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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Lessons Learned During Instrument Testing for the Thermal Infrared Sensor (TIRS)

The Themal InfraRed Sensor (TIRS) instrument, set to launch on the Landsat Data Continuity Mission in 2013, features a passively cooled telescope and IR detectors which are actively cooled by a two stage cryocooler. In order to proceed to the instrument level test campaign, at least one full functional test was required, necessitating a thermal vacuum test to sufficiently cool the detectors and demonstrate performance. This was fairly unique in that this test occurred before the Pre Environmental Review, but yielded significant knowledge gains before the planned instrument level test. During the pre-PER test, numerous discrepancies were found between the model and the actual hardware, which were revealed by poor correlation between model predictions and test data. With the inclusion of pseudo-balance points, the test also provided an opportunity to perform a pre-correlation to test data prior to the instrument level test campaign. Various lessons were learned during this test related to modeling and design of both the flight hardware and the Ground Support Equipment and test setup. The lessons learned in the pre-PER test resulted in a better test setup for the nstrument level test and the completion of the final instrument model correlation in a shorter period of time. Upon completion of the correlation, the flight predictions were generated including the full suite of off-nominal cases, including some new cases defined by the spacecraft. For some of these ·new cases, some components now revealed limit exceedances, in particular for a portion of the hardware that could not be tested due to its size and chamber limitations.. Further lessons were learned during the completion of flight predictions. With a correlated detalled instrument model, significant efforts were made to generate a reduced model suitable for observatory level analyses. This proved a major effort both to generate an appropriate network as well as to convert to the final model to the required format and yielded additional lessons learned. In spite of all the challenges encountered by TIRS, the instrument was successfully delivered to the spacecraft and will soon be tested at observatory level in preparation for a successful mission launch.

Peabody, Hume L.↗
NASA NTRSDate not supplied

Graph Representation Learning for Dengue Forecasting

In 2017, the largest recorded dengue outbreak in Sri Lanka’s history occurred. Since then, dengue has continued to threaten national health across Sri Lanka. The development of an effective Early Warning System (EWS) for dengue outbreaks is essential for Sri Lanka’s Ministry of Health to take preventative measures. We propose the use of Graph Neural Networks as EWS. Using earth observational data from NASAs global satellites and dengue incidence data from Sri Lanka s Ministry of Health, we developed a series of traditional and graph representation EWS to forecast Dengue cases across Sri Lanka’s 25 districts between 2013 and 2022. We demonstrate empirically that Graph Neural Networks which incorporate spatiotemporal relations significantly outperform traditional EWS such as Autoregressive Integrated Moving Average (ARIMA), Random Forest, and Long Short-Term Memory (LSTM). Our source code is available on GitHub and will be provided in the final submission.

Graph Neural Networks↗
NASA NTRS2024

EdgeCortix SAKURA-I Machine-Learning, PCIe Accelerator SEE Heavy Ion Test Report

To enable autonomy in space, machine-learning and computer vision applications become invaluable for sensor processing. However, these algorithms are computationally complex and unfeasible for many embedded central processing units (CPUs) and usually require external coprocessors, such as graphics processing units (GPUs) or accelerators specific to the application, including application specific integrated circuits (ASICs). In power-constrained systems, GPUs tend to consume more power than is acceptable (>40W), so lower-power accelerators have shown promise to provide the performance needed under spacecraft constraints. For radiation engineers, developing methodologies that can properly test CPUs, GPUs, and accelerators, and enable comparisons between them remains a necessary complication to solve as the devices become more complex. The methodology in this test aims to be a start in developing a baseline single-event effect (SEE) test for client-device machine learning accelerators. This category of devices do not host their own operating system. This testing campaign is a continuation of a previous 200 MeV proton test performed in January 2024. This report covers two heavy ion tests of the SAKURA-I card: one in April 2024, and one in June 2024. Additional data was needed after the April test due to ion-range issues experienced at higher linear-energy transfers (LETs). These range issues are described in more detail in Section 8. This experiment characterizes SEEs and data error susceptibility of the EdgeCortix SAKURA-I machine-learning accelerator under heavy ions. The device was monitored for single event upsets (SEUs) and single event functional interrupts (SEFIs) at the Lawrence Berkeley National Laboratory’s 88-inch cyclotron. The SAKURA-I board accelerates machine-learning inference applications on a host computer through a PCIex16 connection. For the purposes of devising an end to end automated analysis workflow for this experiment, the YOLO-V5 and SSD300 objection-detection models, and the ResNet-50, EfficientNet, and MobileNetV2 image classification models were used as a representative suite of analytical machine-learning models.

Seth S Roffe↗
NASA NTRS1996

Learning from Inconsistency

This position paper argues that inconsistencies that occur during the development of a software specification offer an excellent way of learning more about the development process. We base this argument on our work on inconsistency management. Much attention has been devoted recently to the need to allow inconsistencies to occur during software development, to facilitate flexible development strategies, especially for collaborative work. Recent work has concentrated on reasoning in the presence of inconsistency, tracing inconsistencies with 'pollution markers' and supporting resolution. We argue here that one of the most important aspects of inconsistency is the learning opportunity it provides. We are therefore concerned with how to capture this learning outcome so that its significance is not lost. We present a small example of how apprentice software engineers learn from their mistakes, and outline how an inconsistency management tool could support this learning. We then argue that the approach can be used more generally as part of continuous process improvement.

Easterbrook, Steve↗
NASA NTRS1999

SONEX: NASA SASS Ozone and Nitrogen Oxide Experiment

This final report follows closely on our 1998 Annual Progress Report which was forwarded to NASA on November 19, 1998. Rather than reiterate the material therein we note here the continuation of the various items covered. SONEX has proN,Ided a number of opportunities to learn more about the fine-scale structure of the atmosphere. Coupled with the MOZAIC work on layers we can synthesize a new 3-D view of the fine scales that influence atmospheric chemistry. As a side issue we are also relating the features to clear air turbulence which would be, we think, a useful connection for commercial aviation.

Newell, Reginald E.↗
NASA NTRS2004

Cellular Mechanisms Underlying Bone-Forming Cell Proliferative Response to Hypergravity

Life on Earth has evolved under the continuous influence of gravity (1-g). As humans explore and develop space, however, we must learn to adapt to an environment with little or no gravity. Studies indicate that lack of weightbearing for vertebrates occurring with immobilization, paralysis, or in a microgravity environment may cause muscle and bone atrophy through cellular and subcellular level mechanisms. We hypothesize that gravity is needed for the efficient transduction of cell growth and survival signals from the extra-cellular matrix (ECM) (consisting of molecules such as collagen, fibronectin, and laminin) in mechanosensitive tissues. We test for the presence of gravity-sensitive pathways in bone-forming cells (osteoblasts) using hypergravity applied by a cell culture centrifuge. Stimulation of 50 times gravity (50-g) increased proliferation in primary rat osteoblasts for cells grown on collagen Type I and fibronectin, but not on laminin or uncoated surfaces. Survival was also enhanced during hypergravity stimulation by the presence of ECM. Bromodeoxyuridine incorporation in proliferating cells showed an increase in the number of actively dividing cells from about 60% at 1-g to over 90% at 25-g. Reverse transcription-polymerase chain reaction was used to test for all possible integrins. Our combined results indicate that beta1 and/or beta3 integrin subunits may be involved. These data indicate that gravity mechanostimulation of osteoblast proliferation involves specific matrix-integrin signalling pathways which are sensitive to g-level. Further research to define the mechanisms involved will provide direction so that we may better adapt and counteract bone atrophy caused by the lack of weightbearing.

Vercoutere, W.↗
NASA NTRS2021

Role of Cyber-Physical Testing in Developing Resilient Extraterrestrial Habitats

Extraterrestrial long-term habitat systems (henceforth referred to as habitat systems) require groundbreaking technological advances to overcome the extreme demands introduced by isolation and challenging environments. A habitat system must operate as intended under continuous disruptive conditions. Designing for the demands that challenging environments will place on habitat systems (e.g., wild temperature fluctuations, galactic cosmic rays, destructive dust, meteoroid impacts, vibrations, and solar particle events) represents one of the greatest challenges in this endeavor. This engineering problem necessitates that we design and manage habitat systems to be resilient. System resilience requires a comprehensive approach that accounts for disruptions through the design process and adapts to them in operation. As the habitat system evolves—growing in physical size, complexity, population, and connectivity—and diversifies in operations, it must continue to be safe and resilient. In this endeavor, we should take advantage of lessons learned in developing civil infrastructure responsive to catastrophic natural hazards, autonomous robotics platforms, smart buildings, cyber-physical testing, complex systems, and diagnostics and prognostics for intelligent health management. This study highlights the importance of system resilience and cyber-physical testing to address the grand challenge of developing habitat systems.

Public health and safety↗
NASA NTRS2024

System for Photogrammetric Imaging, Detection, and Ranging (SPIDR)

The System for Photogrammetric Imaging, Detection, and Ranging (SPIDR) project aims to advance the state-of-the-art (SOA) technology in camera tracking. SPIDR will advance this technology in the following ways: 1) perform autonomous tracking of rocket launches, 2) allow modularity for camera payloads to enable various types of imagery capture (standard video, high-speed, infrared, etc.), 3) enable additional imagery assets for increased scope of imagery analysis, and 4) serve as a viable tracking replacement for the existing Kineto Tracking Mount (KTM) system used by the Exploration Ground Systems (EGS) program. The project initially aimed to build an in-house mechanical design and control system, but realigned to a commercial off-the-shelf (COTS) mechanical design. During the FY23 CIF timeline, the SPIDR team made significant advances in building a machine-learning (ML) based object detection model for various rockets and their plumes. The team will continue advancing the model, and begin work on the pan and tilt unit (PTU) control system and tracking algorithm development.

Alden Param↗
NASA NTRSDate not supplied

Prediction of High-Latitude Ionospheric Electrodynamics Using the Machine Learning Based Auroral Ionospheric Electrodynamics Model

We introduce a new framework for Machine-Learning (ML) based Auroral Ionosphere Model (ML-AIM). ML-AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents (FACs) of Kunduri et al., 2020 (https://doi.org/10.1029/2020JA027908), the FAC-derived aurora conductance model of Robinson et al., 2020 (https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen & Brekke (1993). The ML-AIM inputs are 60min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pederson/Hall currents, and Joule Heating. We conduct two ML-AIM simulations for a weak geomagnetic activity on 14 May 2013 and a geomagnetic storm on 7-8 September 2017. ML-AIM produces reasonable ionospheric potential patterns such as two cell convection patterns and the enhancement of electric potentials during active times. The cross polar cap potential drop from ML-AIM is also comparable to the ones from the Weimer 2005 model, Super Dual Auroral Radar Network (SuperDARN), and Defense Meteorological Satellite Program (DMSP) F17 observations. ML-AIM is unique in a sense that it predicts ionospheric responses to the time-varying solar wind and geomagnetic conditions, while other traditional empirical model like Weimer 2005 is designed to provide static ionospheric conditions under steady solar wind/IMF conditions. In future, ML-AIM will include ML-based models of aurora precipitation and ionospheric conductance, improving its performance during active times.

H. K. Connor↗
NASA NTRS2011

ISS Regenerative Life Support: Challenges and Success in the Quest for Long-Term Habitability in Space

This presentation will discuss the International Space Station s (ISS) Regenerative Environmental Control and Life Support System (ECLSS) operations with discussion of the on-orbit lessons learned, specifically regarding the challenges that have been faced as the system has expanded with a growing ISS crew. Over the 10 year history of the ISS, there have been numerous challenges, failures, and triumphs in the quest to keep the crew alive and comfortable. Successful operation of the ECLSS not only requires maintenance of the hardware, but also management of the station resources in case of hardware failure or missed re-supply. This involves effective communication between the primary International Partners (NASA and Roskosmos) and the secondary partners (JAXA and ESA) in order to keep a reserve of the contingency consumables and allow for re-supply of failed hardware. The ISS ECLSS utilizes consumables storage for contingency usage as well as longer-term regenerative systems, which allow for conservation of the expensive resources brought up by re-supply vehicles. This long-term hardware, and the interactions with software, was a challenge for Systems Engineers when they were designed and require multiple operational workarounds in order to function continuously. On a day-to-day basis, the ECLSS provides big challenges to the on console controllers. Main challenges involve the utilization of the resources that have been brought up by the visiting vehicles prior to undocking, balance of contributions between the International Partners for both systems and resources, and maintaining balance between the many interdependent systems, which includes providing the resources they need when they need it. The current biggest challenge for ECLSS is the Regenerative ECLSS system, which continuously recycles urine and condensate water into drinking water and oxygen. These systems were brought to full functionality on STS-126 (ULF-2) mission. Through system failures and recovery, the ECLSS console has learned how to balance the water within the systems, store and use water for contingencies, and continue to work with the International Partners for short-term failures. Through these challenges and the system failures, the most important lesson learned has been the importance of redundancy and operational workarounds. It is only because of the flexibility of the hardware and the software that flight controllers have the opportunity to continue operating the system as a whole for mission success.

Bazley, Jesse A.↗
NASA NTRSDate not supplied

Multiclass Flight Anomaly Detection Using Sensor Fusion Based on Dempster-Shafer Theory

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST to reduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Risk detection↗
NASA NTRSDate not supplied

Multiclass Flight Anomaly Detection Using Sensor Fusion Based on Dempster-Shafer Theory

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST toreduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Risk detection↗
NASA NTRSDate not supplied

NASA’s Continued Partnerships with High School Students during a Global Pandemic

NASA HUNCH’s mission is to empower students through Project-Based Learning where they learn skills and can launch their careers through participation in the design and fabrication of real-world valued products. Our goal is to mentor the next generation of students to solve NASA’s greatest challenges even during a global pandemic

HUNCH↗
NASA NTRS2024

Calculating the High-Latitude Ionospheric Electrodynamics Using A Machine Learning-Based Field-Aligned Current Model

We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric electrodynamics Model (ML-AIM). ML-AIM solves a current continuity equation by utilizing the ML model of Field Aligned Currents of Kunduri et al. (2020, https://doi.org/10.1029/2020JA027908), the FAC-derived auroral conductance model of Robinson et al. (2020, https://doi.org/10.1029/2020JA028008), and the solar irradiance conductance model of Moen and Brekke (1993, https://doi.org/10.1029/92gl02109). The ML-AIM inputs are 60-min time histories of solar wind plasma, interplanetary magnetic fields (IMF), and geomagnetic indices, and its outputs are ionospheric electric potential, electric fields, Pedersen/Hall currents, and Joule Heating. We conduct two ML-AIM simulations for a weak geomagnetic activity interval on 14 May 2013 and a geomagnetic storm on 7–8 September 2017. ML-AIM produces physically accurate ionospheric potential patterns such as the two-cell convection pattern and the enhancement of electric potentials during active times. The cross polar cap potentials (ΦPC) from ML-AIM, the Weimer (2005, https://doi.org/10.1029/2004ja010884) model, and the Super Dual Auroral Radar Network (SuperDARN) data-assimilated potentials, are compared to the ones from 3204 polar crossings of the Defense Meteorological Satellite Program F17 satellite, showing better performance of ML-AIM than others. ML-AIM is unique and innovative because it predicts ionospheric responses to the time-varying solar wind and geomagnetic conditions, while the other traditional empirical models like Weimer (2005, https://doi.org/10.1029/2004ja010884) designed to provide a quasi-static ionospheric condition under quasi-steady solar wind/IMF conditions. Plans are underway to improve ML-AIM performance by including a fully ML network of models of aurora precipitation and ionospheric conductance, targeting its characterization of geomagnetically active times.

auroral electrodynamics↗
NASA NTRS2014

Fifteen Years of Chandra Operation: Scientific Highlights and Lessons Learned

NASA's Chandra X-Ray Observatory, designed for three years of operation with a goal of five years is now entering its 15-th year of operation. Thanks to its superb angular resolution, the Observatory continues to yield new and exciting results, many of which were totally unanticipated prior to launch. We discuss the current technical status, review recent scientific highlights, indicate a few future directions, and present what we feel is the most important lesson learned from our experience of building and operating this great observatory.

Weisskopf, Martin C.↗
NASA NTRS1994

Neural network applications in telecommunications

Neural network capabilities include automatic and organized handling of complex information, quick adaptation to continuously changing environments, nonlinear modeling, and parallel implementation. This viewgraph presentation presents Bellcore work on applications, learning chip computational function, learning system block diagram, neural network equalization, broadband access control, calling-card fraud detection, software reliability prediction, and conclusions.

Alspector, Joshua↗
NASA NTRS2002

Research on Intelligent Synthesis Environments

Four research activities related to Intelligent Synthesis Environment (ISE) have been performed under this grant. The four activities are: 1) non-deterministic approaches that incorporate technologies such as intelligent software agents, visual simulations and other ISE technologies; 2) virtual labs that leverage modeling, simulation and information technologies to create an immersive, highly interactive virtual environment tailored to the needs of researchers and learners; 3) advanced learning modules that incorporate advanced instructional, user interface and intelligent agent technologies; and 4) assessment and continuous improvement of engineering team effectiveness in distributed collaborative environments.

Noor, Ahmed K.↗
NASA NTRS2004

Space Exploration Technologies Developed through Existing and New Research Partnerships Initiatives

The Space Partnership Development Program of NASA has been highly successful in leveraging commercial research investments to the strategic mission and applied research goals of the Agency through industry academic partnerships. This program is currently undergoing an outward-looking transformation towards Agency wide research and discovery goals that leverage partnership contributions to the strategic research needed to demonstrate enabling space exploration technologies encompassing both robotic spacecraft missions and human space flight. New Space Partnership Initiatives with incremental goals and milestones will allow a continuing series of accomplishments to be achieved throughout the duration of each initiative, permit the "lessons learned" and capabilities acquired from previous implementation steps to be incorporated into subsequent phases of the initiatives, and allow adjustments to be made to the implementation of the initiatives as new opportunities or challenges arise. An Agency technological risk reduction roadmap for any required technologies not currently available will identify the initiative focus areas for the development, demonstration and utilization of space resources supporting the production of power, air, and water, structures and shielding materials. This paper examines the successes to date, lessons learned, and programmatic outlook of enabling sustainable exploration and discovery through governmental, industrial, academic, and international partnerships. Previous government and industry technology development programs have demonstrated that a focused research program that appropriately shares the developmental risk can rapidly mature low Technology Readiness Level (TRL) technologies to the demonstration level. This cost effective and timely, reduced time to discovery, partnership approach to the development of needed technological capabilities addresses the dual use requirements by the investing partners. In addition, these partnerships help to ensure the attainment of complimenting human and robotic exploration goals for NASA while providing additional capabilities for sustainable scientific research benefiting life and security on Earth.

Nall, Mark↗