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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 757 records · Page 42
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NASA NTRS2017

NASA's Solar System Exploration Research Virtual Institute: Building Collaboration Through International Partnerships

The NASA Solar System Exploration Research Virtual Institute (SSERVI) is a virtual institute focused on re-search at the intersection of science and exploration, training the next generation of lunar scientists, and community development. As part of the SSERVI mission, we act as a hub for opportunities that engage the larger scientific and exploration communities in order to form new interdisciplinary, research-focused collaborations. This talk will describe the international partner re-search efforts and how we are engaging the international science and exploration communities through workshops, conferences, online seminars and classes, student exchange programs and internships.

Virtual Institute↗
NASA NTRS2017

Human-Robot Teaming: Communication, Coordination, and Collaboration

In this talk, I will describe how NASA Ames has been studying how human-robot teams can increase the performance, reduce the cost, and increase the success of a variety of endeavors. The central premise of our work is that humans and robots should support one another in order to compensate for limitations of automation and manual control. This principle has broad applicability to a wide range of domains, environments, and situations. At the same time, however, effective human-robot teaming requires communication, coordination, and collaboration -- all of which present significant research challenges. I will discuss some of the ways that NASA Ames is addressing these challenges and present examples of our work involving planetary rovers, free-flying robots, and self-driving cars.

robotics↗
NASA NTRS2017

Efficient Trajectory Options Allocation for the Collaborative Trajectory Options Program

The Collaborative Trajectory Options Program (CTOP) is a Traffic Management Initiative (TMI) intended to control the air traffic flow rates at multiple specified Flow Constrained Areas (FCAs), where demand exceeds capacity. CTOP allows flight operators to submit the desired Trajectory Options Set (TOS) for each affected flight with associated Relative Trajectory Cost (RTC) for each option. CTOP then creates a feasible schedule that complies with capacity constraints by assigning affected flights with routes and departure delays in such a way as to minimize the total cost while maintaining equity across flight operators. The current version of CTOP implements a Ration-by-Schedule (RBS) scheme, which assigns the best available options to flights based on a First-Scheduled-First-Served heuristic. In the present study, an alternative flight scheduling approach is developed based on linear optimization. Results suggest that such an approach can significantly reduce flight delays, in the deterministic case, while maintaining equity as defined using a Max-Min fairness scheme.

Rodionova, O.↗
NASA NTRS2017

Collaborative, Rapid Mapping of Water Extents During Hurricane Harvey Using Optical and Radar Satellite Sensors

On August 25, 2017, Hurricane Harvey made landfall between Port Aransas and Port O'Connor, Texas, bringing with it unprecedented amounts of rainfall and record flooding. In times of natural disasters of this nature, emergency responders require timely and accurate information about the hazard in order to assess and plan for disaster response. Due to the extreme flooding impacts associated with Hurricane Harvey, delineations of water extent were crucial to inform resource deployment. Through the USGS's Hazards Data Distribution System, government and commercial vendors were able to acquire and distribute various satellite imagery to analysts to create value-added products that can be used by these emergency responders. Rapid-response water extent maps were created through a collaborative multi-organization and multi-sensor approach. One team of researchers created Synthetic Aperture Radar (SAR) water extent maps using modified Copernicus Sentinel data (2017), processed by ESA. This group used backscatter images, pre-processed by the Alaska Satellite Facility's Hybrid Pluggable Processing Pipeline (HyP3), to identify and apply a threshold to identify water in the image. Quality control was conducted by manually examining the image and correcting for potential errors. Another group of researchers and graduate student volunteers derived water masks from high resolution DigitalGlobe and SPOT images. Through a system of standardized image processing, quality control measures, and communication channels the team provided timely and fairly accurate water extent maps to support a larger NASA Disasters Program response. The optical imagery was processed through a combination of various band thresholds and by using Normalized Difference Water Index (NDWI), Modified Normalized Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and cloud masking. Several aspects of the pre-processing and image access were run on internal servers to expedite the provision of images to analysts who could focus on manipulating thresholds and quality control checks for maximum accuracy within the time constraints. The combined results of the radar- and optical-derived value-added products through the coordination of multiple organizations provided timely information for emergency response and recovery efforts.

SERVIR↗
NASA NTRS2017

Remediation Technology Collaboration Development - A Compendium

During its multi-year period of performance, the Remediation Technology Collaboration Development (RTCD) task orders initial goals were to enhance the capability to specifically target reductions in the long-term liabilities associated with NASAs most challenging remediation sites. This was accomplished by identifying existing remediation processes and conditions, researching site-specific technologies (both past and present) while simultaneously looking for parallel situations where these technologies could be applied. In addition, the most promising of these solutions were developed from comprehensive research and bench studies into pilot studies or demonstration projects, which contributed significantly to the success of the RTCD program.

Olsen, W.↗
NASA NTRS2018

Impact of Different Trajectory Option Set Participation Levels within an Air Traffic Management Collaborative Trajectory Option Program

This paper presents the methodology and results of a Human-In-The-Loop (HITL) simulation study conducted in the Airspace Operations Laboratory at NASA Ames Research Center. This study is a part of NASA's ongoing research into developing an Integrated Demand Management (IDM) concept, whose aim is to improve traffic flow management (TFM) by coordinating the FAA's strategic Traffic Flow Management System (TFMS) with its more tactical Time-Based Flow Management (TBFM) system. The purpose of TFM is to regulate air traffic demand so that it is delivered efficiently through constrained airspace resources without exceeding their capacity limits. The IDM concept leverages a new TFMS capability called the Collaborative Trajectory Options Program (CTOP) to strategically pre-condition traffic demand flowing into a TBFM-managed arrival environment, where TBFM is responsible for managing traffic tactically by generating precise arrival schedules. Unlike other TFM tools, CTOP gives flight operators the option of submitting a set of user-preferred alternative trajectories for each flight. CTOP can then use these trajectory option sets (or TOSs) to find user-preferred alternative routes to reduce demand on an overloaded resource. CTOP's effectiveness in redistributing demand is limited, however, by the availability of flights with alternative routes. The research presented in this paper focuses on evaluating the impact on TFM operations by varying the percentage of flights that submit a multiple-option TOS ('TOS participation levels'). Results show the impact on overall system performance and on the rerouted flights themselves. The simulation used a Newark (EWR) airport arrival scenario, with en route weather affecting traffic inbound from the west. Participants were asked to control each of the three arrival flows (north, west, and south) to meet their individual capacity constraints while simultaneously ensuring efficient utilization of the capacity at the destination airport. A large, permeable convective weather cell located southeast of Chicago severely reduced the capacity of the west flow. The study evaluated the impact of five different TOS participation levels on CTOP's ability to re-allocate traffic from the west and improve TFM performance in terms of delay assignment and traffic delivery rate to the airport. Overall, the results showed that increasing TOS submissions allowed the overall system delays to be reduced and fairly distributed among the three arrival flows, at the same time achieving the airport throughput rate. Moreover, it was found that aircraft who submitted a TOS saw a greater reduction in delay, even when they were assigned longer routes. This was particularly true when fewer aircraft submitted a TOS. The results confirm that the CTOP operations with higher TOS participation levels helped utilize the overall National Airspace System (NAS) resources as well as benefited the users who participated.

Yoo, Hyo-Sang↗
NASA NTRS2018

Model of Collaborative Trajectory Options Program Performance

Adoption of Collaborative Trajectory Options Programs (CTOP) in airspace has been hampered by a lack of willingness of a majority of airlines to participate in CTOP as there is a lack of information about benefits of CTOP. At present, there are only selected airlines that are considering participating in CTOP. One open research question is how much benefit an airline get by making a decision to participate in CTOP. Another question is identifying situations in which CTOP is a better alternative to traditional TMIs. For effective use of CTOP, it would be useful to understand how different factors such as capacity and TOS participation influence CTOP performance. Therefore, it would be helpful to develop a model of CTOP performance in terms of these factors.

CTOP↗
NASA NTRS2018

Collaborative Autonomous Logistics

This video shows a demonstration of collaborative autonomous logistics. Turtlebot, a 1-g stand-in for Astrobee, carried REALM sensors around a mockup of ISS in ARGOS to autonomously survey for a cargo bag stowed in a drawer. It provided the location of the bag to Robonaut, which then autonomously climbed across the mockup to the drawer. Robonaut then used the Affordance Template manipulation framework to localize and open the drawer and then again localize and retrieve the cargo bag.

Badger, Julia M.↗
NASA NTRS2018

The NASA Juncture Flow Test as a Model for Effective CFD/Experimental Collaboration

The NASA Juncture Flow test, whose purpose is CFD validation for wing juncture trailing edge separation and progression, was designed from the outset to be a highly collaborative effort between CFD computationalists and experimentalists. This paper highlights key aspects of the planning and execution of the project, which has recently completed its first phase of wind tunnel testing. The joint CFD/experimental team is described, and its accomplishments to date are summarized.

Rumsey, Christopher L.↗
NASA NTRS2018

NASA's Solar System Exploration Research Virtual Institute: Building Collaboration Through International Partnerships

The NASA Solar System Exploration Research Virtual Institute (SSERVI) is a virtual institute focused on research at the intersection of science and exploration, training the next generation of lunar scientists, and community development. As part of the SSERVI mission, we act as a hub for opportunities that engage the larger scientific and exploration communities in order to form new interdisciplinary, research-focused collaborations. This talk will describe the international partnership program, research efforts and how we are engaging the international science and exploration communities through workshops, conferences, online seminars and classes, student exchange programs and internships.

SSERVI International Partnerships↗
NASA NTRS2018

Geonex: A NASA-NOAA Collaboration for Producing Land Surface Products from Geostationary Sensors Using Cloud Computing

The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GEONEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GEONEX pipeline has been deployed on Amazon Web Services cloud platform and it currently leverages near-realtime geostationary data hosted in AWS public datasets under a NOAA-AWS agreement.Initial analyses of various products from ABI/AHI sensors suggest that they are comparable to those from MODIS in representing the spatio-temporal dynamics of land conditions. Cloud computing offers a variety of options for deploying the GEONEX pipeline including choice CPUs, storage media, and automation. We estimate the cost of deploying GEONEX to be $400 - 750 a month for processing data (every 30 minutes) and producing products over the conterminous US. For products such as Fire, latency can be as little as 10 minutes from the time of data acquisition.

Geostationary↗
NASA NTRS2018

GEONEX: Webpage to Display NASA-NOAA Collaboration of Producing Land Surface Products from Geostationary Sensors

The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GEONEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GEONEX pipeline has been deployed on Amazon Web Services cloud platform and it currently leverages near-realtime geostationary data hosted in AWS public datasets under a NOAA-AWS agreement.In order to better introduce the GEONEX products to the science community, we set up a simple webpage (www.geonex.org) to describe the background and the motivation of the project, the algorithms used in deriving the products, and user manuals to the data files. We will also update the status of the data processing, in particular the near-real-time products, on the website and provide links (in text or json files) to the latest datasets.

geostationary↗
NASA NTRS2016

Ames Collaborative Support Facility/Sustainability Base

This presentation is intended to motivate research initiatives at the Collaborative Support Facility on the Ames Research Center Campus. Awards and accomplishments will be highlighted, along with specific research objectives. Information technology challenges with also be introduced as a key initiative to be addressed to enable the deployment of novel technologies that will offer deeper insights into how better reduced energy consumption, improve occupant satisfaction and streamline building operations and maintenance activities.

algortihms↗
NASA NTRS2019

Cumulus: A Community of Collaboration

Cumulus is an open source platform used for ingesting and maintaining archives in the cloud. Sharing and maximizing code re-use are pillars of Cumulus development. Cumulus aims to promote collaboration within the community. This presentation will dive into the principles of Cumulus development, both within the team and the community.

Cumulus↗
NASA NTRS2016

Flight Awareness Collaboration Tool

Flight Awareness Collaboration Tool (FACT) is a software program that provides important information about winter weather operations to airline dispatchers.

winter weather↗
NASA NTRS2019

NASA Electronic Parts and Packaging (NEPP) Program Focus, Strategic Collaborations, and Our Path to the Future

NASA Electronic Parts and Packaging (NEPP) Program Overview Mission Statement: Provide NASA's leadership for developing and maintaining guidance for the screening, qualification, test, and reliable use of EEE parts by NASA, in collaboration with other government agencies and industry. The NASA Electronic Parts Assurance Group (NEPAG) is a core portion of NEPP.

NASA Electronic Parts and Packaging (NEPP) Program↗
NASA NTRS2019

OP3 NASA JAXA Rodent Research Collaboration Opportunities

Space Biology and Human Research Projects Integrated Proposal to use the JAXA MARS facility to be presented to JAXA and JAXA investigator audience at JAXA Kibo Utilization Symposium and OP3 negotiation for 2019 ISS rodent mission. Slides present a pictorial overview of the proposed science and analysis techniques desired from the US investigator team. Prior published data form a recent collaboration with JAXA is also mentioned.

JAXA Rodent Mouse Habitat Unit↗
NASA NTRS2019

Flight Awareness Collaboration Tool (FACT) User Manual

The Flight Awareness Collaboration Tool (FACT) is a web-based software tool that provides important information about winter weather operations to airline dispatchers and airport personnel. This document provides instructions on how to operate FACT. It reviews FACT goals, features, functions, controls, and data displays. The manual uses text and screen shots of the screens to guide new users on how to access FACT features. This manual is required for FACT distribution to airlines and airports and is needed as part of the NASA patent process.

Dispatcher↗