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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 451 records · Page 25

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NASA NTRS1983

Travelling wave effects in large space structures

Several aspects of travelling waves in Large Space Structures(LSS) are discussed. The dynamic similarity among LSS's, electric power systems, microwave circuits and communications network is noted. The existence of time lag between actuation and response is illuminated with the aid of simple examples, and their prediction is demonstrated. To prevent echoes, communications lines have matched terminations; this idea is applied to the design of dampers of one dimensional structures. Periodic structures act as mechanical band pass filters. Implications of this behavior are examined on a simple example. It is noted that the implication is twofold; continuum models of periodic lattice structures may err considerably; on the other hand, it is possible to design favorable transmission (and resonance) characteristics into the structure.

Vonflotow, A.↗

NASA NTRS1992

Implementation of an intelligent control system

A laboratory testbed facility which was constructed at NASA LeRC for the development of an Intelligent Control System (ICS) for reusable rocket engines is described. The framework of the ICS consists of a hierarchy of various control and diagnostic functions. The traditional high speed, closed-loop controller resides at the lowest level of the ICS hierarchy. Above this level resides the diagnostic functions which identify engine faults. The ICS top level consists of the coordination function which manages the interaction between an expert system and a traditional control system. The purpose of the testbed is to demonstrate the feasibility of the OCS concept by implementing the ICS as the primary controller in a simulation of the Space Shuttle Main Engine (SSME). The functions of the ICS which are implemented in the testbed are as follows: an SSME dynamic simulation with selected fault mode models, a reconfigurable controller, a neural network for sensor validation, a model-based failure detection algorithm, a rule based failure detection algorithm, a diagnostic expert system, an intelligent coordinator, and a user interface which provides a graphical representation of the event occurring within the testbed. The diverse nature of the ICS has led to the development of a distributed architecture consisting of specialized hardware and software for the implementation of the various functions. This testbed is made up of five different computer systems. These individual computers are discussed along with the schemes used to implement the various ICS components. The communication between computers and the timing and synchronization between components are also addressed.

Simon, D. L.↗

NASA NTRS1993

Application of artificial neural networks to the design optimization of aerospace structural components

The application of artificial neural networks to capture structural design expertise is demonstrated. The principal advantage of a trained neural network is that it requires trivial computational effort to produce an acceptable new design. For the class of problems addressed, the development of a conventional expert system would be extremely difficult. In the present effort, a structural optimization code with multiple nonlinear programming algorithms and an artificial neural network code NETS were used. A set of optimum designs for a ring and two aircraft wings for static and dynamic constraints were generated by using the optimization codes. The optimum design data were processed to obtain input and output pairs, which were used to develop a trained artificial neural network with the code NETS. Optimum designs for new design conditions were predicted by using the trained network. Neural net prediction of optimum designs was found to be satisfactory for most of the output design parameters. However, results from the present study indicate that caution must be exercised to ensure that all design variables are within selected error bounds.

Berke, Laszlo↗

NASA NTRS1990

Auto-programmable impulse neural circuits

Impulse neural networks use pulse trains to communicate neuron activation levels. Impulse neural circuits emulate natural neurons at a more detailed level than that typically employed by contemporary neural network implementation methods. An impulse neural circuit which realizes short term memory dynamics is presented. The operation of that circuit is then characterized in terms of pulse frequency modulated signals. Both fixed and programmable synapse circuits for realizing long term memory are also described. The implementation of a simple and useful unsupervised learning law is then presented. The implementation of a differential Hebbian learning rule for a specific mean-frequency signal interpretation is shown to have a straightforward implementation using digital combinational logic with a variation of a previously developed programmable synapse circuit. This circuit is expected to be exploited for simple and straightforward implementation of future auto-adaptive neural circuits.

Watula, D.↗

NASA NTRS1993

Trajectory design for the Deep Space Program Science Experiment (DSPSE) mission

In 1994, the Deep Space Program Science Experiment (DSPSE) spacecraft will become the first spacecraft to perform, in succession, both a lunar orbiting mission and a deep-space asteroid encounter mission. The primary mission objective is to perform a long-duration flight-test of various new-technology lightweight components, such as sensors, in a deep-space environment. The mission has two secondary science objectives: to provide high-resolution imaging of the entire lunar surface for mapping purposes and flyby imaging of the asteroid 1620 Geographos. The DSPSE mission is sponsored by the Strategic Defense Initiative Organization (SDIO). As prime contractor, the Naval Research Laboratory (NRL) is building the spacecraft and will conduct mission operations. The Goddard Space Flight Center's (GSFC) Flight Dynamics Division is supporting NRL in the areas of The Deep Space Network (DSN) will provide tracking support. The DSPSE mission will begin with a launch from the Western Test Range in late January 1994. Following a minimum 1.5-day stay in a low-Earth parking orbit, a solid kick motor burn will boost DSPSE into an 18-day, 2.5-revolution phasing orbit transfer trajectory to the Moon. Two burns to insert DSPSE into a lunar polar orbit suitable for the mapping mission will be followed by mapping orbit maintenance and adjustment operations over a period of 2 sidereal months. In May 1994, a lunar orbit departure maneuver, in conjunction with a lunar swingby 26 days later, will propel DSPSE onto a heliocentric transfer that will intercept Geographos on September 1, 1994. This paper presents the characteristics, deterministic delta-Vs, and design details of each trajectory phase of this unique mission, together with the requirements, constraints, and design considerations to which each phase is subject. Numerous trajectory plots and tables of significant trajectory events are included. Following a discussion of the results of a preliminary launch window analysis, a summary of the deterministic impulsive delta-V budget required to establish the baseline mission trajectory design is presented.

Carrington, D.↗

NASA NTRS2001

Recent Troposheric Ozone Observations from Satellite and In-Situ Measurements: An Overview

The past 4-5 years have seen an unprecedented increase in tropospheric ozone (and related) observations thanks to focused campaigns, new satellite data, and to innovative approaches to climatology (commercial aircraft sampling, networks). The result has been new insights into the complex chemistry and dynamics affecting tropospheric ozone in the non-urban environment: global pollution, subtropical stratospheric folds, disturbances associated with the 1997 El-Nino. A synthesis based on selected examples of field and satellite data will be presented.

Thompson, A.↗

NASA NTRS2001

New Insights into Tropospheric Ozone from Satellites and Soundings

Thompson, A.↗

NASA NTRS1993

Terminal Repeller Unconstrained Subenergy Tunneling (TRUST) for Fast Global Optimization

A new method for unconstrained global function optimization, acronymed TRUST, is introduced.

terminal repellers subenergy tunneling function ar↗

NASA NTRS2006

NASA Tech Briefs, December 2006

Topic include: Inferring Gear Damage from Oil-Debris and Vibration Data; Forecasting of Storm-Surge Floods Using ADCIRC and Optimized DEMs; User Interactive Software for Analysis of Human Physiological Data; Representation of Serendipitous Scientific Data; Automatic Locking of Laser Frequency to an Absorption Peak; Self-Passivating Lithium/Solid Electrolyte/Iodine Cells; Four-Quadrant Analog Multipliers Using G4-FETs; Noise Source for Calibrating a Microwave Polarimeter; Hybrid Deployable Foam Antennas and Reflectors; Coating MCPs with AlN and GaN; Domed, 40-cm-Diameter Ion Optics for an Ion Thruster; Gesture-Controlled Interfaces for Self-Service Machines; Dynamically Alterable Arrays of Polymorphic Data Types; Identifying Trends in Deep Space Network Monitor Data; Predicting Lifetime of a Thermomechanically Loaded Component; Partial Automation of Requirements Tracing; Automated Synthesis of Architecture of Avionic Systems; SSRL Emergency Response Shore Tool; Wholly Aromatic Ether-Imides as n-Type Semiconductors; Carbon-Nanotube-Carpet Heat-Transfer Pads; Pulse-Flow Microencapsulation System; Automated Low-Gravitation Facility Would Make Optical Fibers; Alignment Cube with One Diffractive Face; Graphite Composite Booms with Integral Hinges; Tool for Sampling Permafrost on a Remote Planet; and Special Semaphore Scheme for UHF Spacecraft Communications.

Source record↗

NASA NTRS2016

A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures

Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer-Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration's (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size.

passive microwave↗

NASA NTRS2013

Environmentally Friendly Rotorcraft Concepts: 2011-2014: US/France MoA Helicopter Aeromechanics - Task 5

Motivation: Greenhouse gas concentrations have increased well beyond their pre-industrial levels due to human activities; Aircraft emissions are becoming regulated in industrialized nations, so future rotorcraft will need to be designed for minimal environmental impact. Objectives: Determine suitable metrics to measure environmental impact of rotorcraft, focusing first on air pollution; Integrate performance metrics with rotorcraft design codes; Generate rotorcraft designs with minimal environmental impact. Outcomes: Obtain an understanding of how targeting reduced emissions affects the rotorcraft design process; Advance the capability of existing rotorcraft design tools to include environmental performance metrics. Tools: Evaluation and predesign - NDARC (NASA Design and Analysis of Rotorcraft) and CREATION (Concepts of Rotorcraft Enhanced Assessment Through Integrated Optimization Network); Flight simulation - CAMRAD II (Comprehensive Analytical Model of Rotorcraft Aerodynamics and Dynamics) and HOST (Helicopter Overall Simulation Tool).

Environmentally Friendly↗

NASA NTRS2017

Juno Gravity Science: Preparing for Data Collection at Jupiter

One of the primary goals of the Juno mission is to investigate Jupiter’s interior by mapping its gravitational field with the gravity science instrument. The Juno spacecraft has two radio science components that comprise the gravity science instrument: the X-band telecommunications system for a X-up/X-down link and a Ka-band Translator for a Ka-up/Ka-down link. The Deep Space Network’s DSS-25 beam waveguide antenna at the Goldstone Deep Space Communications Complex in California provides the X- and Ka-band uplink alongside an Advanced Water Vapor Radiometer to calibrate tropospheric effects. X-band and Ka-band downlink data are collected with both open-loop and closed-loop receivers located at the complex. Utilization of Ka-band provides scientific benefit to the Doppler measurements, but also adds operational challenges. Pointing of the uplink and downlink Ka-band signals requires additional systems to be calibrated and operated by the Deep Space Network; and the higher frequency of Ka-band means the signal dynamics are increased by a factor of four over X-band signals. Due to the spacecraft’s elliptical orbit and 4000 kilometer perijove altitude, it accelerates at an extreme rate as it approaches Jupiter, inducing large dynamic ranges in Doppler range of approximately 6 MHz over 3 hours at Ka-band. After the installation of a new Ka-band transmitter at DSS-25 for Juno was completed in 2015, end-to-end testing was conducted to ensure readiness for operations at Jupiter and provide an initial assessment of the performance. Cruise testing was conducted in the same operational configuration that the system will be used in during perijove passes. Processed open-loop data yielded uncalibrated Doppler residuals of 1.9 mHz at X-band and 6.0 mHz at Ka-band with 5-second compression time. Conduction of these tests has prepared the instrument and the operations team for science data collection during the science phase of the mission.

Buccino, Dustin↗

NASA NTRSDate not supplied

A Structurally-Adaptive Framework for Distributed Airborne Sensing over Real-time Collaborative Information Sharing Networks

The emergence and maturation of wireless communication technologies continue to transform the aviation industry and are enabling new solutions to challenges faced by NASA’s Advanced Air Mobility (AAM) initiative. AAM is leading towards high-density autonomous aircraft operations in areas underserved by traditional aviation, such as over densely populated urban centers. In this paper, we build on concepts from Smart Spaces - where sensing, processing, and communication are embedded in an environment, and agents are operating within the space can exploit these capabilities in real-time through collaborative information sharing networks. Building from these concepts, we propose a framework to enable a dynamic, topologically-adaptive, and distributed estimation system for man-rated aviation to address challenges faced by autonomous AAM operations. This paper presents the initial concept of operations and system design for this framework, presents a mathematical formulation for abstraction of the problem, identifies requirements and constraints for operation, and presents algorithmic constructs and mathematical formalisms to demonstrate operation. The proposed framework will be evaluated on a regional AAM flight scenario and will focus on two initial applications: (1) GPS-free navigation supporting precision approach and landing (PAL), and (2) surveillance and conformance monitoring of aircraft in vertiport airspaces. Such approaches show promise in addressing gaps in current technologies needed to enable future AAM concepts, while promising greater capabilities, performance, robustness, and safety over current aviation systems and operations.

Structurally-Adaptive↗

NASA NTRSDate not supplied

A Structurally-Adaptive Framework for Distributed Airborne Sensing over Real-time Collaborative Information Sharing Networks

The emergence and maturation of wireless communication technologies continue to transform the aviation industry and are enabling new solutions to challenges faced by NASA’s Advanced Air Mobility (AAM) initiative. AAM is leading towards high-density autonomous aircraft operations in areas underserved by traditional aviation, such as over densely populated urban centers. In this paper, we build on concepts from distributed sensing and smart spaces - where sensing, processing, and communication are embedded in an environment, and agents are operating within the space can exploit these capabilities in real-time through collaborative information sharing networks. Building from these concepts, we propose a framework to enable a dynamic, topologically-adaptive, and distributed estimation system for man-rated aviation to address challenges faced by autonomous AAM operations. This paper presents the initial concept of operations and system design for this framework, presents a mathematical formulation for abstraction of the problem, identifies requirements and constraints for operation, and presents algorithmic constructs to demonstrate operation. The initial framework design will focus on supporting precision navigation and independent surveillance supporting conformance monitoring of aircraft in airspace corridors and vertiport airspaces. Preliminary results from this framework shows promise in addressing gaps in current technologies needed to enable future AAM concepts, while promising greater capabilities, performance, robustness, and safety over current aviation systems and operations.

Distributed sensing↗

NASA NTRS1993

Venus Interior Structure Mission (VISM): Establishing a Seismic Network on Venus

Magellan radar data show the surface of Venus to contain a wide range of geologic features (large volcanoes, extensive rift valleys, etc.). Although networks of interconnecting zones of deformation are identified, a system of spreading ridges and subduction zones like those that dominate the tectonic style of the Earth do not appear to be present. In addition, the absence of a mantle low-viscosity zone suggests a strong link between mantle dynamics and the surface. As a natural follow-on to the Magellan mission, establishing a network of seismometers on Venus will provide detailed quantitative information on the large scale interior structure of the planet. When analyzed in conjunction with image, gravity, and topography information, these data will aid in constraining mechanisms that drive surface deformation.

Stofan, E. R.↗

NASA NTRS2010

Implementing and Simulating Dynamic Traffic Assignment with Intelligent Transportation Systems in Cube Avenue

As urban populations and traffic congestion levels increase, effective use of information and communication tools and intelligent transportation systems as becoming increasingly important in order to maximize the efficiency of transportation networks. The appropriate placement and employment of these tools within a network is critical to their effectiveness. This presentation proposes and demonstrates the use of a commercial transportation simulation tool to simulate dynamic traffic assignment and rerouting to model route modifications as a result of traffic information.

Foytik, Peter↗

NASA NTRS2017

A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Nets convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign. Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

NeMO=NET↗

NASA NTRS1989

Analysis of random drop for gateway congestion control

Lately, the growing demand on the Internet has prompted the need for more effective congestion control policies. Currently No Gateway Policy is used to relieve and signal congestion, which leads to unfair service to the individual users and a degradation of overall network performance. Network simulation was used to illustrate the character of Internet congestion and its causes. A newly proposed gateway congestion control policy, called Random Drop, was considered as a promising solution to the pressing problem. Random Drop relieves resource congestion upon buffer overflow by choosing a random packet from the service queue to be dropped. The random choice should result in a drop distribution proportional to the bandwidth distribution among all contending TCP connections, thus applying the necessary fairness. Nonetheless, the simulation experiments demonstrate several shortcomings with this policy. Because Random Drop is a congestion control policy, which is not applied until congestion has already occurred, it usually results in a high drop rate that hurts too many connections including well-behaved ones. Even though the number of packets dropped is different from one connection to another depending on the buffer utilization upon overflow, the TCP recovery overhead is high enough to neutralize these differences, causing unfair congestion penalties. Besides, the drop distribution itself is an inaccurate representation of the average bandwidth distribution, missing much important information about the bandwidth utilization between buffer overflow events. A modification of Random Drop to do congestion avoidance by applying the policy early was also proposed. Early Random Drop has the advantage of avoiding the high drop rate of buffer overflow. The early application of the policy removes the pressure of congestion relief and allows more accurate signaling of congestion. To be used effectively, algorithms for the dynamic adjustment of the parameters of Early Random Drop to suite the current network load must still be developed.

Hashem, Emam Salaheddin↗