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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2009

Phase Fluctuations at Goldstone Derived from 1-Year Site Testing Interferometer Data

A two-element site test interferometer has been deployed at the NASA Deep Space Network (DSN) tracking complex in Goldstone, California, since May 2007. The interferometer system consists of two offset-fed 1.2 m parabolic reflectors which monitor atmospheric-induced amplitude and phase fluctuations on an unmodulated beacon signal (20.199 GHz) broadcast from a geostationary satellite (Anik F2). The geometry of the satellite and the ground-based infrastructure imposes a 48.5 elevation angle with a separation distance of 256 m along an east-west baseline. The interferometer has been recording phase fluctuation data, to date, for 1 yr with an overall system availability of 95 percent. In this paper, we provide the cumulative distribution functions (CDFs) for 1 year of recorded data, including phase rms, spatial structure function exponent, and surface meteorological measurements: surface wind speed, relative humidity, temperature, barometric pressure, and rain rate. Correlation between surface measurements, phase rms, and amplitude rms at different time scales are discussed. For 1 year, phase fluctuations at the DSN site in Goldstone, are better than 23 for 90 percent of the time (at 48.5 elevation). This data will be used to determine the suitability of the Goldstone site as a location for the Next Generation Deep Space Network.

Nessel, James A.↗
NASA NTRS1995

Accelerated Test Methods

Neural network systems were evaluated for use in predicting wear of mechanical systems. Three different neural network software simulation packages were utilized in order to create models of tribological wear tests. Representative simple, medium, and high complexity simulation packages were selected. Pin-on-disk, rub shoe, and four-ball tribological test data was used for training, testing, and verification of the neural network models. Results showed mixed success. The neural networks were able to predict results with some accuracy if the number of input variables was low or the amount of training data was high. Increased neural network complexity resulted in more accurate results, however there was a point of diminishing return. Medium complexity models were the best trade off between accuracy and computing time requirements. A NASA Technical Memorandum and a Society of Tribologists and Lubrication Engineers paper are being published which detail the work.

Jansen, Ralph↗
NASA NTRS1978

Model verification of mixed dynamic systems

A parameter-estimation method is described for verifying the mathematical model of mixed (combined interactive components from various engineering fields) dynamic systems against pertinent experimental data. The model verification problem is divided into two separate parts: defining a proper model and evaluating the parameters of that model. The main idea is to use differences between measured and predicted behavior (response) to adjust automatically the key parameters of a model so as to minimize response differences. To achieve the goal of modeling flexibility, the method combines the convenience of automated matrix generation with the generality of direct matrix input. The equations of motion are treated in first-order form, allowing for nonsymmetric matrices, modeling of general networks, and complex-mode analysis. The effectiveness of the method is demonstrated for an example problem involving a complex hydraulic-mechanical system.

Chrostowski, J. D.↗
NASA NTRS1992

Characterization of low thermal conductivity PAN-based carbon fibers

The microstructure and surface chemistry of eight low thermal conductivity (LTC) PAN-based carbon fibers were determined and compared with PAN-based fibers heat treated to higher temperatures. Based on wide-angle x ray diffraction, the LTC PAN fibers all appear to have a similar turbostratic structure with large 002 d-spacings, small crystallite sizes, and moderate preferred orientation. Limited small-angle x ray scattering (SAXS) results indicate that, with the exception of LTC fibers made by BASF, the LTC fibers do not have well developed pores. Transmission electron microscopy shows that the texture of the two LTC PAN-based fibers studied (Amoco T350/23X and /25X) consists of multiple sets of parallel, wavy, bent layers that interweave with each other forming a complex three dimensional network oriented randomly around the fiber axis. X ray photoelectron spectroscopy (XPS) analysis finds correlations between heat treated temperatures and the surface composition chemistry of the carbon fiber samples.

Katzman, Howard A.↗
NASA NTRS1992

Absolute positioning using DORIS tracking of the SPOT-2 satellite

The ability of the French DORIS system operating on the SPOT-2 satellite to provide absolute site positioning at the 20-30-centimeter level using 80 d of data is demonstrated. The accuracy of the vertical component is comparable to that of the horizontal components, indicating that residual troposphere error is not a limiting factor. The translation parameters indicate that the DORIS network realizes a geocentric frame to about 50 nm in each component. The considerable amount of data provided by the nearly global, all-weather DORIS network allowed this complex parameterization required to reduce the unmodeled forces acting on the low-earth satellite. Site velocities with accuracies better than 10 mm/yr should certainly be possible using the multiyear span of the SPOT series and Topex/Poseidon missions.

Watkins, M. M.↗
NASA NTRS2002

Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data

Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic coefficients to an accuracy of 110% . In our problem, we would like to get an optimized neural network architecture and minimum data set. This has been accomplished within 500 training cycles of a neural network. After removing training pairs (outliers), the GA has produced much better results. The neural network constructed is a feed forward neural network with a back propagation learning mechanism. The main goal has been to free the network design process from constraints of human biases, and to discover better forms of neural network architectures. The automation of the network architecture search by genetic algorithms seems to have been the best way to achieve this goal.

Rajkumar, T.↗
NASA NTRS1995

Compensation of Gravity-Induced Structural Deformations on a Beam- Waveguide Antenna Using a Deformable Mirror

At the NASA Deep Space Network (DSN) Goldstone Complex, a 34-meter- diameter beam-waveguide antenna, DSS-13, was constructed in 1988-1990 and has become an integral part of an advanced systems program and a test bed for technologies being developed to introduce Ka-band (32 GHz) frequencies into the DSN. A method for compensating the gravity- induced structural deformations in this large antenna is presented.

technologies DSN↗
NASA NTRS2005

Requirements to Design to Code: Towards a Fully Formal Approach to Automatic Code Generation

A general-purpose method to mechanically transform system requirements into a provably equivalent model has yet to appear. Such a method represents a necessary step toward high-dependability system engineering for numerous possible application domains, including distributed software systems, sensor networks, robot operation, complex scripts for spacecraft integration and testing, and autonomous systems. Currently available tools and methods that start with a formal model of a system and mechanically produce a provably equivalent implementation are valuable but not sufficient. The gap that current tools and methods leave unfilled is that their formal models cannot be proven to be equivalent to the system requirements as originated by the customer. For the classes of systems whose behavior can be described as a finite (but significant) set of scenarios, we offer a method for mechanically transforming requirements (expressed in restricted natural language, or in other appropriate graphical notations) into a provably equivalent formal model that can be used as the basis for code generation and other transformations.

Hinchey, Michael G.↗
NASA NTRS2010

Path Length Fluctuations Derived from Site Testing Interferometer Data

To evaluate possible sites for NASA's proposed Ka-band antenna array, the NASA Glenn Research Center has constructed atmospheric phase monitors (APM) which directly measure the tropospheric phase stability. These instruments observe an unmodulated 20.2 GHz beacon signal broadcast from a geostationary satellite (Anik F2) and measure the phase difference between the signals received by the two antennas. Two APM's have been deployed, one at the NASA Deep Space Network (DSN) Tracking Complex in Goldstone, California, and the other at the NASA White Sands Complex, in Las Cruces, New Mexico. Two station-years of atmospheric phase fluctuation data have been collected at Goldstone since operations commenced in May 2007 and 0.5 station-years of data have been collected at White Sands since operations began February 2009. With identical instruments operating simultaneously, we can directly compare the phase stability at the two sites. Phase stability is analyzed statistically in terms of the root-mean-square (rms) of the tropospheric path length fluctuations over 10 min blocks. Correlation between surface wind speed and relative humidity with interferometer phase are discussed. For 2 years, the path length fluctuations at the DSN site in Goldstone, California, have been better than 757 micrometer (with reference to a 300 m baseline and to Zenith) for 90 percent of the time. For the 6 months of data collected at White Sands, New Mexico, the path length fluctuations have been better than 830 micrometers (with reference to a 300 m baseline and to Zenith) for 90 percent of the time. This type of data analysis, as well as many other site quality characteristics (e.g., rain attenuation, infrastructure, etc.), will be used to determine the suitability of both sites for NASA s future communication services at Ka-band using an array of antennas.

Acosta, Roberto J.↗
NASA NTRS2007

Jet Propulsion Laboratory: Annual Report 2006

Nothing is as gratifying in space exploration as when we are surprised by the unexpected. Much of our work progresses in an orderly way, from concept to plan to creation to finding. But now and then we are caught off-guard by something startlingly new, and it is these moments that make our hearts race and leave us with many of our most compelling memories. And 2006 was an exceptional year for the unforeseen. One of our orbiters shocked many with stark proof that liquid water, the seemingly long-gone force that reshaped so much of the scenery of Mars, still flows there today,at least in occasional bursts. Another spacecraft caught us by surprise with photos of Yellowstone-like geysers on one of Saturn's seemingly nondescript moons, Enceladus. A spaceborne observatory created to plumb the life histories of stars and galaxies showed off a completely unexpected talent when it revealed the day and night faces of a fire and ice planet far beyond our solar system 40 light-years away. A newly launched Earth observer revealed that the clouds that decorate our own planet are not what we thought them to be in many ways. Of course, not all of the high points of the year arrived on our doorstep in such unexpected ways. There was also great drama when missions came off exactly as planned, such as when Stardust's sample return capsule made a flawless landing in the Utah desert, bringing home samples of cometary and interstellar dust. Mars Reconnaissance Orbiter slipped into orbit around the red planet exactly as planned. Numerous other missions and technology programs likewise made great achievements during the year. In all, 17 spacecraft and six instruments were stationed across the solar system, studying our own world, other planets, comets and the deeper universe. All of these achievements were enabled by many teams and systems at the Laboratory. The Deep Space Network of communications complexes across three continents supported all of NASA's solar system missions, and several from our international partners, while looking ahead to reinvent itself for the future. Technologists were at work creating innovations both for NASA missions and terrestrial uses. JPL's community of scientific researchers was equally busy coordinating the science activities of our missions and pursuing independent investigations. None of this would be possible without the support of business and administrative teams dedicated to making the Laboratory's institutional environment as world-class as its technical face, or the public engagement specialists who bring the experience of space to the country's diverse publics. All of our missions in one way or another support our nation's Vision for Space Exploration, which envisages a gradually widening robotic and human presence across the solar system in the years ahead.

National Aeronautics and Space Administration (NAS↗
NASA NTRS2006

Jet Propulsion Laboratory: Annual Report 2005

What an amazing host of new sights 2005 brought us. With impeccable choreography, one spacecraft sent an impactor slamming into a comet, reversing the traditional view of these space wayfarers by revealing it to be buried in deep drifts of a fine talcum-like powder. Another spacecraft delivered a probe from our European partners to the surface of Saturn's haze-shrouded moon Titan, disclosing a landscape eerily like Earth's, if we had methane rivers cascading down hillsides of ice. An orbiting observatory for the first time showed us the light from planets circling other stars, which astronomers previously knew to exist only from indirect clues. Throughout the year we also amassed continually expanding views of Earth as well as Mars, by far the most-explored planet after our own. In all, 18 spacecraft and five instruments were stationed across the solar system, studying our own world, other planets, comets and the deeper universe. These missions were enabled by the efforts of everyone at JPL. The Deep Space Network of communications complexes across three continents continued to experience a period of remarkable activity. Others were at work creating technologies both for NASA missions and other uses. JPL's contingent of scientific researchers was equally busy coordinating the science activities of our missions or pursuing independent investigations. None of this would be possible without the support of world-class business and administrative teams. All of our missions in one way or another support our nations Vision for Space Exploration, which envisages a gradually widening robotic and human presence across the solar system in the years ahead. The year was not without its challenges. NASA set forth to implement the Vision for Space Exploration, which resulted in some flight projects and technology efforts being terminated. To adjust to this new direction, it was necessary for us to reduce the JPL workforce by about five percent. Taking steps like this is painful, but we tried to make the process as orderly as possible. In the end, the adjustments made have left JPL on a healthy footing for the years ahead.

Jet Propulsion Laboratory (JPL)↗
NASA NTRS2005

Requirements to Design to Code: Towards a Fully Formal Approach to Automatic Code Generation

A general-purpose method to mechanically transform system requirements into a provably equivalent model has yet to appear. Such a method represents a necessary step toward high-dependability system engineering for numerous possible application domains, including distributed software systems, sensor networks, robot operation, complex scripts for spacecraft integration and testing, and autonomous systems. Currently available tools and methods that start with a formal model of a: system and mechanically produce a provably equivalent implementation are valuable but not sufficient. The "gap" that current tools and methods leave unfilled is that their formal models cannot be proven to be equivalent to the system requirements as originated by the customer. For the ciasses of systems whose behavior can be described as a finite (but significant) set of scenarios, we offer a method for mechanically transforming requirements (expressed in restricted natural language, or in other appropriate graphical notations) into a provably equivalent formal model that can be used as the basis for code generation and other transformations.

Hinchey, Michael G.↗
NASA NTRS2012

An Inter-Comparison of Two Independent Site Test Interferometers Located in Goldstone, California: Initial Study Results

Site Test Interferometers (STIs) have been deployed at two different locations at the NASA Deep Space Network (DSN) tracking complex in Goldstone, California. An STI measures the difference in path length between a geostationary satellite and two antennas on the Earth, producing a measure of atmospheric phase fluctuations over spatial distances comparable to those between elements of possible microwave phased arrays. The purposes of the Goldstone STIs are to assess the suitability of Goldstone as an array site and to statistically characterize atmospheric induced delay fluctuations for application to future array scenarios.The two STI's are separated by 13 km across the Goldstone complex. Each instrument is composed of two small-diameter antennas and associated electronics separated by approx. 200 meters in a principally east-west configuration. The antennas continuously observe signals emitted by geo-stationary satellites and produce data that contain information on the phase difference between signals received by both antennas. The fluctuation in delay (or path length difference) statistics derived from these data sets can be used to infer power loss for particular array configurations.We report on a comparison of the root mean square (RMS) phase delay statistics estimated over 10-minute intervals between the two Goldstone STIs. We have achieved good statistical agreement between the data acquired from the two STIs, given that each instrument is observing different satellites, at different frequencies, over different baseline lengths, with very different implementations, and are located 13 km apart in widely separated terrain at different geodetic altitudes.

propagation↗
NASA NTRS2015

Wideband Array for C, X, and Ku-Band Applications with 5.3:1 Bandwidth

Planar arrays that exploit strong intentional coupling between elements have allowed for very wide bandwidths in low-profile configurations. However, such designs also require complex impedance matching networks that must also be very compact. For many space applications, typically occurring at C-, X-, Ku-, and most recently at Ka-band, such designs require specialized and expensive fabrication techniques. To address this issue, a novel ultra-wideband array is presented, using a simplified feed network to reduce fabrication cost. The array operates from 3.5-18.5 GHz with VSWR less than 2.4 at broadside, and is of very low profile, having a total height of lambda/10 at the lowest frequency of operation. Validation is provided using a 64-element prototype array, fabricated using common Printed Circuit Board (PCB) technology. The low size, weight, and cost of this array make it attractive for space-borne applications.

X-↗
NASA NTRS2018

NASA Space Network Project Operations Management: Past, Present and Future for the Tracking and Data Relay Satellite Constellation

The NASA Space Network (SN) Operations began with the launch of the first Tracking and Data Relay Satellite (TDRS-1) on April 4, 1983 with on-orbit operations conducted from the White Sands Ground Terminal (WSGT) in Las Cruces, New Mexico. Over the past 35 years, the SN has evolved to its current configuration comprised of four strategically located satellite ground stations and a constellation of ten geosynchronous Tracking and Data Relay Satellites that provide customer support 24 hours a day, 365 days per year, at an unprecedented 99.9% proficiency. The Space Network provides total global Communications, Data Relay, and Tracking services for Low Earth Orbiting (LEO) satellites, Human Space Flight, Expendable Launch Vehicles (ELV) and Scientific missions. This paper will address the Operations Management of the NASA Space Network and the complexities associated with this responsibility, as well as significant mission support highlights.

Sobchak, Ted↗
NASA NTRS2021

Scheduling the NASA Deep Space Network with Deep Reinforcement Learning

With three complexes spread evenly across the Earth, NASA’s Deep Space Network (DSN) is the primary means of communications as well as a significant scientific instrument for dozens of active missions around the world. A rapidly rising number of spacecraft and increasingly complex scientific instruments with higher bandwidth requirements have resulted in demand that exceeds the network’s capacity across its 12 antennae. The existing DSN scheduling process operates on a rolling weekly basis and is time-consuming; for a given week, generation of the final baseline schedule of spacecraft tracking passes takes roughly 5 months from the initial requirements submission deadline, with several weeks of peer-to-peer negotiations in between. This paper proposes a deep reinforcement learning (RL) approach to generate candidate DSN schedules from mission requests and spacecraft ephemeris data with demonstrated capability to address real-world operational constraints. A deep RL agent is developed that takes mission requests for a given week as input, and interacts with a DSN scheduling environment to allocate tracks such that its reward signal is maximized. A comparison is made between an agent trained using Proximal Policy Optimization and its random, untrained counterpart. The results represent a proof-of-concept that, given a well-shaped reward signal, a deep RL agent can learn the complex heuristics used by experts to schedule the DSN. A trained agent can potentially be used to generate candidate schedules to bootstrap the scheduling process and thus reduce the turnaround cycle for DSN scheduling.

Wilson, Brian↗
NASA NTRS1991

NMESys: An expert system for network fault detection

The problem of network management is becoming an increasingly difficult and challenging task. It is very common today to find heterogeneous networks consisting of many different types of computers, operating systems, and protocols. The complexity of implementing a network with this many components is difficult enough, while the maintenance of such a network is an even larger problem. A prototype network management expert system, NMESys, implemented in the C Language Integrated Production System (CLIPS). NMESys concentrates on solving some of the critical problems encountered in managing a large network. The major goal of NMESys is to provide a network operator with an expert system tool to quickly and accurately detect hard failures, potential failures, and to minimize or eliminate user down time in a large network.

Nelson, Peter C.↗
NASA NTRS2019

Exploring Network-Related Optimization Problems Using Quantum Heuristics

Network-related connectivity optimization problems are underlying a wide range of applications and are also of high computational complexity. We consider studying network optimization problems using two types of quantum heuristics.One is quantum annealing, and the other Quantum Alternating Operator Ansatz, an extension of the Quantum Approximate Optimization Algorithms for gate-model quantum computation, in which a cost-function based unitary and a non-commuting mixing unitary are applied alternately. We present problem mappings for problems of finding the spanning-tree or spanning-graph of a graph that optimizes certain costs, and a variant that further requires the spanning-tree be degree-bounded. With quantum annealing, all constraints are cast into penalty terms in the cost Hamiltonian, and the solution is encoded as the ground state of the Hamiltonian. We provide three mappings to the quadratic unconstrained binary optimization (QUBO) form, compare the resource requirements, and analyze the tradeoffs. For QAOA, we give special focus on the design of mixers based on the constraints presented in the problem, such that the system evolution remains in a subspace of the full Hilbert space where all constraints are satisfied. In the spanning-tree problem, one such hard constraint is that a mixer applied to a spanning-tree needs also be a spanning tree. This involves checking the connectivity of a subgraph, which is a global condition common for most network-related problems. We show how this feature can be efficiently represented in the mixer in a quantum coherent way, based on manipulation of a descendant-matrix and an adjacent matrix. We further develop a mixer for the spanning-graphs based on the spanning-tree mixer.

Wang, Zhihui↗