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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A comprehensive analysis of transient pressure and rate data from CO 2 storage projects in a depleted pinnacle reef oil field complex, Michigan, USA

Pressure and rate data are commonly recorded as part of a basic monitoring program in CCS projects. This paper discusses the application of multiple analytical techniques to interpret pressure and rate transient data from CO 2 injection and storage operations. The techniques of interest, i.e., injection-falloff analysis, injectivity/productivity index analysis and pressure pulse arrival time analysis, are commonly used in the oil and gas industry to assess reservoir properties, but not well known in the CCS literature (especially the last two). Injection-falloff analysis involves log-log pressure derivative plotting for the falloff data and history-matching of the entire injection-falloff sequence to determine permeability. In the injectivity/productivity index analysis, rate-normalized pressure buildup is plotted against material balance time or ratio of cumulative injection to injection rate to determine the injectivity index (ratio of injection rate to stabilized pressure buildup) which can be related to the permeability-thickness product. The arrival time analysis identifies the arrival of a pressure disturbance (~0.1 psi change from ambient) to determine the hydraulic diffusivity from which permeability can be estimated. The applicability of these techniques is demonstrated via illustrative examples from multiple wells in different pinnacle carbonate reefs undergoing CO 2 -EOR in Northern Michigan. The paper ends with a discussion of the relative merits of each interpretive technique, as well as recommendations that could be useful for other field projects.

42 ENGINEERING↗
DOE OSTI2024

The Value of Hyperparameter Optimization in Phase-Picking Neural Networks

The effectiveness of neural networks for picking seismic phase arrival times has been demonstrated through several case studies, and seismic monitoring programs are starting to adopt the technology into their workflows. However, published models were designed and trained using rather arbitrary choices of hyperparameters, limiting their performance. In this study, we use phase picks from both routine and template-matching analyses from multiple regions (Ridgecrest, California; Kilauea, Hawaii; Yellowstone, Wyoming–Montana–Idaho) to test a hyperparameter optimization scheme for phase-picking neural networks and to evaluate their performance. We show that a published model, namely PhaseNet (Zhu and Beroza, 2019), can be simplified and improved with reasonable effort and there are preferred choices of hyperparameters that increase the performance. We also show that models optimized based on the arrival times reported in routine event catalogs consistently perform well when picking arrival times of smaller events, which is crucial for many tasks from microseismicity to explosion monitoring.

58 GEOSCIENCES↗
DOE OSTI2022

Monitoring Fracture Saturation With Internal Seismic Sources and Twin Neural Networks

Seismic coda-wave analysis is a well-developed method for detecting subtle physical changes in complex media by measuring arrival times in the late-arriving energy from multiply scattered or reflected waves. However, a challenge arises when multiply scattered waves are not sufficiently separated in time from the direct arrivals to provide a clear coda wave train. Additional complications for monitoring changes in fracture systems arise when the signals originate from unsynchronized internal sources, such as natural or induced seismicity, from acoustic emission, or from transportable intra-fracture sources (chattering dust), that generate uncontrolled signals that vary in arrival time, amplitude and frequency content. Here, we use a twin neural network (TNN also known as a Siamese neural network) for dimensionality reduction to analyze signals from chattering dust to classify the fluid saturation state of a synthetic fracture system. The TNN with shared weights generates a low-dimensional representation of the data input by minimizing contrastive loss, serving as the input to a multiclass classifier that accurately classifies whether multiple fractures in a fracture system are fully saturated or partially saturated, or whether a change in saturation has occurred in different fractures in the system. Furthermore, these results show that information buried in unresolved codas from uncontrolled sources can be extracted using machine learning to monitor the evolution of fracture systems caused by physical and chemical processes even when the scattered and direct wave fields overlap.

58 GEOSCIENCES↗
DOE OSTI2022

Data for Monitoring Fracture Saturation with Internal Transportable Seismic Sources and Twin Neural Networks

Seismic coda-wave analysis is a well-developed method for detecting subtle physical changes in complex media by measuring arrival times in the late-arriving energy from multiply-scattered or reflected waves. However, a challenge arises when multiply-scattered waves are not sufficiently separated in time from the direct arrivals to provide a clear coda wave train. Additional complications for monitoring changes in fracture systems arise when the signals originate from unsynchronized internal sources, such as natural induced seismicity, from acoustic emission, or from transportable intra-fracture sources (chattering dust), that generate uncontrolled signals that vary in arrival time, amplitude and frequency content. Here, we use a twin neural network (also known as a Siamese neural network) for dimensionality reduction to analyze signals from chattering dust to classify the fluid saturation state of a synthetic fracture system. The twin neural network with shared weights generates a low-dimensional representation of the data input by minimizing contrastive loss, serving as the input to a multiclass classifier that accurately classifies whether multiple fractures in a fracture system are fully saturated or partially saturated, or whether a change in saturation has occurred in different fractures in the system. These results show that information buried in unresolved codas from uncontrolled sources can be extracted using machine learning to monitor the evolution of fracture systems caused by physical and chemical processes even when the scattered and direct wave fields overlap.

58 GEOSCIENCES↗
DOE OSTI2025

A new probe of μ Hz gravitational waves with FRB timing

We propose Fast Radio Burst (FRB) timing, which uses the precision measurements of the arrival time differences of repeated FRB signals along multiple sightlines, as a new probe of gravitational waves (GWs) around nHz to μHz frequencies, with the highest frequency limited by FRB repeating period. The anticipated experiment requires a sightline separation of tens of AU, achieved by sending radio telescopes to space. We find the signal of arrival time difference induced by GWs depends only on the local GWs in the solar system and we can correlate the measurements from different FRB sources or the same source with different repeaters, which leads to a better sensitivity with a larger number of FRB repeaters detected. Here, the projected sensitivity shows this method is a competitive probe in the nHz to μ Hz frequency range. It can fill the 'μHz gap' between pulsar timing arrays and Laser Interferometer Space Antenna (LISA) and is complementary to other proposals of GW detection in this frequency band.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A new probe of μ Hz gravitational waves with FRB timing

We propose Fast Radio Burst (FRB) timing, which uses the precision measurements of the arrival time differences of repeated FRB signals along multiple sightlines, as a new probe of gravitational waves (GWs) around nHz to μ Hz frequencies, with the highest frequency limited by FRB repeating period. The anticipated experiment requires a sightline separation of tens of AU, achieved by sending radio telescopes to space. We find the signal of arrival time difference induced by GWs depends only on the local GWs in the solar system and we can correlate the measurements from different FRB sources or the same source with different repeaters, which leads to a better sensitivity with a larger number of FRB events detected. The projected sensitivity shows this method is a competitive probe in the nHz to μ Hz frequency range. It can fill the ‘ μ Hz gap’ between pulsar timing arrays and Laser Interferometer Space Antenna (LISA) and is complementary to other proposals of GW detection in this frequency band.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Evaluation of a preliminary regional Earth model through comparison of synthetic and observed waveform data

In this report, we document the process related to developing a regional geologic model of a 605 x 1334 km area centered around Utah and encompassing surrounding states. This model is developed to test the effect that composition of a model has on the generation of synthetic data with the intent of using this information to improve upon full waveform moment tensor inversions. We compare observed data from three seismic events and five stations to the synthetic data generated by a preliminary model derived from a geologic framework model (GFM) developed by the USGS. The synthetic data and observed data comparisons indicate that our preliminary model performs well at smaller offset distances in the northern and central sections of the model. However, the southern stations consistently display synthetic data P- and S-wave arrival times that do not match the observed data arrival times, indicating that the velocity structure of the southern part of the model especially is inaccurate.

58 GEOSCIENCES↗
DOE OSTI2022

Practical Event Location Estimation Algorithm for Power Transmission System Based on Triangulation and Oscillation Intensity

Event location in power systems is quite essential information for system operators to enhance control-room situational awareness capability. Therefore, it is of great importance to develop an event location estimation algorithm for transmission systems with high accuracy. With the development of wide-area measurement system (WAMS) such as FNET/GridEye, and the synchrophasor measurement devices (SMDs) such as frequency disturbance recorders (FDRs), the synchronous measurement data including frequency, voltage amplitude and phase angle can be collected and used for event location estimation. First, the phase angle and rate of change of frequency (RoCoF) trajectories are respectively used for determining two sets of wave arrival time associated with each FDR. Then, a convolutional neural network (CNN) is utilized to determine the wave arrival order to select the more suitable set of wave arrival times for a given case and to perform corresponding modifications. Next, the oscillation intensity associated with each FDR is determined based on phase angle trajectories in the center of inertia (COI) coordinate system. Finally, the multiple criteria for event location estimation are represented. In conclusion, case studies and comparisons between the proposed and previous algorithms using actual and confirmed cases in U.S. power systems are performed to demonstrate the effectiveness and improvement of the proposed algorithm in practical applications.

frequency disturbance recorder (FDR)↗
DOE OSTI2024

Time-of-flight vs time-of-arrival in neutron spectroscopic measurements for high energy density plasmas

The neutron time-of-flight (nToF) diagnostic technique has a lengthy history in Inertial Confinement Fusion (ICF) and High Energy Density (HED) Science experiments. Its initial utility resulted from the simple relationship between the full width half maximum of the fusion peak signal in a distant detector and the burn averaged conditions of an ideal plasma producing the flux [Lehner and Pohl, Z. Phys. 207, 83–104 (1967)]. More recent precision measurements [Gatu-Johnson et al., Phys. Rev. E 94(8), 021202 (2016)] and theoretical studies [Munro, Nucl. Fusion 56, 035001 (2016)] have shown the spectrum to be more subtle and complicated, driving the desire for an absolute calibration of the spectrum to disambiguate plasma dynamics from the conditions producing thermonuclear reactions. In experiments where the neutron production history is not well measured, but the neutron signal is preceded by a concomitant flux of photons, the spectrum can be in situ calibrated using a set of collinear detectors to obtain a true “time-of-flight” measurement. This article presents the motivation and overview of this technique along with estimates of the experimental precision needed to make useful measurements in existing and future nToF systems such as the pulsed power Z-machine located in Albuquerque, NM, at Sandia National Laboratories.

Fusion experiments↗
DOE OSTI2023

Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering

The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating the positions of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determining the signal arrival time, and then locating the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transformers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Reverse Time Migration of Explosion-generated Infrasound and Ground-Coupled Airwaves

Accurately locating sources of seismic and infrasonic energy is integral to global monitoring of earthquakes and explosions. Infrasound arrivals times can be used to calculate the origins of events that generate acoustic energy. Picking times of emergent infrasound arrivals, however, can be difficult and prone to uncertainty. Reverse time migration (RTM) is a waveform based location method that does not rely on picked arrival times. Here we use RTM to locate a known chemical explosion that generated acoustic and acoustic-to-seismic signals on 26 and 108 receivers, respectively. All location predictions are less than 24 km from the known location with time errors of less than three minutes. We find strong overall agreement between our results and those of existing RTM and arrival time based methods. Our initial results suggest that RTM is a promising method of event location using acoustic arrivals recorded on both infrasound and seismic instrumentation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Center-of-Mass Corrections in Associated Particle Imaging

Associated particle imaging (API) utilizes the inelastic scattering of neutrons produced in deuterium-tritium (DT) fusion reactions to obtain 3-D isotopic distributions within an object. The locations of the inelastic scattering centers are calculated by measuring the arrival time and position of the associated alpha particle produced in the fusion reactions, and the arrival time of the prompt gamma created in the neutron scattering event. While the neutron and its associated particle move in opposite directions in the center-of-mass (COM) system, in the laboratory system the angle is slightly less than 180°, and the COM movement must be taken into account in the reconstruction of the scattering location. Furthermore, the fusion reactions are produced by ions of different momenta, and thus the COM velocity varies, resulting in an uncertainty in the reconstructed positions. In this article, we analyze the COM corrections to this reconstruction by simulating the energy loss of beam ions in the target material and identifying sources of uncertainty in these corrections. Here we show that an average COM velocity calculated using the ion beam direction and energy can be used in the reconstruction and discuss errors as a function of ion beam energy, composition, and alpha detection location. When accounting for the COM effect, the mean of the reconstructed locations can be considered a correctable systematic error leading to a shift/tilt in the reconstruction. However, the distribution of reconstructed locations also have a spread that will introduce an error in the reconstruction that cannot be corrected. In this article, we will use the known stopping powers of ions in materials and reaction cross sections to examine the reconstruction uncertainties. We also discuss the impact of this effect on our API system.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Development of an MCP-Based Timing Layer for the LHCb ECAL Upgrade-2

The increase in instantaneous luminosity during the high-luminosity phase of the LHC represents a significant challenge for future detectors. A strategy to cope with high-pileup conditions is to add a fourth dimension to the measurements of the hits, by exploiting the time separation of the various proton–proton primary collisions. According to LHCb simulation studies, resolutions of about 10–20 picoseconds, at least an order of magnitude shorter than the average time span between primary interactions, would be greatly beneficial for the physics reach of the experiment. Microchannel plate (MCP) photomultipliers are compact devices capable of measuring the arrival time of charged particles with the required resolution. The technology of large-area picosecond photodetectors (LAPPDs) is under investigation to implement a timing layer that can be placed within a sampling calorimeter module with the purpose of measuring the arrival time of electromagnetic showers. LAPPD performances, using a Gen-I tile with a delay-line anode and a Gen-II with a capacitively coupled anode, have been measured thoroughly both with laser (wavelength of 405 nm and pulse width of 27.5 ps FWHM) and high-energy electron (1–5.8 GeV) beams. Time resolutions of the order of 30 ps for single photoelectrons and 15 ps for electromagnetic showers initiated by 5-GeV electrons, as measured at the shower maximum, are obtained.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Probing primordial power spectrum and non-Gaussianities with fast radio bursts

We use the precision measurements of the arrivaltime differences of the same fast radio burst (FRB) source along multiple sightlines to measure the primordial power spectrum and Non-Gaussianities. The anticipated experiment requires a sightline separation of 100 AU, achieved by sending three or more radio telescopes to the outer solar system.The Shapiro time delays, measured relatively between different telescopes, are sensitive to the gradient field of the gravitational potential between different sightlines. Since the arrival time difference is independent of when the transient signal is emitted from the source, every measurement of the detected FRB source can be correlated. With enough FRB sources discovered, we can map the gravitational potential across the sky. We further calculate the two-point and three-point correlation function of the arrival time difference between telescopes for different FRB sources in the sky. If 10$^{4}$ FRBs were to be detected, our results suggest that this technique can test the inflationary scale-invariant power spectrum down to ∼ 10$^{3}$ Mpc$^{-1}$ and primordial Non-Gaussianities at a level of f$_{NL}$ ∼ 1.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Shared automated vehicle fleet operations for first-mile last-mile transit connections with dynamic pooling

Shared automated vehicles (SAVs) have the potential to promote transit ridership by providing efficient first-mile last-mile (FMLM) connections through reduced operational costs to fleet providers and lower out-of-pocket costs to riders. To help plan for a future of integrated mobility, this paper investigates the impacts of SAVs serving FMLM connections, as a mode that provides flexibility in access/egress decisions and is well coordinated with train station schedules. To achieve this objective, a novel dynamic pooling algorithm was introduced to match SAVs with riders while coordinating the riders' arrival times at the light-rail station to a known train schedule. Microsimulations of SAVs and travelers throughout two central Austin neighborhoods show how larger service areas, higher levels of SAV demand, and longer arrival times between successive trains require larger SAV fleet sizes and higher SAV utilization rates to deliver close traveler wait times. Four-person SAVs appear to perform similar to 6-seat SAVs but will cost less to provide. Using a dynamic pooling algorithm tightly coordinated with train arrivals (every 15 min) delivers 87% of travelers to their stations in time to catch the next train, whereas uncoordinated assignments deliver just 58% of travelers in time.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Data Analysis of the 2020 Central Idaho Mainshock-Aftershock Sequence

In an effort to inform the Senior Seismic Hazard Analysis Committee for the Idaho National Laboratory, we provide an improved aftershock catalog related to the March 31, 2020, Mw6.5 Stanley, Idaho earthquake from picks related to a temporary network of two real-time and 15 non-telemetered seismometers within the epicentral area. From the permanent and temporary (XP) real-time network, the USGS cataloged 1,946 aftershocks between April 1, 2020 and October 31, 2020. To improve aftershock location and magnitudes, we manually picked arrival times of P and S waves from off-line stations in the XP temporary network, generated a new crustal velocity model, and independently relocated each event using the HypoDD double-difference earthquake algorithm. We created our new velocity model from existing broadband and active source seismic campaign data that were acquired near the epicentral region prior to the 2020 earthquake. We compare arrival time differences, epicentral locations and depths between aftershocks recorded with the two catalogs. We find the addition of local stations provides tighter aftershock clustering that suggests an improved aftershock locations. To detect lower magnitude events, we employed deep learning. Our method solves common problems associated with detecting many events that have a low signal-to-noise ratio. From the machine learning database, we detected more than 74,000 aftershocks. Based on the number of identified earthquakes and Gutenberg-Richter relationships derived from the USGS catalog, we estimate that we have reduced the completion magnitude for the Stanley earthquake sequence to below M1 using this machine learning approach. We located each aftershock with our new velocity model. Our new velocity model and picks suggests aftershocks occurred mostly at shallower depths than assessed in the USGS catalog. These aftershocks align along two linear trends that suggest the activation of two unnamed primary faults.

58 GEOSCIENCES↗
DOE OSTI2022

Earthquake Phase Association Using a Bayesian Gaussian Mixture Model

Earthquake phase association algorithms aggregate picked seismic phases from a network of seismometers into individual seismic events and play an important role in earthquake monitoring and research. Dense seismic networks and improved phase picking methods produce massive seismic phase datasets, particularly for earthquake swarms and aftershocks occurring closely in time and space, making phase association a challenging problem. Here, we present a new association method, the Gaussian Mixture Model Association (GaMMA), that combines the Gaussian mixture model with earthquake location, origin time, and magnitude estimation. We treat earthquake phase association as an unsupervised clustering problem in a probabilistic framework, where each earthquake corresponds to a cluster of P and S phases with a hyperbolic moveout of arrival times and a decay of amplitude with distance. We use the multivariate Gaussian distribution to model the collection of phase picks of an event; and the mean of the multivariate Gaussian distribution is given by the predicted arrival time and amplitude from the causative event. We carry out the pick assignment to each earthquake and determine earthquake source parameters (i.e., earthquake location, origin time, and magnitude) under the maximum likelihood criterion using the Expectation-Maximization algorithm. The GaMMA method does not require typical association steps of other algorithms, such as grid-search or supervised training. The results for both synthetic tests and for the 2019 Ridgecrest earthquake sequence show that GaMMA effectively associates phases from a temporally and spatially dense earthquake sequence while producing useful estimates of earthquake location and magnitude.

58 GEOSCIENCES↗