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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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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Application of Modern Fortran to Spacecraft Trajectory Design and Optimization

In this paper, applications of the modern Fortran programming language to the field of spacecraft trajectory optimization and design are examined. Modern object-oriented Fortran has many advantages for scientific programming, although many legacy Fortran aerospace codes have not been upgraded to use the newer standards (or have been rewritten in other languages perceived to be more modern). NASA's Copernicus spacecraft trajectory optimization program, originally a combination of Fortran 77 and Fortran 95, has attempted to keep up with modern standards and makes significant use of the new language features. Various algorithms and methods are presented from trajectory tools such as Copernicus, as well as modern Fortran open source libraries and other projects.

Trajectory Optimizatio↗
NASA NTRS2017

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Remote Sensin↗
NASA NTRS2017

Very High Resolution Tree Cover Mapping for Continental United States using Deep Convolutional Neural Networks

Uncertainties in input land cover estimates contribute to a significant bias in modeled above ground biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products.

High Resolution↗
NASA NTRS2018

NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

satellite data↗
NASA NTRS2017

NASA Advanced Supercomputing Facility Expansion

The NASA Advanced Supercomputing (NAS) Division enables advances in high-end computing technologies and in modeling and simulation methods to tackle some of the toughest science and engineering challenges facing NASA today. The name "NAS" has long been associated with leadership and innovation throughout the high-end computing (HEC) community. We play a significant role in shaping HEC standards and paradigms, and provide leadership in the areas of large-scale InfiniBand fabrics, Lustre open-source filesystems, and hyperwall technologies. We provide an integrated high-end computing environment to accelerate NASA missions and make revolutionary advances in science. Pleiades, a petaflop-scale supercomputer, is used by scientists throughout the U.S. to support NASA missions, and is ranked among the most powerful systems in the world. One of our key focus areas is in modeling and simulation to support NASA's real-world engineering applications and make fundamental advances in modeling and simulation methods.

NASA↗
NASA NTRS2018

Web-Based Geospatial Visualization of GPM Data with CesiumJS

Advancements in the capabilities of JavaScript frameworks and web browsing technology have made online visualization of large geospatial datasets such as those coming from precipitation satellites viable. These data benefit from being visualized on and above a three-dimensional surface. The open-source JavaScript framework CesiumJS (http://cesiumjs.org), developed by Analytical Graphics, Inc., leverages the WebGL protocol to do just that. This presentation will describe how CesiumJS has been used in three-dimensional visualization products developed as part of the NASA Precipitation Processing System (PPS) STORM data-order website. Existing methods of interacting with Global Precipitation Measurement (GPM) Mission data primarily focus on two-dimensional static images, whether displaying vertical slices or horizontal surface/height-level maps. These methods limit interactivity with the robust three-dimensional data coming from the GPM core satellite. Integrating the data with CesiumJS in a web-based user interface has allowed us to create the following products. We have linked with the data-order interface an on-the-fly visualization tool for any GPM/partner satellite orbit. A version of this tool also focuses on high-impact weather events. It enables viewing of combined radar and microwave-derived precipitation data on mobile devices and in a way that can be embedded into other websites. We also have used CesiumJS to visualize a method of integrating gridded precipitation data with modeled wind speeds that animates over time. Emphasis in the presentation will be placed on how a variety of technical methods were used to create these tools, and how the flexibility of the CesiumJS framework facilitates creative approaches to interact with the data.

3D↗
NASA NTRS2017

Vector Radiative Transfer Code SORD: Performance Analysis and Quick Start Guide

We present a new open source polarized radiative transfer code SORD written in Fortran 9095. SORD numerically simulates propagation of monochromatic solar radiation in a plane-parallel atmosphere over a reflecting surface using the method of successive orders of scattering (hence the name). Thermal emission is ignored. We did not improve the method in any way, but report the accuracy and runtime in 52 benchmark scenarios. This paper also serves as a quick start users guide for the code available from ftp:maiac.gsfc.nasa.govpubskorkin, from the JQSRT website, or from the corresponding (first) author.

polarized radiative transfer↗
NASA NTRS2017

PandExo: A Community Tool for Transiting Exoplanet Science with JWST and HST

As we approach the James Webb Space Telescope (JWST) era, several studies have emerged that aim to (1) characterize how the instruments will perform and (2) determine what atmospheric spectral features could theoretically be detected using transmission and emission spectroscopy. To some degree, all these studies have relied on modeling of JWST's theoretical instrument noise. With under two years left until launch, it is imperative that the exoplanet community begins to digest and integrate these studies into their observing plans, as well as think about how to leverage the Hubble Space Telescope (HST) to optimize JWST observations. To encourage this and to allow all members of the community access to JWST & HST noise simulations, we present here an open-source Python package and online interface for creating observation simulations of all observatory-supported timeseries spectroscopy modes. This noise simulator, called PandExo, relies on some aspects of Space Telescope Science Institute's Exposure Time Calculator, Pandeia. We describe PandExo and the formalism for computing noise sources for JWST. Then we benchmark PandExoʼs performance against each instrument team's independently written noise simulator for JWST, and previous observations for HST. We find that PandExo is within 10% agreement for HST/WFC3 and for all JWST instruments.

Batalha, Natasha E.↗
NASA NTRS2018

Astrobee Robot Software: A Modern Software System for Space

Astrobee is a new free-flyer robot designed to operate inside the International Space Station (ISS). Astrobee capabilities include markerless navigation, autonomous docking for recharge, perching on handrails to minimize power and modular payloads. Astrobee will operate without crew support, controlled by teleoperation, plan execution, or on-board third parties software. This paper presents the Astrobee Robot Software, a NASA Open-Source project, powering the Astrobee robot. The Astrobee Robot Software relies on a distributed architecture based on the Robot Operating System (ROS). The software runs on three interconnected smart phone class processors. We present the software approach, infrastructure required, and main software components. The Astrobee Robot Software embrace modern software practices while respecting flight constraints. The paper concludes with the lessons learned, including examples usage of the software. Several research teams are already using the Astrobee Robot Software to develop novel projects that will fly on Astrobee.

Astrobee↗
NASA NTRS2018

Supplier Research & Analysis Approach

"The implementation of NASA GSFC's portfolio of mission projects relies upon inter-connected, multi-tiered supply chains of organizations operating under direct and indirect contracts and other agreements throughout the U.S. and around the world. These supply chains are subject to an inter-related array of technical/production, business, market and security risks that are amplified by the ongoing globalization of industry and technology and which can disrupt or threaten the production and delivery of products and services when needed and in conformance with requirements. In recognition of such risks and associated challenges, GSFC's SMA directorate launched an innovative Supplier Research and Analysis (SRA) capability three years ago to gain greater insight into the operating environment, performance, capabilities and viability of current and prospective suppliers for GSFC projects and proposals. The capability uses business intelligence techniques and primarily open source information resources as part of a cost-effective, non-intrusive methodology to produce several types of research and analysis reports. The reports are based on a holistic analytical framework encompassing key technical/production, business enterprise management, market and security factors, and feature in-depth information, summary information profiles, SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis, and candidate risk concerns in order to pro-actively support SMA and project management needs. The SRA capability, which is designed to complement and support ongoing SMA/project management activities and practices, has produced over 95 reports since its start-up in early 2015. This presentation addresses the approach, methodology and performance of the Supplier Research and Analysis (SRA) capability and its value in assuring the success of NASA mission projects. In doing so, the presentation provides lessons-learned, best practices, case examples and address how it fits into the development of an enterprise-level Supply Chain Risk Management capability."

supplier research and analysis↗
NASA NTRS2018

Restructuring Big Data to Improve Data Access and Performance in Analytic Services Making Research More Efficient for the Study of Extreme Weather Events and Application User Communities

By developing and enhancing various services and tools, the GES DISC provides users with the capability to access and visualize data, and to make comparisons of data from multiple sensor and models via a number of cross-discipline projects. Discovering Data via Faceted Web Interface Web interface to data products and services Search and Download mechanisms Dataset Landing Pages Accessing Data through Interoperable Services: GDS – GrADS Data Server OPeNDAP - Open-source Project for a Network Data Access Protocol WMS – OGC service GIS connector – allowing IS tools to access data easier (coming soon) HTTPS -- direct online access Downloading Data Basics: Subset and egridding Service – Parameter, Spatial, Time, Vertical, Mean averaging, format conversion, and regridding for L3/L4 gridded data Swath Data Subsetter – Parameter, spatial subset of L2 /L1 data. Visualizing Data Online: Giovanni –Visualization and Analysis L3/L4 gridded data AIRS NRT Viewer – AIRS near-real-time DQVis – L2 data quality visualization

data cube↗
NASA NTRS2018

NASA NeMO-Net

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low- resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

NASA↗
NASA NTRS2018

Robots and Robots and More

Over the course of this year I worked on several projects across different departments, from physical robots at Swamp Works, to automated robots running functional tests on launch critical Graphical User Interfaces (GUIs). My overarching project this year was designing a new automated testing framework. It began with becoming familiar with the existing automated testing framework, which used image matching to perform programmatic GUI testing. After writing a few dozen tests with the existing framework, I was able to identify the strengths and weaknesses of the system and begin the search for another tool to replace it. I ended up selecting an open source library, which I modified to fit both National Aeronautics and Space Administration (NASA) requirements and those of the specific Computer Software Configuration Item (CSCI) I worked on. After the new framework was feature-rich enough to meet the team requirements, I helped roll out the changes, managed the transition to the new framework, and directed continuing automation work in regards to the library. The result was a comprehensive new way of efficiently testing the GUI that was robust enough to handle small changes to the GUI itself. Additionally, for approximately six weeks I worked on rebuilding, repairing, and updating the software of four "Swarmie" robots for a NASA event. By the close of the six weeks, the four robots were operational and had improved obstacle detection software that solved a faulty stop signal issue that the robots were experiencing.

Siegel, Alex↗
NASA NTRS2018

Command and Control Software Development

As NASA’s role in spaceflight operations change, so too does its design priorities. NASA’s flagship venture, the Space Launch System (SLS), is being created primarily to explore deep space. While commercial partners conduct low Earth orbit resupplies, tests, and even transportation of astronauts, NASA can divert more resources to more exciting projects - going back to the moon, or onwards to Mars. In the short term, this goal manifests in the upcoming Exploration Mission-1 (EM-1) test of the SLS, slated for as early as 2019. SLS, with the Orion capsule, will travel farther than any human-rated spacecraft has gone before. As the most powerful launch vehicle ever created, SLS requires many new innovations to ensure mission success. One such technology is the launch control software, which is the focus of this internship. The SLS launch control software is composed of many functions, all of which require rigorous testing to meet the standard of life-critical code. To facilitate easier testing, the first project I undertook was to customize the open-source tool Wireshark to the software team’s needs. Wireshark is a network protocol analyzer that can take in information about custom information packets. The launch control software will have several protocols that are custom to NASA. I ensured that all necessary component files were the correct file type and in the correct structure for Wireshark to compile. Having allowed Wireshark access to understand custom NASA data packets, testers of the launch control software will be able to find anomalies easier.

Wang, Zhengdong↗
NASA NTRS2018

UAS Autonomous Hazard Mitigation through Assured Compliance with Conformance Criteria

The behavior of a drone depends on the integrity of the data it uses and the reliability of the avionics systems that process that data to affect the operation of the aircraft. Commercial unmanned aircraft systems frequently rely on commercial-off-the-shelf and open source avionics components and data sources whose reliability and integrity are not easily assured. To mitigate failure events for aircraft that do not comply with conventional aviation safety standards, operational limitations are typically prescribed by regulators. Part 107 of the Federal Aviation Regulations serves as a good example of operational limitations that mitigate risk for small unmanned aircraft systems. These limitations, however, restrict growth possibilities for the industry. Any reasonable path toward achieving routine operation of all types of drones will have to address the need for assurance of avionics systems, especially their software. This paper discusses the possibility of strategically using assured systems as a stepping stone to routine operation of drones. A specimen system for assured geofencing, called Safeguard, is described as an example of such a stepping stone.

Dill, Evan, T.↗
NASA NTRS2018

An Open Avionics and Software Architecture to Support Future NASA Exploration Missions

Final Document is attached. The presentation describes an avionics and software architecture that has been developed through NASAs Advanced Exploration Systems (AES) division. The architecture is open-source, highly reliable with fault tolerance, and utilizes standard capabilities and interfaces, which are scalable and customizable to support future exploration missions. Specific focus areas of discussion will include command and data handling, software, human interfaces, communication and wireless systems, and systems engineering and integration. Final Paper, not the Abstract, is attached.

Avionics↗
NASA NTRS2018

Supply Chain Research and Analysis for Space Systems

The implementation of NASA GSFC's portfolio of mission projects relies upon inter-connected, multi-tiered supply chains of organizations operating under direct and indirect contracts and other agreements throughout the U.S. and around the world. These supply chains are subject to an inter-related array of technical/production, business, market and security risks that are amplified by the ongoing globalization of industry and technology and which can disrupt or threaten the production and delivery of products and services when needed and in conformance with requirements. In recognition of such risks and associated challenges, GSFC's SMA directorate launched an innovative Supply Chain Research and Analysis capability three years ago to gain greater insight into the operating environment, performance, capabilities and viability of current and prospective suppliers for GSFC projects and proposals. The capability uses business intelligence techniques and primarily open source information resources as part of a cost-effective, non-intrusive methodology to produce several types of research and analysis reports. The reports are based on a holistic analytical framework encompassing key technical/production, business enterprise management, market and security factors, and feature in-depth information, summary information profiles, SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis, and candidate risk concerns in order to pro-actively support SMA and project management needs. The SRA capability, which is designed to complement and support ongoing SMA/project management activities and practices, has produced over 105 reports since its start-up in early 2015.This presentation addresses the approach, methodology and performance of the Supply Chain Research and Analysiscapability and its value in assuring the success of NASA mission projects. In doing so, the presentation provides lessons-learned, best practices, case examples and address how it fits into the development of an enterprise-level Supply Chain Risk Management capability.

supply chain risk management↗
NASA NTRS2018

Climates of Warm Earth-Like Planets. I. 3D Model Simulations

We present a large ensemble of simulations of an Earth-like world with increasing insolation and rotation rate. Unlike previous work utilizing idealized aquaplanet congurations we focus our simulations on modern Earth-like topography. The orbital period is the same as modern Earth, but with zero obliquity and eccentricity. The atmosphere is 1 bar N2-dominated with CO2=400 ppmv and CH4=1 ppmv. The simulations include two types of oceans; one without ocean heat transport (OHT) between grid cells as has been commonly used in the exoplanet literature, while the other is a fully coupled dynamic bathtub type ocean. The dynamical regime transitions that occur as day length increases induce climate feedbacks producing cooler temperatures, rst via the reduction of water vapor with increasing rotation period despite decreasing shortwave cooling by clouds, and then via decreasing water vapor and increasing shortwave cloud cooling, except at the highest insolations. Simulations without OHT are more sensitive to insolation changes for fast rotations while slower rotations are relatively insensitive to ocean choice. OHT runs with faster rotations tend to be similar with gyres transporting heat poleward making them warmer than those without OHT. For slower rotations OHT is directed equator-ward and no high latitude gyres are apparent. Uncertainties in cloud parameterization preclude a precise determination of habitability but do not a affect robust aspects of exoplanet climate sensitivity. This is the first paper in a series that will investigate aspects of habitability in the simulations presented herein. The datasets from this study are open source and publicly available.

Planetary systems↗