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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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This tutorial describes research in the development of air traffic metrics to guide responses to weather and explores the development of metrics needed to model and mitigate man-made events.
The GISS climate model, ModelE, [an atmosphere-ocean model] has been extended to support the simulation of climates for exoplanets. As part of this process, a more general calendaring system has been implemented that is derived from orbital parameters. The calendar design attempts to preserve the basic notions of the 4 seasons as well as the time of day. These slides provide the basic underlying assumptions for the design and detail how to configure the model to use non-Earth orbital parameters. [One of a series of videos taken of the ROCKE-3D (Resolving Orbital and Climate Keys of Earth and Extraterrestrial Environments with Dynamics), a 3-Dimensional General Circulation Model (GCM) exoplanet modeling tutorial sessions, May 21-22, 2018, (this slide set was prepared for Day 2, Parts 1 and 2, May 22, 2018) on YouTube (https://www.youtube.com)]
Two widely accepted techniques for lowering the cost and risk of developing systems are (1) the use of a defined systems engineering (SE) process or methodology and (2) the reuse of existing (previously built) system components. The first technique is represented, for example, in materials published by NASA (e.g., NASA Systems Engineering Handbook) or by professional societies such as INCOSE (International Council on Systems Engineering). Well-formed SE techniques provide value by establishing the proper scope of the system (e.g., requirements), and by identifying and resolving problems relatively early in project lifecycles, when fixes are less expensive. The second technique (reuse) is applied most commonly to hardware and software; it seeks to avoid replicating design and implementation costs while also reducing risk by placing proven capabilities into operational use. In this paper, we outline a methodology combining these two techniques and extending reuse beyond hardware and software to foundational aspects of a Mission Operation System’s (MOS) design. We describe the system design artifacts that result (e.g., requirements, design documentation), as well as the reusable patterns and elements of the design, and their interrelationships. This approach is enabled by model-based systems engineering (MBSE) techniques and tools and is currently available in SysML form as a plug-in to MagicDraw. Additionally, usage of a rigorous MBSE approach allows for training materials and tutorials to be packaged within the overall model itself. The results of such an approach include decreased cost and risk during the design phase, improved ability of the MOS development team to investigate trade spaces and identify impacts to important flight-ground trade studies. Such results extend into decreased costs and risk in later phases due to improved design, decreased need for late fixes or development of "glue-ware" or scripts to fill unanticipated gaps in functionality, and improved ability to identify and plan testing and other validation activities. Finally, lower operational costs can be expected, both due to improved quality of the MOS, increased ease of maintaining updated knowledge of system configuration, and the fact that training and procedural materials are also updated at the same time as accepted system changes.
NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network aimed at accurately assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. We present here the active learning component of the project, which consists of an interactive video game prototype for tablet and mobile devices where players are able to intuitively label morphology classifications over mm-scale 3D coral reef imagery. Active learning applications present a novel methodology for engaging the public while efficiently providing large-scale training and test data for increasingly complex and data-intensive machine learning algorithms. NeMO-Net trains players on domain-specific knowledge through interactive tutorials and periodically checks players' input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide personalized classification training. Players can rate the classifications of other players, unlock rewards and join a global community as they explore and classify coral reefs and other shallow marine environments.
In this semi-tutorial paper, we revisit the interference phenomena caused by pairs of copropagating or counter-propagating transverse electromagnetic waves by letting the host medium be absorbing. We first consider plane waves in an unbounded medium, summarize the standingwave solution of the Maxwell equations, and discuss specific effects caused by nonvanishing absorption. We then consider the superposition of plane and spherical waves in the context of fartfield electromagnetic scattering by a particle. To this end we modify the classical Jones lemma by allowing nonzero absorption in the host medium and consider its most obvious consequences such as forward- and backscattering interference. The physical similarity of the two scenarios (superpositions of plane waves and superpositions of plane and spherical waves) is discussed.
This is a tutorial that covers the basic concepts associated with the SISO Space Reference Federation Object Model. It will be given at the SISO 2020 SIW in Orlando, Florida.
FUN3D Core Capabilities - Established as a research code in late 1980’s; now supports numerous internal and external efforts across the speed range - Solves 2D/3D steady and unsteady Euler and RANS equations on node-based mixed element grids for compressible and incompressible flows - General dynamic mesh capability: any combination of rigid / overset / morphing grids, including 6-DOF effects - Aeroelastic modeling using mode shapes, full FEM, CC, etc. - Constrained / multipoint adjoint-based design and mesh adaptation - Distributed development team using agile/extreme software practices including 24/7 regression and performance testing - Capabilities fully integrated, online documentation, training videos, tutorials
The NASA DEVELOP Program conducted 10-week long feasibility projects in a remote work setting, including partnering with The Nature Conservancy’s Washington Chapter and the Puget Sound Clean Air Agency to investigate wildfire smoke from 2000 - 2020 in the Pacific Northwest using satellite-derived data. The team engaged with platforms for collaboration both internally with NASA affiliates and externally with community organizations. Working from multiple states, the team members used a variety of software including Google Meet, Microsoft Teams, and Google Earth Engine to foster communication and work with data in a shared virtual environment. Throughout the project, the team learned that executing the project in a distanced work setting made it easier to reach out to scientists across the country for expertise and guidance. To study changes in air quality resulting from wildfire smoke, the team utilized data from NASA’s Fire Information from Resource Management System (FIRMS) and the ESA’s Sentinel-5 TROPOspheric Monitoring Instrument (TROPOMI). The team created a Google Earth Engine web-based tool, “Plume Hazards and Observations of Emissions by Navigating an Interactive eXplorer” (PHOENIX), to visualize changes in pollutants and aerosol optical depth after fire events. The potential relationship between plume height and fire radiative power was evaluated by using NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua and Terra satellites and NASA’s Multi-angle Imaging SpectroRadiometer (MISR) aboard Terra with the MISR INteractive eXplorer (MINX). The PHOENIX smoke assessment tool and science communication infographics will be shared electronically with the partner organizations. Furthermore, the team introduced the partners to MINX and will provide a tailored tutorial that included a recorded video and a written component with a live virtual workshop. These resources build capacity for further research and education on wildfire smoke and air quality within communities.
This is a summarized version of a tutorial given at the 2019 NASA SmallSat Technical Interchange Meeting providing an experienced-based overview, suggestions, and guidance for SmallSat development focused on new investigators. Topics addressed include the basics of SmallSats, Mission Formulation, Realities of Flight Development, Access to Space, Mission Operations, and Remarks on the Future Directions of SmallSat Science and Technology Missions.
In 2017-2018 the Global Data Assembly Center (GDAC) at NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) provided ingest, archive, distribution and user services for GHRSST operational data streams with improved and evolved tools, services, and tutorials and interfaced with the user community to address technical inquiries. The GDAC provided access to new GHRSST datasets as well retired a significant number of deprecated GHRSST datasets from its discovery services. The following sections summarize and document the specific achievements of the GDAC to the GHRSST community.
In 2017-2018 the Global Data Assembly Center (GDAC) at NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) provided ingest, archive, distribution and user services for GHRSST operational data streams with improved and evolved tools, services, and tutorials and interfaced with the user community to address technical inquiries. The GDAC provided access to new GHRSST datasets as well retired a significant number of deprecated GHRSST datasets from its discovery services. The following sections summarize and document the specific achievements of the GDAC to the GHRSST community.
The purpose of this tutorial is to provide an introduction to various space mission types, link together the various mission systems engineering concepts which go into the design and development of a space mission with the ground system architecture. Attendees will learn about: Types of satellite missions and characteristics of them, Types of satellite orbits and why a particular mission is in a particular orbit, Characteristics of different satellite operators (commercial, DoD, civil), Mission drivers, Mission operations concepts and how various challenges have been addressed, Space mission data standards, How to deliver data, General ground system and Mission Operations Center design.
In 2019, the Great Plains experienced unprecedented catastrophic flooding. Large flood events are predicted to increase in frequency and severity, posing risks to communities in this region, particularly Tribal Nations. We used data from Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), imagery from the Sentinel-2 MultiSpectral Instrument (MSI), and digital elevation models (DEMs) from the Shuttle Radar Topography Mission (SRTM) within Google Earth Engine to map historical floods in the region beginning in 2014 with particular attention to the Rosebud Sioux Reservation and the tribal lands of other Great Plains Tribal Water Alliance members. This historical mapping used C-SAR for a combined method approach with a Z-score algorithm in addition to an index for flooded short vegetation. We also developed a flood risk map by weighting different flood predictor variables according to flood risk literature. These variables included soil drainage from the Soil Survey Geographic Database (SSURGO); elevation, slope, and Topographic Wetness Index (TWI) derived from digital elevation models; precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS); land cover from the National Land Cover Database (NLDC); and Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 Operational Land Imager (OLI). From the flood extent and risk maps, we identified widespread flooding in short vegetation (including cropland) and noted flood susceptibility in regions exhibiting high social vulnerability and low community resilience (FEMA indices). We created an ArcGIS Online StoryMap to share project background, results, and data. Additionally, we provided a written tutorial so partners may replicate the flood mapping for future flood events.
One of the critical challenges in emerging autonomous systems is timely mitigation of hazards encountered during operation which may not be known or accounted for at the time of design. Efficient execution of unmanned systems therefore demands a paradigm shift from scheduled periodic maintenance to predictive risk analysis that includes condition-based-monitoring, real-time reliability assessment and hazard mitigation. Particularly, the state-of-health parameters needs to be computed at the component level, unit level as well as the integrated system level. While in the former two levels, the physics of health propagation may be based on underlying electro-mechanical properties, system level prognostics often relies on data-driven models. Further, uncertainty from model, measurements and input sources should be accurately quantified to generate meaningful prediction results that can be fed into reliable decision making processes. Finally, the expected risk and time to failure has to be computed based on the current state-of-health of the overall system. This talk presents a conceptual design of such an in-time safety assurance approach for unmanned aerial vehicles (UAV) operating at low altitudes near and over populated areas. Typical in-flight hazard incidents include unplanned detour, proximity to obstacles, mid-flight component faults, limited battery life and poor quality of GPS measurements. Safety assessment therefore comprises trajectory generation and re-plan, battery RUL computation, distributed fault diagnostics and uncertainty management of predicted trajectory based on GPS measurement noise. The entire monitoring framework will be demonstrated on simulated as well as real UAV flight experiments conducted at the NASA Langley Research Center. This tutorial will therefore guide the audience through a step-by-step tracking of an autonomous system with focus on in-time risk prediction in the presence of unforeseen hazards and uncertain environment.
In November 2020, Hurricanes Eta and Iota hit Central America within weeks of each other, causing severe flooding, landslides, and widespread damage. NASA DEVELOP partnered with Comité Regional de Recursos Hidráulicos (CRRH), Centro de Coordinación para la Prevención de los Desastres en América Central y República Dominicana (CEPREDENAC), and Sistema de la Integración Centroamericana (SICA) to better understand how flooding throughout Central America has impacted and will continue to affect communities, focusing on sites in Guatemala, Honduras, El Salvador, Belize, Nicaragua, western Panama, and eastern Costa Rica from January 2015 to October 2021. The team utilized surface reflectance data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), Sentinel-2 MultiSpectral Instrument (MSI), Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS), and Terra Moderate Resolution Imaging Spectroradiometer (MODIS). This project also utilized backscatter data from Sentinel-1 C-band Synthetic Aperture Radar (C-SAR) and elevation data from the Shuttle Radar Topography Mission (SRTM). Incorporating these Earth observations in NASA SERVIR’s Hydrologic Remote Sensing Analysis for Floods (HYDRAFloods) tool run on Google Earth Engine (GEE), the team produced historical surface water maps, a case study analysis of the two hurricanes, and a code tutorial. These results indicated that surface water increased in priority sites from 2015 to 2021, optical and SAR imagery detected similar flood patterns and extent after the hurricanes, and rainfall was concentrated on the east coast of the region. These products allow partners to make informed decisions around flooding preparation and disaster mitigation.
DARTS is a rigid/flexible multibody dynamics toolkit for themodeling and simulation of aerospace and robotic vehicles forengineering applications. In this paper we describe an on-line,browser-based environment using Jupyter notebooks to supporttraining needs for the DARTS software. The suite of curated tutorial notebooks is organized into different topic areas, and intomultiple themes within each topic area. The notebooks within atheme use a progression of examples for users to expand theirunderstanding of the software. The topic areas include one onthe DARTS multibody dynamics software and another one on thetheory underlying the multibody dynamics formulation. We alsodescribe a number of Jupyter extensions that were used - andsome developed in house - to enhance the notebook interface foruse with the dynamics simulation software. One significant extension we implemented allows the embedding of live 3D visualizations within simulation notebooks.
In 2019-2020 the Global Data Assembly Center (GDAC) at NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) provided ingest, archive, distribution and user services for GHRSST operational data streams with improved and evolved tools, services, and tutorials, and interfaced with the user community to address technical inquiries. Several new GHRSST datasets including reprocessed MODIS Aqua/Terra L2P with improved cloud screening were made available. The PO.DAAC participated in the evolving GHRSST data management re architecture and development activities. GHRSST Science Team member Edward Armstrong assumed the role of co-leader of the CEOS SST Virtual Constellation. The following sections summarize and document the specific achievements of the GDAC to the GHRSST community.
This paper presents a case study of using FLOCAD to model the heat transfer and boiling flow behavior of a Liquid Nitrogen (LN2) ground station equipment heat exchanger. The unique aspect of this work is that is one of very few studies documenting the use of FLOCAD in the spacecraft thermal control community. The results of the simulations described herein were used to guide and plan the execution of thermal vacuum testing spacecraft hardware using a ground test heat exchanger such as the one modeled herein. The paper will review the theory of two-phase boiling flow and correlations used by SINDA/FLUINT as well as the terminology and nomenclature used within Thermal Desktop / FLOCAD regarding two-phase boiling flow heat transfer modeling and simulation. Results for using the ground station heat exchanger as a heat sink in a thermal vacuum test set up are shown in order to demonstrate the set-up and execution of a typical FLOCAD model. Results indicate that properly modeling of the two-phase behavior is critical in order to ascertain the time constant associated with the heat exchanger, as well as understanding the temperature distribution across the test equipment and prediction of required LN2 flowrates to be used during testing. The paper is meant to augment the FLOCAD user’s manual and serve as a tutorial for thermal engineers and analysts wishing to learn and apply FLOCAD to industrial problems.