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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Evolution of KSC EGS Post Space Shuttle - Success Factors and Lessons Learned

The Human Exploration and Operations Mission Directorate (HEOMD) Knowledge Capture & Transfer (KCT) team conducted video interviews with element managers in the Exploration Ground Systems (EGS) Program Office at Kennedy Space Center (KSC). The immediate goal was to capture a point-in-time profile of challenges, solutions, and lessons learned derived from EGS element development activity from the end of the Space Shuttle Program (SSP) to the present time.

Johnson, Patrick↗
NASA NTRS2019

Leakage Correlation

The allowable leakage rate for space hardware is typically specified as scc/sec of helium. It is important to be able to use the measured helium leakage rate to calculate the expected leakage rate of the working fluid. In this U.S. Spacesuit Knowledge Capture seminar, Dr. Eugene Ungar will explore the physical configuration of typical leak paths, discuss the physics of molecular, transition, and continuum flow, and present the accepted method of conservatively calculating the expected leakage rate of the working fluid.

Ungar, Eugene↗
NASA NTRS2020

Lessons Learned from Sonic Boom Flight Research Projects conducted by NASA Armstrong Flight Research Center

Over the course of four years, a team of aerospace engineers at the National Aeronautics and Space Administration Armstrong Flight Research Center completed four projects, each with the objective to research sonic boom signatures from a ground and building level perspective. The relatively compressed timeline of these projects resulted in the team amassing a large number of lessons learned. With each successive project, these lessons have been more relied upon and referenced. This report serves as a written record of the teams tribal knowledge capturing the relevant lessons learned and their importance for future projects.

Erin R Waggoner↗
NASA NTRSDate not supplied

Spacecraft Natural Thermal Environments

During this U.S. Spacesuit Knowledge Capture Program event, Dr. Eugene Ungar presented the background on the Earth and Moon solar, albedo, and outgoing longwave radiation (OLR) natural thermal environment parameters. He reviewed the interrelationship of the parameters and explore the physically correct method of combining them. Dr. Ungar also presented a sample analysis of a representative case.

Eugene K Ungar↗
NASA NTRSDate not supplied

My Life as a NASA Test Subject

In addition to Joe McMann’s duties as project engineer and technical manager, he was a test subject for spacesuits, life support systems, and other systems under his purview for over 18 years. During this Spacesuit Knowledge Capture event, he shared his experiences as a test subject during the Gemini, Apollo, and Space Shuttle Programs, which include both comical and serious lessons learned.

Joe McMann↗
NASA NTRS2021

Remote Concurrent Engineering: A-Team Studies in the Virtual World

NASA Jet Propulsion Laboratory’s (JPL’s)Architecture Team (A-Team) has nearly a decade of experiencein maturing early formulation mission and technology conceptsby combining innovative collaborative engineering methodswith cutting-edge subject matter expertise and advancedanalysis tools in an in-person environment. When COVID-19forced JPL’s workforce to work remotely in March 2020, ATeamhad to quickly pivot from an in-person collaborativeenvironment to a remote working environment.Through introspection, careful planning, and considerablepractice, A-Team was able to develop new operating proceduresto effectively continue early formulation studies in a virtualenvironment. A-Team has held over 57 remote studies in the 10months since the start of mandatory telework at JPL in March2020. In the remote setting, A-Team conducts studies in half-daysessions with clients and subject matter experts (SMEs) viavideoconferencing, shared computer screens, and digitalcollaborative tools.The key lesson is that increased staffing and planning is neededto prepare and successfully run remote A-Team studies. RemoteA-Team studies require careful selection of the appropriatetools for security, accessibility, and usability within theNASA/JPL environment. Knowledge capture methods andtemplates need to be thought out and agreed upon in advance asthere is less room for improvising in a remote format. Variouscommunication channels have to be monitored to allow for teamcoordination while maintaining fruitful participant engagementduring a session. In addition, technical backup for all roleswithin the A-Team have to be identified to allow the study tocontinue even if a team member’s connectivity is temporarilyinterrupted. Finally, careful thought has to be put into methodsand processes to create a collaborative environment in a virtualspace such that a group of experts who are only connected viathe internet can experience the creative spark and flow of a greatcollaborative and innovative study.

Zusack, Steven↗
NASA NTRSDate not supplied

MUSTANG: A Workhorse for NASA Spaceflight Avionics

The Modular Unified Space Technology Avionics for Next Generation (MUSTANG) is a small integrated Avionics system including Command and Data Handling (C&DH), Power System Electronics (PSE), Attitude Control System Interfaces (ACS), and Propulsion Electronics. The MUSTANG Avionics Architecture is built upon many years of knowledge capture and lessons learned at the Goddard Space Flight Center. With a motivation towards modularity and keeping board redesign costs to a minimum, MUSTANG offers flexibility in features with a backplane-less design and allows the user to choose the options (cards) needed for their system. It incorporates a distributed power system that provides secondary power to all its subcomponents reducing the number of primary services needed for an Avionics. MUSTANG can be integrated into one system or divided into several smaller components. MUSTANG supports redundancy and cross-strap ability for a more robust and reliable Avionics system. A variation of MUSTANG exists for Instrument Electronics called iMUSTANG and allows the user to select functionality applicable to the instrument electronics. MUSTANG is not meant to replace Avionics for all spacecraft. There are limitations due to its relatively compact size, but the MUSTANG design has proven broadly applicable on many spacecraft and instrument bus avionics architectures.

MUSTANG↗
NASA NTRS2024

Integrated (Physical and Digital) Collaborative Experimentation: Advancing Dialog and Leveraging by Aerospace Researchers and Developers

This paper 1) documents findings and observations from the American Institute of Aeronautics and Astronautics (AIAA) Ground Test Technical Committee (GTTC) Future of Ground Test Working Group and the Applied Aeronautics Technical Committee (APATC) Collaborative Experiments & Computation Discussion Group, and 2) develops a more focused approach for sharing and advancing integrated development and use of physical experimental and computational capabilities for aerospace research and development. The GTTC and APATC are engaging with the larger AIAA technical community by creating a Focus Group on this topical area that will support working together on common interests in the public domain. This paper summarizes the knowledge capture from the last ten+ years and proposes a structure and scope going forward for the new, combined Focus Group.

ground testing↗
NASA NTRS2024

Enhancing Future Commercial Space Stations: Applying ISS Insights to Environmental Control and Life Support Systems (ECLSS) Development

The expansion of space exploration to commercial space stations represents a shift from government-led endeavors to a new competitive era driven by private companies and innovative forces. The journey to actualize these celestial outposts is fraught with difficulties, including the intricate establishment of Environmental Control and Life Support systems (ECLSS) within these commercial space stations. By applying the insights gleaned from the ISS, particularly during the initial conceptual design phase of the commercial developments, there is an opportunity to circumvent some of the teething issues and optimize the development process. Furthermore, applying insights from the ISS ECLSS design, development, test, and maintenance phases has the potential to enhance system compatibility and mitigate the challenges associated with upgrading a fixed design. This approach can lead to lower costs due to fewer iterations and streamlined processes. This paper will conduct an in-depth evaluation of the ECLS system from both system and operational perspectives with the aim to provide a knowledge capture for early-stage design considerations to bolster the development of sustainable, efficient, and habitable commercial space stations.

ISS↗
NASA NTRSDate not supplied

Enhancing Future Commercial Space Stations: Applying ISS Insights to Environmental Control and Life Support Systems (ECLSS) Development

The expansion of space exploration to commercial space stations represents a shift from government-led endeavors to a new competitive era driven by private companies and innovative forces. The journey to actualize these celestial outposts is fraught with difficulties, including the intricate establishment of Environmental Control and Life Support systems (ECLSS) within these commercial space stations. By applying the insights gleaned from the ISS, particularly during the initial conceptual design phase of the commercial developments, there is an opportunity to circumvent some of the teething issues and optimize the development process. Furthermore, applying insights from the ISS ECLSS design, development, test, and maintenance phases has the potential to enhance system compatibility and mitigate the challenges associated with upgrading a fixed design. This approach can lead to lower costs due to fewer iterations and streamlined processes. This paper will conduct an in-depth evaluation of the ECLS system from both system and operational perspectives with the aim to provide a knowledge capture for early-stage design considerations to bolster the development of sustainable, efficient, and habitable commercial space stations.

ISS↗
NASA NTRSDate not supplied

Towards Computational Functional Hazard Assessment (CFHA): A Gap Analysis and Concept for Emerging Aviation Systems

Given the current evolution of the National Airspace and future trajectory towards novel and evolving operations with varying levels of autonomy, complexity, and acceptable risk, there is an opportunity to support safety assurance by extending existing methodologies, such as Functional Hazard Assessment (FHA). In response to challenges in performing FHA for novel aviation concepts, we propose a concept for Computational Functional Hazard Assessment (CFHA), which provides processes, methods, and tools for incorporating external data to facilitate further exploration of the hazard space iterativelty throughout the design process. The core components of CFHA involve knowledge capture from historical and operational data, functional architecture specification via a formal modeling language, and simulation for hazardous scenario analysis. Through this concept, we aim to adapt conventional safety assessment to address the increasingly complex hazard space generated from emerging operations.

Seydou Mbaye↗
NASA NTRSDate not supplied

Towards Functional Hazard Assessment (CFHA): A Gap Analysis and Concept for Emerging Aviation Systems

Given the current evolution of the National Airspace and future trajectory towards novel and evolving operations with varying levels of autonomy, complexity, and acceptable risk, there is an opportunity to support safety assurance by extending existing methodologies, such as Functional Hazard Assessment (FHA). In response to challenges in performing FHA for novel aviation concepts, we propose a concept for Computational Functional Hazard Assessment (CFHA), which provides processes, methods, and tools for incorporating external data to facilitate further exploration of the hazard space iterativelty throughout the design process. The core components of CFHA involve knowledge capture from historical and operational data, functional architecture specification via a formal modeling language, and simulation for hazardous scenario analysis. Through this concept, we aim to adapt conventional safety assessment to address the increasingly complex hazard space generated from emerging operations.

Seydou Mbaye↗
DOE OSTI2012

Automated Expert Modeling and Student Evaluation

AEMASE searches a database of recorded events for combinations of events that are of interest. It compares matching combinations to a statistical model to determine similarity to previous events of interest and alerts the user as new matching examples are found. AEMASE is currently used by weapons tactics instructors to find situations of interest in recorded tactical training scenarios. AEMASE builds on a sub-component, the Relational Blackboard (RBB), which is being released as open-source software. AEMASE builds on RBB adding interactive expert model construction (automated knowledge capture) and re-evaluation of scenario data.

Source record↗
DOE OSTI2014

Sandia Cyber Omni Tracker

SCOT cyber security team enhancement tool that coordinates activities, captures knowledge, and serves as a platform to automate time-consuming tasks that a cyber security team needs to perform in its daily operations.

Nick Peterson, Todd Bruner↗
DOE OSTI2022

2.3.3.404 - National Lab and University Collaboration for MHK Instrumentation and Data Processing Tools

Field and laboratory validation, testing, demonstration, and operation are critical steps for increasing the technology readiness level of marine energy (ME) converters because they provide high-quality testing and performance data that are critical information used to feed all aspects of technology development. This project, in partnership with industry, enables the marine and hydrokinetic energy (MHK) community to reliably and efficiently collect, process, manage, and share quality data by facilitating access to and development of instrumentation, guidelines and data processing/QA tools. Under this project, open-source data processing code (MHKiT) and tools (ME Data Pipeline, MRE Code Hub, PRIMRE Code Catalog), instrumentation (loads measurements), data acquisition systems (miniDAQ), and measurement guidance tools (Telesto, high EMI guidance) were developed to facilitate the collection and processing of quality laboratory and field data. Overall, this project is intended to improve the quality of the data collected during laboratory and field demonstration projects by standardizing the collection and processing techniques, as well as by improving access to instrumentation, code, and measurement guidance. Quality data will, in turn, lead to improved knowledge capture following ME device testing.

data processing↗
NASA NTRS2006

Safety and Mission Assurance Knowledge Management Retention: Managing Knowledge for Successful Mission Operations

Knowledge Management is a proactive pursuit for the future success of any large organization faced with the imminent possibility that their senior managers/engineers with gained experiences and lessons learned plan to retire in the near term. Safety and Mission Assurance (S&MA) is proactively pursuing unique mechanism to ensure knowledge learned is retained and lessons learned captured and documented. Knowledge Capture Event/Activities/Management helps to provide a gateway between future retirees and our next generation of managers/engineers. S&MA hosted two Knowledge Capture Events during 2005 featuring three of its retiring fellows (Axel Larsen, Dave Whittle and Gary Johnson). The first Knowledge Capture Event February 24, 2005 focused on two Safety and Mission Assurance Safety Panels (Space Shuttle System Safety Review Panel (SSRP); Payload Safety Review Panel (PSRP) and the latter event December 15, 2005 featured lessons learned during Apollo, Skylab, and Space Shuttle which could be applicable in the newly created Crew Exploration Vehicle (CEV)/Constellation development program. Gemini, Apollo, Skylab and the Space Shuttle promised and delivered exciting human advances in space and benefits of space in people s everyday lives on earth. Johnson Space Center's Safety & Mission Assurance team work over the last 20 years has been mostly focused on operations we are now beginning the Exploration development program. S&MA will promote an atmosphere of knowledge sharing in its formal and informal cultures and work processes, and reward the open dissemination and sharing of information; we are asking "Why embrace relearning the "lessons learned" in the past?" On the Exploration program the focus will be on Design, Development, Test, & Evaluation (DDT&E); therefore, it is critical to understand the lessons from these past programs during the DDT&E phase.

Johnson, Teresa A.↗
DOE OSTI2012

Capturing and Using Knowledge about the Use of Visualization Toolkits

When constructing visualization pipelines using toolkits such as Visualization Toolkit (VTK) and Generic Mapping Tools (GMT), developers must understand (1) what toolkit operators will transform their data from its raw state to some required view state and (2) what viewers are available to present the generated view. Traditionally, developers learn about how to construct visualization pipelines by reading documentation and inspecting code examples, which can be costly in terms of the time and effort expended. Once an initial pipeline is constructed, developers may still have to undergo a trial and error process before a satisfactory visualization is generated. This paper presents the Visualization Knowledge Project (VisKo) that is built on a knowledge base of visualization toolkit operators and how they can be piped together to form visualization pipelines. Developers may now rely on VisKo to guide them when constructing visualization pipelines and in some cases, when VisKo has complete knowledge about some set of operators (i.e., sequencing and parameter settings), automatically generate a fully functional visualization pipeline.

Chemical Imaging↗