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Results for “Waveform Averaging”

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

Continuous dithered waveform averaging for high-fidelity digitization of repetitive signals

Methods and devices for digitizing an analog repetitive signal using waveform averaging are described. An example method includes generating a time-varying dither signal, receiving the analog repetitive signal comprising multiple instances of a waveform, wherein each waveform has a waveform duration, wherein an average of the time-varying dither signal over multiple waveform durations is substantially zero, and wherein the time-varying dither signal varies over each waveform duration, generating a timing alignment, combining each waveform with the corresponding portion of the time-varying dither signal over each waveform duration to produce an analog output signal, converting the analog output signal to a digital output signal, and producing, based on the timing alignment, a digital averaged signal based on averaging the multiple instances of the waveform in the analog output signal, wherein the timing alignment is used to align the multiple instances of the waveform in the analog output signal.

Buckley, Brandon Walter↗
DOE OSTI2024

Detuned synchronized waveform averaging for reducing distortions due to sample interleaving

This invention introduces a method for optimally detuning the ratio of analog-to-digital (ADC) sample rate and trigger rate during waveform averaging to minimize the deleterious effects from sample interleaving. A prescription is described for determining the optimal detuning factor for the ADC or trigger rate in any scenario, depending on the number of ADC cores used and the limited tuning resolution of the clock rates for a specific implementation.

42 ENGINEERING↗
DOE OSTI2023

Discrete offset dithered waveform averaging for high-fidelity digitization of repetitive signals

Methods and devices for digitizing an analog repetitive signal using waveform averaging are described. An example method includes generating a discrete set of analog dither offset voltages, wherein at least two of the discrete set of analog dither offset voltages are different from each other, receiving the analog repetitive signal comprising multiple instances of a waveform, wherein the waveform has a waveform duration, generate a timing alignment to align each waveform of the analog repetitive signal and the corresponding analog dither offset voltage over the waveform duration, combining, based on the timing alignment, each waveform and the corresponding analog dither offset voltage over the waveform duration to produce an analog output signal, converting the analog output signal to a digital output signal, and producing, based on the timing alignment, a digital averaged signal based on averaging the multiple instances of the waveform in the analog output signal.

Buckley, Brandon Walter↗
DOE OSTI2022

Probing D-region globally using lightning waveforms detected by ENTLN [Slides]

This report includes research and data that the author's state demonstrates "the technique from [Shao et al., Nature Geoscience, 2012] can be used with ENTLN waveforms. Furthermore, an "automated processing has been developed to produce averaged waveforms and associated measurements." Ideas for future work is also presented.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaluation method of beam instability in laser ion source using solenoid

In a laser ion source using solenoid field confinement, it is known that an ion beam becomes unstable in the certain range of a magnetic field. Although it is essential to quantify the instability when discussing the unstable region of the beam, it is difficult to evaluate the beam instability by peak current or the amount of charge because an irregular change of temporal profile occurs in addition to the shot-by-shot fluctuation of amplitude. In this study, I propose the most appropriate method to evaluate the beam instability using the difference from an average waveform. The validity of the new method was evaluated by comparing three evaluation methods (variation of maximum value, variation of integral of waveforms, and the proposed evaluation method) with the experimentally obtained waveforms with the stable and unstable regions of a solenoid field. The proposed method was verified to best represent the beam instability by laser-induced plasma.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Deep Learning Denoising Applied to Regional Distance Seismic Data in Utah

Seismic waveform data are generally contaminated by noise from various sources. Suppressing this noise effectively so that the remaining signal of interest can be successfully exploited remains a fundamental problem for the seismological community. To date, the most common noise suppression methods have been based on frequency filtering. These methods, however, are less effective when the signal of interest and noise share similar frequency bands. Inspired by source separation studies in the field of music information retrieval (Jansson et al., 2017) and a recent study in seismology (Zhu et al., 2019), we implemented a seismic denoising method that uses a trained deep convolutional neural network (CNN) model to decompose an input waveform into a signal of interest and noise. In our approach, the CNN provides a signal mask and a noise mask for an input signal. The short-time Fourier transform (STFT) of the estimated signal is obtained by multiplying the signal mask with the STFT of the input signal. To build and test the denoiser, we used carefully compiled signal and noise datasets of seismograms recorded by the University of Utah Seismograph Stations network. Results of test runs involving more than 9000 constructed waveforms suggest that on average the denoiser improves the signal-to-noise ratios (SNRs) by ~5 db, and that most of the recovered signal waveforms have high similarity with respect to the target waveforms (average correlation coefficient of ~0.80) and suffer little distortion. Application to real data suggests that our denoiser achieves on average a factor of up to ~2-5 improvement in SNR over band-pass filtering and can suppress many types of noise that band-pass filtering cannot. For individual waveforms, the improvement can be as high as ~15 db.

58 GEOSCIENCES↗
DOE OSTI2024

Characterization of ORNL PSD ASIC

The performance of the ORNL ASIC and its readout system was tested with pixelated organic scintillators. We use a pixelated trans-Stilbene scintillator array from Inrad Optics and a pixelated organic glass scintillator array developed at Sandia National Laboratories to characterize the energy and timing resolutions and the pulse-shape discrimination (PSD) figure-of-merit (FoM). The results are compared to previous work in which the same metrics were measured on waveforms digitized at 250 MHz with 14-bit resolution. We found that the PSD FoM at 340 keVee of the ASIC configuration compared to waveform data varied with the scintillator type. We measured a PSD FoM of 1.12 ± 0.14 with the ASIC configuration versus 1.39 ± 0.23 with waveform data using the trans-Stilbene array. We measured a PSD FoM of 0.52 ± 0.18 with the ASIC configuration versus 1.25 ± 0.19 with waveform data using the the organic glass scintillator array. The coincidence timing resolution was measured using two 6x6x6 mm 3 cubes of trans-Stilbene. It was measured to be 805 ± 9 ps with the ASIC configuration versus 300 ps on average with waveform data.

42 ENGINEERING↗
DOE OSTI2026

Detection of scintillation light in noble gases with wavelength-shifting optical fibers

Wavelength-shifting (WLS) techniques enable particle detectors based on noble gases, whose scintillation light is predominantly emitted in the vacuum-ultraviolet. We investigate WLS fibers coated with tetraphenyl butadiene (TPB) for scintillation light detection in gaseous xenon and argon at pressures up to 8.5 bar, motivated by future high-pressure xenon time-projection chambers of the NEXT program. Two detector configurations are studied: an elongated high-pressure vessel with four PTFE panels equipped with WLS fibers read by temperature-stabilized SiPMs, and a compact box-shaped detector operated at 1 bar Xe with WLS fibers read out by PMTs. Both operate with continuous gas purification. The detector response is characterized using cosmic muons and alpha particles from a $^{241}$Am source. With the SiPM setup, we measure a light collection efficiency (LCE) of ${1.18 \pm 0.01~\mathrm{(sta.)}~^{+0.07}_{-0.09}~\mathrm{(sys.)}~\%}$ for xenon and ${1.07 \pm 0.01~\mathrm{(sta.)}~^{+0.06}_{-0.08}~\mathrm{(sys.)}~\%}$ for argon. With PMT readout, we measure a LCE of ${0.45 \pm 0.01~\mathrm{(sta.)} \pm 0.05~\mathrm{(sys.)}~\%}$ in xenon, in agreement with the SiPM result once photon detection efficiency is accounted for. Average scintillation waveforms in xenon and argon are studied to assess the time structure of the emitted light. Cosmic-muon measurements yield a mean energy required to produce a scintillation photon $45\pm7~\mathrm{(sta.)}~^{+4}_{-5}~\mathrm{(sys.)}~\mathrm{eV}$ at 1.5 bar, in agreement with the literature. The results demonstrate that TPB-coated WLS fiber systems can reliably detect scintillation light in high-pressure gaseous noble detectors, with a LCE representing an upper limit for realistic large-scale TPCs, where additional photon losses from materials and fiber attenuation are expected.

Soleti, S. R. [Donostia Intl. Phys. Ctr., San Seba↗
DOE OSTI2022

Interpretable AI forecasting for numerical relativity waveforms of quasicircular, spinning, nonprecessing binary black hole mergers

We present a deep-learning artificial intelligence model (AI) that is capable of learning and forecasting the late-inspiral, merger and ringdown of numerical relativity waveforms that describe quasicircular, spinning, nonprecessing binary black hole mergers. We used the NRHybSur3dq8 surrogate model to produce train, validation and test sets of ℓ = |m| = 2 waveforms that cover the parameter space of binary black hole mergers with mass ratios q ≤ 8 and individual spins |s$^{z}_ {(1,2)}$| ≤ 0.8. These waveforms cover the time range t ∊ [-5000 M, 130 M], where t = 0M marks the merger event, defined as the maximum value of the waveform amplitude. We harnessed the ThetaGPU supercomputer at the Argonne Leadership Computing Facility to train our AI model using a training set of 1.5 million waveforms. We used 16 NVIDIA DGX A100 nodes, each consisting of 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, to fully train our model within 3.5 h. Our findings show that artificial intelligence can accurately forecast the dynamical evolution of numerical relativity waveforms in the time range t ∊ [-100 M, 130 M]. Sampling a test set of 190,000 waveforms, we find that the average overlap between target and predicted waveforms is ≳99% over the entire parameter space under consideration. We also combined scientific visualization and accelerated computing to identify what components of our model take in knowledge from the early and late-time waveform evolution to accurately forecast the latter part of numerical relativity waveforms. This work aims to accelerate the creation of scalable, computationally efficient and interpretable artificial intelligence models for gravitational wave astrophysics

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Full-waveform tomography reveals iron spin crossover in Earth’s lower mantle

Abstract Three-dimensional models of Earth’s seismic structure can be used to identify temperature-dependent phenomena, including mineralogical phase and spin transformations, that are obscured in 1-D spherical averages. Full-waveform tomography maps seismic wave-speeds inside the Earth in three dimensions, at a higher resolution than classical methods. By providing absolute wave speeds (rather than perturbations) and simultaneously constraining bulk and shear wave speeds over the same frequency range, it becomes feasible to distinguish variations in temperature from changes in composition or spin state. We present a quantitative joint interpretation of bulk and shear wave speeds in the lower mantle, using a recently published full-waveform tomography model. At all depths the diversity of wave speeds cannot be explained by an isochemical mantle. Between 1000 and 2500 km depth, hypothetical mantle models containing an electronic spin crossover in ferropericlase provide a significantly better fit to the wave-speed distributions, as well as more realistic temperatures and silica contents, than models without a spin crossover. Below 2500 km, wave speed distributions are explained by an enrichment in silica towards the core-mantle boundary. This silica enrichment may represent the fractionated remains of an ancient basal magma ocean.

58 GEOSCIENCES↗
DOE OSTI2021

Seismic Imaging of the Mw 7.1 Ridgecrest Earthquake Rupture Zone From Data Recorded by Dense Linear Arrays

Abstract We analyze seismograms recorded by four arrays (B1–B4) with 100 m station spacing and apertures of 4–8 km that cross the surface rupture of the 2019 Mw 7.1 Ridgecrest earthquake. The arrays extend from B1 in the northwest to B4 in the southeast of the surface rupture. Delay times between P wave arrivals associated with ∼1,200 local earthquakes and four teleseismic events are used to estimate local velocity variations beneath the arrays. Both teleseismic and local P waves travel faster on the northeast than the southwest side of the fault beneath arrays B1 and B4, but the velocity contrast is less reliably resolved at arrays B2 and B3. We identify several 1–2 km wide low‐velocity zones with much slower inner cores that amplify S waveforms, inferred as damage zones, beneath each array. The damage zones at arrays B2 and B4 also generate fault‐zone head and trapped waves. An automated detector, based on peak ground velocities and durations of high‐amplitude waves, identifies candidate fault‐zone trapped waves (FZTWs) in a localized zone for ∼600 earthquakes at array B4. Synthetic waveform modeling of averaged FZTWs, generated by ∼30 events with high‐quality signals, indicates that the trapping structure at array B4 has a width of ∼300 m, depth of 3–5 km, S wave velocity reduction of ∼20% with respect to the surrounding rock, Q ‐value of ∼30, and S wave velocity contrast of ∼4% across the fault (faster on the northeast side). The results show complex fault‐zone internal structures (velocity contrasts and low‐velocity zones) that vary along fault strike.

Qiu, Hongrui↗
DOE OSTI2022

Discriminating Types of Volcanic Electrical Activity: Toward an Eruption Detection Algorithm

Abstract We present a method for classification of the two distinct types of electrical activity that occur during an explosive volcanic eruption: vent discharges and lightning. Vent discharges occur at the onset of an explosion and create a distinctive radio frequency signature called continual radio frequency. Seconds to minutes after the onset of the eruption, lightning begins to occur throughout the eruption column. We use logistic regression to classify a radio frequency impulse as being part of either a lightning flash or a period of continual radio frequency. The classifier uses the number of peaks in the amplitude envelope from 1 ms windows before and after the impulsive very high frequency waveform, with an average accuracy of 97.9%. We propose that this method could be used in an algorithm to determine when explosive eruptions occurred by identification of the distinctive signatures of vent discharges and lightning.

58 GEOSCIENCES↗
DOE OSTI2021

BPM System for the PIP-II Injector Test Facility

A new BPM system was used for commissioning and operation of the PIP2 Injector Test Facility. The system of 13 warm and 12 cold BPMs was based upon custom 250 MS/s digitizers controlled and readout over gigabit ethernet by a single multi-core rackmount server running linux. The system provided positions, intensity, and phase for each bpm as a pulse average or pulse waveform from 10 us to 4 ms at a 20 Hz pulse repetition rate.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

AC losses of Roebel and CORC ® cables at higher AC magnetic fields and ramp rates

We have measured ReBCO coated conductor-based conductor on round core (CORC ® ) and Roebel cables at 77 K in a spinning magnet calorimeter, which subjected the tapes in the samples to a radial magnetic field of 566 mT (peak) at frequencies up to 120 Hz (272 T s -1 , cyclic average) with an approximately sinusoidal waveform. The samples were oriented such that the field applied to the tapes within the cables was entirely radial, simplifying subsequent analysis. An expression for loss which included hysteretic, flux creep and eddy current losses was fit to both the CORC ® and the Roebel cables. This expression allowed easy comparison of the relative influence of eddy currents and flux creep (or power-law behavior) effects. The loss of both the CORC ® and Roebel cables measured here were seen to be essentially the sum of the hysteretic loss, flux creep effects, and the normal metal eddy current losses of the individual tapes. The losses of these cables were measured at high B × d B /d t with no coupling current loss observed under the present preparation conditions. The influence of flux creep effects on loss were not negligible. The losses of the CORC ® cable per meter of tape were seen to be reduced from the case of a flat tape because of the helical geometry of the tapes.

Physics↗
DOE OSTI2026

Improved Regional Moment Tensor Inversion for Moderately Large Earthquakes in the Western United States Using a 3D Earth Model Based on Full Waveform Tomography

The nature of seismic sources for moderately large (moment magnitude, M w 5.0–6.5) events are commonly characterized by their moment tensor (MT) solutions and obtained by inversion of regional distance (200–1600 km) long‐period (20–50 s) waveforms. Regional MT estimates are often calculated from average plane‐layered, one‐dimensional (1D) velocity models. However, 1D model calculations can produce misfits in the arrival times and waveform shapes that introduce errors, particularly at longer distances or for shorter periods, which are necessary for analyzing lower magnitude events. Approximate Earth models (e.g., 1D) representing broad areas may be inadequate, particularly in the crust and uppermost mantle of tectonically complex regions. In this study, we show how a three‐dimensional (3D) Earth model obtained from full waveform inversion tomography can improve waveform fits and decrease phase errors. We developed a platform and workflow to perform routine 3D MT inversions and inverted MTs for 25 earthquakes in the western United States and seven nuclear explosions using an average 1D and a recent 3D Earth model, WUS256 (Rodgers et al., 2022). Using the 3D model improves waveform fits (variance reduction and phase time shifts) compared with the 1D model, and the 3D MT solutions are stable across large distances. This study shows that 3D models obtained from full waveform tomography can improve MTs and source characterization especially at far regional distances (>800 km).

Geosciences↗
DOE OSTI2020

Assessing Collision Cross Section Calibration Strategies for Traveling Wave-Based Ion Mobility Separations in Structures for Lossless Ion Manipulations

The collision cross section (CCS) is an important property that aids in the structural characterization of molecules. Here, we investigated the CCS calibration accuracy with traveling waves ion mobility spectrometry (TWIMS) separations in structures for lossless ion manipulations (SLIM) using three sets of calibrants. A series of singly negatively-charged phospholipids and bile acids were calibrated in nitrogen buffer gas using two different TW waveform profiles (square and sine) and amplitudes (20 V0-p, 25 V0-p, and 30 V 0-p ). The calibration errors for the three calibrant sets Agilent tuning mixture and polyalanine, and one assembled in-house showed negligible differences using a sine-shaped TW waveform. Furthermore, calibration errors were all within 1-2 % of the DTIMS measurements, with lower errors for sine waveforms, presumably due to the lower average and maximum fields experienced by ions. Finally, ultrahigh resolution multi-pass (long path length) SLIM TWIMS demonstrated improved CCS calibration for phospholipid and bile acid isomers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Effects-Based Monitoring of Geomagnetically-Induced Current Using a Convolutional Neural Network

Geomagnetically-induced current (GIC) due to space weather can flow in the power grid causing undesirable effects such as transformer overheating, misoperation of protection devices, and potential blackouts. It is therefore important to monitor GIC in the power grid to improve online situational awareness and decision-making of system operators during a geomagnetic disturbance. To avoid the costly installation of GIC monitors at transformers’ neutrals, it is desirable to find correlations between GIC and already-monitored parameters. Hence, this work proposed the use of a convolutional neural network (CNN) to compute GIC amplitudes from learned patterns in the time-series data of transformer even harmonic currents. Using an electromagnetic transient program, GIC injection simulations were performed for a modeled Dominion Energy Virginia (DEV) substation with two 504 MVA, 500/230 kV transformers. Data collected from these offline simulations were used to train the CNN to provide online GIC monitoring. Testing the CNN performance involved using real GIC measurements from published literature and from a physical GIC monitor in the DEV area. Finally, the results showed that the proposed method was able to provide GIC readings with a root mean squared error of 1.56 A/phase (equivalent to an average accuracy of 94%) for these real GIC waveforms.

42 ENGINEERING↗
DOE OSTI2022

Reanalysis of crackle perception data using logistic and logarithmic fits and sound quality metrics

Mathematical models of human auditory perception (sound quality metrics) are explored for the purpose of fitting human subject data from a prior published study. A variety of linearizing transforms and techniques are employed. Ultimately, a model is identified that predicts 98.9% of the variance in average human subject ratings of crackliness of jet noise waveforms.

Swift, Stephen Hales↗