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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 NTRS2023

Easy, Scalable Subsetting of GEDI Point Clouds

The GEDI Subsetter, a Python tool developed for NASA’s Multi-mission Algorithm and Analysis Platform (MAAP), optimizes the accessibility and visualization of GEDI point clouds by enabling users to efficiently subset data in a convenient, scalable manner. Complex science data often requires users to learn new software skills and handle many large files. Handling and cleaning large data sets is tedious and error-prone. These challenges significantly impede analysis. One of the goals of NASA's MAAP is to provide a platform that lowers the barrier to conducting research and analysis at scale. When a group of MAAP users wanted to conduct above-ground biomass estimation using GEDI data, we found that their existing workflow for leveraging GEDI data suffered from the barriers mentioned above. Furthermore, their workflow did not scale easily beyond a small number of granules. We found that existing tools related to GEDI data retrieval and subsetting were too limiting, so the GEDI Subsetter was written to support MAAP users’ needs. Being able to run many subsetting jobs simultaneously in the MAAP, and parallelizing the code itself, has led to significant speed improvements in obtaining relevant data, reducing subsetting time from hours to minutes. MAAP users can now more quickly and easily obtain only the data relevant to their research, by choosing which GEDI collection they want to work with (L1A, L2A, L2B, or L4A), and how they want to subset it, by specifying an area of interest, a temporal range, and relevant attributes. This has significantly reduced the feedback loop for users, allowing them to much more quickly subset GEDI data and begin their analysis. Although the GEDI Subsetter originally targeted users of the MAAP, it is generalized such that it can also be used outside of the MAAP and includes a command-line interface for convenience. Furthermore, with minor modifications, it should be possible to use it with non-GEDI data as the general pattern should be applicable to other sparse/track-based sensors.

Charles Daniels↗
NASA NTRSDate not supplied

Turbo-Design: Open-Source Radial Equilibrium Turbomachinery Solver: Part I - Turbines

Advances in 3D Geometrical Designs and Cooling have played a significant role in improving the efficiency of turbomachinery. However, these advancements must be effectively translated back to the modeler. Machine learning can facilitate this transition. Specifically, machine learning–based loss models can bridge the gap between 3D and 1D designs, enabling modelers not only to predict velocity triangles but also to extract additional geometric features. Currently, the design tools used at NASA have not been updated to support such integration—until now. TurboDesign is an open-source, Python-based framework that replaces TD2 (LEW-11029-1) and AXOD2 (LEW-16323-1), both of which are radial equilibrium solvers for axial turbines. The goal of this update is to enable the integration of machine learning loss models into radial equilibrium equations. Additionally, TurboDesign is designed to support radial machines. This paper presents the governing equations, the assumptions underlying the code, the integration of legacy loss models, an example of machine learning model integration, and a validation comparison with CFD. All code, tutorials, and documentation are available at: https://www.github.com/nasa/turbo-design

Radial Equilibrium↗
NASA NTRS2008

James Webb Space Telescope - Applying Lessons Learned to I&T

The James Webb Space Telescope (JWST) is part of a new generation of spacecraft acquiring large data volumes from remote regions in space. To support a mission such as the JWST, it is imperative that lessons learned from the development of previous missions such as the Hubble Space Telescope and the Earth Observing System mission set be applied throughout the development and operational lifecycles. One example of a key lesson that should be applied is that core components, such as the command and telemetry system and the project database, should be developed early, used throughout development and testing, and evolved into the operational system. The purpose of applying lessons learned is to reap benefits in programmatic or technical parameters such as risk reduction, end product quality, cost efficiency, and schedule optimization. In the cited example, the early development and use of the operational command and telemetry system as well as the establishment of the intended operational database will allow these components to be used by the developers of various spacecraft components such that development, testing, and operations will all use the same core components. This will reduce risk through the elimination of transitions between development and operational components and improve end product quality by extending the verification of those components through continual use. This paper will discuss key lessons learned that have been or are being applied to the JWST Ground Segment integration and test program.

Johns, Alan↗
NASA NTRSDate not supplied

Problems in Machine Learning-Based Systems for Safety-Critical Avionics

To explore, discover, and understand the impact on safety of growing complexity introduced by modernization aimed at improving the efficiency of flight, the access to airspace, and/or the expansion of services provided by air vehicles. To develop and demonstrate innovative solutions that enable this modernization and the aviation transformation envisioned by ARMD through proactive mitigation of risks in accordance with target levels of safety.

Safety Critical Systems↗
NASA NTRS2002

Solving and Learning Soft Temporal Constraints: Experimental Setting and Results

Soft temporal constraints problems allow to describe in a natural way scenarios where events happen over time and preferences are associated to event distances and durations. However, sometimes such local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. Machine learning techniques can be useful in this respect. In this paper we describe two solvers (one more general and the other one more efficient) for tractable subclasses of soft temporal problems, and we show some experimental results. The random generator used to build the problems on which tests are performed is also described. We also compare the two solvers highlighting the tradeoff between performance and representational power. Finally, we present a learning module and we show its behavior on randomly-generated examples.

Rossi, F.↗
NASA NTRSDate not supplied

Application of Agile for Systems Engineering, Project Management and Modeling and Lessons Learned

The Systems Engineering team within the Human Research Program (HRP) Exploration Medical Capability (ExMC) Element has been transforming its development processes to be more efficient, robust,and responsive to change and to its stakeholders. To these ends, the Systems Engineering team trialed the integration of agile development techniques into existing and new processes. Agile development methods are well understood within the software development community. Outside ofsoftware development, however, how non-software project management (PM) and systems engineering (SE) teams implement agile development techniques is less well understood. In its transformation efforts, the ExMC SE team focused on three main areas: Improving the project communications among subsystem teams and stakeholders by adopting a scrum-like process, Changing the status and reporting mechanisms to improve schedule coordination between the subsystem team, SE leadership, and ExMC Element leadership, and Unifying the model-based SE workflow to improve understanding of Concepts of Operations across projects.This presentation highlights several of these transformations and what the SE team learned while undergoing the transformation.

S Lumpkins↗
NASA NTRS2016

The International Space Station (ISS) Solar Alpha Rotary Joint (SARJ): Materials & Processes (M&P) Lessons Learned for a Large, Spacecraft Rotating Mechanism

The ISS utilizes two large rotating mechanisms, the SARJ, as part of the solar arrays alignment system for more efficient power generation. The SARJ is a 10.3m circumference, nitrided 15-5PH steel race ring of triangular cross-section, with 12 sets of trundle bearing assemblies transferring load across the rolling joint. The SARJ mechanism rotates continuously and slowly - once every orbit, or every 90 minutes. In 2008, the starboard SARJ suffered a lubrication failure, resulting in severe damage (spalling) of one of the race ring surfaces. Extensive effort was conducted to prevent the port SARJ from suffering the same failure, and fortunately was ultimately successful in recovering the functionality of the starboard SARJ. The M&P function was key in determining the cause of failure and the means for mechanism recovery. From a M&P lessons-learned perspective, observations are made concerning the original SARJ design parameters (boundary conditions), the perceived need for nitriding the race ring, the test conditions employed during qualification, the environmental controls used for the hardware preflight, and the lubrication robustness necessary for complex kinematic mechanisms expecting high-reliability and long-life.

Golden, Johnny L.↗
NASA NTRS2013

Summary of Stirling Convertor Testing at NASA Glenn Research Center in Support of Stirling Radioisotope Power System Development

The NASA Glenn Research Center (GRC) has been testing 100 We class, free-piston Stirling convertors for potential use in Stirling Radioisotope Power Systems (RPS) for space science and exploration missions. Free-piston Stirling convertors are capable of achieving a 38% conversion efficiency, making Stirling attractive for meeting future power system needs in light of the shrinking U.S. plutonium fuel supply. Convertors currently on test include four Stirling Technology Demonstration Convertors (TDCs), manufactured by the Stirling Technology Company (STC), and six Advanced Stirling Convertors (ASCs), manufactured by Sunpower, Inc. Total hours of operation is greater than 514,000 hours (59 years). Several tests have been initiated to demonstrate the functionality of Stirling convertors for space applications, including: in-air extended operation, thermal vacuum extended operation. Other tests have also been conducted to characterize Stirling performance in anticipated mission scenarios. Data collected during testing has been used to support life and reliability estimates, drive design changes and improve quality, and plan for expected mission scenarios. This paper will provide a summary of convertors tested at NASA GRC and discuss lessons learned through extended testing.

Energy Conversion Efficiency↗
NASA NTRS2002

Digital Control Technologies for Modular DC-DC Converters

Recent trends in aerospace Power Management and Distribution (PMAD) systems focus on using commercial off-the-shelf (COTS) components as standard building blocks. This move to more modular designs has been driven by a desire to reduce costs and development times, but is also due to the impressive power density and efficiency numbers achieved by today's commercial DC-DC converters. However, the PMAD designer quickly learns of the hidden "costs" of using COTS converters. The most significant cost is the required addition of external input filters to meet strict electromagnetic interference (MIAMI) requirements for space systems. In fact, the high power density numbers achieved by the commercial manufacturers are greatly due to the lack of necessary input filters included in the COTS module. The NASA Glenn Research Center is currently pursuing a digital control technology that addresses this problem with modular DC-DC converters. This paper presents the digital control technologies that have been developed to greatly reduce the input filter requirements for paralleled, modular DC-DC converters. Initial test result show that the input filter's inductor size was reduced by 75 percent, and the capacitor size was reduced by 94 percent while maintaining the same power quality specifications.

Button, Robert M.↗
NASA NTRS2016

Evidence Report: Risk of Performance Errors Due to Training Deficiencies

Substantial evidence supports the claim that inadequate training leads to performance errors. Barshi and Loukopoulos (2012) demonstrate that even a task as carefully developed and refined over many years as operating an aircraft can be significantly improved by a systematic analysis, followed by improved procedures and improved training (see also Loukopoulos, Dismukes, & Barshi, 2009a). Unfortunately, such a systematic analysis of training needs rarely occurs during the preliminary design phase, when modifications are most feasible. Training is often seen as a way to compensate for deficiencies in task and system design, which in turn increases the training load. As a result, task performance often suffers, and with it, the operators suffer and so does the mission. On the other hand, effective training can indeed compensate for such design deficiencies, and can even go beyond to compensate for failures of our imagination to anticipate all that might be needed when we send our crew members to go where no one else has gone before. Much of the research literature on training is motivated by current training practices aimed at current training needs. Although there is some experience with operations in extreme environments on Earth, there is no experience with long-duration space missions where crews must practice semi-autonomous operations, where ground support must accommodate significant communication delays, and where so little is known about the environment. Thus, we must develop robust methodologies and tools to prepare our crews for the unknown. The research necessary to support such an endeavor does not currently exist, but existing research does reveal general challenges that are relevant to long-duration, high-autonomy missions. The evidence presented here describes issues related to the risk of performance errors due to training deficiencies. Contributing factors regarding training deficiencies may pertain to organizational process and training programs for spaceflight, such as when training programs are inadequate or unavailable. Furthermore, failure to match between tasks on the one hand, and learning and memory abilities on the other hand is a contributing factor, especially when individuals' relative efficiency with which new information is acquired, and adjustments made in behavior or thinking, are inconsistent with mission demands. Thus, if training deficiencies are present, the likelihood of errors or of the inability to successfully complete a task increases. What's more, the overall risk to the crew, the vehicle, and the mission increases.

Barshi, Immanuel↗
NASA NTRSDate not supplied

Experiences in the Practice of Design of Experiments at NASA

Statistical design of experiments (DOE) has gained recognition within NASA by spurring innovation and efficiency, and it has demonstrated significant impact. Aerospace research and development benefits DOE techniques by accelerating learning, maximizing knowledge, ensuring strategic resource investment, and informing data-driven decisions. In practice, DOE relies on multidisciplinary collaboration to develop solution strategies that integrate statistical methods with subject-matter expertise to meet challenging research objectives. This presentation shares experiences in the practice of design of experiments at NASA in aeronautics, space exploration, and atmospheric science.

Peter A. Parker↗
NASA NTRSDate not supplied

Statistical Practice and Research at NASA

The discipline of statistics has gained recognition within NASA by spurring innovation and efficiency, and it has demonstrated significant impact and value. In aerospace research and development, it accelerates learning, maximizes knowledge, ensures strategic resource investment, and informs rigorous data-driven decisions. In practice, it requires immersive multidisciplinary collaboration to develop solution strategies that integrate statistical methods with subject-matter expertise to address challenging research objectives. This presentation provides an overview of statistical case studies in aeronautics, space exploration, and atmospheric science, and it highlights statistical research motivated by NASA’s challenging applications.

Peter A. Parker↗
NASA NTRS2016

A Tale of Two Chambers: Iterative Approaches and Lessons Learned from Life Support Systems Testing in Altitude Chambers

With a brand new fire set ablaze by a serendipitous convergence of events ranging from a science fiction novel and movie ("The Martian"), to ground-breaking recent discoveries of flowing water on its surface, the drive for the journey to Mars seems to be in a higher gear than ever before. We are developing new spacecraft and support systems to take humans to the Red Planet, while scientists on Earth continue using the International Space Station as a laboratory to evaluate the effects of long duration space flight on the human body. Written from the perspective of a facility test director rather than a researcher, and using past and current life support systems tests as examples, this paper seeks to provide an overview on how facility teams approach testing, the kind of information they need to ensure efficient collaborations and successful tests, and how, together with researchers and principal investigators, we can collectively apply what we learn to execute future tests.

Callini, Gianluca↗
NASA NTRSDate not supplied

Generation of Continental Scale Percent Tree Cover Product Using Deep-learning and Multi-scale Remote Sensing Data

Spatially explicit percent tree cover (TC) estimation is critical for mapping forest aboveground biomass and its dynamics. While various TC products have been developed, there has not been a generalized framework that can be applied to diverse terrestrial ecosystems due to underlain extreme complexities. Deep learning algorithms can learn a spatial pattern and radiometric characteristics of tree canopy as a robust approximation of physical or empirical models, and thus have emerged as promising and efficient tools for large-scale TC mapping. In this study, we synergistically use very high-resolution aerial imageries (National Agriculture Imagery Program, NAIP) and medium resolution Landsat data to map continental-scale TC (CONUS and Mexico) through a hierarchical deep learning approach (Convolutional Neural Network), i.e., NAIP TC generated from a NAIP model is utilized to train a Landsat model. The produced TC product (hereafter, NEX-TC) is able to capture the spatial pattern of TC distribution and its changes driven by natural disturbance and human land management. We further explore and analyze the reliability and potential uncertainty of the NEX-TC by comparing it to lidar- (lidar-TC), National Land Cover Database (NLCD-TC), and MODIS Vegetation Continuous Field (MODIS-TC). This evaluation practice reveals that TC products based on passive optical sensors tend to underestimate TC across all land cover types while Landsat-based TCs (i.e., NEX-TC & NLCD-TC) perform better than the coarser MODIS TC estimate. Our results show that the NEX-TC is generally comparable to NLCD-TC but it particularly outperforms NLCD-TC and MODIS-TC over the dense forests where lidar-TC indicates >80% TC. These results indicate that our hierarchical deep learning approach and TC product will be effective and useful for characterizing large-scale tree cover and possibly associated carbon dynamics.

Landsat↗
NASA NTRS2006

Qualification and Issues with Space Flight Laser Systems and Components

The art of flight quality solid-state laser development is still relatively young, and much is still unknown regarding the best procedures, components, and packaging required for achieving the maximum possible lifetime and reliability when deployed in the harsh space environment. One of the most important issues is the limited and unstable supply of quality, high power diode arrays with significant technological heritage and market lifetime. Since Spectra Diode Labs Inc. ended their involvement in the pulsed array business in the late 1990's, there has been a flurry of activity from other manufacturers, but little effort focused on flight quality production. This forces NASA, inevitably, to examine the use of commercial parts to enable space flight laser designs. System-level issues such as power cycling, operational derating, duty cycle, and contamination risks to other laser components are some of the more significant unknown, if unquantifiable, parameters that directly effect transmitter reliability. Designs and processes can be formulated for the system and the components (including thorough modeling) to mitigate risk based on the known failures modes as well as lessons learned that GSFC has collected over the past ten years of space flight operation of lasers. In addition, knowledge of the potential failure modes related to the system and the components themselves can allow the qualification testing to be done in an efficient yet, effective manner. Careful test plan development coupled with physics of failure knowledge will enable cost effect qualification of commercial technology. Presented here will be lessons learned from space flight experience, brief synopsis of known potential failure modes, mitigation techniques, and options for testing from the system level to the component level.

Ott, Melanie N.↗
NASA NTRS2006

Qualification and Issues with Space Flight Laser Systems and Components

The art of flight quality solid-state laser development is still relatively young, and much is still unknown regarding the best procedures, components, and packaging required for achieving the maximum possible lifetime and reliability when deployed in the harsh space environment. One of the most important issues is the limited and unstable supply of quality, high power diode arrays with significant technological heritage and market lifetime. Since Spectra Diode Labs Inc. ended their involvement in the pulsed array business in the late 199O's, there has been a flurry of activity from other manufacturers, but little effort focused on flight quality production. This forces NASA, inevitably, to examine the use of commercial parts to enable space flight laser designs. System-level issues such as power cycling, operational derating, duty cycle, and contamination risks to other laser components are some of the more significant unknown, if unquantifiable, parameters that directly effect transmitter reliability. Designs and processes can be formulated for the system and the components (including thorough modeling) to mitigate risk based on the known failures modes as well as lessons learned that GSFC has collected over the past ten years of space flight operation of lasers. In addition, knowledge of the potential failure modes related to the system and the components themselves can allow the qualification testing to be done in an efficient yet, effective manner. Careful test plan development coupled with physics of failure knowledge will enable cost effect qualification of commercial technology. Presented here will be lessons learned from space flight experience, brief synopsis of known potential failure modes, mitigation techniques, and options for testing from the system level to the component level.

Ott, Melanie N.↗
NASA NTRS2008

Developing Humanoid Robots for Real-World Environments

Humanoids are steadily improving in appearance and functionality demonstrated in controlled environments. To address the challenges of operation in the real-world, researchers have proposed the use of brain-inspired architectures for robot control, and the use of robot learning techniques that enable the robot to acquire and tune skills and behaviours. In the first part of the paper we introduce new concepts and results in these two areas. First, we present a cerebellum-inspired model that demonstrated efficiency in the sensory-motor control of anthropomorphic arms, and in gait control of dynamic walkers. Then, we present a set of new ideas related to robot learning, emphasizing the importance of developing teaching techniques that support learning. In the second part of the paper we propose the use in robotics of the iterative and incremental development methodologies, in the context of practical task-oriented applications. These methodologies promise to rapidly reach system-level integration, and to early identify system-level weaknesses to focus on. We apply this methodology in a task targeting the automated assembly of a modular structure using HOAP-2. We confirm this approach led to rapid development of a end-to-end capability, and offered guidance on which technologies to focus on for gradual improvement of a complete functional system. It is believed that providing Grand Challenge type milestones in practical task-oriented applications accelerates development. As a meaningful target in short-mid term we propose the 'IKEA Challenge', aimed at the demonstration of autonomous assembly of various pieces of furniture, from the box, following included written/drawn instructions.

Stoica, Adrian↗
NASA NTRS2014

Re-Engineering the ISS Payload Operations Control Center During Increased Utilization and Critical Onboard Events

With an increase in utilization and hours of payload operations being executed onboard the International Space Station (ISS), upgrading the NASA Marshall Space Flight Center (MSFC) Huntsville Operations Support Center (HOSC) ISS Payload Control Area (PCA) was essential to gaining efficiencies and assurance of current and future payload health and science return. PCA houses the Payload Operations Integration Center (POIC) responsible for the execution of all NASA payloads onboard the ISS. POIC Flight Controllers are responsible for the operation of voice, stowage, command, telemetry, video, power, thermal, and environmental control in support of ISS science experiments. The methodologies and execution of the PCA refurbishment were planned and performed within a four-month period in order to assure uninterrupted operation of ISS payloads and minimal impacts to payload operations teams. To vacate the PCA, three additional HOSC control rooms were reconfigured to handle ISS real-time operations, Backup Control Center (BCC) to Mission Control in Houston, simulations, and testing functions. This involved coordination and cooperation from teams of ISS operations controllers, multiple engineering and design disciplines, management, and construction companies performing an array of activities simultaneously and in sync delivering a final product with no issues that impacted the schedule. For each console operator discipline, studies of Information Technology (IT) tools and equipment layouts, ergonomics, and lines of sight were performed. Infusing some of the latest IT into the project was an essential goal in ensuring future growth and success of the ISS payload science returns. Engineering evaluations led to a state of the art Video Wall implementation and more efficient ethernet cabling distribution providing the latest products and the best solution for the POIC. These engineering innovations led to cost savings for the project. Constraints involved in the management of the project included executing over 450 crew-hours of ISS real-time payload operations including a major onboard communications upgrade, SpaceX un-berth, a Soyuz launch, roll-out of ISS live video and interviews from the POIC, annual BCC certification and hurricane season, and ISS simulations and testing. Continuous ISS payload operations were possible during the PCA facility modifications with the reconfiguration of four control rooms and standup of two temporary control areas. Another major restriction to the project was an ongoing facility upgrade that included a NASA Headquarters mandated replacement of all electrical and mechanical systems and replacement of an external generator. These upgrades required a facility power outage during the PCA upgrades. The project also encompassed console layout designs and ordering, amenities selections and ordering, excessing of old equipment, moves, disposal of old IT equipment, camera installations, facility tour re-schedules, and contract justifications. These were just some of the tasks needed for a successful project. This paper describes the logistics and lessons learned in upgrading a control center capability in the middle of complex real-time operations. Combining the efficiencies of controller interaction and new technology infusion were prime drivers for this upgrade to handle the increased utilization of science research on ISS. The success of this project could not jeopardize the current operations while these facility upgrades occurred.

Dudley, Stephanie R. B.↗