful and without human error. An interface architecture is proposed for the GNC suite thatallows both the guidance and navigation subsystems to provide code algorithms directly in C++, and thecontrols 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 fromthe GNC team to the FSW team. The GNC/FSW teams also worked together to develop a cFS-friendlywrapper which abstracts the integration of the GNC algorithm code into an interface-level API that iscompatible with cFS. Several iterations of vehicle GNC code have been produced between the GNC/FSWteam’s partnership, and this strong interface between these two teams have allowed the GNC/FSWteams to greatly increase confidence of efficient and error-free implementation of the GNC code ontoMAV for a successful flight.

GNC↗