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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Automated integration gate selection for Gaussian mixture model pulse shape discrimination

Pulse shapes differ between neutron and gamma particles when measured with detector devices employing pulse shape discriminating (PSD) scintillators. Digitized waveforms can be used in detection systems to perform pulse shape discrimination for this application. Prior Gaussian Mixture Model (GMM) methods require access to the pulse full-waveform. Reducing the waveform to a smaller set of combined samples reduces computational cost while affecting PSD performance. In this work, we develop a method for selecting the best performing combination of integration gates, or contiguous summed segments of the digitized pulse for PSD. The method uses a discrimination score based on the GMM PSD approach. Furthermore, the final selection is performed using Bayesian Optimization. PSD detection results are compared with varying numbers of selected gates on time-of-flight (TOF) data. This method can be used to fully automate the selection of gates in an unsupervised (without ground truth labels) setting.

42 ENGINEERING↗
DOE OSTI2021

A generalized machine learning workflow to visualize mechanical discontinuity

Accurate detection and mapping of mechanical discontinuity in materials has widespread industrial and research applications. Herein, we developed a generalized machine-learning framework for visualizing single mechanical discontinuity embedded in material of any composition, velocity, density, porosity, and size with limited data. The proposed visualization of discontinuity requires accurate estimations of the length, location, and orientation of the embedded discontinuity by processing multipoint wave-transmission measurements. k-Wave simulator is used to create a large dataset of elastic waveforms recorded during multi-point wave-transmission measurements through materials containing single mechanical discontinuity. k-Wave simulator considers the wave attenuation, dispersion, and mode conversion in wave motion. Discrete wavelet transform (DWT) and statistical feature extraction are essential for data preprocessing prior to the data-driven model development. DWT also minimizes the effect of noise. Using hyper-parameter tuning and cross validation, gradient boosting regression can visualize the mechanical discontinuity with an accuracy of 0.85, in terms of coefficient of determination. A double-layered neural network-based regression has better performance with an accuracy of 0.95. Use of convolutional neural network converts the predictive task from a waveform processing to an image processing problem. Convolutional neural network achieved a generalization performance of 0.91. The proposed generalized workflow requires robust simulation of wave propagation, signal processing, feature engineering, and model evaluation. Sensors closest to the source and those located opposite the source are the most significant for the desired visualization. Notably, the sensors closest to the source capture the non-linear associations, whereas the sensor on the border opposite to the source capture the linear associations between the measured waveforms and the properties of the mechanical discontinuity.

42 ENGINEERING↗
DOE OSTI2018

Target tracking via recursive Bayesian state estimation in cognitive radar networks

To cope with complicated environments and stealthier targets, incorporating intelligence and cognition cycles into target tracking is of great importance in modern sensor network management. With remarkable advances in sensor techniques and deployable platforms, a sensing system has freedom to select a subset of available radars, plan their trajectories, and transmit designed waveforms. In this paper, we propose a general framework for single target tracking in cognitive networks of radars, including consideration of waveform design, path planning, and radar selection, which are separately but not jointly taken into account in existing work. The tracking procedure, built on the theories of dynamic graphical models (DGM) and recursive Bayesian state estimation (RBSE), is formulated as two iterative steps: (i) solving a combinatorial optimization problem to select the optimal subset of radars, waveforms, and locations for the next tracking instant, and (ii) acquiring the recursive Bayesian state estimation to accurately track the target. Further, an illustrative example introduces a specific scenario in 2-D space. Simulation results based on the scenario demonstrate that the proposed framework can accurately track the target under the management of the network of radars.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Frequency analysis and resonant operation for efficient capacitive deionization

Capacitive deionization (CDI) performance metrics can vary widely with operating methods. Conventional CDI operating methods such as constant current and constant voltage show advantages in either energy or salt removal performance, but not both. We here develop a theory around and experimentally demonstrate a new operation for CDI that uses sinusoidal forcing voltage (or sinusoidal current). We use a dynamic system modeling approach, and quantify the frequency response (amplitude and phase) of CDI effluent concentration. Using a wide range of operating conditions, we demonstrate that CDI can be modeled as a linear time invariant system. We validate this model with experiments, and show that a sinusoid voltage operation can simultaneously achieve high salt removal and strong energy performance, thus very likely making it superior to other conventional operating methods. Based on the underlying coupled phenomena of electrical charge (and ionic) transfer with bulk advection in CDI, we derive and validate experimentally the concept of using sinusoidal voltage forcing functions to achieve resonance-type operation for CDI. Despite the complexities of the system, we find a simple relation for the resonant time scale: the resonant time period (frequency) is proportional (inversely proportional) to the geometric mean of the flow residence time and the electrical (RC) charging time. Operation at resonance implies the optimal balance between absolute amount of salt removed (in moles) and dilution (depending on the feed volume processed), thus resulting in the maximum average concentration reduction for the desalinated water. We further develop our model to generalize the resonant time-scale operation, and provide responses for square and triangular voltage waveforms as two examples. To this end, we develop a general tool that uses Fourier analysis to construct CDI effluent dynamics for arbitrary input waveforms. Using this tool, we show that most of the salt removal (~95%) for square and triangular voltage forcing waveforms is achieved by the fundamental Fourier (sinusoidal) mode. The frequency of higher Fourier modes precludes high flow efficiency for these modes, so these modes consume additional energy for minimal additional salt removed. This deficiency of higher frequency modes further highlights the advantage of DC-offset sinusoidal forcing for CDI operation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 OSTI2020

Identifying Different Classes of Seismic Noise Signals Using Unsupervised Learning

Abstract Proper classification of nontectonic seismic signals is critical for detecting microearthquakes and developing an improved understanding of ongoing weak ground motions. We use unsupervised machine learning to label five classes of nonstationary seismic noise common in continuous waveforms. Temporal and spectral features describing the data are clustered to identify separable types of emergent and impulsive waveforms. The trained clustering model is used to classify every 1 s of continuous seismic records from a dense seismic array with 10–30 m station spacing. We show that dominate noise signals can be highly localized and vary on length scales of hundreds of meters. The methodology demonstrates the complexity of weak ground motions and improves the standard of analyzing seismic waveforms with a low signal‐to‐noise ratio. Application of this technique will improve the ability to detect genuine microseismic events in noisy environments where seismic sensors record earthquake‐like signals originating from nontectonic sources.

Johnson, Christopher W.↗
DOE OSTI2021

Learning the Low Frequency Earthquake Activity on the Central San Andreas Fault

Abstract Low frequency earthquakes (LFEs) originating below the central San Andreas Fault are associated with slow‐slip beneath the seismogenic zone within the more ductile portion of the crust. Monitoring efforts over 15 years detected >1 million LFEs. We train a gradient boosted tree model using statistical features describing the seismic waveforms to estimate the hourly LFE event count. The burst‐like LFE behavior is reproduced, while lower amplitudes are predicted during the most active periods. The hourly event counts are up to 18% greater than the catalog. The ability to continuously monitor LFE activity provides insight to when geodetic measurements of slow slip are possible, without the need for developing a computational‐intensive template‐matching catalog. Similar waveform statistical features are found between detecting LFEs and tremors, which provides additional evidence tremors are composed of LFEs. The approach extracts information contained in continuous seismic waveforms that might benefit detecting precursory signals.

58 GEOSCIENCES↗
DOE OSTI2021

Attention Network Forecasts Time-to-Failure in Laboratory Shear Experiments

Rocks under stress deform by creep mechanisms that include formation and slip on small-scale internal cracks. Intragranular cracks and slip along grain contacts release energy as elastic waves termed acoustic emissions (AE). AEs are thought to contain predictive information that can be used for fault failure forecasting. Here, we present a method using unsupervised classification and an attention network to forecast labquakes using AE waveform features. Our data were generated in a laboratory setting using a biaxial shearing device with granular fault gouge intended to mimic the conditions of tectonic faults. Here, we analyzed the temporal evolution of AEs generated throughout several hundred laboratory earthquake cycles. We used a Conscience Self-Organizing Map (CSOM) to perform topologically ordered vector quantization based on waveform properties. The resulting map was used to interactively cluster AEs. We examined the clusters over time to identify those with predictive ability. Finally, we used a variety of LSTM and attention-based networks to test the predictive power of the AE clusters. By tracking cumulative waveform features over the seismic cycle, the network is able to forecast the time-to-failure (TTF) of lab earthquakes. Our results show that analyzing the data to isolate predictive signals and using a more sophisticated network architecture are key to robustly forecasting labquakes. In the future, this method could be applied on tectonic faults to monitor earthquakes and augment early warning systems.

58 GEOSCIENCES↗
DOE OSTI2022

Yield Estimation of the August 2020 Beirut Explosion by Using Physics‐Based Propagation Simulations of Regional Infrasound

Abstract The August 2020 Beirut explosion is the largest single‐fired ammonium nitrate explosion documented in history. The massive explosion excited loud infrasound in the atmosphere, and clear waveforms were recorded by a regional infrasound array at an epicentral distance of 100 km, allowing for accurate measurements of explosion energy. We estimate the explosion size based on the infrasound waveform inversion. Unlike conventional inversions using empirical models, we perform full 3‐D finite‐difference simulations to obtain a physics‐based propagation model for the inversion. Accurate numerical modeling of infrasound is challenging as the propagation is substantially affected by the turbulent atmosphere. Instead of a single deterministic prediction, we provide a range of waveform predictions by running multiple simulations with stochastic weather forecast models, which allows for comprehensive uncertainty analysis of numerical modeling and estimated yields. Finally, we expand the yield estimation technique for seismoacoustic analysis and demonstrate the substantial advantage of the joint approach.

58 GEOSCIENCES↗
DOE OSTI2024

Machine Learning‐Assisted Microearthquake Location Workflow for Monitoring the Newberry Enhanced Geothermal System

Abstract Enhanced geothermal systems (EGS) offer a sustainable energy source but face challenges in accurately locating microearthquakes induced during reservoir stimulation. Locating these microearthquakes provides reliable feedback on the stimulation progress. Current deep learning methods for locating earthquakes require extensive data sets for training, which is problematic as detected microearthquakes are often limited. To address the scarcity of training data, we propose a practical workflow using probabilistic multilayer perceptron (PMLP) which predicts microearthquake locations from cross‐correlation time lags in waveforms. Utilizing a 3D velocity model of Newberry site derived from ambient noise interferometry, we generate numerous synthetic microearthquakes and 3D acoustic waveforms for PMLP training. Accurate synthetic tests prompt us to apply the trained network to the 2012 and 2014 stimulation field waveforms. To enhance the accuracy of source localization, we carefully handpick the P‐arrival times. Predictions on the 2012 stimulation data set show major microseismic activity at depths of 0.5–1.2 km, correlating with a known casing leakage scenario. In the 2014 data set, the majority of predictions concentrate at 2.0–2.9 km depths, consistent with results obtained from conventional physics‐based inversion, and align with the presence of natural fractures from 2.0 to 2.7 km. We validate our findings by comparing the synthetic and field picks, demonstrating a satisfactory match for the first arrivals. By combining the benefits of quick inference speeds and accurate location predictions, we demonstrate the feasibility of using realistic synthetic data set to locate microseismicity for EGS monitoring.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Light-wave-controlled Haldane model in monolayer hexagonal boron nitride

In recent years, the stacking and twisting of atom-thin structures with matching crystal symmetry has provided a unique way to create new superlattice structures in which new properties emerge. In parallel, control over the temporal characteristics of strong light fields has allowed researchers to manipulate coherent electron transport in such atom-thin structures on sublaser-cycle timescales. Here we demonstrate a tailored light-wave-driven analogue to twisted layer stacking. Tailoring the spatial symmetry of the light waveform to that of the lattice of a hexagonal boron nitride monolayer and then twisting this waveform result in optical control of time-reversal symmetry breaking and the realization of the topological Haldane model in a laser-dressed two-dimensional insulating crystal. Further, the parameters of the effective Haldane-type Hamiltonian can be controlled by rotating the light waveform, thus enabling ultrafast switching between band structure configurations and allowing unprecedented control over the magnitude, location and curvature of the bandgap. This results in an asymmetric population between complementary quantum valleys that leads to a measurable valley Hall current, which can be detected by optical harmonic polarimetry. The universality and robustness of our scheme paves the way to valley-selective bandgap engineering on the fly and unlocks the possibility of creating few-femtosecond switches with quantum degrees of freedom.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Petahertz electronics

Petahertz, or lightwave, electronics uses tailored optical waveforms to control charge carriers in an electronic circuit at petahertz frequencies. This may enable faster processing than conventional pulsed electronics, which cannot be scaled beyond gigahertz frequencies. In recent years, petahertz-scale currents driven by optical fields have been measured in solid-state systems and nanoscale structures, with several proof-of-principle demonstrations of sub-optical-cycle current generation and optical-field-resolved waveform detection at the sub-femtosecond to few-femtosecond scale. Recent work has taken the first steps towards digital and quantum operation by exploring optical-field-driven logic and memory functionality. Here, in this Review, we discuss the progress towards sub-cycle field-driven current injection, highlighting key theoretical concepts, experimental milestones, and questions remaining as we push towards realizing petahertz electronics for ultrafast optical waveform analysis, digital logic, communications, and quantum computation.

Heide, Christian↗
DOE OSTI2014

Application of gas-coupled laser acoustic detection to gelatins and underwater sensing

Gas-coupled Laser Acoustic Detection (GCLAD) has been used as a method to sense ultrasound waves in materials without contact of the material surface. To sense the waveform, a laser beam is directed parallel to the material surface and displaced or deflected when the radiated waveform traverses the beam. We present recent tests that demonstrate the potential of using this technique for detecting ultrasound in gelatin phantoms and in water. As opposed to interferometric detection, GCLAD operates independently of the optical surface properties of the material. This allows the technique to be used in cases where the material is transparent or semi-transparent. We present results on sensing ultrasound in gelatin phantoms that are used to mimic biological materials. As with air-coupled transducers, the frequency response of GCLAD at high frequencies is limited by the high attenuation of ultrasound in air. In contrast, water has a much lower attenuation. Here we demonstrate the use of a GCLAD-like system in water, measuring the directivity response at 1 MHz and sensing waveforms with higher frequency content.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Periodic and stochastic thermal modulation of protein folding kinetics

Chemical reactions are usually observed either by relaxation of a bulk sample after applying a sudden external perturbation, or by intrinsic fluctuations of a few molecules. Here we show that the two ideas can be combined to measure protein folding kinetics, either by periodic thermal modulation, or by creating artificial thermal noise that greatly exceeds natural thermal fluctuations. We study the folding reaction of the enzyme phosphoglycerate kinase driven by periodic temperature waveforms. As the temperature waveform unfolds and refolds the protein, its fluorescence color changes due to FRET (Förster resonant Energy Transfer) of two donor/acceptor fluorophores labeling the protein. We adapt a simple model of periodically driven kinetics that nicely fits the data at all temperatures and driving frequencies: The phase shifts of the periodic donor and acceptor fluorescence signals as a function of driving frequency reveal reaction rates. We also drive the reaction with stochastic temperature waveforms that produce thermal fluctuations much greater than natural fluctuations in the bulk. Such artificial thermal noise allows the recovery of weak underlying signals due to protein folding kinetics. This opens up the possibility for future detection of a stochastic resonance for protein folding subject to noise with controllable amplitude.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Generalization of the Child-Langmuir law to the alternate extraction of positive and negative ions

Using a combined analytical and simulation approach, we investigate positive and negative ion extraction between two electrodes from an ion-ion plasma source. With a square voltage waveform applied to the electrodes, we obtain approximate analytical solutions for the time-averaged extracted current densities, which are given simply by: J{sub p}{sup ac}=[α−fL√((M{sub p})/(q{sub p}V{sub 0}) )]J{sub p}{sup dc}, and J{sub n}{sup ac}=[(1−α)−fL√((M{sub n})/(q{sub n}V{sub 0}) )]J{sub n}{sup dc}, where J{sup ac} is the time-averaged current density, α is the square waveform duty cycle, f is the frequency, L is the electrode gap length, M is the ion mass, q is the ion charge, V{sub 0} is the applied voltage amplitude, J{sup dc} is the dc extracted current density, and the subscripts p and n refer to positive and negative ions, respectively. In particular, if J{sup dc} is the dc space-charge limited current density, then these equations describe the square waveform generalization of the Child-Langmuir law.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Dynamical cancellation of pulse-induced transients in a metallic shielded room for ultra-low-field magnetic resonance imaging

Pulse-induced transients such as eddy currents can cause problems in measurement techniques where a signal is acquired after an applied preparatory pulse. In ultra-low-field magnetic resonance imaging, performed in magnetic fields typically of the order of 100 μT, the signal-to-noise ratio is enhanced in part by prepolarizing the proton spins with a pulse of much larger magnetic field and in part by detecting the signal with a Superconducting QUantum Interference Device (SQUID). The pulse turn-off, however, can induce large eddy currents in the shielded room, producing an inhomogeneous magnetic-field transient that both seriously distorts the spin dynamics and exceeds the range of the SQUID readout. It is essential to reduce this transient substantially before image acquisition. We introduce dynamical cancellation (DynaCan), a technique in which a precisely designed current waveform is applied to a separate coil during the later part and turn off of the polarizing pulse. This waveform, which bears no resemblance to the polarizing pulse, is designed to drive the eddy currents to zero at the precise moment that the polarizing field becomes zero. We present the theory used to optimize the waveform using a detailed computational model with corrections from measured magnetic-field transients. SQUID-based measurements with DynaCan demonstrate a cancellation of 99%. Dynamical cancellation has the great advantage that, for a given system, the cancellation accuracy can be optimized in software. This technique can be applied to both metal and high-permeability alloy shielded rooms, and even to transients other than eddy currents.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Automated setup for magnetic hysteresis characterization based on a voltage controlled current source with 500 kHz full power bandwidth and 10 A peak-to-peak current

This paper describes the design of a system for the characterization of magnetic hysteresis behavior in soft ferrite magnetic cores. The proposed setup can test magnetic materials exciting them with controlled arbitrary magnetic field waveforms, including the capability of providing a DC bias, in a frequency bandwidth up to 500 kHz, with voltages up to 32 V peak-to-peak, and currents up to 10 A peak-to-peak. In order to have an accurate control of the magnetic field waveform, the system is based on a voltage controlled current source. The electronic design is described focusing on closed loop feedback stabilization and passive components choice. The system has real-time hysteretic loop acquisition and visualization. The comparisons between measured hysteresis loops of sample magnetic materials and datasheet available ones are shown. Results showing frequency and thermal behavior of the hysteresis of a test sample prove the system capabilities. Moreover, the B-H loops obtained with a multiple waveforms excitation signal, including DC bias, are reported. The proposal is a low-cost and replicable solution for hysteresis characterization of magnetic materials used in power electronics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Dual-frequency glow discharges in atmospheric helium

In this paper, the dual-frequency (DF) glow discharges in atmospheric helium were experimented by electrical and optical measurements in terms of current voltage characteristics and optical emission intensity. It is shown that the waveforms of applied voltages or discharge currents are the results of low frequency (LF) waveforms added to high frequency (HF) waveforms. The HF mainly influences discharge currents, and the LF mainly influences applied voltages. The gas temperatures of DF discharges are mainly affected by HF power rather than LF power.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗