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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Strong Upper-Plate Heterogeneity at the Hikurangi Subduction Margin (North Island, New Zealand) Imaged by Adjoint Tomography

We use earthquake-based adjoint tomography to invert for three-dimensional structure of the North Island, New Zealand, and the adjacent Hikurangi subduction zone. The study area, having a shallow depth to the plate interface below the North Island, offers a rare opportunity for imaging material properties at an active subduction zone using land-based measurements. Starting from an initial model derived using ray tomography, we perform iterative model updates using spectral element and adjoint simulations to fit waveforms with periods ranging from 4–30 s. We perform 28 model updates using an L-BFGS optimization algorithm, improving data fit and introducing P- and S-wave velocity changes of up to ±30%. Resolution analysis using point spread functions show that our measurements are most sensitive to heterogeneities in the upper 30 km. The most striking velocity changes coincide with areas related to the active Hikurangi subduction zone. Lateral velocity structures in the upper 5 km correlate well with New Zealand geology. Here, the inversion reveals increased along-strike heterogeneity on the margin. In Cook Strait we observe a low-velocity zone interpreted as deep sedimentary basins. In the central North Island, low-velocity anomalies are linked to surface geology, and we relate velocity structures at depth to crustal magmatic activity below the Taupō Volcanic Zone.

58 GEOSCIENCES↗
DOE OSTI2026

Comparing ICME simulations with scaled laboratory experiment

In stellar physics and astrophysics, numerical simulations and laboratory experiments are often compared to observational data to support their representation of the real world. However, there is also merit in comparing numerical simulations to properly scaled experiments, especially when the experiment and the simulation are both emulating the solar phenomena. Confirming the credibility of scaled experiments and their scaling with well-validated models is important to expand our knowledge of the associated physical phenomena. This is significant because experiments and simulations can be performed frequently, whereas observations may be limited by location, field of view, and missing data. In this work, we use the Alfvén Wave Solar atmosphere Model, a well-validated magnetohydrodynamic model, to simulate an interplanetary coronal mass ejection (ICME) and compare it to an experiment which provides a scaled analog to a physical ICME. The experiment was performed on the Big Red Ball facility and scaled using dimensionless parameters such as plasma β and magnetosonic Mach number to reproduce the main structure of an ICME. We compare the model-simulated temperature, density, and magnetic field to those from the experiment, as well as the scaling parameters used in the experiment, to those calculated from the simulation. This comparison is performed to further justify the scaling arguments made by the experiment. Additionally, the comparison would lead to the development of stronger scaling arguments for future experiments.

Bryant, K. [University of Michigan, Ann Arbor, MI ↗
DOE OSTI2022

DeepShake: Shaking Intensity Prediction Using Deep Spatiotemporal RNNs for Earthquake Early Warning

Abstract We propose a deep spatiotemporal recurrent neural network, DeepShake, to project future shaking intensity directly from current ground-motion observations. DeepShake is a network-based forecasting model, able to predict future shaking intensity at all stations within a network given previously measured ground shaking. The model is not given any a priori knowledge of station locations; instead, it learns wave propagation amplitudes and delays solely from training data. We developed DeepShake with the 35,679 earthquakes from the 2019 Ridgecrest sequence. Tasked with alerting for modified Mercalli intensity (MMI) IV+ shaking on 3568 validation earthquakes at least 5 s in advance, DeepShake achieves an equal error rate of 11.4%. For the Mw 7.1 earthquake that hit Ridgecrest on 5 July 2019, DeepShake was able to provide targeted alerts to all stations inside the network 5 s prior to the arrival of MMI IV+ waveforms. DeepShake demonstrates that deep spatiotemporal neural networks can effectively provide one-step earthquake early warning with reasonable accuracy and latency.

Geochemistry & Geophysics↗
DOE OSTI2021

Bayesian Optimal Sensor Augmentation Via Estimated Mutual Information

We consider the problem of designing a sensor network to most efficiently locate the source of a seismic event. The left panel of Figure 1 shows 982 possible sensor locations in a region of interest. The objective is to infer the unknown location of an earthquake, indicated by the gold dot for illustration purposes. Assume that three sensors have already been placed for data collection. Travel times of the P-wave from the source to the three sensors are observed and used to infer the unknown source location via a Bayesian statistical model described subsequently. The right panel of Figure 1 shows samples from the resulting posterior distribution for source location along with a posterior estimate of this location.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Feasibility of Using Fourier Neural Operators for 3D Elastic Seismic Simulations

Seismic simulations in three-dimensional (3D) Earth models are important in many of the seismological applications related to our lab’s mission, however, accurate high-fidelity simulations are computationally costly. We investigated the capabilities of the newly developed Fourier Neural Operator (FNO) to solve the 3D elastic wave equations for seismic simulations. We generated simulation data for training the FNO model, and analyzed its performance on various test cases, such as the performance with different number of training data, different resolutions, and on more canonical structures. We found the FNO model can reproduce 3D seismic simulations with high accuracy and ~169 times faster on a small spatial grid size of 16 x 16 x16. When applied on higher resolution data, we found that transfer learning with fine-tuning on a small amount of data achieves reasonable results. This feasibility study showed promising results for using FNO’s for 3D seismic simulations and represents the foundation of further research to develop this into a more mature approach for different seismological applications. The potential impact of this project will provide techniques for large-scale or real-time applications of solving PEDs in support of national lab’s programs.

58 GEOSCIENCES↗
DOE OSTI2026

Wind and Temperature Consensus at Horn Point, HU-Beltsville, Piney Run (Maryland) in support of CoURAGE

The Maryland Department of the Environment (MDE) operates a ground-based atmospheric profiling network consisting of collocated radar wind profilers (RWP) and radio acoustic sounding systems (RASS) as part of its Ambient Air Monitoring Program. This network provides continuous observations of wind and temperature structure in the lower troposphere to support air quality forecasting, regulatory analysis, and atmospheric research. The network currently includes three fixed sites across Maryland: Horn Point (HP, lower eastern shore) [38.587525°,-76.141006°], Howard University-Beltsville (HUB, central Maryland) [39.055277°, -76.878632°], and Piney Run (PR, western Maryland) [39.705950°, -79.012000°] The network is designed to capture regional variability in atmospheric transport and boundary-layer processes. These systems measure vertical profiles of horizontal wind speed and direction using Doppler radar techniques, with observations typically spanning from ~100 m above ground level up to approximately 2.5–4 km. Measurements are derived from the Doppler shift of backscattered electromagnetic signals, enabling retrieval of wind vectors at multiple altitudes with high temporal resolution (e.g., 30-minute averages reported every 6 minutes). Each radar wind profiler is paired with a Radio Acoustic Sounding System (RASS) to provide profiles of virtual temperature in the lower atmosphere (~100–200 m AGL) by measuring the propagation speed of acoustic waves. Together, the RWP/RASS system yields a coupled data set of thermodynamic and kinematic atmospheric structure, including additional parameters such as vertical velocity, radial velocity, signal-to-noise ratio, and spectral width for advanced analysis. There are two types of files for each station: wind data (files with a "w" prefix) and virtual temperature RASS data (files with a "t" prefix). The wind data files are in the format wYYDDD.cns, where YY is the 2-digit year and DDD is the day of the year. The RASS virtual temperature data files are in the format tYYDDD.cns. Each record has the following header structure: Line 1 : Station Name RASS files Line 2 : RASS rev DeTect_2.0, WINDS files Line 2 : WINDS rev ATI 5.1 Line 3 : N latitude, W longitude, and site elevation (m) Line 4 : Date and begin time of consensus: yy mm dd hh mn ss plus # minutes to add to get UTC Line 5 : Consensus averaging time (minutes); number of beams; number of range gates Line 6 : Number of records required to make consensus (num) total number of records (tot) and the consensus window size (m/s) in the format: num:tot (window) RASS files Line 7 : no. of coded cells, no. of spec, pulse width (ns), and inter-pulse period (µs), WINDS files Line 7 : No. of coded cells, no. of spectra, pulse width (ns), and inter-pulse period (µs), each with a pair of values: first value is for oblique beams, second for vertical RASS files Line 8 : Full scale Doppler value (m/s) Delay to first gate (ns) Number of gates Spacing of gates (ns), WINDS files Line 8 : Full scale Doppler velocity (m/s), oblique and vertical Vertical correction applied to oblique beams? (0 = no, 1 = yes) Delay to first gate (ns), oblique and vertical Number of gates, oblique and vertical Spacing of gates (ns), oblique and vertical Line 9 : Azimuth and elevation (9s indicate vertical beam not used) RASS files Line 10, values : HT = Height above ground (km), T = Uncorrected virtual temperature consensus (deg C), Tc = Corrected virtual temperature consensus (deg C), W = Vertical wind consensus (9s indicate vertical beam not used, w-component, positive upward, m/s), CNT = Number of records that made consensus (for the 3 values in same order), SNR = Average signal to noise ratio (dB) of records in consensus (same order) WINDS files Line 10, values : HT = Height above ground (km), SPD = Wind speed (m/s), DIR = Wind direction (deg E of N from N), RAD = Radial velocities for each beam (m/s) in order given in azimuth and elevation line (positive toward radar; 9s indicate vertical beam not used, CNT = Number of records that made consensus, SNR = Average signal to noise ratio (dB) of records in consensus

{"wind speed and direction",temperature}↗
DOE OSTI2025

Deciphering Reaction Mechanisms of Molecular Proton Reduction Catalysts with Cyclic Voltammetry: Kinetic vs Thermodynamic Control

The kinetics and thermodynamics of elementary reaction steps involved in the catalytic reduction of protons to hydrogen define the reaction landscape for catalysis. The mechanisms can differ in the order of the elementary proton transfer, electron transfer, and bond-forming steps and can be further differentiated by the sites at which protons and electrons localize. Access to fully elucidated mechanistic, kinetic, and thermochemical details of molecular catalysts is crucial to facilitate the development of new catalysts that operate with optimal efficiency, selectivity, and durability. The mechanism by which a catalyst operates, as well as the kinetics and thermodynamics associated with the individual steps, can often be accessed through electroanalytical studies. Here, this Account details the application of cyclic voltammetry to interrogate reaction mechanisms and quantify the kinetics and thermodynamics of elementary reaction steps for a series of molecular catalysts that mediate electrochemical proton reduction. I distinguish the limiting scenarios wherein a catalyst operates under kinetic control vs thermodynamic control, with a focus on detecting how cyclic voltammetry features shift with proton source strength and concentration, as well as scan rate. For systems that operate under kinetic control, catalytic currents are observed at, or slightly positive toward, the formal potential for the redox process that triggers catalysis. Under thermodynamic control, catalytic responses shift as a function of the proton source pKa and effective pH of the solution. After drawing this distinction, we introduce the appropriate voltammetry experiments and accompanying analytical expressions for extracting key metrics from the data. To illustrate analytical strategies to quantify elementary reaction steps of catalysts operating under kinetic control, I describe our studies of proton reduction catalysts Co(dmgBF 2 ) 2 (CH 3 CN) 2 (dmgBF 2 = difluoroboryl-dimethylglyoxime) and [Ni(P 2 Ph N 2 Ph ) 2 ] 2+ (P 2 Ph N 2 Ph = 1,5-phenyl-3,7-phenyl-1,5-diaza-3,7-diphosphacyclooctane). Here, peak shift analysis, foot-of-the-wave analysis, and plateau current analysis are applied to data sets wherein voltammetric response are recorded as a function of catalyst concentration, proton source concentration, proton source strength, and scan rate to quantify rate constants for elementary proton transfer and bond-forming steps in a catalytic cycle. Further, the case study of [Ni(P 2 Ph N 2 Ph ) 2 ] 2+ illustrates how complementary spectroscopic methods can bolster the mechanistic assignment. Collectively, these two studies showcase how detailed mechanistic studies inform on rate-limiting elementary steps in catalysis and other key processes underpinning catalysis. Second, I present analytical strategies to interrogate catalysts operating under thermodynamic control, centered on the case study of [Ni II (P 2 Ph N 2 Bn ) 2 ] 2+ (P 2 Ph N 2 Bn = 1,5-dibenzyl-3,7-diphenyl-1,5-diaza-3,7-diphosphacyclooctane). Here, the application of nonaqueous Pourbaix theory to extract thermodynamic information is introduced, and the construction of a coupled Pourbaix diagram is detailed. This study identifies ligand-based protonation as the key process that places catalysis under thermodynamic control and influences the reaction mechanism. Together, the work detailed in this Account showcases the utility of electroanalytical methods to disentangle complex reaction mechanisms and extract key thermochemical and kinetic parameters for elementary steps of catalysis. Through detailed presentation of the key analytical expressions that underpin these analyses, this Account seeks to facilitate the adoption of cyclic voltammetry by the community to fully extract kinetic, thermochemical, and mechanistic information on electrochemical small-molecule activation.

catalysts↗
DOE OSTI2023

ANN-based ground motion model for Turkey using stochastic simulation of earthquakes

SUMMARY Turkey is characterized by a high level of seismic activity attributed to its complex tectonic structure. The country has a dense network to record earthquake ground motions; however, to study previous earthquakes and to account for potential future ones, ground motion simulations are required. Ground motion simulation techniques offer an alternative means of generating region-specific time-series data for locations with limited seismic networks or regions with seismic data gaps, facilitating the study of potential catastrophic earthquakes. In this research, a local ground motion model (GMM) for Turkey is developed using region-specific simulated records, thus constructing a homogeneous data set. The simulations employ the stochastic finite-fault approach and utilize validated input-model parameters in distinct regions, namely Afyon, Erzincan, Duzce, Istanbul and Van. To overcome the limitations of linear regression-based models, artificial neural network is used to establish the form of equations and coefficients. The predictive input parameters encompass fault mechanism (FM), focal depth (FD), moment magnitude (Mw), Joyner and Boore distance (RJB) and average shear wave velocity in the top 30 m (Vs30). The data set comprises 7359 records with Mw ranging between 5.0 and 7.5 and RJB ranging from 0 to 272 km. The results are presented in terms of spectral ordinates within the period range of 0.03–2.0 s, as well as peak ground acceleration and peak ground velocity. The quantification of the GMM uncertainty is achieved through the analysis of residuals, enabling insights into inter- and intra-event uncertainties. The simulation results and the effectiveness of the model are verified by comparing the predicted values of ground motion parameters with the observed values recorded during previous events in the region. The results demonstrate the efficacy of the proposed model in simulating physical phenomena.

Karimzadeh, Shaghayegh (ORCID:0000000337531676)↗
DOE OSTI2023

AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers

We introduce spatiotemporal-graph models that concurrently process data from the twin advanced LIGO detectors and the advanced Virgo detector. We trained these AI classifiers with 2.4 million IMRPhenomXPHM waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers with component masses m{1,2}∈[3M⊙,50M⊙], and individual spins sz{1,2}∈[−0.9,0.9]; and which include the (ℓ,|m|)={(2,2),(2,1),(3,3),(3,2),(4,4)} modes, and mode mixing effects in the ℓ=3,|m|=2 harmonics. We trained these AI classifiers within 22 hours using distributed training over 96 NVIDIA V100 GPUs in the Summit supercomputer. We then used transfer learning to create AI predictors that estimate the total mass of potential binary black holes identified by all AI classifiers in the ensemble. We used this ensemble, 3 classifiers for signal detection and 2 total mass predictors, to process a year-long test set in which we injected 300,000 signals. This year-long test set was processed within 5.19 minutes using 1024 NVIDIA A100 GPUs in the Polaris supercomputer (for AI inference) and 128 CPU nodes in the ThetaKNL supercomputer (for post-processing of noise triggers), housed at the Argonne Leadership Computing Facility. These studies indicate that our AI ensemble provides state-of-the-art signal detection accuracy, and reports 2 misclassifications for every year of searched data. This is the first AI ensemble designed to search for and find higher order gravitational wave mode signals.

Tian, Minyang↗
DOE OSTI2022

Multiscale and multiphysics FEA simulation and materials optimization for laser ultrasound transducers

In this study, the relationship between the nanocomposite design and the laser ultrasound transducer (LUT) characteristics was investigated through simulations in multiple scale levels for material behavior, device response, and acoustic wave propagation in media. First, the effects of the nanoparticle size and concentration on the effective properties of composites were quantitatively investigated with the finite element analysis (FEA) method. Second, the effective properties of the nanocomposite were assigned to the layer, which is modeled as a homogeneous material, in the FEA for the LUT simulating the energy conversion from the incident laser to the acoustic wave. Finally, the ultrasound propagation in the water was calculated by a theoretical wave propagation model. The FEA-based prediction was compared with the experimental data in the literature and a theoretical analysis for LUT based on Thermal-Acoustic coupling. As a result, the ultrasound waves on the transducer surface and at a distance in the water could be predicted. Based on the hierarchically integrated prediction procedure, the optimal conditions of the photoacoustic nanocomposites were investigated through the parametric study with the particle size and concentration as variables. The results guide the material designs optimized for different device characteristics, such as high pressure and broad bandwidth.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Data–Driven Velocity Model Evaluation Using K–Means Clustering

In this work, we develop a data-driven clustering method to evaluate a velocity model using surface wave velocity dispersion. This is done by first computing theoretical dispersion curves for 1-D velocity profiles of all the grid locations and then splitting the resulting dispersion curves into a certain number of groups via the K-means clustering. The observed dispersion curves are also clustered following the same procedure and the velocity model is assessed by comparing the spatial patterns obtained for the observed and synthetic data sets. The method is applied to evaluate two community velocity models in southern California, CVM-S4.26 and CVM-H15.1, using phase velocity maps derived for 3–16 s Rayleigh waves. We found a good correlation in the spatial distribution of clusters between the result of CVM-S4.26 and that of the observed data, suggesting that the CVM-S4.26 fits the observed dispersion maps better than the CVM-H15.1 in terms of features extracted from the clustering analysis.

58 GEOSCIENCES↗
DOE OSTI2020

Noninteracting electrons in a prototypical one-dimensional sinusoidal potential

A prototypical model of a one-dimensional metallic monatomic solid containing noninteracting electrons is studied, where the argument of the cosine potential energy, periodic with the lattice, contains the first reciprocal lattice vector G 1 =2π/a, where a is the lattice constant. The time-independent Schrödinger equation can be written in reduced variables as a Mathieu equation for which numerically exact solutions for the band structure and wave functions are obtained. The band structure has band gaps that increase with increasing amplitude q of the cosine potential. In the extended-zone scheme, the energy gaps decrease with increasing index n of the Brillouin-zone boundary ka=nπ, where k is the crystal momentum of the electron. The wave functions of the band electron are derived for various combinations of k and q as complex combinations of the real Mathieu functions with even and odd parity, and the normalization factor is discussed. The wave functions at the bottoms and tops of the bands are found to be real or imaginary, respectively, corresponding to standing waves at these energies. Irrespective of the wave vector k within the first Brillouin zone, the electron probability density is found to be periodic with the lattice. The Fourier components of the wave functions are derived versus q, which reveal multiple reciprocal-lattice-vector components with variable amplitudes in the wave functions unless q = 0. The magnitudes of the Fourier components are found to decrease exponentially as a power of n for n~3 to 45 for ka = π/2 and q = 2, and a precise fit is obtained to the data. The probability densities and probability currents obtained from the wave functions are also discussed. The probability currents are found to be zero for crystal momenta at the tops and bottoms of the energy bands, because the wave functions for these crystal momenta are standing waves. To conlcude, the band structure is calculated from the central equation and compared to the numerically exact band structure.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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 OSTI2022

MBARI-WEC September and October 2022 Field Data

This data is needed to simulate a model of the MBARI-WEC (Monterey Bay Aquarium Research Institute, Wave Energy Converter device) in a simulation environment (e.g. Gazebo) for 56 observation dates in the time between September and October 2022, and to compare the simulation outputs to the corresponding field data of the physical MBARI-WEC. There were 50 observations chosen in Sept and 6 observations in Oct. To help understand terms below, a summary of the system can be found at the github link in the downloads section below. The Gazebo MBARI-WEC model is also provided, should users wish to simulate using this platform. There are 4 *mat files included. ................................................................................................................................................................................................................................... Spectrum and Simulation Inputs: September2022_spectrum_siminputs.mat and October2022_spectrum_siminputs.mat has data needed for simulation inputs in table format. These include the ocean spectrum for an observation and operating parameters of the MBARI-WEC during that observation. They are organized as rows representing an observation and columns representing data. For example, for the September *mat there are 50 rows. The first 7 columns are Datetime, sig_waveheight, peak_period, mean_period, heaveconedoor_status, pistonpos_mean, and scale_factor: - Datetime is the date and time the observation occurred in PST - sig_waveheight is the significant wave height of the ocean spectrum during that observation in meters - peak_period is the peak period of the ocean spectrum during that observation in seconds - mean_period is the mean period of the ocean spectrum during that observation in seconds - heaveconedoor_status is the status of the heave cone doors where 0 represents the doors are open and 1 represents they are closed - pistonpos_mean is the mean position of the PTO ram (piston) in meters - scale_factor is an additional factor of 0.5 --1.4 applied to a default damping relationship The next columns are data needed to represent the ocean spectrum. First are the frequencies [Hz] labeled as "f0-f38", then the variance density [m2/Hz] labeled as "vardens0-vardens38". October2022_spectrum_siminputs.mat follows as a similar format as above, but includes a larger amount of ocean spectrum frequencies and variance density elements. ................................................................................................................................................................................................................................... Field data: The field data is found in MBARIWEC_septdata.mat and MBARIWEC_octdata.mat for the observations of September and October, respectively. These contain data in a struct format. The struct contains the following fields for each observation: PC_BattCurr, PC_LoadCurr, PC_RPM, PC_Voltage, SC_Range, SC_Velocity, DateTime, where: - PC_BattCurr is the current flowing to or from the onboard batteries in Amps - PC_LoadCurr is the current flowing to the load dump in Amps - PC_RPM is the electric/hydraulic motor shaft speed (directly coupled) in RPM - PC_Voltage is the bus voltage at the power converter in Volts - SC_Range is the PTO ram (piston) position in meters where 0 is fully retracted and 2.03 is fully extended - SC_Velocity is the PTO ram (piston) velocity in meters/sec - DateTime is the date and time of the sampled field data in each observation in PST - Electric Power is equal to: PC_Voltage*(PC_BattCurr + PC_LoadCurr) in Watts For example, upon loading MBARIWEC_octdata.mat, the aforementioned fields would be loaded, each with {6x1} cells for the 6 observations chosen in October. Within the first cell of e.g. SC_Range would be sampled data representing the field data of the MBARIWEC PTO piston position for say, one hour, of the first October observation. The corresponding field DateTime would...

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Constraining ΛCDM cosmological parameters with Einstein Telescope mock data

ABSTRACT We investigate the capability of Einstein Telescope to constrain the cosmological parameters of the non-flat ΛCDM cosmological model. Two types of mock data sets are considered depending on whether or not a short gamma-ray burst is detected, and associated with the gravitational wave emitted by binary neutron stars merger, using the THESEUS satellite. Depending on the mock data set, two statistical estimators are applied: one assumes that the redshift is known, while the other marginalizes over it assuming a specific redshift prior distribution. We demonstrate that (i) using mock catalogues collecting gravitational wave signals emitted by binary neutron stars systems to which a short gamma-ray burst has been associated, Einstein Telescope may achieve an accuracy on the cosmological parameters of $\sigma _{H_0}\approx 0.40$ km s−1 Mpc−1, $\sigma _{\Omega _{k,0}}\approx 0.09$, and $\sigma _{\Omega _{\Lambda ,0}}\approx 0.07$; while (ii) using mock catalogues collecting all gravitational wave signals emitted by binary neutron stars systems for which an electromagnetic counterpart has not been detected, Einstein Telescope may achieve an accuracy on the cosmological parameters of $\sigma _{H_0}\approx 0.04$ km s−1 Mpc−1, $\sigma _{\Omega _{k,0}}\approx 0.01$, and $\sigma _{\Omega _{\Lambda ,0}}\approx 0.01$, once the redshift probability distribution of GW events is known from from population synthesis simulations and/or the measure of the tidal deformability parameter. These results show an improvement of a factor 2–75 with respect to earlier results using complementary data sets.

Califano, Matteo↗
DOE OSTI2026

Snow-eater heat waves of the western United States

Abrupt snowmelt, triggered by rain-on-snow events or "snow-eater heat waves," can cause flooding, initiate or accelerate snow drought, and affect water availability. However, the characteristics (e.g., area, duration, and frequency), impacts, and trends of snow-eater heat waves have received little attention. To address this gap, we developed a method to identify snow-eater heat waves and estimate their melt potential using 20th Century Reanalysis version 3 air temperature data, the TempestExtremes algorithm, and an operational snowmelt model (SNOW-17) across 1850-2015. Melt season snow-eater heat waves typically last 3 to 5 days, with three to five events, doubling snowmelt rates. Seven of 11 spring superfloods are shown to coincide with snow-eater heat waves. Since the 1850s, snow-eater heat waves have increased in area and frequency, decreased in duration, and shifted earlier in the melt season. Incorporating snow-eater heat-wave impacts into SNOW-17 enhances extreme melt estimates, improving water management support tools.

Rhoades, Alan M↗
DOE OSTI2024

Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments

Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips.

58 GEOSCIENCES↗