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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Baseband Digital Network Analyzer Upgrade for LLRF Controllers

Digital Network Analyzers (DNA) have been implemented in many Low-Level Radio Frequency (LLRF) systems, notably NSLS-II and CERN, to help tune feedback loops. DNA characterizes feedback loops by measuring the frequency-dependent magnitude and phase transfer functions. It enables the measurement of open loop gains, gain/phase margins, and loop delays to help fine-tune feedback loops. An FPGA-based DNA has been developed and integrated into the current Relativistic Heavy Ion Collider (RHIC) LLRF infrastructure. Its performance has been tested with an implementation of one-turn delay feedback (OTFB) on the bench to maximize gain and stability. The DNA has been used to characterize a RHIC 28 MHz cavity in a RHIC Accelerator Physics Experiment (APEX) to test transient beam loading compensation strategies.

43 PARTICLE ACCELERATORS↗
NASA NTRS2006

On-wafer vector network analyzer measurements in the 220-325 Ghz frequency band

We report on a full two-port on-wafer vector network analyzer test set for the 220-325 GHz (WR3) frequency band. The test set utilizes Oleson Microwave Labs frequency extenders with the Agilent 8510C network analyzer. Two port on-wafer measurements are made with GGB Industries coplanar waveguide (CPW) probes. With this test set we have measured the WR3 band S-parameters of amplifiers on-wafer, and the characteristics of the CPW wafer probes. Results for a three stage InP HEMT amplifier show 10 dB gain at 235 GHz [1], and that of a single stage amplifier, 2.9 dB gain at 231 GHz. The approximate upper limit of loss per CPW probe range from 3.0 to 4.8 dB across the WR3 frequency band.

coplanar waveguides↗
NASA NTRS1989

W-band six-port network analyzer for two-port characterization of millimeter wave transistors

A W-band (75-100 GHz) six-port junction network analyzer was constructed from commercially available descrete waveguide components and was used for the direct two-port S-parameter measurement of active three-terminal devices. A comparison between the six-port and a down-converter-type frequency extender for a conventional network analyzer revealed the superior performance of the six-port. The application of the six-port to characterize a 0.1-micron gate-length HEMT at W-band is described, and representative results are presented.

Moeller, Karl J.↗
NASA NTRS1990

Using a modified Hewlett Packard 8410 network analyzer as an automated farfield antenna range receiver

A Hewlett Packard 8410 Network Analyzer was modified to be used as an automated far-field antenna range receiver. By using external mixers, analog to digital signal conversion, and an external computer/controller, the HP8410 is capable of measuring signals as low as -110 dBm. The modified receiver is an integral part of an automated far-field range which features computer controlled test antenna positioning, system measurement parameters, and data acquisition, as well as customized measurement file management. The system described was assembled and made operational, taking advantage of off-the-shelf hardware available at minimal cost.

Terry, John D.↗
NASA NTRS1990

Near-field antenna testing using the Hewlett Packard 8510 automated network analyzer

Near-field antenna measurements were made using a Hewlett-Packard 8510 automated network analyzer. This system features measurement sensitivity better than -90 dBm, at measurement speeds of one data point per millisecond in the fast data acquisition mode. The system was configured using external, even harmonic mixers and a fiber optic distributed local oscillator signal. Additionally, the time domain capability of the HP8510, made it possible to generate far-field diagnostic results immediately after data acquisition without the use of an external computer.

Kunath, Richard R.↗
NASA NTRS1986

Effects of specimen preparation on the electromagnetic property measurements of solid materials with an automatic network analyzer

Effects of specimen preparation on measured values of an acrylic's electomagnetic properties at X-band microwave frequencies, TE sub 1,0 mode, utilizing an automatic network analyzer have been studied. For 1 percent or less error, a gap between the specimen edge and the 0.901-in. wall of the specimen holder was the most significant parameter. The gap had to be less than 0.002 in. The thickness variation and alignment errors in the direction parallel to the 0.901-in. wall were equally second most significant and had to be less than 1 degree. Errors in the measurement f the thickness were third most significant. They had to be less than 3 percent. The following parameters caused errors of 1 percent or less: ratios of specimen-holder thicknesses of more than 15 percent, gaps between the specimen edge and the 0.401-in. wall less than 0.045 in., position errors less than 15 percent, surface roughness, hickness variation in the direction parallel to the 0.401-in. wall less than 35 percent, and specimen alignment in the direction parallel to the 0.401-in. wall mass than 5 degrees.

Long, E. R., Jr.↗
NASA NTRS2008

Measurements by a Vector Network Analyzer at 325 to 508 GHz

Recent experiments were performed in which return loss and insertion loss of waveguide test assemblies in the frequency range from 325 to 508 GHz were measured by use of a swept-frequency two-port vector network analyzer (VNA) test set. The experiments were part of a continuing effort to develop means of characterizing passive and active electronic components and systems operating at ever increasing frequencies. The waveguide test assemblies comprised WR-2.2 end sections collinear with WR-3.3 middle sections. The test set, assembled from commercially available components, included a 50-GHz VNA scattering- parameter test set and external signal synthesizers, augmented with recently developed frequency extenders, and further augmented with attenuators and amplifiers as needed to adjust radiofrequency and intermediate-frequency power levels between the aforementioned components. The tests included line-reflect-line calibration procedures, using WR-2.2 waveguide shims as the "line" standards and waveguide flange short circuits as the "reflect" standards. Calibrated dynamic ranges somewhat greater than about 20 dB for return loss and 35 dB for insertion loss were achieved. The measurement data of the test assemblies were found to substantially agree with results of computational simulations.

Fung, King Man↗
NASA NTRS1995

Neural Networks Analyze Data In Particle-Impact-Noise Tests

Electronic neural networks and computers put to use in analyzing data acquired in particle-impact-noise-detection (PIND) tests of packaged electronic components. PIND tests detect loose particles in packages that cause failures during subsequent operation of packages in presence of accelerations or other effects - for example, loose electrically conductive particles that bounce into positions in which they cause short circuits. Interpretation of test data more objective and accurate. Preliminary results suggest use of neural networks result in significant improvement in quality and reliability and decrease in cost of PIND testing.

Scaglione, Lois J.↗
NASA NTRS1990

Electro-optic network analyzer

The bandwidth of frequency domain measurement methods of electrical signals has usually been far greater than the bandwidth of time domain methods. The primary limits of the time domain approach have been the 20 to 30 GHz bandwidth limit for electronic waveform acquisition instrumentation, and the lack of usable electrical pulse generators for excitation of a test device. The bandwidth of frequency domain network analysis appears to have reached a plateau of between 100 to 200 GHz, while time domain measurement have improved markedly in both bandwidth and sensitivity with the introduction of the pulsed laser based electro-optic sampling approach. Network analysis or the measurement of device scattering parameters provides information necessary to the design of electronic network such as high frequency amplifiers, mixers, and phase shifter. The bandwidth of frequency domain network analysis is currently being exceeded by the next generations of high frequency transistors and devices. Thus the electro-optic approach is a natural means of extending network analysis into the range above 100 GHz by employing time domain methods. In this approach, a suitable electrical excitation pulse is generated and propagated along a transmission line toward a test device. In the picosecond domain, laser driven photoconductive switches provide a unique method of generating electrical transients. Several materials were studied for generating short electrical pulses using photoconductive switches. The various semiconductive materials tested for photoconductive switching, and the electro-optic measurement technique used to characterize the material performance are described.

Jackson, Todd A.↗
NASA NTRS1969

Thermal Network Analyzer Program

Computer program solves transient or steady-state heat flow problems through the concept of lumped parameters expressed as the electrical analog of the heat transfer problem using finite differences techniques.

Sakakura, H. D.↗
NASA NTRS2005

A Comparison of Geographic Information Systems, Complex Networks, and Other Models for Analyzing Transportation Network Topologies

This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.

Alexandrov, Natalia↗
DOE OSTI2022

pnnl/FLOWER

Network FLOW analyzER - Network bi-directional deep packet header inspection. This software is designed to generate custom network flow records by monitoring bi-directional traffic from a span port, tap or other source. This function is independent of proprietary hardware or software solutions. This tool solves the problem of existing COTS solutions being either too complex or too slow.

Curtis, Darren↗
DOE OSTI2021

RivGraph: Automatic extraction and analysis of river and delta channel network topology

River networks sustain life and landscapes by carrying and distributing water, sediment, and nutrients throughout ecosystems and communities. At the largest scale, river networks drain continents through tree-like tributary networks. At typically smaller scales, river deltas and braided rivers form loopy, complex distributary river networks via avulsions and bifurcations.In order to model flows through these networks or analyze network structure, the topology, or connectivity, of the network must be resolved. Additionally, morphologic properties of each river channel as well as the direction of flow through the channel inform how fluxes travel through the network’s channels. Riv Graphis a Python package that automates the extraction and characterization of river channel networks from a user-provided binary image, or mask, of a channel network (Fig. 1). Masks may be derived from (typically remotely-sensed) imagery, simulations, or even hand-drawn. RivGraph will create explicit representations of the channel network by resolving river centerlines as links, and junctions as nodes. Flow directions are solved for each link of the network without using auxiliary data, e.g., a digital elevation model (DEM). Morphologic properties are computed as well, including link lengths, widths, sinuosities, branching angles,and braiding indices. If provided,RivGraph will preserve georeferencing information of the mask and will export results as ESRI shapefiles, GeoJSONs, and GeoTIFFs for easy import into GIS software.RivGraph can also return extracted networks as networkx objects for convenient interfacing with the full-featured networkx package (Hagberg et al., 2008). Finally, RivGraph offers a suite of topologic metrics that were specifically designed for river channel network analysis (Tejedor et al., 2015b).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

Structural factoring approach for analyzing stochastic networks

The problem of finding the distribution of the shortest path length through a stochastic network is investigated. A general algorithm for determining the exact distribution of the shortest path length is developed based on the concept of conditional factoring, in which a directed, stochastic network is decomposed into an equivalent set of smaller, generally less complex subnetworks. Several network constructs are identified and exploited to reduce significantly the computational effort required to solve a network problem relative to complete enumeration. This algorithm can be applied to two important classes of stochastic path problems: determining the critical path distribution for acyclic networks and the exact two-terminal reliability for probabilistic networks. Computational experience with the algorithm was encouraging and allowed the exact solution of networks that have been previously analyzed only by approximation techniques.

Hayhurst, Kelly J.↗
DOE OSTI2024

Z-Target Radiography Postprocessing With A Deep Convolution Neural Network

Analyzing X-ray radiographs is crucial for understanding target behavior in Inertial Confinement Fusion (ICF) and High Energy Density (HED) platforms. However, the density of Magneto Raleigh Taylor (MRT) bands and limitations of target materials often obscure relevant spike growth and density information. To address this issue, machine learning postprocessing techniques can be applied to remove darkened regions in radiography images. In this study, a novel method is presented for removing MRT darkened regions from z-target radiographs using a convolutional neural network (CNN). The CNN, consisting of six layers, treats the darkened regions as noise and employs a mixed loss function and end-to-end frameworks to suppress them while preserving sharpness. The six-layer architecture is designed to effectively learn features when provided with a larger volume of learning space. Each layer is optimized using a mixed loss function that combines a standard loss pixel approach with a multi-scaled structural similarity index loss, which considers luminance, contrast, and structure in local neighborhoods. This approach is particularly beneficial for capturing the stochastic structure of MRT limbs. Due to the limited availability of experimental data, training is conducted using synthetic target radiography from 3D Alegra simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1995

The Deep Space Network stability analyzer

A stability analyzer for testing NASA Deep Space Network installations during flight radio science experiments is described. The stability analyzer provides realtime measurements of signal properties of general experimental interest: power, phase, and amplitude spectra; Allan deviation; and time series of amplitude, phase shift, and differential phase shift. Input ports are provided for up to four 100 MHz frequency standards and eight baseband analog (greater than 100 kHz bandwidth) signals. Test results indicate the following upper bounds to noise floors when operating on 100 MHz signals: -145 dBc/Hz for phase noise spectrum further than 200 Hz from carrier, 2.5 x 10(exp -15) (tau =1 second) and 1.5 x 10(exp -17) (tau =1000 seconds) for Allan deviation, and 1 x 10(exp -4) degrees for 1-second averages of phase deviation. Four copies of the stability analyzer have been produced, plus one transportable unit for use at non-NASA observatories.

Breidenthal, Julian C.↗