Thermally induced acoustic emissions in salt at the Waste Isolation Pilot Plat (WIPP)
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Choens, Robert Charles [Sandia National Laboratori↗
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Thermally induced acoustic emissions in salt at the Waste Isolation Pilot Plant (WIPP)
Explore the source record for details and available documents.
Choens, Robert Charles [Sandia National Laboratori↗
Dielectric loss due to charged-defect acoustic phonon emission
The coherence times of state-of-the-art superconducting qubits are limited by bulk dielectric loss, yet the microscopic mechanism leading to this loss is unclear. Here, we propose that the experimentally observed loss can be attributed to the presence of charged defects that enable the absorption of electromagnetic radiation by the emission of acoustic phonons. Our explicit derivation of the absorption coefficient for this mechanism allows us to derive a loss tangent of 7.2 × 10−9 for Al2O3, in good agreement with recent high-precision measurements [Read et al., Phys. Rev. Appl. 19, 034064 (2023)]. We also find that for temperatures well below ∼0.2 K, the loss should be independent of temperature, which is also in agreement with observations. Our investigations show that the loss per defect depends mainly on properties of the host material, and a high-throughput search suggests that diamond, cubic BN, AlN, and SiC are optimal in this respect.
Turiansky, Mark E. (ORCID:0000000291543582)↗
Acoustic Energy Release During the Laboratory Seismic Cycle: Insights on Laboratory Earthquake Precursors and Prediction
Abstract Machine learning can predict the timing and magnitude of laboratory earthquakes using statistics of acoustic emissions. The evolution of acoustic energy is critical for lab earthquake prediction; however, the connections between acoustic energy and fault zone processes leading to failure are poorly understood. Here, we document in detail the temporal evolution of acoustic energy during the laboratory seismic cycle. We report on friction experiments for a range of shearing velocities, normal stresses, and granular particle sizes. Acoustic emission data are recorded continuously throughout shear using broadband piezo‐ceramic sensors. The coseismic acoustic energy release scales directly with stress drop and is consistent with concepts of frictional contact mechanics and time‐dependent fault healing. Experiments conducted with larger grains (10.5 μm) show that the temporal evolution of acoustic energy scales directly with fault slip rate. In particular, the acoustic energy is low when the fault is locked and increases to a maximum during coseismic failure. Data from traditional slide‐hold‐slide friction tests confirm that acoustic energy release is closely linked to fault slip rate. Furthermore, variations in the true contact area of fault zone particles play a key role in the generation of acoustic energy. Our data show that acoustic radiation is related primarily to breaking/sliding of frictional contact junctions, which suggests that machine learning‐based laboratory earthquake prediction derives from frictional weakening processes that begin very early in the seismic cycle and well before macroscopic failure.
58 GEOSCIENCES↗
Directional Cooling-Induced Fracturing: Directional Cooling Data Under True-Triaxial Stress State
Data includes Directional Cooling-Induced Fracturing (DCIF) testing data using westerly granite blocks. This submission includes data from two samples of westerly granite, lab sample 7 and 8. Files contain stress, temperature and acoustic emission data acquired during polyaxial, laboratory testing of westerly granite blocks for each sample. FILES: .tradb -- files containing acoustic emission waveforms; sqlite3 database .pridb -- files containing basic acoustic emission information (no waveforms); sqlite3 database .geom -- geometry of the AE sensor network (ASCII)
15 GEOTHERMAL ENERGY↗
IC Project: w19_hossfault “Modelling of stick-slip behavior in sheared granular fault gouge & nonlinear elasticity behavior in cracked solid”
Low frequency earthquakes, non-volcanic tremor, and acoustic emissions are examples of weak seismic signals that may help detect major seismic events, i.e., earthquakes. In a laboratory setting, in which stick-slip events are simulated, acoustic emissions are detected far from the stick-slip events. In both, field and laboratory scale cases, the acoustic emission signals are sourced or detected in a volume remote from the volume that spawns the earthquake. Therefore, it is relevant to establish the causal relationship between signals detected on passive, remote monitors and the dynamics of the elastic structures that launch important seismic events. The work conducted under this IC allocation allowed us to utilize a numerical model that let us follow this causality, i.e., examine and connect the dynamics in a granular system (fault gouge), to signals detected on passive remote monitors. It was demonstrated that stress chains are key in the dynamics of granular systems and that their evolution is the source of the acoustic emission.
58 GEOSCIENCES↗
Impact of ultrafast laser pulse repetition rate on filament acoustic and emission signatures
Femtosecond filaments formed at standoff distances can be used for interrogating various types of matter, making them of potential interest for remote sensing. However, the physical aspects of the filaments generated at standoff distances are influenced by various environmental and laser properties, including the repetition rate of the laser. In this study, we evaluate the role of the predecessor pulse on the properties of filaments by varying the repetition rate of the laser in the range of 10–1000 Hz and using acoustic and emission signatures to diagnose filament properties. Our results show that filaments are brighter at high repetition rates while they are elongated at low repetition rates. The repetition rate has minimal influence on the downstream edge of the filament generation, while the upstream edge becomes elongated. The shadowgram images showed that the turbulence generated by the predecessor pulse may influence the next pulse when the laser is operating at high repetition rates.
47 OTHER INSTRUMENTATION↗
Underwater Noise Measurements around a Tidal Turbine in a Busy Port Setting
Acoustic emissions from current energy converters remain an environmental concern for regulators because of their potential effects on marine life and uncertainties about their effects stemming from a lack of sufficient observational data. Several recent opportunities to characterize tidal turbine sound emissions have begun to fill knowledge gaps and provide a context for future device deployments. In July 2021, a commercial-off-the-shelf hydrophone was deployed in a free-drifting configuration to measure underwater acoustic emissions and characterize a 25 kW-rated tidal turbine at the University of New Hampshire’s Living Bridge Project in Portsmouth, New Hampshire. Sampling methods and analysis were performed in alignment with the recently published IEC 62600-40 Technical Specification for acoustic characterization of marine energy converters. Results from this study indicate acoustic emissions from the turbine were below ambient sound levels and therefore did not have a significant impact on the underwater noise levels of the project site. As a component of Pacific Northwest National Laboratory’s Triton Field Trials (TFiT) described in this Special Issue, this effort provides a valuable use case for the IEC 62600-40 Technical Specification framework and further recommendations for cost-effective technologies and methods for measuring underwater noise at future current energy converter project sites.
tidal turbine↗
Utah FORGE: Slide-Hold-Slide Experiments on Gneiss at Increased Temperature
Included are data from triaxial, single-inclined-fracture friction experiments. The experiments were performed with slide-hold-slide protocol on Utah FORGE gneiss at increased temperature. With a ~10 MPa normal stress, temperatures vary between experiments from room temperature up to 163 Celsius. Hold times vary during experiment from ~10^1 to ~10^5 seconds. Measured are the frictional response upon reactivation after a hold period, active acoustic data (P-wave velocity and amplitude) and passive acoustic data (acoustic emission occurrence and amplitude). There are two types of datafiles: (1) Datafiles containing the friction data, including the temperature and the active acoustic data measured during the experiment (AEXX_Gneiss_Vp_mixref4). The underscore _Vp means that it includes the Vp or P-wave velocity data, with _mixref meaning that we use a mixed reference point for calculating the P-wave velocity. And (2) the datafiles containing the passive acoustics data, a catalog of the acoustic emissions (AE's) measured during the experiment (AEcatalog_AEXX_runX), where AEXX matches the experiment number and runX denotes which part of the experiment the data was collected, matching the times where active acoustic data was collected. AE catalogs are split in two parts when the file size exceeds 1 GB to aid download/opening times.
15 GEOTHERMAL ENERGY↗
DCIF Westerly Granite AE Stress Effect Test (Task 3-1)
Directional Cooling-Induced Fracturing (DCIF) experiments were conducted on rectangular Westerly granite blocks (width=depth=4.0", height=2.0"). Liquid nitrogen was poured in a small, 1"-diameter copper cup attached to the top of the sample, and the resulting acoustic emissions (AEs) and temperature changes on the surface of the sample were monitored. Several confining stresses were applied bi-axially to the sides of the samples so that the onset of AE activity and the stress applied to the sample were correlated. The obtained AEs were used to determine the microcracking source locations and amplitude, and the associated moment tensors. Included in this submission are the animations of the AE locations and graphics displaying the measured temperature-AE activity changes for different stresses.
15 GEOTHERMAL ENERGY↗
Enhanced Design of Radiation Tolerant High-Temperature Structural Health Monitoring Sensors
Acoustic emission sensors are vital in the nuclear industry for real-time structural health monitoring and early detection of material degradation. By capturing high-frequency stress waves emitted from defects like cracks, corrosion, or fatigue, acoustic emission sensors enable non-invasive monitoring of critical components such as reactor vessels, piping, and containment structures. This technology supports predictive maintenance, enhances safety, and ensures regulatory compliance by providing early warnings of potential failures. It is also instrumental in research, particularly in material testing reactors, where it is used to monitor the behavior of fuels and materials under irradiation, by allowing the detection of cracking or other acoustic signals in real time. This enables the evaluation of performance and accident behavior of advanced fuel concepts.
46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
Hydraulic fracture mapping using wavelet-based fusion of wave transmission and emission measurements
Fracture characterization is essential to hydrocarbon and geothermal exploration and production as well as carbon geo-sequestration. Active and passive measurements have been extensively applied to map fractures across various scales. Active and passive methods have inherent advantages and limitations that complement each other. Here, in this study, to improve the mapping of embedded fractures and the surrounding geomechanically altered regions, we integrate active shear-wave transmission measurement with passive acoustic-emission measurement collected during a lab-scale hydraulic-fracturing experiment. The proposed data fusion leverages the advantages of the two modalities of measurements, while minimizing their individual limitations. Polarized shear-wave transmission (active) measurement was collected before and after the hydraulic fracturing, and the acoustic emission (passive) measurement was collected during hydraulic fracturing. Two sets of two-dimensional maps of fracture and fracture-induced damage in axial, median and frontal planes were obtained by separately processing active and passive measurements. These 2D maps are then fed to wavelet-based image fusion technique to integrate the two sources of information for the reliable mapping/imaging of the embedded fractures and the surrounding geomechanically altered regions.
58 GEOSCIENCES↗
Triton Field Trials (TFiT) underwater noise - University of New Hampshire Living Bridge turbine Processed Data
In July 2021, a commercial-off-the-shelf hydrophone was deployed in a free-drifting configuration to measure underwater acoustic emissions and characterize a 25 kW-rated tidal turbine at the University of New Hampshire's Living Bridge Project in Portsmouth, New Hampshire. Sampling methods and analysis were performed in alignment with the recently published IEC 62600-40 Technical Specification for acoustic characterization of marine energy converters. Results from this study indicate acoustic emissions from the turbine were below ambient sound levels and therefore did not have a significant impact on the underwater noise levels of the project site. As a component of Pacific Northwest National Laboratory's Triton Field Trials (TFiT) described in a paper published in a Special Issue of Journal of Marine Energy Science and Engineering, this study provides a valuable use case for the IEC 62600-40 Technical Specification framework and further recommendations for cost-effective technologies and methods for measuring underwater noise at future current energy converter project sites. The paper can be accessed in the link bellow.
16 TIDAL AND WAVE POWER↗
Online Monitoring System for Concrete Structures Affected by Alkali-Silica Reaction
This report presents a comprehensive study of developing ultrasonic wave and acoustic emission (AE) techniques for long-term monitoring of alkali-silica reaction (ASR) development in concrete. Small, medium, and full-scale concrete specimens were cast, conditioned, and monitored for various periods, from at least one year to 2.4 years. The reactive concrete specimens contain coarse or fine reactive aggregates to study the effects of different types of reactive aggregates. Confinements were also designed to simulate the 2-dimensional confinement effects of reinforcement in the shield building of nuclear power plants. The concrete specimens were stored in a environmental chamber with high humidity and high temperature to accelerate the ASR development. The ultrasonic monitoring data shows high sensitivity to ASR development and could detect cracking initiation well before visible surface cracks occurred. However, the linear ultrasonic analysis based on wave velocity is strongly affected by temperature variation. Therefore, a nonlinear ultrasonic method was proposed to measure thermally induced nonlinear acoustic responses of concrete (thermal modulation of ultrasonic wave). The measured nonlinear acoustic parameter shows a high correlation with ASR expansion across specimens with different reactive aggregates and confinement conditions. The same conclusion was obtained from nonlinear resonance tests on small concrete prisms. Compared to the linear acoustic methods, the nonlinear tests show high sensitivities to ASR damage from internal microcracking initiation at the early stage to visible cracks at the late stage of ASR. The attributes of the thermal modulation of nonlinear ultrasonic method include high sensitivity, immunity to temperature effects, and strong correlation with ASR expansion, which demonstrate great potentials of the nonlinear ultrasonic method for diagnosis of ASR damage and prediction of concrete deterioration process. Acoustic emission is a passive sensing technique for damage assessment, and access to only one surface is needed even for thick and heavily reinforced elements such as the walls utilized for nuclear shield building. Additionally, relatively few sensors are required to monitor the progression of the damage process. Results indicate that the AE data can be related to the damage rating index, which is a petrography-based means of assessing damage due to ASR in reinforced concrete. Furthermore, AE is capable of detecting ASR damage long before surface cracking is visually noticeable. Boundary conditions play in important role in the progression of ASR damage and differences in boundary conditions are reflected in the AE data. Entropy based data assessment methods provide a means to assess the damage state, and convolutional neural network based data assessment provided a means to assess the damage state in real-time. Results indicate that artificial neural network models may be used as a means to predict volumetric expansion based on AE data.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
A Weakly Supervised Machine Learning Procedure for Magnet Quench Diagnostics
Voltage taps remain the standard and reliable diagnostic tool for detecting quenches in superconducting magnets. However, they identify a quench only at the time of voltage rise and do not provide information on earlier physical precursors. In this work, we investigate whether acoustic emission data can reveal precursor activity that occurs before conventional voltage detection using machine learning techniques. We introduce an event selection method and a weakly supervised machine learning procedure to learn data-driven criteria for identifying potential acoustic precursors to quenches. Two Convolutional Neural Network (CNN) architectures are trained: one on acoustic sensor events from our selection procedure and one on the Fast Fourier Transforms (FFTs) of these events. Both networks are trained iteratively using confidence-weighted loss functions to associate certain subsets of training data with a precursor label. We evaluate the performance of these models by examining the time distribution of events classified as potential precursors relative to the quench onset. Results indicate that the proposed approach can possibly distinguish acoustic emission events occurring closer to the quench from earlier acoustic activity during ramping, suggesting the potential for flagging quench precursors in acoustic data.
Khan, Maira [Fermilab] (ORCID:0009000891602387)↗
Processed Lab Data for Neural Network-Based Shear Stress Level Prediction
Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a non-overlapping window from the original acoustic data. The first column is the time of the window. The second and third columns are the mean and the variance of the acoustic data in this window, respectively. The 4th-11th column is the the power spectrum density ranging from low to high frequency. And the last column is the corresponding label (shear stress level). The name of the file means which driving velocity the sequence is generated from. Data were generated from laboratory friction experiments conducted with a biaxial shear apparatus. Experiments were conducted in the double direct shear configuration in which two fault zones are sheared between three rigid forcing blocks. Our samples consisted of two 5-mm-thick layers of simulated fault gouge with a nominal contact area of 10 by 10 cm^2. Gouge material consisted of soda-lime glass beads with initial particle size between 105 and 149 micrometers. Prior to shearing, we impose a constant fault normal stress of 2 MPa using a servo-controlled load-feedback mechanism and allow the sample to compact. Once the sample has reached a constant layer thickness, the central block is driven down at constant rate of 10 micrometers per second. In tandem, we collect an AE signal continuously at 4 MHz from a piezoceramic sensor embedded in a steel forcing block about 22 mm from the gouge layer The data from this experiment can be used with the deep learning algorithm to train it for future fault property prediction.
15 GEOTHERMAL ENERGY↗
Failure prediction and estimation of failure parameters
Machine-learning methods and apparatus are disclosed to determine frictional state or other parameters in an earthquake zone or other failing medium, using acoustic emission, seismic waves, or other detectable indicators of microscopic processes. Predictions of future failures are demonstrated in different regimes. A classifier is trained using time series of acoustic emission data along with historic data of frictional state or failure events. In disclosed examples, random forests and gradient boost trees are used, and grid-search or EGO procedures are used for hyperparameter tuning. Once trained, the classifier can be applied to testing or live data in order to assess a frictional state, assess seismic hazard, or make predictions regarding a future failure event. The technology has been developed in a double direct shear apparatus, but can be widely applied to seismic faults, other terrestrial failures, or failures in man-made structures. Variations are disclosed.
Johnson, Paul Allan↗
Experimental study correlating damage and permeability in concrete using confined, flattened Brazilian disks
This research experimentally studies coupled hydro-mechanical relationships using flattened Brazilian disks under confinement, with concurrent permeability measurements and acoustic emission monitoring. A series of tests are performed on concrete, as representative samples of brittle geo-materials, under a range of confining stresses between 2.76 and 13.79 MPa. Acoustic emission data is used to quantify damage and identify damage thresholds. Damage is then correlated to pre-peak changes in permeability. The advancement of models that couple transport properties to mechanical responses are of interest in the fields of carbon sequestration, hydrofracking, production of geothermal energy, induced seismicity, and underground nuclear waste storage, and these experiments validate a novel experimental approach towards investigating hydro-mechanical relationships.
Boyce, S.↗