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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 397 records · Page 22
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NASA NTRS1992

Lessons learned from an historical look at flight testing

A brief historical review of accidents was made to examine lessons learned in flight testing with major emphasis on human factors limitations. The results of this survey show undeniably that new aircraft and new pilots are not immune to old problems. Of three related human factors limitations, pilot skill frequently showed up as the primary factor responsible for accidents due to inadequate training (or proficiency) to handle an unexpected situation. A primary contributing factor was unsatisfactory aircraft handling qualities which increased pilot work load and therefore were less forgiving to 'pilot error,' particularly when flown in a stressful situation at the extremes of the flight envelope. Historically, pilot induced oscillation (PIO) has persisted as a major control problem particularly in first-flight operation. Deliberate errors involving a conscious decision to 'take a chance' by flying an aircraft with known deficiencies occurred more frequently in early times. Finally, inadvertent errors involving forgetfulness, indecision, and confusion are occurring more frequently with the current trend toward automated computerized controlled cockpits.

Seth B. Anderson↗
NASA NTRS2011

2010 CEOS Field Reflectance Intercomparisons Lessons Learned

This paper summarizes lessons learned from the 2009 and 2010 joint field campaigns to Tuz Golu, Turkey. Emphasis is placed on the 2010 campaign related to understanding the equipment and measurement protocols, processing schemes, and traceability to SI quantities. Participants in both 2009 and 2010 used an array of measurement approaches to determine surface reflectance. One lesson learned is that even with all of the differences in collection between groups, the differences in reflectance are currently dominated by instrumental artifacts including knowledge of the white reference. Processing methodology plays a limited role once the bi-directional reflectance of the white reference is used rather than a hemispheric-directional value. The lack of a basic set of measurement protocols, or best practices, limits a group s ability to ensure SI traceability and the development of proper error budgets. Finally, rigorous attention to sampling methodology and its impact on instrument behavior is needed. The results of the 2009 and 2010 joint campaigns clearly demonstrate both the need and utility of such campaigns and such comparisons must continue in the future to ensure a coherent set of data that can span multiple sensor types and multiple decades.

Thome, Kurtis↗
NASA NTRS2012

The Astro-H Soft X-Ray Mirror

The Astro-H is led by the Japanese Space Agency (JAXA) in collaboration with many other institutions including the NASA Goddard Space Flight Center. Goddard's contributions include two soft X-ray telescopes (SXTs). The telescopes have an effective area of 562 square cm at 1 keV and 425 square cm at 6 keV with an image quality requirement of 1.7 arc-minutes half power diameter (HPD). The engineering model has demonstrated 1.1 arc-minutes HPD error. The design of the SXT is based on the successful Suzaku mission mirrors with some enhancements to improve the image quality. Two major enhancements are bonding the X-ray mirror foils to alignment bars instead of allowing the mirrors to float, and fabricating alignment bars with grooves within 5 microns of accuracy. An engineering model SXT was recently built and subjected to several tests including vibration, thermal, and X-ray performance in a beamline. Several lessons were learned during this testing that will be incorporated in the flight design. Test results and optical performance are discussed, along with a description of the design of the SXT.

Robinson, David↗
NASA NTRS2012

Identification of Robust Terminal-Area Routes in Convective Weather

Convective weather is responsible for large delays and widespread disruptions in the U.S. National Airspace System, especially during summer. Traffic flow management algorithms require reliable forecasts of route blockage to schedule and route traffic. This paper demonstrates how raw convective weather forecasts, which provide deterministic predictions of the vertically integrated liquid (the precipitation content in a column of airspace) can be translated into probabilistic forecasts of whether or not a terminal area route will be blocked. Given a flight route through the terminal area, we apply techniques from machine learning to determine the likelihood that the route will be open in actual weather. The likelihood is then used to optimize terminalarea operations by dynamically moving arrival and departure routes to maximize the expected capacity of the terminal area. Experiments using real weather scenarios on stormy days show that our algorithms recommend that a terminal-area route be modified 30% of the time, opening up 13% more available routes that were forecast to be blocked during these scenarios. The error rate is low, with only 5% of cases corresponding to a modified route being blocked in reality, whereas the original route is in fact open. In addition, for routes predicted to be open with probability 0.95 or greater by our method, 96% of these routes (on average over time horizon) are indeed open in the weather that materializes

Pfeil, Diana Michalek↗
NASA NTRS2026

Incremental Learning for Passive Microwave Precipitation Retrievals using Advanced Technology Microwave Sounder

Spaceborne passive microwave (PMW) radiometry is central to global precipitation monitoring, yet retrieval uncertainties remain substantial, particularly for cross-track sounders whose variable footprints and channel configurations are optimized for atmospheric temperature and moisture profiling rather than precipitation. Consequently, existing operational products often exhibit angular-dependent biases, limited effective swath utilization, unrealistic rainfall probability distributions, and systematic misclassification of precipitation phase. These limitations are further compounded by the scarcity of globally accurate and representative precipitation observations, as training data from the Dual-frequency Precipitation Radar (DPR) and the Cloud Profiling Radar (CPR) are spatially sparse, lack uniform global coverage, and exhibit heterogeneous error characteristics across precipitation regimes. To address these challenges, this study presents a supervised retrieval algorithm that incrementally trains an ensemble of extreme gradient-boosted decision trees by augmenting base learners with pre-training on reanalysis data and post-training on coincident DPR and CPR observations matched with the Advanced Technology Microwave Sounder (ATMS). By transferring prior information from reanalysis to posterior constraints from radar observations and adopting a sequential detection–estimation strategy for precipitation phase and rate retrieval, the proposed approach yields retrievals across the full ATMS swath that are largely free from persistent deficiencies in current Global Precipitation Measurement (GPM) passive microwave operational products. In particular, the method resolves bimodal artifacts in rainfall retrievals and mitigates systematic high-latitude snowfall biases, including overestimation across the Arctic and underestimation across the Antarctic. Validation against independent Multi-Radar Multi-Sensor (MRMS) data over the Contiguous United States (CONUS) further demonstrates improved performance in precipitation phase detection and rate estimation relative to both reanalysis and current GPM PMW products.

Mahyar Garshasbi↗
NASA NTRS2003

Shuttle Payload Ground Command and Control: An Experiment Implementation Combustion Module-2 Software Development, STS-107

This presentation covers the design of a command and control architecture developed by the author for the Combustion Module-2 microgravity experiment, which flew aboard the STS-107 Shuttle mission, The design was implemented to satisfy a hybrid network that utilized TCP/IP for both the onboard segment and ground segment, with an intermediary unreliable transport for the space to ground segment. With the infusion of Internet networking technologies into Space Shuttle, Space Station, and spacecraft avionics systems, comes the need for robust methodologies for ground command and control. Considerations of high bit error links, and unreliable transport over intermittent links must be considered in such systems. Internet protocols applied to these systems, coupled with the appropriate application layer protections, can provide adequate communication architectures for command and control. However, there are inherent limitations and additional complexities added by the use of Internet protocols that must be considered during the design. This presentation will discuss the rationale for the: framework and protocol algorithms developed by the author. A summary of design considerations, implantation issues, and learned lessons will be will be presented. A summary of mission results using this communications architecture will be presented. Additionally, areas of further needed investigation will be identified.

Carek, David Andrew↗
NASA NTRS2012

Can Selforganizing Maps Accurately Predict Photometric Redshifts?

We present an unsupervised machine-learning approach that can be employed for estimating photometric redshifts. The proposed method is based on a vector quantization called the self-organizing-map (SOM) approach. A variety of photometrically derived input values were utilized from the Sloan Digital Sky Survey's main galaxy sample, luminous red galaxy, and quasar samples, along with the PHAT0 data set from the Photo-z Accuracy Testing project. Regression results obtained with this new approach were evaluated in terms of root-mean-square error (RMSE) to estimate the accuracy of the photometric redshift estimates. The results demonstrate competitive RMSE and outlier percentages when compared with several other popular approaches, such as artificial neural networks and Gaussian process regression. SOM RMSE results (using delta(z) = z(sub phot) - z(sub spec)) are 0.023 for the main galaxy sample, 0.027 for the luminous red galaxy sample, 0.418 for quasars, and 0.022 for PHAT0 synthetic data. The results demonstrate that there are nonunique solutions for estimating SOM RMSEs. Further research is needed in order to find more robust estimation techniques using SOMs, but the results herein are a positive indication of their capabilities when compared with other well-known methods

Way, Michael J.↗
NASA NTRS2004

Real-Time Adaptive Color Segmentation by Neural Networks

Artificial neural networks that would utilize the cascade error projection (CEP) algorithm have been proposed as means of autonomous, real-time, adaptive color segmentation of images that change with time. In the original intended application, such a neural network would be used to analyze digitized color video images of terrain on a remote planet as viewed from an uninhabited spacecraft approaching the planet. During descent toward the surface of the planet, information on the segmentation of the images into differently colored areas would be updated adaptively in real time to capture changes in contrast, brightness, and resolution, all in an effort to identify a safe and scientifically productive landing site and provide control feedback to steer the spacecraft toward that site. Potential terrestrial applications include monitoring images of crops to detect insect invasions and monitoring of buildings and other facilities to detect intruders. The CEP algorithm is reliable and is well suited to implementation in very-large-scale integrated (VLSI) circuitry. It was chosen over other neural-network learning algorithms because it is better suited to realtime learning: It provides a self-evolving neural-network structure, requires fewer iterations to converge and is more tolerant to low resolution (that is, fewer bits) in the quantization of neural-network synaptic weights. Consequently, a CEP neural network learns relatively quickly, and the circuitry needed to implement it is relatively simple. Like other neural networks, a CEP neural network includes an input layer, hidden units, and output units (see figure). As in other neural networks, a CEP network is presented with a succession of input training patterns, giving rise to a set of outputs that are compared with the desired outputs. Also as in other neural networks, the synaptic weights are updated iteratively in an effort to bring the outputs closer to target values. A distinctive feature of the CEP neural network and algorithm is that each update of synaptic weights takes place in conjunction with the addition of another hidden unit, which then remains in place as still other hidden units are added on subsequent iterations. For a given training pattern, the synaptic weight between (1) the inputs and the previously added hidden units and (2) the newly added hidden unit is updated by an amount proportional to the partial derivative of a quadratic error function with respect to the synaptic weight. The synaptic weight between the newly added hidden unit and each output unit is given by a more complex function that involves the errors between the outputs and their target values, the transfer functions (hyperbolic tangents) of the neural units, and the derivatives of the transfer functions.

Duong, Tuan A.↗
NASA NTRS2019

Lessons Learned in Fabrication of a High-Specific-Torque Concentric Magnetic Gear

Magnetic gearing is being investigated at NASA as a replacement to conventional mechanical gearing in aerospace applications. Some potential benefits of magnetic gears over mechanical gearing are torque transmission without mechanical contact, decreased transmission noise, and no required lubrication. However, in order to be a viable alternative for aerospace applications, magnetic gearing must be shown to provide high enough specific torque (torque per unit mass). NASA's second magnetic gearing prototype (PT-2) was able to achieve promising specific torque on par with low torque mechanical gearboxes. This work will briefly review the electromagnetic and structural design of PT-2, provide detailed information on fabrication and assembly, examine build errors, walk through rebuild efforts to improve operation, and conclude with remarks on build difficulties and opportunities for improvement in future prototypes.

Magnetic Gearing↗
NASA NTRS2007

Acoustics

The acoustics environment in space operations is important to maintain at manageable levels so that the crewperson can remain safe, functional, effective, and reasonably comfortable. High acoustic levels can produce temporary or permanent hearing loss, or cause other physiological symptoms such as auditory pain, headaches, discomfort, strain in the vocal cords, or fatigue. Noise is defined as undesirable sound. Excessive noise may result in psychological effects such as irritability, inability to concentrate, decrease in productivity, annoyance, errors in judgment, and distraction. A noisy environment can also result in the inability to sleep, or sleep well. Elevated noise levels can affect the ability to communicate, understand what is being said, hear what is going on in the environment, degrade crew performance and operations, and create habitability concerns. Superfluous noise emissions can also create the inability to hear alarms or other important auditory cues such as an equipment malfunctioning. Recent space flight experience, evaluations of the requirements in crew habitable areas, and lessons learned (Goodman 2003; Allen and Goodman 2003; Pilkinton 2003; Grosveld et al. 2003) show the importance of maintaining an acceptable acoustics environment. This is best accomplished by having a high-quality set of limits/requirements early in the program, the "designing in" of acoustics in the development of hardware and systems, and by monitoring, testing and verifying the levels to ensure that they are acceptable.

Goodman, Jerry R.↗
NASA NTRS2021

Mapping Global Forest Age from Forest Inventories, Biomass and Climate Data

Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. However, a lack of global diagnostics that reflect the forest stage and associated disturbance regimes hampers the quantification of age-related differences in forest carbon dynamics. This study provides a new global distribution of forest age circa 2010, estimated using a machine learning approach trained with more than 40 000 plots using forest inventory, biomass and climate data. First, an evaluation against the plot-level measurements of forest age reveals that the data-driven method has a relatively good predictive capacity of classifying old-growth vs. non-old-growth (precision = 0.81 and 0.99 for old-growth and non-old-growth, respectively) forests and estimating corresponding forest age estimates (NSE = 0.6 – Nash–Sutcliffe efficiency – and RMSE = 50 years – root-mean-square error). However, there are systematic biases of overestimation in young- and underestimation in old-forest stands, respectively. Globally, we find a large variability in forest age with the old-growth forests in the tropical regions of Amazon and Congo, young forests in China, and intermediate stands in Europe. Furthermore, we find that the regions with high rates of deforestation or forest degradation (e.g. the arc of deforestation in the Amazon) are composed mainly of younger stands. Assessment of forest age in the climate space shows that the old forests are either in cold and dry regions or warm and wet regions, while young–intermediate forests span a large climatic gradient. Finally, comparing the presented forest age estimates with a series of regional products reveals differences rooted in different approaches and different in situ observations and global-scale products. Despite showing robustness in cross-validation results, additional methodological insights on further developments should as much as possible harmonize data across the different approaches. The forest age dataset presented here provides additional insights into the global distribution of forest age to better understand the global dynamics in the forest water and carbon cycles. The forest age datasets are openly available at https://doi.org/10.17871/ForestAgeBGI.2021 (Besnard et al., 2021).

Simon Besnard↗
NASA NTRS1994

Software interface verifier

A Telos study of 40 recent subsystem deliveries into the DSN at JPL found software interface testing to be the single most expensive and error-prone activity, and the study team suggested creating an automated software interface test tool. The resulting Software Interface Verifier (SIV), which was funded by NASA/JPL and created by Telos, employed 92 percent software reuse to quickly create an initial version which incorporated early user feedback. SIV is now successfully used by developers for interface prototyping and unit testing, by test engineers for formal testing, and by end users for non-intrusive data flow tests in the operational environment. Metrics, including cost, are included. Lessons learned include the need for early user training. SIV is ported to many platforms and can be successfully used or tailored by other NASA groups.

Soderstrom, Tomas J.↗
NASA NTRS1994

Structure of merger remnants. 4: Isophotal shapes

This paper examines the shapes of isophotes of galaxy merger remnants. More specifically, we perform a series of numerical experiments to study galaxy mergers. The simulations explore a variety of encounter geometries, types of progenitor galaxies, and particle numbers. We 'observe' each of the remnants from 64 viewpoints to estimate how the isophotal shapes vary with the orientation of the remnant. Also, by comparing the results from the various simulations, we learn how encounter geometry and the structure of the progenitors can affect the shape of the remnants. The encounter geometry, the structure of the progenitors, and the orientation of the remnant play deciding roles in the shape of the 'observed' isophotes, so much so that these simulated merger remnants have both 'boxy' and 'disky' isophotes and ellipticities ranging from E0 to E7. Thus, the shapes seen in our merger simulation remnants span a similar range to observed isophotal shapes of real galaxies. Furthermore, to estimate the errors in this analysis, we introduce the statistical bootstrap. Bootstrapping is used rather widely in observational astronomy when dealing with small samples; however, its use is practically unheard of for tackling theoretical problems. We believe that bootstrapping can be extremely useful for dealing with the small samples found in numerical simulations; therefore, we present the rudiments and basis of the technique with emphasis on its use in N-body calculations. Also, by comparing the errors for the smaller simulations with those of the larger ones, we speculate on the number of particles required to accurately explore isophotal shapes in simulations.

Heyl, Jeremy S.↗
NASA NTRS2018

Preparing to Assure Mission Success

Space flight missions are inherently risky. The mission operations assurance discipline grew out of strategies to control error and evolved into what today is an independent technical authority dedicated to achieving mission success. After the Mars Climate Orbiter loss, NASA made a set of recommendations for missions. Two of those recommendations led to the creation of today’s Mission Operations Assurance Manager role: (1) require an independent Mission Assurance representative during the operational phase of every flight project, and (2) require all flight projects to report and track post-launch anomalies. Since then, the MOAM role has been continually refined by lessons learned from its practice on more than 20 concurrent missions. The MOAM role requires significant operations experience as well as an additional set of skills. In the interests of developing a cadre of potential MOAMs, the MOAM training program was developed. It is a 14-unit set of presentations/discussions covering the 11 categories of MOAM tasks. It is offered to new MOAMs and interested systems engineers with an eye to preparing both types of personnel for present and future assignments, as well as building relationships among personnel of different organizations. MOAM training sessions include presentations on the various tools and processes employed by MOAMs, interspersed with mindset lessons illustrated via “war story” discussions of previous missions.

Bryant, Larry↗
NASA NTRS2010

Space Flight Resource Management for ISS Operations

Although the astronaut training flow for the International Space Station (ISS) spans 2 years, each astronaut or cosmonaut often spends most of their training alone. Rarely is it operationally feasible for all six ISS crewmembers to train together, even more unlikely that crewmembers can practice living together before launch. Likewise, ISS Flight Controller training spans 18 months of learning to manage incredibly complex systems remotely in plug-and-play ground teams that have little to no exposure to crewmembers before a mission. How then do all of these people quickly become a team - a team that must respond flexibly yet decisively to a variety of situations? The answer implemented at NASA is Space Flight Resource Management (SFRM), the so-called "soft skills" or team performance skills. Based on Crew Resource Management, SFRM was developed first for shuttle astronauts and focused on managing human errors during time-critical events (Rogers, et al. 2002). Given the nature of life on ISS, the scope of SFRM for ISS broadened to include teamwork during prolonged and routine operations (O'Keefe, 2008). The ISS SFRM model resembles a star with one competency for each point: Communication, Cross-Culture, Teamwork, Decision Making, Team Care, Leadership/Followership, Conflict Management, and Situation Awareness. These eight competencies were developed with international participation by the Human Behavior and Performance Training Working Group. Over the last two years, these competencies have been used to build a multi-modal SFRM training flow for astronaut candidates and flight controllers that integrates team performance skills into the practice of technical skills. Preliminary results show trainee skill increases as the flow progresses; and participants find the training invaluable to performing well and staying healthy during ISS operations. Future development of SFRM training will aim to help support indirect handovers as ISS operations evolve further with the retirement of the Space Shuttle Program.

Schmidt, Lacey L.↗
NASA NTRS2021

Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States

Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we present a model using a 3-D convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud, combined with coincident satellite data (spectral, phenology, and disturbance history). We compared this approach to traditional modeling used for making forest predictions from LiDAR data (height metrics and random forest) and found that the CNN had consistently lower uncertainty. We then applied the CNN to public data over six New England states in the USA, generating maps of 14 forest attributes at a 10 m resolution over 85% of the region. Aboveground biomass estimates produced a root mean square error of 36 Mg ha−1 (44%) and were within the 97.5% confidence of independent county-level estimates for 33 of 38 or 86.8% of the counties examined. CNN predictions for stem density and percentage of conifer attributes were moderately successful, while predictions for detailed species groupings were less successful. The approach shows promise for improving the prediction of forest attributes from regional LiDAR data and for combining disparate LiDAR datasets into a common framework for large-scale estimation.

Elias Ayrey↗
NASA NTRS2004

Accessing FMS Functionality: The Impact of Design on Learning

In modern commercial and military aircraft, the Flight Management System (FMS) lies at the heart of the functionality of the airplane. The nature of the FMS has also caused great difficulties learning and accessing this functionality. This study examines actual Air Force pilots who were qualified on the newly introduced advanced FMS and shows that the design of the system itself is a primary source of difficulty learning the system. Twenty representative tasks were selected which the pilots could be expected to accomplish on an ' actual flight. These tasks were analyzed using the RAFIV stage model (Sherry, Polson, et al. 2002). This analysis demonstrates that a great burden is placed on remembering complex reformulation of the task to function mapping. 65% of the tasks required retaining one access steps in memory to accomplish the task, 20% required two memorized access steps, and 15% required zero memorized access steps. The probability that a participant would make an access error on the tasks was: two memorized access steps - 74%, one memorized access step - 13%, and zero memorized access steps - 6%. Other factors were analyzed as well, including experience with the system and frequency of use. This completed the picture of a system with many memorized steps causing difficulty with the new system, especially when trying to fine where to access the correct function.

Fennell, Karl↗
NASA NTRS2019

Human Performance Contributions to Safety in Commercial Aviation

Every day in aviation, pilots, air traffic controllers, and other front-line personnel perform countless correct judgments and actions in a variety of operational environments. These judgments and actions are often the difference between an accident and a non-event. Ironically, data on these behaviors are rarely collected or analyzed. Data-driven decisions about safety management and design of safety-critical systems are limited by the available data, which influence how decision makers characterize problems and identify solutions. Large volumes of data are collected on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that result in routine successful outcomes, safety management and system design decisions are based on a small sample of nonrepresentative safety data. This assessment aimed to find and document “safety successes” made possible by human operators. With many Aeronautics Research Mission Directorate (ARMD) Programs and Projects focusing on increased automation and autonomy and decreased human involvement, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant risk — a risk that has not been recognized to date. Without understanding how humans contribute to safety, any estimate of predicted safety of autonomous capabilities is incomplete and inherently suspect. Furthermore, understanding the ways in which humans contribute to safety can promote strategic interactions among safety technologies, functions, procedures and the people using them. Without this understanding, the full benefits of an integrated, optimized human/technology or autonomous system will not be realized. Historically, safety has been consistently defined in terms of the occurrence of accidents or recognized risks (i.e., in terms of things that go wrong). These adverse outcomes are explained by identifying their causes, and safety is restored by eliminating or mitigating these causes. An alternative to this approach is to focus on what goes right and identify how to replicate that process. Focusing on the rare cases of failures attributed to “human error” provides little information about why human performance routinely prevents adverse events. Hollnagel has proposed that things go right because people continuously adjust their work to match their operating conditions. These adjustments become increasingly important as systems continue to grow in complexity. Thus, the definition of safety should reflect not only “avoiding things that go wrong” but “ensuring that things go right.” The basis for safety management requires developing an understanding of everyday activities. However, few mechanisms to monitor everyday work exist in the aviation domain, which limits opportunities to learn how designs function in reality. This concept of safety thinking and safety management is reflected in the emerging field of resilience engineering. According to Hollnagel, a system is resilient if it can sustain required operations under expected and unexpected conditions by adjusting its functioning prior to, during, or following changes, disturbances, and opportunities. To explore “positive” behaviors that contribute to resilient performance in commercial aviation, the assessment team examined a range of existing sources of data about pilot and air traffic control (ATC) tower controller performance, including subjective interviews with domain experts and objective aircraft flight data records. These data were used to identify strategies that support resilient performance, methods for exploring and refining those strategies in existing data, and proposed methods for capturing and analyzing new data.

Null, Cynthia H.↗