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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Performance of the Muon g - 2 calorimeter and readout systems measured with test beam data

A single calorimeter station for the Muon $g-2$ experiment at Fermilab includes the following subsystems: a 54-element array of PbF$_{2}$ Cherenkov crystals read out by large-area SiPMs, bias and slow-control electronics, a suite of 800 MSPS waveform digitizers, a clock and control distribution network, a gain calibration and monitoring system, and a GPU-based frontend read out through a MIDAS data acquisition environment. The entire system performance was evaluated using 2.5 - 5 GeV electrons at the End Station Test Beam at SLAC. This paper includes a description of the individual subsystems and the results of measurements of the energy response and resolution, energy-scale stability, timing resolution, and spatial uniformity. All measured performances meet or exceed the $g-2$ experimental requirements. Based on the success of the tests, the complete production of the required 24 calorimeter stations has been made and installation into the main experiment is complete. Furthermore, the calorimeter response measurements determined here informed the design of the reconstruction algorithms that are now employed in the running $g-2$ experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Abiotic influences on continuous conifer forest structure across a subalpine watershed

Understanding the abiotic drivers of high-elevation forest physiognomy is essential for forecasting how mountain ecosystems will respond to emerging environmental pressures. Most prior studies of these relationships have relied on small samples of the full landscape, resulting in limited power to detect dominant covariates and their interactions. Here we report the first evaluation of abiotic influences on a complement of accurate, wall-to-wall estimates of conifer forest structure and composition at the watershed scale. In a subalpine conifer domain in the Colorado Rocky Mountains (USA), we developed a novel method for deriving stand structure metrics from waveform LiDAR data, which showed high fidelity with field inventory. We quantified the relationships between structural and compositional metrics and climate, topographic, edaphic, and geologic factors. Our results showed that peak snow water equivalent (SWE), snow disappearance rate, and elevation explained most of the variation in forest structure. The highest stand density, basal area, maximum canopy height, and quadratic mean diameter occurred in sites with SWE around one standard deviation below mean, but with long snow residence times. Stand density decreased linearly with elevation, while other metrics peaked between 3000 m and 3200 m. Substrate properties had weaker influence. Continuous mapping of through-canopy forest structure enabled our novel findings of the dominant role of snowpack in explaining structural and compositional variation, and of elevation thresholds. Our reproducible approach facilitates assessment of forest-topoclimate relationships in other conifer-dominated landscapes and improves understanding of the baseline patterns controlling forest structure, which is needed for predicting long-term ecological change.

forest ecology↗
DOE OSTI2026

Designing the Protocols for Programmable Ammonia Catalysis

Programmable catalysis can provide a more energy-efficient and cost-effective route to enhancing commercial ammonia production, a key process in the advancement of renewable energy technologies and the manufacture of fertilizers and basic chemicals. This work explores the computational discovery of optimal forcing protocols to drive such dynamic catalysis models. By employing matrix-free time-stepper methods, coupled with an optimization approach, that integrates Bayesian optimization with a Bayesian continuation strategy to efficiently discover the periodic steady states of such periodically forced systems, we enable the discovery of complex optimal catalyst strain waveforms, while ensuring robust solver convergence. We demonstrate the flexibility of our approach to discover optimized forcing protocols under varying physical constraints on strain modulation or other catalyst operating parameters. We show that these can have a temporal structure more complex than simple step functions. In order to detect undesirable catalytic loops that may correlate with overall reduced performance, we perform a study using graph-theoretical analysis to investigate the dynamics of catalytic kinetic networks formed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Reverse battery model for anodic arc discharges near atmospheric pressure

In this study, the macroscopic electrical parameters of anodic arc discharges performed with solid carbon and molybdenum anodes are described in terms of an equivalent circuit. The proposed 1D model shows that the plasma volume behaves as a dissipative medium combined with self-generated voltage from the plasma sheaths. We test this model by fitting virtual circuits of resistors and batteries to the measured voltage–current (V–I) characteristics representative of atmospheric arc processes (300 and 500 Torr He), resulting in the identification of two phenomena relevant for arc technology. First, steady DC arc experiments show that a minimal arc voltage (V ≈ 20–30 V) or threshold voltage is necessary to keep the plasma arc on. Second, we prove that pulsed arc discharges excited with rectangular current waveforms (1–5 Hz) are never extinguished but remain ignited during the inactive time of the pulse as long as the aforementioned threshold voltage is maintained. In summary, an atmospheric anodic arc discharge with solid electrodes consumes electrical power as a voltage source opposed to the external voltage supply together with a resistor that accounts for the plasma impedance. This approach permits us to estimate basic plasma parameters from simple electrical diagnostics, as well as to predict how power consumption is distributed within an anodic arc and to understand dynamics of arc plasmas excited by periodic signals.

25 ENERGY STORAGE↗
DOE OSTI2026

Machine learning for seismic low-frequency extrapolation

The cycle-skipping problem that plagues full waveform inversion (FWI) can be at least partially mitigated if low frequencies (which encode the kinematics of wave propagation in seismic data) are recorded. However, seismic sources and receivers are band-limited, so seismic data does not generally include signals down to 0 Hz. To improve our ability to solve the seismic inverse problem, one can synthesize this missing low-frequency (LF) content from the recorded high-frequency (HF) data using machine learning (ML) models. Deep learning models such as convolutional neural networks (CNNs) demonstrate impressive ability to perform low frequency extrapolation. However, such models require powerful hardware (GPU machines) and careful training. We assess the extrapolation capabilities of three different ML models that do not require GPU machines, namely, random forest, Gaussian process regression and gradient boosting, on both synthetic and real data. Experimental results on two synthetic data sets (generated from a low velocity lens embedded in a homogeneous medium, and the Marmousi model) demonstrate that FWI applied to the extrapolated data consistently improves inversion accuracy relative to FWI applied to the original data sets that do not contain low frequencies. Application of low-frequency extrapolation to real data from the Northwest Shelf of Australia demonstrates that tree-based ML models such as gradient boosting can outperform CNNs in terms of both accuracy and computational cost on non-GPU architectures.

58 GEOSCIENCES↗
DOE OSTI2016

Global adjoint tomography: first-generation model

We present the first-generation global tomographic model constructed based on adjoint tomography, an iterative full-waveform inversion technique. Synthetic seismograms were calculated using GPU-accelerated spectral-element simulations of global seismic wave propagation, accommodating effects due to 3-D anelastic crust & mantle structure, topography & bathymetry, the ocean load, ellipticity, rotation, and self-gravitation. Fréchet derivatives were calculated in 3-D anelastic models based on an adjoint-state method. The simulations were performed on the Cray XK7 named ‘Titan’, a computer with 18 688 GPU accelerators housed at Oak Ridge National Laboratory. The transversely isotropic global model is the result of 15 tomographic iterations, which systematically reduced differences between observed and simulated three-component seismograms. Our starting model combined 3-D mantle model S362ANI with 3-D crustal model Crust2.0. We simultaneously inverted for structure in the crust and mantle, thereby eliminating the need for widely used ‘crustal corrections’. We used data from 253 earthquakes in the magnitude range 5.8 ≤ M w ≤ 7.0. We started inversions by combining ~30 s body-wave data with ~60 s surface-wave data. The shortest period of the surface waves was gradually decreased, and in the last three iterations we combined ~17 s body waves with ~45 s surface waves. We started using 180 min long seismograms after the 12th iteration and assimilated minor- and major-arc body and surface waves. The 15th iteration model features enhancements of well-known slabs, an enhanced image of the Samoa/Tahiti plume, as well as various other plumes and hotspots, such as Caroline, Galapagos, Yellowstone and Erebus. Furthermore, we see clear improvements in slab resolution along the Hellenic and Japan Arcs, as well as subduction along the East of Scotia Plate, which does not exist in the starting model. Point-spread function tests demonstrate that we are approaching the resolution of continental-scale studies in some areas, for example, underneath Yellowstone. Here, this is a consequence of our multiscale smoothing strategy in which we define our smoothing operator as a function of the approximate Hessian kernel, thereby smoothing gradients less wherever we have good ray coverage, such as underneath North America.

58 GEOSCIENCES↗
DOE OSTI2018

Analysis of seismic moment tensors and in situ stress during Enhanced Geothermal System development at The Geysers geothermal field, California

Seismicity in the vicinity of an Enhanced Geothermal System (EGS) demonstration project in the Northwest Geysers at the Prati 32 (P-32) injection well and the Prati State 31 (PS-31) production well is investigated to determine earthquake focal mechanisms, moment magnitudes and in situ stress of events that occur before and during reservoir stimulation starting 2011 October 6. We present a catalogue of 167 waveform-based seismic moment tensors ranging in moment magnitude from 0.6 to 3.9. The moment tensor catalogue is subsequently used to invert for the stress tensor and to investigate possible temporal stress changes resulting from fluid injection. We find an approximate 15-degree counter-clockwise rotation of the least compressive stress σ3 during injection. More remarkable is a change in orientation of the maximum compressive stress σ1 from subhorizontal to vertical when injection operations temporarily cease. The orientation of σ1 returns to subhorizontal when injection operations resume. It is found that there was a systematic reduction in the stress shape factor, R, as the injected water volume increases, indicating an evolution towards a more transtensional stress state.

58 GEOSCIENCES↗
DOE OSTI2017

Decentralized Interleaving of Paralleled Dc-Dc Buck Converters

We present a decentralized control strategy that yields switch interleaving among parallel-connected dc-dc buck converters. The proposed method is based on the digital implementation of the dynamics of a nonlinear oscillator circuit as the controller. Each controller is fully decentralized, i.e., it only requires the locally measured output current to synthesize the pulse width modulation (PWM) carrier waveform and no communication between different controllers is needed. By virtue of the intrinsic electrical coupling between converters, the nonlinear oscillator-based controllers converge to an interleaved state with uniform phase-spacing across PWM carriers. To the knowledge of the authors, this work presents the first fully decentralized strategy for switch interleaving in paralleled dc-dc buck converters.

14 SOLAR ENERGY↗
DOE OSTI2022

Making Invisible Visible: Data-Driven Seismic Inversion With Spatio-Temporally Constrained Data Augmentation

Deep learning and data-driven approaches have shown great potential in scientific domains. The promise of data-driven techniques relies on the availability of a large volume of high-quality training datasets. Due to the high cost of obtaining data through expensive physical experiments, instruments, and simulations, data augmentation techniques for scientific applications have emerged as a new direction for obtaining scientific data recently. However, existing data augmentation techniques originating from computer vision yield physically unacceptable data samples that are not helpful for the domain problems that we are interested in. In this article, we develop new data augmentation techniques based on convolutional neural networks. Specifically, our generative models leverage different physics knowledge (such as governing equations, observable perception, and physics phenomena) to improve the quality of the synthetic data. To validate the effectiveness of our data augmentation techniques, we apply them to solve a subsurface seismic full-waveform inversion using simulated CO 2 leakage data. Our interest is to invert for subsurface velocity models associated with very small CO 2 leakage. We validate the performance of our methods using comprehensive numerical tests. Here via comparison and analysis, we show that data-driven seismic imaging can be significantly enhanced by using our data augmentation techniques. Particularly, the imaging quality has been improved by 15% in test scenarios of general-sized leakage and 17% in small-sized leakage when using an augmented training set obtained with our techniques.

58 GEOSCIENCES↗
DOE OSTI2020

Time Resolution Measurements of EJ-232Q With Single- and Dual-Sided Readouts

In view of its importance to applications in high energy physics, nuclear security, and time-of-flight PET, the time resolution of 0.5% benzophenone-quenched EJ-232Q plastic scintillators of various lengths was studied using a fast waveform digitizer and fast PMTs. The scintillator sizes were 5 mm × 5 mm × 10, 25, 50, 75, or 100 mm. First, the timing measurements were performed by using a single PMT reading out one end. The degradation in time resolution with increasing scintillator length was studied. The irradiation geometry was also observed to have a non-negligible impact on the time resolution due to the variation of depth-of-interaction positions, especially for the scintillators with a high aspect ratio. Second, two PMTs were employed for a dual-sided readout. This readout together with the mean time method was demonstrated to be an effective approach to improve the timing performance since the depth-of-interaction-related effect was considerably mitigated. A time resolution of 79 ± 1 ps (full-width at half-maximum) has been achieved for the 5 × 5 × 10 mm 3 scintillator. The 5 × 5 × 100 mm 3 scintillator can still provide a time resolution as good as 172 ± 1 ps. Here, the energy range used for each was between 340 keVee and 450 keVee. In addition, the time information from individual PMTs in a dual-sided readout was able to show a time resolution almost identical to that determined with the mean-time-based technique after correcting for the depth-of-interaction variation for the 100 mm long scintillator.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Multistep inversion workflow for 3D long-offset damped elastic waves in the Fourier domain

We present a new workflow for imaging damped three-dimensional elastic wavefields in the Fourier domain. The workflow employs a multiscale imaging approach, in which offset lengths are laddered, where frequency content and damping of the data are changed cyclically. Thus, the inversion process is launched using short-offset and low-frequency data to recover the long spatial wavelength of the image at a shallow depth. Increasing frequency and offset length leads to the recovery of the fine-scale features of the model at greater depths. For the fixed offset, we employ (in the imaging process) a few discrete frequencies with a set of Laplace damping parameters. The forward problem is solved with a finite-difference frequency-domain method based on a massively parallel iterative solver. The inversion code is based upon the solution of a least squares optimisation problem and is solved using a nonlinear gradient method. It is fully parallelised for distributed memory computational platforms. Our full-waveform inversion workflow is applied to the 3D Marmousi-2 and SEG/EAGE Salt models with long-offset data. The maximum inverted frequencies are 6 Hz for the Marmousi model and 2 Hz for the SEG/EAGE Salt model. The detailed structures are imaged successfully up to the depth approximately equal to one-third of the maximum offset length at a resolution consistent with the inverted frequencies.

3D full waveform inversion↗
DOE OSTI2022

Flicker: A review of temporal light modulation stimulus, responses, and measures

Flicker has been an important lighting system consideration for over a century. More precise terms are temporal light modulation (TLM) as the stimulus, and responses to TLM as the unwanted visual, cognitive, or physiological consequences. As lighting technology evolved, different forms of TLM emerged, and so did responses to them. Today, some LED systems – encompassing the LED, driver, and control – can result in TLM causing severe unwanted effects, while other LED systems produce no unwanted effects at all. LED systems can deliver a much wider range of luminous waveforms than conventional lighting systems, some exhibiting very high modulation depths. More than any light source before, they can elicit perceptions of the phantom array. Direct flicker effects at modulation frequencies less than about 80 Hz and the stroboscopic effect at frequencies greater than 80 Hz are fairly well understood, but the phantom array effect needs more exploration and characterisation. This review focuses on the technology and research history that led to current metrics for quantifying TLM and human responses to TLM. Notably, visually impaired individuals may exhibit alterations in their response to TLM, but such a discussion is beyond the intent of this review. Thus, the focus is on individuals with normal visual function.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Joint physics-based and data-driven time-lapse seismic inversion: Mitigating data scarcity

In carbon capture and sequestration (CCS), developing rapid and effective imaging techniques is crucial for real-time monitoring of the spatial and temporal dynamics of CO 2 propagation during/after injection. With continuing improvements in computational power and data storage, data-driven techniques based on machine learning (ML) have been effectively applied to seismic inverse problems. In particular, ML helps alleviate the ill-posedness and high computational cost of full-waveform inversion (FWI). However, such data-driven inversion techniques require massive high-quality training data sets to ensure prediction accuracy, which hinders their application to time-lapse monitoring of CO 2 sequestration. We propose an efficient “hybrid” time-lapse workflow that combines physics-based FWI and data-driven ML inversion. The scarcity of the available training data is addressed by developing a new data-generation technique with physics constraints. The method is vali dated on a synthetic CO 2 -sequestration model based on the Kimberlina storage reservoir in California. The proposed approach is shown to synthesize a large volume of high-quality, physically realistic training data, which is critically important in accurately characterizing the CO 2 movement in the reservoir. In conclusion, the developed hybrid methodology can also simultaneously predict the variations in velocity and saturation and achieve high spatial resolution in the presence of realistic noise in the data.

58 GEOSCIENCES↗
DOE OSTI2022

Assessing the Effectiveness of Generalized Likelihood Ratio Test Detector Schemes in Seismic Event Detection and the Avoidance of Nontarget Signals

Cross-correlation techniques have played a long-standing and pivotal role in seismic event monitoring. However, the performance of correlation-based detectors is challenged by nuisance seismicity, or nontarget signals. Such detections are a problem when the mission is to automatically map events to the correct source region. Using aftershocks of the 2014 $M_w$ 6.0 South Napa, California, earthquake, we demonstrate the effectiveness of utilizing a dynamic correlation processor framework in a generalized likelihood ratio test (GLRT) detector configuration to minimize nontarget detections. A GLRT maximizes a detection statistic with respect to one or more unknown parameters. In this case, the detection statistic is a template signal match against the waveform in a window sliding over a data stream, and the unknown parameter is an index variable indicating group membership of the template event or events. Detected events are assigned to the event group that yields the largest detection statistic. In this work, our results show that a GLRT detector will outperform a suite of independently operating correlation and subspace detectors in terms of having a lower nontarget detection rate at a given missed detection rate. We also show that a GLRT detector composed of a few high-rank subspace detectors has a slightly higher nontarget detection rate, but a significantly lower missed detection rate, than a GLRT detector composed of many low-rank subspace detectors. The high-rank GLRT configuration produced impressive results even with marginal data (single channel, single station, and very low time bandwidth product), which bodes well for the utility of building efficient aftershock classification systems and global monitoring systems at larger scales. However, future work is required to assess performance at the regional scale and to assess the performance of the system at detecting target events not used in the detector template creation.

58 GEOSCIENCES↗
DOE OSTI2017

Continued Data Acquisition Development

This task focused on improving techniques for integrating data acquisition of secondary particles correlated in time with detected cosmic-ray muons. Scintillation detectors with Pulse Shape Discrimination (PSD) capability show the most promise as a detector technology based on work in FY13. Typically PSD parameters are determined prior to an experiment and the results are based on these parameters. By saving data in list mode, including the fully digitized waveform, any experiment can effectively be replayed to adjust PSD and other parameters for the best data capture. List mode requires time synchronization of two independent data acquisitions (DAQ) systems: the muon tracker and the particle detector system. Techniques to synchronize these systems were studied. Two basic techniques were identified: real time mode and sequential mode. Real time mode is the preferred system but has proven to be a significant challenge since two FPGA systems with different clocking parameters must be synchronized. Sequential processing is expected to work with virtually any DAQ but requires more post processing to extract the data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Seismic Characterization of the Blue Mountain Geothermal Field

Subsurface characterization is crucial for geothermal energy exploration and production. Yet hydrothermal reservoirs usually reside in highly fractured and faulted zones where accurate characterization is very challenging because of low signal-to-noise ratios of land seismic data and lack of coherent reflection signals. We perform an active-source seismic characterization for the Blue Mountain geothermal field in Nevada using active seismic data to reveal the elastic medium property complexity and fault distribution at this field. We first employ an unsupervised machine learning method to attenuate groundroll and near-surface guided-wave noise and enhance coherent reflection and scattering signals from noisy seismic data. We then build a smooth initial P-wave velocity model based on an existing magnetotellurics survey result, and use 3D first-arrival traveltime tomography to refine the initial velocity model. We then derive a set of elastic wave velocities and anisotropic parameters using elastic full-waveform inversion, and obtain PP and PS images using elastic reverse-time migration. We identify major faults by analyzing the variations of seismic velocities and anisotropy parameters, and reveal mid- to small-scale faults by applying a supervised machine learning method to the seismic migration images. Our characterization reveals complex velocity heterogeneities and anisotropies, as well as faults, with a high spatial resolution. These results can provide valuable information for optimal placement of future injection and production wells to increase geothermal energy production at the Blue Mountain geothermal power plant.

58 GEOSCIENCES↗
DOE OSTI2019

Overlapping localized exponential time differencing methods for diffusion problems

The localized exponential time differencing (ETD) based on overlapping domain decomposition has been recently introduced for extreme-scale phase field simulations of coarsening dynamics, which displays excellent parallel scalability in supercomputers. This paper serves as the first step toward building a solid mathematical foundation for this approach. We study the overlapping localized ETD schemes for a model time-dependent diffusion equation discretized in space by the standard central difference. Two methods are proposed and analyzed for solving the fully discrete localized ETD systems: the first one is based on Schwarz iteration applied at each time step and involves solving stationary problems in the subdomains at each iteration, while the second one is based on the Schwarz waveform relaxation algorithm in which time-dependent subdomain problems are solved at each iteration. The convergences of the associated iterative solutions to the corresponding fully discrete localized ETD solution and to the exact semidiscrete solution are rigorously proved. Finally, numerical experiments are also carried out to confirm theoretical results and to compare the performance of the two methods.

58 GEOSCIENCES↗
DOE OSTI2017

Decentralized Interleaving of Paralleled Dc-Dc Buck Converters: Preprint

We present a decentralized control strategy that yields switch interleaving among parallel connected dc-dc buck converters without communication. The proposed method is based on the digital implementation of the dynamics of a nonlinear oscillator circuit as the controller. Each controller is fully decentralized, i.e., it only requires the locally measured output current to synthesize the pulse width modulation (PWM) carrier waveform. By virtue of the intrinsic electrical coupling between converters, the nonlinear oscillator-based controllers converge to an interleaved state with uniform phase-spacing across PWM carriers. To the knowledge of the authors, this work represents the first fully decentralized strategy for switch interleaving of paralleled dc-dc buck converters.

14 SOLAR ENERGY↗