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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Modeling Weather Impact on Ground Delay Programs

Scheduled arriving aircraft demand may exceed airport arrival capacity when there is abnormal weather at an airport. In such situations, Federal Aviation Administration (FAA) institutes ground-delay programs (GDP) to delay flights before they depart from their originating airports. Efficient GDP planning depends on the accuracy of prediction of airport capacity and demand in the presence of uncertainties in weather forecast. This paper presents a study of the impact of dynamic airport surface weather on GDPs. Using the National Traffic Management Log, effect of weather conditions on the characteristics of GDP events at selected busy airports is investigated. Two machine learning methods are used to generate models that map the airport operational conditions and weather information to issued GDP parameters and results of validation tests are described.

Wang, Yao↗
NASA NTRS2012

Maturing Pump Technology for EVA Applications in a Collaborative Environment

The transition from low earth orbit Extravehicular Activity (EVA) for construction and maintenance activities to planetary surface EVA on asteroids, moons, and, ultimately, Mars demands a new spacesuit system. NASA's development of that system has resulted in dramatically different pumping requirements from those in the current spacesuit system. Hamilton Sundstrand, Cascon, and NASA are collaborating to develop and mature a pump that will reliably meet those new requirements in space environments and within the design constraints imposed by spacesuit system integration. That collaboration, which began in the NASA purchase of a pump prototype for test evaluation, is now entering a new phase of development. A second generation pump reflecting the lessons learned in NASA's testing of the original prototype will be developed under Hamilton Sundstrand internal research funding and ultimately tested in an integrated Advanced Portable Life Support System (APLSS) in NASA laboratories at the Johnson Space Center. This partnership is providing benefit to both industry and NASA by supplying a custom component for EVA integrated testing at no cost to the government while providing test data for industry that would otherwise be difficult or impossible to duplicate in industry laboratories. This paper discusses the evolving collaborative process, component requirements and design development based on early NASA test experience, component stand alone test results, and near term plans for integrated testing at JSCs.

Hodgson, Edward↗
NASA NTRS2014

Analysis and Prediction of Weather Impacted Ground Stop Operations

When the air traffic demand is expected to exceed the available airport's capacity for a short period of time, Ground Stop (GS) operations are implemented by Federal Aviation Administration (FAA) Traffic Flow Management (TFM). The GS requires departing aircraft meeting specific criteria to remain on the ground to achieve reduced demands at the constrained destination airport until the end of the GS. This paper provides a high-level overview of the statistical distributions as well as causal factors for the GSs at the major airports in the United States. The GS's character, the weather impact on GSs, GS variations with delays, and the interaction between GSs and Ground Delay Programs (GDPs) at Newark Liberty International Airport (EWR) are investigated. The machine learning methods are used to generate classification models that map the historical airport weather forecast, schedule traffic, and other airport conditions to implemented GS/GDP operations and the models are evaluated using the cross-validations. This modeling approach produced promising results as it yielded an 85% overall classification accuracy to distinguish the implemented GS days from the normal days without GS and GDP operations and a 71% accuracy to differentiate the GS and GDP implemented days from the GDP only days.

Analysis↗
NASA NTRS2018

Artificial Intelligence - The Future of Space Communications

Presentation focus on the Cognitive Communications Project Overview. The project aims to develop cognitive communications technologies to increase mission science return and improve resource efficiencies. Another key aspect of the project is understanding the fundamental aspects of cognitive technology and developing artificial intelligence to advance the future of space communications. This involves the use of machine learning algorithms in the next generation architecture for space communications in the effort to increase efficiency, autonomy and increased performance of the space communication and navigations next generation architecture.

Briones, Janette C.↗
NASA NTRS2018

NASA's Satellite Laser Ranging Systems for the 21st Century

For over 40 years, NASA’s global network of satellite laser ranging (SLR) stations has provided a significant percentage of the global orbital data used to define the International Terrestrial Reference Frame (ITRF). The current NASA legacy network is reaching its end-of-life and a new generation of systems must be ready to take its place. Scientific demands of sub-millimeter precision ranging and the ever-increasing number of tracking targets give aggressive performance requirements to this new generation of systems. Using lessons learned from the legacy systems and the successful development of a prototype station, a new network of SLR stations, called the Space Geodesy Satellite Laser Ranging (SGSLR) systems, is being developed. These will be the state-of-the-art SLR component of NASA’s Space Geodesy Project (SGP). Each of SGSLR’s nine subsystems has been designed to produce a robust, kilohertz laser ranging system with 24/7 operational capability and with minimal human intervention. SGSLR’s data must support the aggressive goals of the Global Geodetic Observing System (GGOS), which are 1 millimeter (mm) position accuracy and 0.1 mm per year stability of the ITRF. This paper will describe the major requirements and accompanying design of the new SGSLR systems, how the systems will be tested, and the expected system performance.

J. F. Mcgarry↗
NASA NTRS2017

Reducing V&V Cost of Flight Critical Systems: Myth or Reality?

This paper presents an overview of NASA research program on the V&V of flight critical systems. Five years ago, NASA started an effort to reduce the cost and possibly increase the effectiveness of V&V for flight critical systems. It is the right time to take a look back and realize what progress has been made. This paper describes our overall approach and the tools introduced to address different phases of the software lifecycle. For example, we have improved testing by developing a statistical learning approach tor defining test cases. The tool automatically identifies possible unsafe conditions by analyzing outliers in output data; using an iterative learning process, it can then generate more test cases that represent potentially unsafe regions of operation. At the code level, we have developed and made available as open source a static analyzer for C and C++ programs called IKOS. We have shown that IKOS is very precise in the analysis of embedded C programs (very few false positives) and a bit less for regular C and C++ code. At the design level, in collaboration with our NRA partners, we have developed a suite of analysis tools for Simulink models. The analysis is done in a compositional framework for scalability.

Brat, Guillaume P.↗
NASA NTRS2020

Overview of MultiLayer Metal Insulation Development for Small Stirling Convertors at NASA Glenn Research Center

A small Stirling convertor is currently under development at the NASA Glenn Research Center to produce one watt of electrical power from eight watts of heat. Previous radioisotope power systems made use of the General-Purpose Heat Source (GPHS) which produces 250 watts of heat but is unsuitable for a one-watt Stirling convertor. The only other qualified heat source available is the Light-Weight Radioisotope Heating Unit (LWRHU), which produces one watt of heat and is primarily used to provide heat to electronics and instrumentation to maintain their appropriate operating temperature. Unfortunately, the LWRHU has a heat flux of 272 W/meters squared compared to the GPHS heat flux of 6000 W/m2 which greatly increases the demands on the insulation to ensure that enough of the heat produced is available to the convertor and not lost to the environment. An analysis was performed that showed that the insulation must have an effective thermal conductivity of 0.005 W/m·K or better for the system to function. A multi-layer metal insulation package was designed and a prototype was fabricated and tested to investigate the feasibility of this design. While the prototype did not meet the requirements perfectly, the lessons learned are being used to generate an improved thermal model using the test data so that a second iteration can developed that will meet the performance requirements with a much higher confidence.

Daniel D Goodell↗
NASA NTRSDate not supplied

Improved Breathing Cartridge (IBC)

The Fire Cartridge (FC) is designed to work with the Emergency Mask (EM) to protect the crew by cleaning the inhaled air in a post fire environment. The Improved Breathing Cartridge (IBC) is designed as a next generation FC with the lessons learned from years of building and testing the FC design, as well as novel catalyst materials to improve the performance and robustness of the cartridge. The FC was designed with minimal margin to save on mass, while the IBC was sized to have over 100% excess catalyst to achieve the required conversion, and still reduce the production cost. Kinetic data taken from another project was used to size the catalyst bed from the standard plug flow reactor conversion equation. The cartridge has been 3D printed and will soon be filled with the required levels of media. Testing has not yet been performed to determine final performance.

Fire Response↗
NASA NTRS1994

Two frameworks for integrating knowledge in induction

The use of knowledge in inductive learning is critical for improving the quality of the concept definitions generated, reducing the number of examples required in order to learn effective concept definitions, and reducing the computation needed to find good concept definitions. Relevant knowledge may come in many forms (such as examples, descriptions, advice, and constraints) and from many sources (such as books, teachers, databases, and scientific instruments). How to extract the relevant knowledge from this plethora of possibilities, and then to integrate it together so as to appropriately affect the induction process is perhaps the key issue at this point in inductive learning. Here the focus is on the integration part of this problem; that is, how induction algorithms can, and do, utilize a range of extracted knowledge. Preliminary work on a transformational framework for defining knowledge-intensive inductive algorithms out of relatively knowledge-free algorithms is described, as is a more tentative problems-space framework that attempts to cover all induction algorithms within a single general approach. These frameworks help to organize what is known about current knowledge-intensive induction algorithms, and to point towards new algorithms.

Rosenbloom, Paul S.↗
NASA NTRSDate not supplied

Using Artificial Intelligence (AI) and Machine Learning (ML) to conduct Space Missions Solid Waste Management Survey

The National Aeronautics and Space Administration (NASA) Solid Waste Management team has been focusing on technologies that can operate in microgravity. NASA aims to conduct both short and long-term transit and planetary missions on the lunar and Mars surfaces. Therefore, an updated waste survey is needed to explore technologies for operation in microgravity for transit missions and partial gravity for planetary missions. This paper will utilize Artificial Intelligence and Machine Learning techniques to conduct the survey and generate knowledge graphs for Spacecraft Waste Management.

Artificial Intelligence↗
NASA NTRS2012

EVA Skills Training

Dr. Parazynski and a colleague from Extravehicular Activity (EVA), Robotics, & Crew Systems Operations (DX) worked closely to build the EVA Skills Training Program, and for the first time, defined the gold standards of EVA performance, allowing crewmembers to increase their performance significantly. As part of the program, individuals had the opportunity to learn at their own rate, taking additional water time as required, to achieve that level of performance. This focus on training to one's strengths and weaknesses to bolster them enabled the Crew Office and DX to field a much larger group of spacewalkers for the daunting "wall of EVA" required for the building and maintenance of the ISS. Parazynski also stressed the need for designers to understand the capabilities and the limitations of a human in a spacesuit, as well as opportunities to improve future generations of space. He shared lessons learned (how the Crew Office engaged in these endeavors) and illustrated the need to work as a team to develop these complex systems.

Parazynski, Scott↗
NASA NTRS2019

Deep Learning Emulation of Atmospheric Correction for Geostationary Sensors

New generation geostationary satellites make reflectance observations available at a continental scale with unprecedented spatiotemporal resolution and spectral range. Generating Earth monitoring products from these observations requires retrieval of the basic parameter, surface reflectance (SR), by atmospheric correction (AC). Algorithms for atmospheric correction, including Multi-Angle Implementation of Atmospheric Correction (MAIAC), are adapted for each sensor and are too computationally complex to be run in real time, relying instead on look-up tables with precomputed values. Machine learning methods, including convolutional neural networks, have demonstrated performance in learning complex, nonlinear mappings and extracting insight from high-dimensional remote sensing data. In this work, we present a deep learning emulator of MAIAC to retrieve both SR and cloud products. Using this adaptation of deep learning-based emulation to remote sensing, we demonstrate stable SR retrieval over a variety of land covers and viewing conditions and accurate cloud detection. Further, a comparison of computation time suggests emulation as a compelling alternative for expensive physical simulation, especially for applications benefited by near-real time data, such as agricultural management and disaster response.

Duffy, Kate↗
NASA NTRS1991

Acquisition and improvement of human motor skills: Learning through observation and practice

Skilled movement is an integral part of the human existence. A better understanding of motor skills and their development is a prerequisite to the construction of truly flexible intelligent agents. We present MAEANDER, a computational model of human motor behavior, that uniformly addresses both the acquisition of skills through observation and the improvement of skills through practice. MAEANDER consists of a sensory-effector interface, a memory of movements, and a set of performance and learning mechanisms that let it recognize and generate motor skills. The system initially acquires such skills by observing movements performed by another agent and constructing a concept hierarchy. Given a stored motor skill in memory, MAEANDER will cause an effector to behave appropriately. All learning involves changing the hierarchical memory of skill concepts to more closely correspond to either observed experience or to desired behaviors. We evaluated MAEANDER empirically with respect to how well it acquires and improves both artificial movement types and handwritten script letters from the alphabet. We also evaluate MAEANDER as a psychological model by comparing its behavior to robust phenomena in humans and by considering the richness of the predictions it makes.

Iba, Wayne↗
NASA NTRSDate not supplied

VFR Trajectory Forecasting using Deep Generative Model for Autonomous Airspace Operations

To enable the airspace integration of autonomous operations, such as uncrewed aircraft conducting cargo deliveries, there is a need to forecast the positions of the surrounding traffic with which they may interact. This paper focuses on forecasting Visual Flight Rules traffic, a significant source of uncertainty and risk in the airspace, especially around small regional airports, due to the unplanned and often untracked nature of such flights. A deep generative model is developed, trained on historical traffic data at example towered and non-towered airports, and used to predict flight trajectories. Experimental results are presented comparing the performance of variational autoencoder and classical machine learning forecasting when applied to both the towered and non-towered airports over varying time horizons. The results show the advantages of the variational autoencoder in producing accurate probabilistic forecasts over varying time horizons.

uncrewed aircraft↗
NASA NTRSDate not supplied

VFR Trajectory Forecasting using Deep Generative Model for Autonomous Airspace Operations

To enable the airspace integration of autonomous operations, such as uncrewed aircraft conducting cargo deliveries, there is a need to forecast the positions of the surrounding traffic with which they may interact. This paper focuses on forecasting Visual Flight Rules traffic, a significant source of uncertainty and risk in the airspace, especially around small regional airports, due to the unplanned and often untracked nature of such flights. A deep generative model is developed, trained on historical traffic data at example towered and non-towered airports, and used to predict flight trajectories. Experimental results are presented comparing the performance of variational autoencoder and classical machine learning forecasting when applied to both the towered and non-towered airports over varying time horizons. The results show the advantages of the variational autoencoder in producing accurate probabilistic forecasts over varying time horizons.

uncrewed aircraft↗
NASA NTRS1995

Pseudogradient Training For A Class Of Neural Networks

Developmental second-order recurrent neural networks of special type modified to enhance stability in face of inputs beyond range of inputs on which trained. Second-order recurrent neural networks contain product feedback units and can be trained, by use of example inputs and outputs, to act as finite-state automatons. Particular second-order recurrent neural networks in question learn grammars in sense they are trained to generate binary responses to input training sequences of ones and zeros, each sequence being marked "legal" or "illegal" according to grammar to be learned.

Zeng, Zheng↗
NASA NTRS1990

Fast Feature-Recognizing Optoelectronic System

Proposed optoelectronic system recognizes features or classifies images by processing outputs of photosensors rapidly, in parallel, through circuits developed in research on neural networks. Array of photoconductive elements serve as photomodulated connections in electronic neural network, which provides high speed data compression to generate feature vector. System able to "learn" new patterns for subsequent recognition. Potential applications in robotic vision systems and pattern recognition.

Thakoor, S.↗