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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Distributed Training and Optimization of Neural Networks

Deep learning models are yielding increasingly better performances thanks to multiple factors. To be successful, model may have large number of parameters or complex architectures and be trained on large dataset. This leads to large requirements on computing resource and turn around time, even more so when hyperparameter optimization is done (e.g. search over model architectures). While this is a challenge that goes beyond particle physics, we review the various ways to do the necessary computations in parallel, and put it in the context of high-energy physics.

Vlimant, Jean-Roch↗
NASA NTRS2002

Automated testing of the Deep Space Network's Uplink Subsystem

This paper describes some of the lessons we learned in implementing two different Tcl-basedtest frameworks used to automate testing of the Deep Space Network's new Uplink Subsystem, and discusses the advantages and disadvantages of each. The first framework was external to the Uplink Subsystem's software and provided complete control of the environment; the second was integrated with the software and provided less control, but motivated more and better testing. Some implementation details of the second frameworkare discussed as well.

DSN automated testing Tcl/Tk↗
NASA NTRS2007

Deep Space Network Antenna Logic Controller

The Antenna Logic Controller (ALC) software controls and monitors the motion control equipment of the 4,000-metric-ton structure of the Deep Space Network 70-meter antenna. This program coordinates the control of 42 hydraulic pumps, while monitoring several interlocks for personnel and equipment safety. Remote operation of the ALC runs via the Antenna Monitor & Control (AMC) computer, which orchestrates the tracking functions of the entire antenna. This software provides a graphical user interface for local control, monitoring, and identification of faults as well as, at a high level, providing for the digital control of the axis brakes so that the servo of the AMC may control the motion of the antenna. Specific functions of the ALC also include routines for startup in cold weather, controlled shutdown for both normal and fault situations, and pump switching on failure. The increased monitoring, the ability to trend key performance characteristics, the improved fault detection and recovery, the centralization of all control at a single panel, and the simplification of the user interface have all reduced the required workforce to run 70-meter antennas. The ALC also increases the antenna availability by reducing the time required to start up the antenna, to diagnose faults, and by providing additional insight into the performance of key parameters that aid in preventive maintenance to avoid key element failure. The ALC User Display (AUD) is a graphical user interface with hierarchical display structure, which provides high-level status information to the operation of the ALC, as well as detailed information for virtually all aspects of the ALC via drill-down displays. The operational status of an item, be it a function or assembly, is shown in the higher-level display. By pressing the item on the display screen, a new screen opens to show more detail of the function/assembly. Navigation tools and the map button allow immediate access to all screens.

Ahlstrom, Harlow↗
NASA NTRS2016

Traffic Modeling for Deep Space Network in the Human Mars Exploration Era

In this article we describe the analysis and simulation effort of the end-to-end traffic flow for the Deep Space Network (DSN) in the Human Exploration Era, when DSN will provide communication and navigation services for human missions to distant celestial objects like the Moon, asteroids, and Mars. Using the network traffic derived for the 30-day period within July/August 2039 from the Space Communications Mission Model (SCMM), we simulate the bandwidths of the ground links and the buffer profiles of the network nodes. We also use a 2-state Markov scheme that models the store-and-forward mechanism that regulates the ground network traffic. The network traffic modeling and simulation generates ground bandwidth and buffer statistics, which in turn are used to formulate the future DSN ground network bandwidth and storage requirements.

Cheung, Kar-ming↗
NASA NTRS2016

Characterization of Operational Performance of Ka-Band Links in Deep Space Network

This paper presents a characterization of performance of 32-GHz Ka-band link in an operational environment. The data come from tracking of Kepler spacecraft by the NASA Deep Space Network (DSN) in the past few years. Ka-band link offers a significant signal-to-noise advantage compared to the more common X- or S-band; however, it is subject to more signal fluctuation caused by the weather. Kepler is the first mission supported by the DSN that solely rely on the higher deep space Ka-band communications link to return its high-rate science data. A well characterization of the operational performance of Kepler would benefit future Ka-band missions, especially for those operating with smaller link margin. The study examines how weather conditions at the DSN facilities (e.g., winds, clouds, rains) affect the received signal, particularly on telemetry data. It addresses questions such as how often the weather affects the link and how much degradation the link could suffer. Among the 22 Ka-band passes in 2012, heavy clouds affected one pass and two passes were impacted by high winds. The adverse weather caused at time as much as 3-dB instantaneous change in the signal to noise ratio (SNR). Estimates of degradation to the SNR as a function of wind speeds are captured based on observations. This study also quantifies the probability distribution of the variation of received signal power. With such information, future missions can better plan their link design. An optimal link design aims for just having enough margin to realize the data return with a targeted probability, with neither having too much margin that it reduces the downlink data rate nor insufficient reserve that causes frequent data outages. The study tries to provide some answers to the questions such as how much does SNR vary from one tracking pass to the next, and what is the cumulative distribution of signal fluctuation. Regarding the signal variation over many tracking passes, we found that the averaged symbol SNR (SSNR) for each pass changed by as much as 4 dB. Some variations were due to geometry such as the changing distance between spacecraft and Earth and different antenna pointing elevations. Other variations seem to be random in nature, reflecting the randomness of operational environments. Some passes were found to be quite stable, with a standard deviation of symbol SNR around 0.25 dB while others had greater variation, up to 1.4 dB. Overall, the cumulative distribution of the symbol SNR reflects that 50% of the fluctuations was less than 0.6 dB, 90% of fluctuation was within 1.6 dB and 95% within 2.2 dB. Some of the challenges in data processing, validation and modeling were captured in this paper. We observed some inconsistent measurements where operational data significantly deviate from a normal expectation. These inconsistent measurements made it hard to develop an accurate and consistent performance model, such as the degradation of signal SNR as a function of high wind. The difficulty was compounded by the fact that there were only a few Ka-band passes observed in high winds.

Pham, Timothy↗
DOE OSTI2024

Achieving precise multiparameter measurements with distributed optical fiber sensor using wavelength diversity and deep neural networks

The development of advanced distributed optical fiber sensing systems that are capable of performing accurate and spatially resolved multiparameter measurements is of great interest to a wide range of scientific and industrial applications. Here, in this paper, we propose and experimentally demonstrate a wavelength diversity based advanced distributed optical fiber sensor system to accomplish multiparameter sensing while greatly enhancing measurement accuracy. A suite of deep neural network (DNN) algorithms are developed and verified for data denoising, rapid Brillouin frequency shift estimation, and vibration data event classification. As a proof-of-concept, we demonstrate the effectiveness of the proposed advanced wavelength diversity distributed fiber sensor system assisted by DNN for simultaneous, independent measurements of static strain, temperature, and acoustic vibrations over a 25 km long sensing fiber at 3 m spatial resolution. These results suggest the potential for an intelligent multiparameter monitoring system with enhanced performance in advanced structural health monitoring applications.

47 OTHER INSTRUMENTATION↗
NASA NTRS1993

Estimating the Deep Space Network modification costs to prepare for future space missions by using major cost drivers

This paper develops a cost model to do long range planning cost estimates for Deep Space Network (DSN) support of future space missions. The paper focuses on the costs required to modify and/or enhance the DSN to prepare for future space missions. The model is a function of eight major mission cost drivers and estimates both the total cost and the annual costs of a similar future space mission. The model is derived from actual cost data from three space missions: Voyager (Uranus), Voyager (Neptune), and Magellan. Estimates derived from the model are tested against actual cost data for two independent missions, Viking and Mariner Jupiter/Saturn (MJS).

Remer, Donald S.↗
DOE OSTI2021

Augmented signal processing in Liquid Argon Time Projection Chambers with a deep neural network

The Liquid Argon Time Projection Chamber (LArTPC) is an advanced neutrino detector technology widely used in recent and upcoming accelerator neutrino experiments. It features a low energy threshold and high spatial resolution that allow for comprehensive reconstruction of event topologies. In current-generation LArTPCs, the recorded data consist of digitized waveforms on wires produced by induced signal on wires of drifting ionization electrons, which can also be viewed as two-dimensional (2D) (time versus wire) projection images of charged-particle trajectories. For such an imaging detector, one critical step is the signal processing that reconstructs the original charge projections from the recorded 2D images. For the first time, we introduce a deep neural network in LArTPC signal processing to improve the signal region of interest detection. By combining domain knowledge (e.g., matching information from multiple wire planes) and deep learning, this method shows significant improvements over traditional methods. This work details the method, software tools, and performance evaluated with realistic detector simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2006

Deep Space Network, Cryogenic HEMT LNAs

Exploration of the Solar System with automated spacecraft that are more than ten astronomical units (1 AU = 149,597,870.691 km) from earth requires very large antennae employing extremely sensitive receivers. A key figure of merit in the specification of the spacecraft-to-earth telecommunications link is the ratio of the antenna gain to operatio nal noise temperature (G/Top) of the system. The Deep Space Network (DSN) receivers are cryogenic, low-noise amplifiers (LNAs) which addres s the need to maintain Top as low as technology permits. Historicall y, the extra-ordinarily sensitive receive systems operated by the DSN have required ctyogenically cooled, ruby masers, operating at a physi cal temperature near the boiling point of helium, as the LNA. Althoug h masers continue to be used today, they are hand crafted at JPL and expensive to manufacture and maintain. Recent advances in the developm ent of indium phosphide (InP) based high electron mobility transistor s (HEMTs) combined with cryogenic cooling near the boiling point of h ydrogen have made this alternate technology comparable with and a fraction of the cost of maser technology. InP HEMT LNA modules are demons trating noise temperatures less than ten times the quantum noise limi t (10hf/k) from 1 to 100 GHz. To date, the lowest noise LNA modules developed for the DSN have demonstrated noise temperatures of 3.5 K and 8.5 K at 8.5 K at 32 GHz, respectively. Front-end receiver packages employing these modules have demonstrated operating system noise temperatures of 17 K at 8.4 GHz (on a 70m antenna at zenith) and 39 K at 3 2 GHz (on a 34m antenna at zenith). The development and demonstration of cryogenic, InP HEMT based front-end amplifiers for the DSN requir es accurate component and module characterization, and modeling from 1 to 100 GHz at physical temperatures down to 12 K. The characterizati on and modeling begins with the HEMT chip, proceeds to the multi-stag e HEMT LNA module, and culminates with the complete front-end cryogenic receiver package for the antenna. This presentation will provide a n overview of this development process. Examples will be shown for de vices, LNA modules, front-end receiver packages, antennae employing these packages and the improvements to the down-link capacity.

low-noise amplifiers (LNAs)↗
NASA NTRS2004

Power Spectrum of Atmospheric Scintillation for the Deep Space Network Goldstone Ka-Band Downlink

Dynamic signal fluctuations due to atmospheric scintillations may impair the Ka-band (around 32-GHz) link sensitivities for a low-margin Deep Space Network (DSN) receiving system. The ranges of frequency and power of the fast fluctuating signals (time scale less than 1 min) are theoretically investigated using the spatial covariance and turbulence theory. Scintillation power spectrum solutions are derived for both a point receiver and a finite-aperture receiver. The aperture-smoothing frequency ((omega(sub s)), corner frequency ((omega(sub c)), and damping rate are introduced to define the shape of the spectrum for a finite-aperture antenna. The emphasis is put on quantitatively describing the aperture-smoothing effects and graphically estimating the corner frequency for a large aperture receiver. Power spectral shapes are analyzed parametrically in detail through both low- and high-frequency approximations. It is found that aperture-averaging effects become significant when the transverse correlation length of the scintillation is smaller than the antenna radius. The upper frequency or corner frequency for a finite-aperture receiver is controlled by both the Fresnel frequency and aperture-smoothing frequency. Above the aperture-smoothing frequency, the spectrum rolls off at a much faster rate of exp (-omega(sup 2)/omega(sup 2, sub s), rather than omega(sup -8/3), which is customary for a point receiver. However, a relatively higher receiver noise level can mask the fast falling-off shape and make it hard to be identified. We also predict that when the effective antenna radius a(sub r) less than or = 6 m, the corner frequency of its power spectrum becomes the same as that for a point receiver. The aperture-smoothing effects are not obvious. We have applied these solutions to the scenario of a DSN Goldstone 34-m-diameter antenna and predicted the power spectrum shape for the receiving station. The maximum corner frequency for the receiver (with omega(sub s) = 0.79 omega(sub 0) is found to be 0.44 Hz (or 1.0 omega(sub 0), while the fading rate (or fading slope) is about 0.06 dB/s.

Ho, C.↗
NASA NTRS2005

Antennas for the array-based Deep Space Network: current status and future designs

Development of very large arrays1,2 of small antennas has been proposed as a way to increase the downlink capability of the NASA Deep Space Network DSN) by two or three orders of magnitude thereby enabling greatly increased science data from currently configured missions or enabling new mission concepts. The current concept is for an array of 400 x 12-m antennas at each of three longitudes. The DSN array will utilize radio astronomy sources for phase calibration and will have wide bandwidth correlation processing for this purpose. NASA has undertaken a technology program to prove the performance and cost of a very large DSN array. Central to that program is a 3-element interferometer to be completed in 2005. This paper describes current status of the low cost 6-meter breadboard antenna to be used as part of the interferometer and the RF design of the 12-meter antenna.

antennas↗
DOE OSTI2020

A deep neural network to search for new long-lived particles decaying to jets

A tagging algorithm to identify jets that are significantly displaced from the proton-proton (pp) collision region in the CMS detector at the LHC is presented. Displaced jets can arise from the decays of long-lived particles (LLPs), which are predicted by several theoretical extensions of the standard model. The tagger is a multiclass classifier based on a deep neural network, which is parameterised according to the proper decay length $\mathrm{c}\tau_0$ of the LLP. A novel scheme is defined to reliably label jets from LLP decays for supervised learning. Samples of pp collision data, recorded by the CMS detector at a centre-of-mass energy of 13 TeV, and simulated events are used to train the neural network. Domain adaptation by backward propagation is performed to improve the simulation modelling of the jet class probability distributions observed in pp collision data. The potential performance of the tagger is demonstrated with a search for long-lived gluinos, a manifestation of split supersymmetric models. The tagger provides a rejection factor of 10 000 for jets from standard model processes, while maintaining an LLP jet tagging efficiency of 30-80% for gluinos with 1 mm $\leq$ $c\tau_0$ $\leq$ 10 m. The expected coverage of the parameter space for split supersymmetry is presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Deep material network via a quilting strategy: visualization for explainability and recursive training for improved accuracy

Recent developments integrating micromechanics and neural networks offer promising paths for rapid predictions of the response of heterogeneous materials with similar accuracy as direct numerical simulations. The deep material network is one such approaches, featuring a multi-layer network and micromechanics building blocks trained on anisotropic linear elastic properties. Once trained, the network acts as a reduced-order model, which can extrapolate the material’s behavior to more general constitutive laws, including nonlinear behaviors, without the need to be retrained. However, current training methods initialize network parameters randomly, incurring inevitable training and calibration errors. Here, we introduce a way to visualize the network parameters as an analogous unit cell and use this visualization to “quilt” patches of shallower networks to initialize deeper networks for a recursive training strategy. The result is an improvement in the accuracy and calibration performance of the network and an intuitive visual representation of the network for better explainability.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1971

Radio tracking with the Deep Space Network

Range and Doppler measurements for the Mariner 6 and 7 spacecraft are described. The Doppler tracking system and the two systems of spacecraft ranging apparatus being used in the Deep Space Network are illustrated. Charged particle calibration is considered. Equipment characteristics are presented for the ranging apparatus.

Martin, W. L.↗
NASA NTRS1991

Stochastic availability analysis of operational data systems in the Deep Space Network

Existing availability models of standby redundant systems consider only an operator's performance and its interaction with the hardware performance. In the case of operational data systems in the Deep Space Network (DSN), in addition to an operator system interface, a controller reconfigures the system and links a standby unit into the network data path upon failure of the operating unit. A stochastic (Markovian) process technique is used to model and analyze the availability performance and occurrence of degradation due to partial failures are quantitatively incorporated into the model. Exact expressions of the steady state availability and proportion degraded performance measures are derived for the systems under study. The interaction among the hardware, operator, and controller performance parameters and that interaction's effect on data availability are evaluated and illustrated for an operational data processing system.

Issa, T. N.↗