Improving Earth System Models: What Can Be Learned from the Arctic-Boreal Region? As Part of the Arctic-Boreal Vulnerability Experiment
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Braghiere, Renato↗
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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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The Application of Remote Sensing and Machine Learning to Improve Early Warning Systems for Harmful Algal Events in the Highland Lake Chains, TX
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Shuyu Chang↗
NASA’s Quiet Electric ENgines (QUEEN): Summary of the QUEEN V2 Test
A liquid-cooled electric ducted fan system was designed, built, and tested at the NASA Glenn Research Center. Main components of the system include a Commercial-Off- the-Shelf (COTS) fan and motor, and a custom-designed heat exchanger integrated into the fan duct. Fan speed, thrust, and cooling system thermal performance was measured in this static ground test of the propulsor. This propulsor prototype is one of NASA’s Quiet Electric ENgines (QUEENs) and is designated the ‘QUEEN V2.’ The Quiet Electric Engines are being developed for the 25% scale model of the Subsonic Aft Engine (SUSAN) Flight Research Vehicle and are intended to explore the potential of distributed electric propulsion for regional single-aisle aircraft. This test demonstrated the functionality of the QUEEN V2, quantified the thrust produced by the electrofan, and characterized the electrical and thermal performance of the system. Lessons learned will be used to guide development of future QUEEN prototypes.
Aerodynamics↗
NASA’s Quiet Electric ENgines (QUEEN): Summary of the QUEEN V2 Test
A liquid-cooled electric ducted fan system was designed, built, and tested at the NASA Glenn Research Center. Main components of the system include a Commercial-Off- the-Shelf (COTS) fan and motor, and a custom-designed heat exchanger integrated into the fan duct. Fan speed, thrust, and cooling system thermal performance was measured in this static ground test of the propulsor. This propulsor prototype is one of NASA’s Quiet Electric ENgines (QUEENs) and is designated the ‘QUEEN V2.’ The Quiet Electric Engines are being developed for the 25% scale model of the Subsonic Aft Engine (SUSAN) Flight Research Vehicle and are intended to explore the potential of distributed electric propulsion for regional single-aisle aircraft. This test demonstrated the functionality of the QUEEN V2, quantified the thrust produced by the electrofan, and characterized the electrical and thermal performance of the system. Lessons learned will be used to guide development of future QUEEN prototypes.
Aeronautics-General↗
NASA’s Quiet Electric ENgines (QUEEN): Summary of the Acoustic Tests of the QUEEN V1
Noise produced by an electric ducted fan system was measured in tests at the NASA Glenn Research Center Acoustical Testing Laboratory. Main components of the system include a Commercial-Off-the-Shelf fan, shroud, motor, and Electronic Speed Controller, plus a custom-designed inlet bellmouth, and four experimental inlet duct acoustic liners. Fan speed and noise were measured in this static ground test of the propulsor. This propulsor prototype is one of NASA’s Quiet Electric ENgines (QUEENs) and is designated the ‘QUEEN V1.’ The Quiet Electric Engines are being developed for the 25% scale model of the Subsonic Aft Engine (SUSAN) Flight Research Vehicle and are intended to explore the potential of distributed electric propulsion for large single-aisle aircraft. Results indicated that inlet duct acoustic liners reduced tone and broadband noise as compared to a hardwall inlet duct. Predicted performance of the honeycomb liner compared well with measurements from an array of far field microphones. Inlet acoustic liners are just one method for mitigating noise for electric ducted fans for aircraft propulsion systems. Lessons learned will be used to guide development of future QUEEN prototypes. Results of a thermal test of the Electronic Speed Controller are presented in a separate report.
Aircraft propulsion and power↗
NASA’s Quiet Electric ENgines (QUEEN): Summary of the Acoustic Tests of the QUEEN V1
Noise produced by an electric ducted fan system was measured in tests at the NASA Glenn Research Center Acoustical Testing Laboratory. Main components of the system include a Commercial-Off-the-Shelf fan, shroud, motor, and Electronic Speed Controller, plus a custom-designed inlet bellmouth, and four experimental inlet duct acoustic liners. Fan speed and noise were measured in this static ground test of the propulsor. This propulsor prototype is one of NASA’s Quiet Electric ENgines (QUEENs) and is designated the ‘QUEEN V1.’ The Quiet Electric Engines are being developed for the 25% scale model of the Subsonic Aft Engine (SUSAN) Flight Research Vehicle and are intended to explore the potential of distributed electric propulsion for large single-aisle aircraft. Results indicated that inlet duct acoustic liners reduced tone and broadband noise as compared to a hardwall inlet duct. Predicted performance of the honeycomb liner compared well with measurements from an array of far field microphones. Inlet acoustic liners are just one method for mitigating noise for electric ducted fans for aircraft propulsion systems. Lessons learned will be used to guide development of future QUEEN prototypes. Results of a thermal test of the Electronic Speed Controller are presented in a separate report.
Aircraft propulsion and power↗
Lessons Learned Study Final Report for the Exploration Systems Mission Directorate
This report is the final product of a 90-day study performed for the Exploration Systems Mission Directorate. The study was to assemble lessons NASA has learned from previous programs that could help the Exploration Systems Mission Directorate pursue the Exploration vision. It focuses on those lessons that should have the greatest significance to the Directorate during the formulation of program and mission plans. The study team reviewed a large number of lessons learned reports and data bases, including the Columbia Accident Investigation Board and Rogers Commission reports on the Shuttle accidents, accident reports from robotic space flight systems, and a number of management reviews by the Defense Sciences Board, Government Accountability Office, and others. The consistency of the lessons, findings, and recommendations validate the adequacy of the data set. In addition to reviewing existing databases, a series of workshops was held at each of the NASA centers and headquarters that included senior managers from the current workforce as well as retirees. The full text of the workshop reports is included in Appendix A. A lessons learned website was opened up to permit current and retired NASA personnel and on-site contractors to input additional lessons as they arise. These new lessons, when of appropriate quality and relevance, will be brought to the attention of managers. The report consists of four parts: Part 1 provides a small set of lessons, called the Executive Lessons Learned, that represent critical lessons that the Exploration Systems Mission Directorate should act on immediately. This set of Executive Lessons and their supporting rationale have been reviewed at length and fully endorsed by a team of distinguished NASA alumni; Part 2 contains a larger set of lessons, called the Selected Lessons Learned, which have been chosen from the lessons database and center workshop reports on the basis of their specific significance and relevance to the near-term work of the Exploration Directorate. These lessons frequently support the Executive lessons but are more general in nature; Part 3 consists of the reports of the center workshops that were conducted as part of this activity. These reports are included in their entirety (approximately 200 pages) in Appendix G and have significance for specific managers; Part 4 consists of the remainder of the lessons that have been selected by this effort and assembled into a database for the use of the Explorations Directorate. The database is archived and hosted in the Lessons Learned Knowledge Network, which provides a flexible search capability using a wide variety of search terms. Finally, a spreadsheet lists databases searched and a bibliography identifies reports that have been reviewed as sources of lessons for this task. NASA has been presented with many learning opportunities. We have conducted numerous programs, some extremely successful and others total failures. Most have been documented with a formal lessons learned activity, but we have not always incorporated these learning opportunities into our normal modes of business. For example, the Robbins Report of 2001 clearly indicates that many project failures of the past two decades were the result of violating well documented best practices, often in direct violation of management instructions and directives. An overarching lesson emerges: that disciplined execution in accordance with proven best practices is the greatest single contributor to a successful program. The Lessons Learned task team offers a sincere hope that the lessons presented herein will be helpful to the Exploration Systems Directorate in charting and executing their course. The success of the Directorate and of NASA in general depends on our collective ability to move forward without having to relearn the lessons of those who have gone before.
Van Laak, Jim↗
High Gain Antenna System Deployment Mechanism Integration, Characterization, and Lessons Learned
The integration and deployment testing of the High Gain Antenna System for the Global Precipitation Measurement mission is summarized. The HGAS deployment mechanism is described. The gravity negation system configuration and its influence on vertical, ground-based, deployment tests are presented with test data and model predictions. A focus is made on the late discovery and resolution of a potentially mission degrading deployment interference condition. The interaction of the flight deployment mechanism, gravity negation mechanism, and use of dynamic modeling is described and lessons learned presented.
000000↗
High Gain Antenna System Deployment Mechanism Integration, Characterization, and Lessons Learned
The integration and deployment testing of the High Gain Antenna System (HGAS) for the Global Precipitation Measurement mission is summarized. The HGAS deployment mechanism is described. The gravity negation system configuration and its influence on vertical, ground-based deployment tests are presented with test data and model predictions. A focus is made on the late discovery and resolution of a potentially mission-degrading deployment interference condition. The interaction of the flight deployment mechanism, gravity-negation mechanism, and use of dynamic modeling is described and lessons learned presented
Parong, Fil↗
ISS Regenerative Life Support: Challenges and Success in the Quest for Long-Term Habitability in Space
The International Space Station's (ISS) Regenerative Environmental Control and Life Support System (ECLSS) was launched in 2008 to continuously recycle urine and crew sweat into drinking water and oxygen using brand new technologies. This functionality was highly important to the ability of the ISS to transition to the long-term goal of 6-crew operations as well as being critical tests for long-term space habitability. Through the initial activation and long-term operations of these systems, important lessons were learned about the importance of system redundancy and operational workarounds that allow Systems Engineers to maintain functionality with limited on-orbit spares. This presentation will share some of these lessons learned including how to balance water through the different systems, store and use water for use in system failures and creating procedures to operate the systems in ways that they were not initially designed to do.
Bazley, Jesse↗
Deepti: Deep-Learning-Based Tropical Cyclone Intensity Estimation System
Tropical cyclones are one of the costliest natural disasters globally because of the wide range of associated hazards. Thus, an accurate diagnostic model for tropical cyclone intensity can save lives and property. There are a number of existing techniques and approaches that diagnose tropical cyclone wind speed using satellite data at a given time with varying success. This paper presents a deep learning-based objective, diagnostic estimate of tropical cyclone intensity from infrared satellite imagery with 13.24 kt Root Mean Squared Error (RMSE). In addition, a visualization portal in a production system is presented that displays deep learning output and contextual information for end users, one of the first of its kind.
Manil Maskey↗
Lessons Learned from the Crew Health Care System (CHeCS) Rack 1 Environmental Control and Life Support (ECLS) Design
This paper will provide an overview of the International Space Station (ISS) Environmental Control and Life Support (ECLS) design of the Crew Health Care System (CHeCS) Rack 1 and it will document some of the lessons that have been learned to date for the ECLS equipment in this rack.
Williams, David E.↗
Space Shuttle Cargo Integration Coupled Loads Analysis Lessons Learned
When a system experiences a loading environment characterized by rapidly varying forces, such as a rocket launch, a transient analysis is used to analyze the response of the system. The most common transient analysis methodology is the Coupled Loads Analysis (CLA). CLAs are used by the automotive and aerospace industry to analyze cars, trucks, planes, helicopters, spacecraft, etc. The Space Shuttle program used the CLA methodology to assess the compatibility of the payload with the Orbiter and the flight environment. The Space Shuttle Verification Loads Analysis (VLA) was a standardized CLA process that started between ten and thirteen months prior to launch, and included several meetings as well as analysis by both the Space Shuttle Program and the payload developers to verify that the payloads were compatible with the flight loads environment and would not interact negatively with the vehicle. Over the course of the Space Shuttle Program, many improvements were made to the process, which reduced cycle time and improved manifest flexibility. There were several issues which were never fully addressed, but workarounds were developed to keep the process flowing. The lessons learned included automation of some processes and standardization of others, early assessments, improved documentation and better coordination with all stakeholders in the process. Lessons learned also included the limitations in the current process, and what to do to avoid the same issues in the future.
Erica E. Bruno↗
Space Shuttle Cargo Integration Coupled Loads Analysis Lessons Learned
When a system experiences a loading environment characterized by rapidly varying forces, such as a rocket launch, a transient analysis is used to analyze the response of the system. The most common transient analysis methodology is the Coupled Loads Analysis(CLA). CLAs are used by the automotive and aerospace industry to analyze cars, trucks,planes, helicopters, spacecraft, etc. The Space Shuttle program used the CLA methodology to assess the compatibility of the payload with the Orbiter and the flight environment. The Space Shuttle Verification Loads Analysis (VLA) was a standardized CLA process that started between ten and thirteen months prior to launch, and included several meetings as well as analysis by both the Space Shuttle Program and the payload developers to verify that the payloads were compatible with the flight loads environment and would not interact negatively with the vehicle. Over the course of the Space Shuttle Program, many improvements were made to the process, which reduced cycle time and improved manifest flexibility. There were several issues which were never fully addressed, but work-arounds were developed to keep the process flowing. The lessons learned included automation of some processes and standardization of others, early assessments, improved documentation and better coordination with all stakeholders in the process. Lessons learned also included the limitations in the current process, and what to do to avoid the same issues.
ERICA E. BRUNO↗
System and Safety Analysis with SysAI A Statistical Learning Framework
This is a tutorial on how to use the SYSAI (System Analysis using Statistical AI), a flexible statistical learning framework for the V&V and analysis of complex and high-dimensional Aerospace systems with DNN and AI components. SYSAI provides functionality for a variety of analyses and V&V tasks, including statistical data analysis, high dimensional safety-envelope and time-series analysis, property checking, as well as intelligent test-case generation. The tutorial will demonstrate SYSAI with our industrial partner’s Autonomous Centerline Tracking system, which uses a DNN to enable autonomous aircraft taxiing as an example. Video & Tutorial
Statistical V&V for Complex safety-critical system↗
Implementation of A Drone Delivery System for Healthcare in Mexico: International Precedents and Domestic Considerations
One of the Mexican healthcare system’s principal problems is providing access to its rural communities. Drone delivery has repeatedly proven to be a compelling and cost-effective solution to this problem elsewhere, yet there is currently no scholarship connecting global developments in drone technology to problems in the Mexican healthcare system. This paper is to outline lessons learned from drone systems in several countries, providing information on the potential target communities, technical drone and nest specs, goods of delivery, and regulation of a drone system for healthcare in Mexico.
Drone↗
Active Learning for Directed Exploration of Complex Systems
Physics-based simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. Such codes provide the highest-fidelity representation of system behavior, but are often so slow to run that insight into the system is limited. For example, conducting an exhaustive sweep over a d-dimensional input parameter space with k-steps along each dimension requires k(sup d) simulation trials (translating into k(sup d) CPU-days for one of our current simulations). An alternative is directed exploration in which the next simulation trials are cleverly chosen at each step. Given the results of previous trials, supervised learning techniques (SVM, KDE, GP) are applied to build up simplified predictive models of system behavior. These models are then used within an active learning framework to identify the most valuable trials to run next. Several active learning strategies are examined including a recently-proposed information-theoretic approach. Performance is evaluated on a set of thirteen synthetic oracles, which serve as surrogates for the more expensive simulations and enable the experiments to be replicated by other researchers.
oracles↗
Learning In networks
Intelligent systems require software incorporating probabilistic reasoning, and often times learning. Networks provide a framework and methodology for creating this kind of software. This paper introduces network models based on chain graphs with deterministic nodes. Chain graphs are defined as a hierarchical combination of Bayesian and Markov networks. To model learning, plates on chain graphs are introduced to model independent samples. The paper concludes by discussing various operations that can be performed on chain graphs with plates as a simplification process or to generate learning algorithms.
Buntine, Wray L.↗