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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2000

A Distributed Multi-Agent System for Collaborative Information Management and Learning

In this paper, we present DIAMS, a system of distributed, collaborative agents to help users access, manage, share and exchange information. A DIAMS personal agent helps its owner find information most relevant to current needs. It provides tools and utilities for users to manage their information repositories with dynamic organization and virtual views. Flexible hierarchical display is integrated with indexed query search-to support effective information access. Automatic indexing methods are employed to support user queries and communication between agents. Contents of a repository are kept in object-oriented storage to facilitate information sharing. Collaboration between users is aided by easy sharing utilities as well as automated information exchange. Matchmaker agents are designed to establish connections between users with similar interests and expertise. DIAMS agents provide needed services for users to share and learn information from one another on the World Wide Web.

Chen, James R.↗
NASA NTRS2017

IPPW Enabled International Collaborations in EDL Lessons Learned and Recommendations:

The International Planetary Probe Workshop (IPPW) is a forum for exchanging information and encourage collaboration. The IPPW-14 (2017) in its 14th year and attracts participants mainly from US and Europe. The authors of this proposed talk are exploring and have established international collaboration in multiple areas of interest to IPPW community. The authors will present examples that illustrate the motivations for the partnership, the unique capabilities and the potential benefits of international collaboration and how to approach the collaboration in order to overcome the challenges.

Venkatapathy, Ethiraj↗
NASA NTRS1998

High-Performance Monitoring Architecture for Large-Scale Distributed Systems Using Event Filtering

Monitoring is an essential process to observe and improve the reliability and the performance of large-scale distributed (LSD) systems. In an LSD environment, a large number of events is generated by the system components during its execution or interaction with external objects (e.g. users or processes). Monitoring such events is necessary for observing the run-time behavior of LSD systems and providing status information required for debugging, tuning and managing such applications. However, correlated events are generated concurrently and could be distributed in various locations in the applications environment which complicates the management decisions process and thereby makes monitoring LSD systems an intricate task. We propose a scalable high-performance monitoring architecture for LSD systems to detect and classify interesting local and global events and disseminate the monitoring information to the corresponding end- points management applications such as debugging and reactive control tools to improve the application performance and reliability. A large volume of events may be generated due to the extensive demands of the monitoring applications and the high interaction of LSD systems. The monitoring architecture employs a high-performance event filtering mechanism to efficiently process the large volume of event traffic generated by LSD systems and minimize the intrusiveness of the monitoring process by reducing the event traffic flow in the system and distributing the monitoring computation. Our architecture also supports dynamic and flexible reconfiguration of the monitoring mechanism via its Instrumentation and subscription components. As a case study, we show how our monitoring architecture can be utilized to improve the reliability and the performance of the Interactive Remote Instruction (IRI) system which is a large-scale distributed system for collaborative distance learning. The filtering mechanism represents an Intrinsic component integrated with the monitoring architecture to reduce the volume of event traffic flow in the system, and thereby reduce the intrusiveness of the monitoring process. We are developing an event filtering architecture to efficiently process the large volume of event traffic generated by LSD systems (such as distributed interactive applications). This filtering architecture is used to monitor collaborative distance learning application for obtaining debugging and feedback information. Our architecture supports the dynamic (re)configuration and optimization of event filters in large-scale distributed systems. Our work represents a major contribution by (1) survey and evaluating existing event filtering mechanisms In supporting monitoring LSD systems and (2) devising an integrated scalable high- performance architecture of event filtering that spans several kev application domains, presenting techniques to improve the functionality, performance and scalability. This paper describes the primary characteristics and challenges of developing high-performance event filtering for monitoring LSD systems. We survey existing event filtering mechanisms and explain key characteristics for each technique. In addition, we discuss limitations with existing event filtering mechanisms and outline how our architecture will improve key aspects of event filtering.

Maly, K.↗
NASA NTRS2020

Enabled International Collaborations in EDL: Lessons Learned and Recommendations

The modern day National Airspace System (NAS) is powered by System Wide Information Management (SWIM) which is a real-time digital data sharing infrastructure that provides a high fidelity view of the lifecycle of a flight. The newly available data within the SWIM feeds can be leveraged to help drive efficiencies in the NAS. In this talk, we investigate the gate conflict prediction problem as a concrete use case which could help drive efficiencies. We begin with a high level description of NASA's Airspace Technology Demonstration 2 which is built upon the real-time SWIM feeds and produces the data used in our investigation. We model gate conflicts as a regression problem and describe the iterative process of model building, model validation, and evaluation used to assess the efficacy of our approach.

William J. Coupe↗
NASA NTRSDate not supplied

2023 NASA SmallSat In-Person LEARN Forum: Announcements

NASA’s SmallSat LEARN Forum is an activity aimed at providing the NASA SmallSat Community with collaboration resources. LEARN stands for Learning from Experiences, Achievements, and Resolution Navigation and through the forum attendees share and learn together as a community to become smarter implementers of small spacecraft missions. Held October 11-12, the Annual In-Person LEARN Forum announcements presentation summarizes current and upcoming events and solicitations for the community.

Bruce D Yost↗
NASA NTRS2018

Collaborative Experiments and Computations in Aircraft Icing

The formation of ice over lifting surfaces can affect aerodynamic performance. The ability to predict ice accumulation and the resulting degradation in vehicle performance is essential to determine the limitations of aircraft in icing encounters. The consequences of underestimating performance degradation can be serious and so it is important to produce accurate predictions, particularly for severe icing conditions. The simulation of ice accretion is a challenging multidisciplinary problem that requires close collaboration between the computational and ground test communities. This paper describes three recent case studies and the lessons learned through collaborative experiments and computations in aircraft icing- one for large commercial transports, one for rotorcraft, and one dealing with icing on regional jets.

and rotorcraft↗
NASA NTRS2018

NASA’s and ESA’s Tracking Networks, A Decade of Strategic Partnership for Solar System Exploration

Planetary missions travel vast distances in the Solar System for valuable scientific exploration. Spacecraft return data to Earth via radio links that suffer power losses inversely proportional to the square of the distance, which forms a tremendous engineering challenge compounded by relatively low onboard transmitter power, typically 20 Watts at X-band (~8 GHz). Deep space missions are enabled only because leading space agencies invested in developing very large communications antennas as well as provided a sophisticated suite of tracking, telemetry and command standard services. NASA’s Deep Space Network and ESA’s ESTRACK network are distributed geographically to provide global coverage and utilize stations ranging in size from 34 meters to 70 meters in diameter. Over time, the expanding number of missions, increased requirements for redundancy during critical events as well as the drive for improved navigation accuracy via long-baseline interferometric techniques, increased the networks’ loading and strongly motivated collaboration between the two agencies. Ten years ago, NASA and ESA established a cross-support agreement in addition to multiple on-going mission-specific agreements to meet this need. This strategic partnership was enabled by the development of international inter-operability technical standards and compatible spectrum usage. Over recent years, many critical events were supported via these agreements and some scientific breakthroughs were enabled. A multi-nation tracking contention period envisioned in the near future will also benefit from this partnership. Due to its success over the last decade, the DSN-ESTRACK partnership is serving as a model for other agencies with deep space communication facilities and similar interest. This paper reviews the benefits and lessons learned from collaborative deep space exploration, especially via communications networks, and discusses the potential implementation of similar NASA agreements with other space agencies.

Costrell, Jim↗
NASA NTRS1998

Case-Based Capture and Reuse of Aerospace Design Rationale

The goal of this project is to apply artificial intelligence techniques to facilitate capture and reuse of aerospace design rationale. The project applies case-based reasoning (CBR) and concept mapping (CMAP) tools to the task of capturing, organizing, and interactively accessing experiences or "cases" encapsulating the methods and rationale underlying expert aerospace design. As stipulated in the award, Indiana University and Ames personnel are collaborating on performance of research and determining the direction of research, to assure that the project focuses on high-value tasks. In the first five months of the project, we have made two visits to Ames Research Center to consult with our NASA collaborators, to learn about the advanced aerospace design tools being developed there, and to identify specific needs for intelligent design support. These meetings identified a number of task areas for applying CBR and concept mapping technology. We jointly selected a first task area to focus on: Acquiring the convergence criteria that experts use to guide the selection of useful data from a set of numerical simulations of high-lift systems. During the first funding period, we developed two software systems. First, we have adapted a CBR system developed at Indiana University into a prototype case-based reasoning shell to capture and retrieve information about design experiences, with the sample task of capturing and reusing experts' intuitive criteria for determining convergence (work conducted at Indiana University). Second, we have also adapted and refined existing concept mapping tools that will be used to clarify and capture the rationale underlying those experiences, to facilitate understanding of the expert's reasoning and guide future reuse of captured information (work conducted at the University of West Florida). The tools we have developed are designed to be the basis for a general framework for facilitating tasks within systems developed by the Advanced Design Technologies Testbed (ADTT) project at ARC. The tenets of our framework are (1) that the systems developed should leverage a designer's knowledge, rather than attempting to replace it; (2) that learning and user feedback must play a central role, so that the system can adapt to how it is used, and (3) that the learning and feedback processes must be as natural and as unobtrusive as possible. In the second funding period we will extend our current work, applying the tools to capturing higher-level design rationale.

Leake, David B.↗
NASA NTRS2011

NASA Tech Briefs, January 2011

The topics include: 1) Distributed Aerodynamic Sensing and Processing Toolbox; 2) Collaborative Supervised Learning for Sensor Networks; 3) Hazard Detection Software for Lunar Landing; 4) Onboard Nonlinear Engine Sensor and Component Fault Diagnosis and Isolation Scheme; 5) Network-Capable Application Process and Wireless Intelligent Sensors for ISHM; 6) Interface Supports Multiple Broadcast Transceivers for Flight Applications; 7) FPGA Sequencer for Radar Altimeter Applications; 8) Miniature Sapphire Acoustic Resonator - MSAR; 9) Process-Hardened, Multi-Analyte Sensor for Characterizing Rocket Plume Constituents; 10) SAD5 Stereo Correlation Line-Striping in an FPGA; 11) Hybrid Composite Cryogenic Tank Structure; 12) Nanoscale Deformable Optics; 13) Reliability-Based Design Optimization of a Composite Airframe Component; 14) Zinc Oxide Nanowire Interphase for Enhanced Lightweight Polymer Fiber Composites; 15) Plasma Igniter for Reliable Ignition of Combustion in Rocket Engines; 16) Wire Test Grip Fixture; 17) A Sub-Hertz, Low-Frequency Vibration Isolation Platform; 18) Carbon Nanofibers Synthesized on Selective Substrates for Nonvolatile Memory and 3D Electronics; 19) Nanoparticle/Polymer Nanocomposite Bond Coat or Coating; 20) High-Resolution Wind Measurements for Offshore Wind Energy Development; 21) Spring Tire; 22) Marsviewer 2008; 23) Mission Services Evolution Center Message Bus; 24) Major Constituents Analysis for the Vehicle Cabin Atmosphere Monitor; 25) Astronaut Health Participant Summary Application; 26) Adaption of the AMDIS Method to Flight Status on the VCAM Instrument; 27) Natural Language Interface for Safety Certification of Safety-Critical Software; 28) Cryogenic Caging for Science Instrumentation; 29) Wide-Range Neutron Detector for Space Nuclear Applications; 30) In Situ Guided Wave Structural Health Monitoring System; 31) Multiplexed Energy Coupler for Rotating Equipment; 32) Attitude Estimation in Fractionated Spacecraft Cluster Systems; 33) Full Piezoelectric Multilayer-Stacked Hybrid Actuation/Transduction Systems; 34) Active Flow Effectors for Noise and Separation Control; 35) Method and System for Temporal Filtering in Video Compression Systems; 36) Apparatus for Measuring Total Emissivity of Small, Low-Emissivity Samples; 37) Multiple-Zone Diffractive Optic Element for Laser Ranging Applications; 38) Simplified Architecture for Precise Aiming of a Deep-Space Communication Laser Transceiver; 39) Two-Photon-Absorption Scheme for Optical Beam Tracking; 40) High-Sensitivity, Broad-Range Vacuum Gauge Using Nanotubes for Micromachined Cavities; 41) Wide-Field Optic for Autonomous Acquisition of Laser Link; 42) Extracting Zero-Gravity Surface Figure of a Mirror; 43) Modeling Electromagnetic Scattering From Complex Inhomogeneous Objects; 44) Visual Object Recognition and Tracking of Tools; 45) Method for Implementing Optical Phase Adjustment; 46) Visual SLAM Using Variance Grid Maps; 47) Rapid Calculation of Spacecraft Trajectories Using Efficient Taylor Series Integration; 48) Efficient Kriging Algorithms; 49) Predicting Spacecraft Trajectories by the WeavEncke Method; 50) An Augmentation of G-Guidance Algorithms; 51) Comparison of Aircraft Icing Growth Assessment Software; 52) Silicon-Germanium Voltage-Controlled Oscillator at 105 GHz; 53) Estimation of Coriolis Force and Torque Acting on Ares-1; 54) Null Lens Assembly for X-Ray Mirror Segments; and 55) High-Precision Pulse Generator.

Source record↗
NASA NTRSDate not supplied

NASA DEVELOP’s Approach to Co-Production and Collaborative STEM Engagement

The NASA DEVELOP Program conducts 50-60 projects each year with the goal of bringing the benefits of NASA Earth science to local decision-making challenges. DEVELOP, part of NASA’s Applied Sciences’ Capacity Building Program, connects end users with students, recent graduates, and early and transitioning career professionals through 10-week rapid feasibility studies. These projects are a collaboration between NASA, DEVELOP office host locations (universities and other federal installations), project partners (federal agencies, state and local governments, non-profit and for-profit organizations, and international organizations), and the project teams who conduct them. The projects take place under the guidance of science advisors from NASA, academia, and partner organizations, and introduces communities to new applications of NASA Earth observations data with the desired outcome of informed decision-making. This presentation will highlight the DEVELOP model of co-production and programmatic collaboration, lessons learned in partnering, and evaluation activities that look at the impact of the program’s efforts.

Capacity Building↗
NASA NTRSDate not supplied

Localizing Earth Science: The NASA DEVELOP Model of Community Science & Engagement

NASA’s DEVELOP Program conducts rapid feasibility studies that apply Earth observations to address community priorities and needs and enable informed decision making. Part of NASA’s Applied Sciences’ Capacity Building Program, DEVELOP builds skills in both participants (students, recent graduates, and early/transitioning career professionals) who conduct projects on small teams, and partner organizations (state and local governments, federal agencies, non-profit and for-profit organizations, and international organizations) that work closely with DEVELOP to design the project. While only a subset of DEVELOP partner organizations are local municipalities or community-led non-profits, the DEVELOP model is an effective engagement tool and often used by NASA as an introduction for communities to engage in Earth science research and familiarization with satellite remote sensing. This presentation will highlight the DEVELOP model of co-production and community collaboration, lessons learned in partnering with local communities, and evaluation activities that look at the program’s reach and efforts.

Robert Cecil Byles↗
NASA NTRSDate not supplied

The NASA DEVELOP Model: Multidisciplinary Teams Conduct Interdisciplinary Projects to Produce Transdisciplinary Outcomes

The DEVELOP Program, part of NASA’s Earth Applied Sciences’ Capacity Building Program, conducts 50-70 feasibility studies each year that utilize Earth observations to address local decision-making challenges and help inform action. DEVELOP uses these projects as the mechanism to build skills in its participants and partner organizations to assess and apply satellite data insights to environmental decision-making processes. While organized by thematic focus (ex. water resource management, ecological forecasting, disaster management), projects use a multidisciplinary team approach with teams of students, recent graduates, early career professionals, and transitioning career professionals, bringing different disciplines, life experience, and perspectives to execute projects that have been collaboratively designed with end-user partner organizations (federal agencies, state and local governments, non-profit and for-profit organizations, and international organizations). Projects are interdisciplinary in nature as they integrate methods from multiple disciplines, with a focus on the incorporation of satellite data with other data sources such as socioeconomic and demographic data, in situ measurements, model outputs, and partners’ knowledge, and take place under the guidance of science advisors from NASA, academia, and other partner organizations. The culmination of these multidisciplinary teams working on projects that draw from interdisciplinary methods and approaches, is a transdisciplinary solution for the partner organizations to explore further for potential adoption. This presentation will highlight the DEVELOP model of co-production and collaboration, lessons learned integrating people and project methodologies, and impact assessment activities surrounding the program’s efforts.

Capacity Building↗
NASA NTRSDate not supplied

Enabling Space Biological Knowledge Discovery Through Image and Video Data Sharing

Increased biomedical risks associated with deep space crewed missions (cis-Lunar, Mars transit/surface) require development of health countermeasures, novel ecosystem support, risk modeling, and fundamental space biological knowledge discovery. Molecular-omics, physiological-phenotypic-behavioral, and environmental-radiation telemetry data from space biological and health studies are needed for reuse by scientists to address these tasks. The data as well as space-relevant biospecimens are being made more findable, accessible, interoperable, and reusable through NASA’s Open Science Data Repository (OSDR). This new OSDR umbrella grouping includes NASA GeneLab, the NASA Ames Life Sciences Data Archive (ALSDA), and the NASA Biological Institutional Scientific Collection. The OSDR system design appropriately handles metadata and processed-tabular results from ALSDA studies collected from space experiments. But raw and processed ALSDA bioimage and video datasets require an expansion of OSDR’s data architecture to handle ingestion, curation, and egress. The academic-industry bioimaging field saw a scientific renaissance in the past several years through leveraging open-source software, international collaborations, machine learning, and other open science/programming approaches. As crewed missions and more biological experiments are on the deep space horizon, OSDR is embracing data stewardship through listening to feedback from subject matter experts and designing an expanded architecture which is appropriate for NASA’s goals to enable analysis and reuse of bioimaging and video data for the public science community.Discovery Through Image and Video Data Sharing

space biology↗
NASA NTRS2004

Work Done For the Safety and Assurance Directorate

The Safety and Assurance Directorate (SAAD) has a vision. The vision is to be an essential part of NASA Glenn's journey to excellence. SAAD is in charge of leading safety, security, and quality and is important to our customers. When it comes to programmatic and technical decision making and implementation, SAAD provides clear safety, reliability, maintainable, quality assurance and security. I worked on a couple different things during my internship with Sandra Hardy. I did a lot of logistics for meeting and trips. I helped run the budget for the SAAD directorate. I also worked with Rich Miller for one week and we took water samples and ran tests. We also calibrated the different equipment. There is a lot more to meetings than people see. I did one for a retirement party. I had to get work orders and set up the facilities where the event is going to take place. I also set up a trip to Plum Brook Station. I had to order vans and talk with the people up there to see when a good time was. I also had to make invitations and coordinate everything. I also help Sandy run the numbers in the budget. We use excel to do this, which makes it a lot easier. things. He is in the environmental safety office. I learned how to collaborate the equipment using alpha and beta sources. I went out with him and we took water samples and tested them for conductivity and chlorine. I have learned a lot in the short time I've been here. It has been a great experience and I have has the pleasure of meeting and working with great people.

Struhar, Paul T., Jr.↗
NASA NTRS2020

Mars 2020 Maxon Commercial Motor Development from COTS to Flight Qualified Motors, Gearbox, and Detent Brake: Issues Overcome and Lessons Learned

Building on previous collaborations, maxon and the Jet Propulsion Laboratory (JPL) established a partnership to modify Ø20mm & Ø32mm Commerical-Off-The-Shelf (COTS) BrushLess Direct Current (BLDC) flat motors and a Ø22mm planetary gearbox.The commercial design was modified to meet the requirements for the Mars2020 rover, a Class B [1], Martian surface environment, interplanetary mission, whilst maintain as much of the industrial heritage as possible. Despite numerous problems encountered during the development, these were all overcome and qualification of the design and acceptance testing for the 10 Flight Model (FM) actuators that were installed on the Mars2020 rover was completed on-time for the rover assembly schedule.

Loschiavo, Michael↗