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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 OSTI2023

RF signature detection for waveguide deformation

A radio frequency (RF) based waveguide health monitoring system is disclosed. The system employs an RF transmitter for launching a probe RF waveform into a waveguide. Reflections, etc., from the interior of the waveguide of the probe RF waveform create a signature RF waveform, with a health RF receiver receiving this resultant signature RF waveform. A health processing system analyzes the signature RF waveform, and when it detects a change indicative of a deformation of the waveguide, generates a warning signal. This change may be due to bends, flexes, vibrations (or changes in vibrations), or separations of the waveguide. The system may have low frequency, high frequency, or high frequency imaging modes. The system may employ a high-power probe RF waveform, thereby enabling a wireless charging system with power RF receivers located along the length of the waveguide providing additional functionality.

Curtis, Shane Keawe↗
DOE OSTI2023

Ammonia generation in Ns pulse and Ns pulse/RF discharges over a catalytic surface

Plasma-catalytic ammonia synthesis in a ns pulse discharge and a 'hybrid' ns pulse/RF discharge in plane-to-plane geometry is studied by Fourier Transform infrared absorption spectroscopy. The data are taken in a preheated H 2 –N 2 mixture, with and without Ni/γ-Al 2 O 3 or Co/γ-Al 2 O 3 catalyst placed in the discharge section. The measurement results are taken using two different approaches. The first is a 'single-stage' process, where a ns pulse discharge in the H 2 –N 2 mixture is sustained continuously. In this case, the ammonia yield increases slowly, over a period of tens of minutes. The second is a 'two-stage' process, where the catalyst is first activated by the ns pulse discharge sustained in pure nitrogen, and then the activated catalyst is exposed to the H 2 –N 2 flow, with or without the discharge. In this case, a strong overshoot of the NH 3 number density at the reactor exit is detected, by over a factor of two compared to the single-stage process. After the initial overshoot, the ammonia yield gradually decreases to the 'single stage' value (with the discharge on), or to near zero (with the discharge off). The results demonstrate that the ammonia yield in the plasma-catalytic reactor is controlled by the N atom accumulation on the catalyst surface, which reacts with H atoms thermally dissociated on the catalyst or generated in the plasma. The results also show that the plasma-catalytic ammonia yield is significantly higher compared to that in the ns pulse discharge without the catalyst. The accumulation of H atoms on the catalyst, with their subsequent reactions with N atoms generated in the plasma, is of relatively minor importance at the present conditions. An additional series of measurements was made with a sub-breakdown RF waveform overlapped with the ns pulse discharge train, to enhance the vibrational excitation of nitrogen. The ammonia yield measured with the RF waveform added is approximately 20% higher compared to that at the baseline ns pulse discharge conditions, both with and without the catalyst. This effect is weaker compared to that of the catalyst activation by N atoms. Additional data are necessary to isolate the possible effect of the vibrationally excited N 2 molecules on the ammonia synthesis in the plasma catalytic reactions.

surface reactions↗
DOE OSTI2022

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of origin. This information is subsequently utilized to identify failure trends and to implement corrective measures on the offending cavity. Manual inspection of large-scale, time-series data, generated by frequent system failures is tedious and time consuming, and thereby motivates the use of machine learning (ML) to automate the task. This study extends work on a previously developed system based on traditional ML methods (Tennant and Carpenter and Powers and Shabalina Solopova and Vidyaratne and Iftekharuddin, Phys. Rev. Accel. Beams, 2020, 23, 114601), and investigates the effectiveness of deep learning approaches. The transition to a DL model is driven by the goal of developing a system with sufficiently fast inference that it could be used to predict a fault event and take actionable information before the onset (on the order of a few hundred milliseconds). Because features are learned, rather than explicitly computed, DL offers a potential advantage over traditional ML. Specifically, two seminal DL architecture types are explored: deep recurrent neural networks (RNN) and deep convolutional neural networks (CNN). We provide a detailed analysis on the performance of individual models using an RF waveform dataset built from past operational runs of CEBAF. In particular, the performance of RNN models incorporating long short-term memory (LSTM) are analyzed along with the CNN performance. Furthermore, comparing these DL models with a state-of-the-art fault ML model shows that DL architectures obtain similar performance for cavity identification, do not perform quite as well for fault classification, but provide an advantage in inference speed.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

The Lightning and Radio Emission Detector (LRD) instrument

The Lightning and Radio Emission Detector (LRD) instrument will be carried by the Galileo Probe into Jupiter's atmosphere. The LRD will verify the existence of lightning in the atmosphere and will determine the details of many of its basic characteristics. The instrument, operated in its magnetospheric mode at distances of about 5, 4, 3, and 2 planetary radii from Jupiter's center, will also measure the RF noise spectrum in Jupiter's magnetosphere. The LRD instrument is composed of a ferrite-core radio frequency antenna and two photodiodes mounted behind individual fisheye lenses. The output of the RF antenna is analyzed both separately and in coincidence with the optical signals from the photodiodes. The RF antenna provides data both in the frequency domain (with three narrow-band channels, primarily for deducing the physical properties of distant lightning) and in the time domain with a priority scheme (primarily for determining from individual RF waveforms the physical properties of closeby-lightning).

Lanzerotti, L. J.↗
DOE OSTI2023

Multiple frequency electron cyclotron resonance thruster

An electron cyclotron resonance (ECR) thruster includes a magnetic field source configured to generate a magnetic field, a thruster body that defines a chamber, the thruster body being disposed relative to the magnetic field source such that the magnetic field is present in the chamber and such that a magnetic nozzle is established, an antenna configured to propagate radio frequency (RF) power within the chamber, and a waveform generator coupled to the antenna to generate an RF waveform for the RF power. The waveform generator is configured such that the RF waveform includes multiple frequencies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2012

Goldstone Solar System Radar Waveform Generator

Due to distances and relative motions among the transmitter, target object, and receiver, the time-base between any transmitted and received signal will undergo distortion. Pre-distortion of the transmitted signal to compensate for this time-base distortion allows reception of an undistorted signal. In most radar applications, an arbitrary waveform generator (AWG) would be used to store the pre-calculated waveform and then play back this waveform during transmission. The Goldstone Solar System Radar (GSSR), however, has transmission durations that exceed the available memory storage of such a device. A waveform generator capable of real-time pre-distortion of a radar waveform to a given time-base distortion function is needed. To pre-distort the transmitted signal, both the baseband radar waveform and the RF carrier must be modified. In the GSSR, this occurs at the up-conversion mixing stage to an intermediate frequency (IF). A programmable oscillator (PO) is used to generate the IF along with a time-varying phase component that matches the time-base distortion of the RF carrier. This serves as the IF input to the waveform generator where it is mixed with a baseband radar waveform whose time-base has been distorted to match the given time-base distortion function producing the modulated IF output. An error control feedback loop is used to precisely control the time-base distortion of the baseband waveform, allowing its real-time generation. The waveform generator produces IF modulated radar waveforms whose time-base has been pre-distorted to match a given arbitrary function. The following waveforms are supported: continuous wave (CW), frequency hopped (FH), binary phase code (BPC), and linear frequency modulation (LFM). The waveform generator takes as input an IF with a time varying phase component that matches the time-base distortion of the carrier. The waveform generator supports interconnection with deep-space network (DSN) timing and frequency standards, and is controlled through a 1 Gb/s Ethernet UDP/IP interface. This real-time generation of a timebase distorted radar waveform for continuous transmission in a planetary radar is a unique capability.

Quirk, Kevin J.↗
NASA NTRS2007

JTRS/SCA and Custom/SDR Waveform Comparison

This paper compares two waveform implementations generating the same RF signal using the same SDR development system. Both waveforms implement a satellite modem using QPSK modulation at 1M BPS data rates with one half rate convolutional encoding. Both waveforms are partitioned the same across the general purpose processor (GPP) and the field programmable gate array (FPGA). Both waveforms implement the same equivalent set of radio functions on the GPP and FPGA. The GPP implements the majority of the radio functions and the FPGA implements the final digital RF modulator stage. One waveform is implemented directly on the SDR development system and the second waveform is implemented using the JTRS/SCA model. This paper contrasts the amount of resources to implement both waveforms and demonstrates the importance of waveform partitioning across the SDR development system.

Oldham, Daniel R.↗
NASA NTRS2014

Ka-Band Link Study and Analysis for a Mars Hybrid RF/Optical Software Defined Radio

The integrated radio and optical communications (iROC) project at the NASA Glenn Research Center (GRC) is investigating the feasibility of a hybrid RF and optical communication subsystem for future deep space missions. The hybrid communications subsystem enables the advancement of optical communications while simultaneously mitigating the risk of infusion by combining an experimental optical transmitter and telescope with a reliable Ka-band RF transmitter and antenna. The iROC communications subsystem seeks to maximize the total data return over the course of a potential 2-year mission in Mars orbit beginning in 2021. Although optical communication by itself offers potential for greater data return over RF, the reliable Ka-band link is also being designed for high data return capability in this hybrid system. A daily analysis of the RF link budget over the 2-year span is performed to optimize and provide detailed estimates of the RF data return. In particular, the bandwidth dependence of these data return estimates is analyzed for candidate waveforms. In this effort, a data return modeling tool was created to analyze candidate RF modulation and coding schemes with respect to their spectral efficiency, amplifier output power back-off, required digital to analog conversion (DAC) sampling rates, and support by ground receivers. A set of RF waveforms is recommended for use on the iROC platform.

communication↗
DOE OSTI2020

Machine Learning Based Cavity Fault Classification and Prediction

We report on the development of machine learning models for classifying C100 superconducting radio frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through 5-passes. Of these, 96 cavities (12 cryomodules) are designed with a digital low-level RF system configured such that a cavity fault triggers waveform recordings of 17 RF signals for each of the eight cavities in the cryomodule. Subject matter experts (SME) can analyze the collected time-series data, identify which of the eight cavities faulted first, and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time – rather than post-mortem – identification of the offending cavity and classification of the fault type has been implemented. We discuss the performance of the ML models during a recent physics run.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Effect of net direct current on the properties of radio frequency sheaths: simulation and cross-code comparison

In order to understand, predict and control ion cyclotron range of frequency (ICRF) interactions with tokamak scrape-off layer plasmas, computational tools which can model radio frequency (RF) sheaths are needed. In particular, models for the effective surface impedance and DC rectified sheath potentials may be coupled with full wave RF simulation codes to predict self-consistent wave fields near surfaces and the resulting power dissipation and plasma-material interactions from ion sputtering. In this study, previous work assuming zero net DC current flow through the sheath is generalized to allow the surface to collect net positive or negative current, as is often observed in experiments. The waveforms, DC potential and RF admittance are investigated by means of analytical theory, nonlinear fluid and particle-in-cell (PIC) codes. Cross-code comparisons provide detailed model verification and elucidate the roles of ion and electron kinetics. When the sheath draws negative (positive) DC current, the voltage rectification is reduced (increased) compared with the zero-current case, and the magnitude of both the real and imaginary parts of the admittance are increased (reduced). In conclusion, a previous four-input parametrization of the sheath rectification and admittance properties is generalized to include a fifth parameter describing the DC sheath current.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2012

An MSK Radar Waveform

The minimum-shift-keying (MSK) radar waveform is formed by periodically extending a waveform that separately modulates the in-phase and quadrature- phase components of the carrier with offset pulse-shaped pseudo noise (PN) sequences. To generate this waveform, a pair of periodic PN sequences is each passed through a pulse-shaping filter with a half sinusoid impulse response. These shaped PN waveforms are then offset by half a chip time and are separately modulated on the in-phase and quadrature phase components of an RF carrier. This new radar waveform allows an increase in radar resolution without the need for additional spectrum. In addition, it provides self-interference suppression and configurable peak sidelobes. Compared strictly on the basis of the expressions for delay resolution, main-lobe bandwidth, effective Doppler bandwidth, and peak ambiguity sidelobe, it appears that bi-phase coded (BPC) outperforms the new MSK waveform. However, a radar waveform must meet certain constraints imposed by the transmission and reception of the modulation, as well as criteria dictated by the observation. In particular, the phase discontinuity of the BPC waveform presents a significant impediment to the achievement of finer resolutions in radar measurements a limitation that is overcome by using the continuous phase MSK waveform. The phase continuity, and the lower fractional out-of-band power of MSK, increases the allowable bandwidth compared with BPC, resulting in a factor of two increase in the range resolution of the radar. The MSK waveform also has been demonstrated to have an ambiguity sidelobe structure very similar to BPC, where the sidelobe levels can be decreased by increasing the length of the m-sequence used in its generation. This ability to set the peak sidelobe level is advantageous as it allows the system to be configured to a variety of targets, including those with a larger dynamic range. Other conventionally used waveforms that possess an even greater spectral efficiency than the MSK waveform, such as linear frequency modulation (LFM) and Costas frequency hopping, have a fixed peak sidelobe level that is therefore not configurable, and can be exceeded by high contrast targets. Furthermore, in the case of a multistatic experiment observing a target in motion, self-interference from the transmitter to the receiver is mitigated by the MSK waveform. Waveforms that have delay Doppler coupling, such as LFM, provide no such protection.

Quirk, Kevin J.↗
NASA NTRS2001

Power Divider for Harmonically Rich Waveforms

A power divider divides an RF signal into two output signals having a phase difference of 180° or a multiple thereof. When the RF signal is a square wave or another harmonically rich signal. the phases of the fundamental and the harmonics have the proper relationship. The divider can be implemented in the form of rnicrostrips on a board, with one of the output microstrips having several bends to provide a different electrical length from the other.

William Herbert Sims, III↗
NASA NTRS2014

General Dynamic (GD) Launch Waveform On-Orbit Performance Report

The purpose of this report is to present the results from the GD SDR on-orbit performance testing using the launch waveform over TDRSS. The tests include the evaluation of well-tested waveform modes, the operation of RF links that are expected to have high margins, the verification of forward return link operation (including full duplex), the verification of non-coherent operational models, and the verification of radio at-launch operational frequencies. This report also outlines the launch waveform tests conducted and comparisons to the results obtained from ground testing.

radio communication↗
NASA NTRS2018

High-Rate Ka-Band Modulator for the NISAR Mission

In order to meet ever-increasing data return requirements, more satellites are using the near-Earth Ka-band (25.5 – 27.0 GHz) to achieve higher downlink rates. The paper discusses the Universal Space Transponder - Ka-band Modulator (UST-KaM) developed at the NASA Jet Propulsion Laboratory for the NASA-ISRO SAR (NISAR) mission, which is capable of transmitting up to 1.74 Gbps with 7/8 LDPC encoding. The UST-KaM utilizes OQPSK with both baseband and RF filtering to contain the 1 Gsps transmission spectrum within the 1.5 GHz Ka-band, even with the use of an external, saturated amplifier. Due to the high data rates involved, several technical hurdles were overcome in both the digital and RF designs. The UST-KaM is a software defined radio with two digital circuit board assemblies: a low speed housekeeper board for commanding and telemetry, and a high-rate signal processing board known as the Signal Processing Module (SPM). The SPM receives data from the spacecraft via a SERDES interface at up to 2 Gbps, processes and encodes the data using a Xilinx Virtex-5 FPGA, and produces 1 Gsps OQPSK I and Q waveforms via synchronized, multiplexed DACs. The RF Electronics in the UST-KaM employ a heterodyne architecture in which the I/Q digital waveforms are filtered and then up-converted using a sub-harmonic IQ mixer. The LO of the converter, which is included in the exciter assembly, is at 13.125GHz, and the RF output is a 26.25GHz carrier which is modulated with the OQPSK waveforms. The output of the mixer is then filtered using low loss quartz thinfilm edge coupled Chebyshev filters and amplified through a series of low gain Ka-Band amplifiers. The exciter assembly also has a 2GHz, low-phase-noise, PLL synthesizer to supply the clock to the DACs for the high rate digital waveforms.

Spurgers, Carl↗
DOE OSTI2022

Automatic Waveform Quality Control for Surface Waves Using Machine Learning

Surface-wave seismograms are widely used by researchers to study Earth’s interior and earthquakes. To extract information reliably and robustly from a suite of surface waveforms, the signals require quality control screening to reduce artifacts from signal complexity and noise. This process has usually been completed by human experts labeling each waveform visually, which is time consuming and tedious for large data sets. We explore automated approaches to improve the efficiency of waveform quality control processing by investigating logistic regression, support vector machines, K-nearest neighbors, random forests (RF), and artificial neural networks (ANN) algorithms. To speed up signal quality assessment, we trained these five machine learning (ML) methods using nearly 400,000 human-labeled waveforms. The ANN and RF models outperformed other algorithms and achieved a test accuracy of 92%. We evaluated these two best-performing models using seismic events from geographic regions not used for training. The results show that the two trained models agree with labels from human analysts but required only 0.4% of the time. Although the original (human) quality assignments assessed general waveform signal-to-noise, the ANN or RF labels can help facilitate detailed waveform analysis. Our investigations demonstrate the capability of the automated processing using these two ML models to reduce outliers in surface-wave-related measurements without human quality control screening.

58 GEOSCIENCES↗