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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Inferring fracture dilation and shear slip from surface deformation utilising trained surrogate models

An important task in energy and CO 2 storage (sequestration) in the subsurface is to verify that the surrounding fractures and faults are not activated, acting as leakage pathways. This is achievable through effective and efficient Measurement, Monitoring and Verification (MMV) plans. In this work, two surrogate models are trained to captures dilation (opening) and shear deformation of fractures, and the associated surface deformation. The trained surrogate model, based on conditional Generative-Adversarial Networks (cGAN) receives fracture apertures from dilational fractures together with fracture slips from shear fractures and predicts the combined surface deformation. An inversion algorithm based on Bayesian framework is proposed to identify the geometry of both types of fractures, as well as volume of dilational fractures and deformation moment induced by shear fractures, all from the measured surface deformation data. The inversion algorithm utilises the Differential Evolution (DE) optimisation technique that has the superior performance in finding the global minimum of cost function. The proposed surrogate-assisted inversion successfully inferred the unknown dip, dip direction and the volume of the dilational fractures as well as the induced deformation moment in shear fractures. The model was further tested for the inversion of a field hydraulic fracturing tilt dataset applying different scenarios with varying unknowns to show the model's performance, as well as incorporating shear deformation for better match with the observed data.

Dilation and shear↗
DOE OSTI2025

Probing the Fine Symmetry Breaking in High-Temperature Superconductor Bi 2 Sr 2 CaCu 2 O 8+δ with Angle-Resolved Nonreciprocal Transport

After decades of research, symmetry breaking in high-temperature cuprate superconductors remains a key issue to resolve and is relevant to understanding their exotic quantum phases. In the prototypical cuprate superconductor, Bi 2 Sr 2 CaCu 2 O 8+δ (Bi2212), the possible symmetry breaking has been mostly examined microscopically with scanning tunneling microscopy and photoemission spectroscopy. However, macroscopic evidence and the direct implications for electronic transport have remained elusive. Using superconductivity-enhanced nonreciprocal transport, we report macroscopic evidence of inversion symmetry breaking in Bi2212. While the inversion symmetry breaking is subtle, its effect on nonreciprocal transport is significantly enhanced by the vortex motion during the superconducting transition, leading to a robust manifestation of inversion symmetry breaking in macroscopic transport. Combining angle-resolved nonreciprocal transport and 3D tight-binding model calculations, we derive that the inversion symmetry breaking is due to subtle crystal distortions that give rise to both in-plane and out-of-plane polar axes. Our work not only establishes nonreciprocal transport as a sensitive macroscopic probe to provide electrical transport-based evidence of fine symmetry breaking in Bi2212, but also paves the way for novel device applications such as high-temperature superconducting diodes and superconducting spintronics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

PyOED: An Extensible Suite for Data Assimilation and Model-Constrained Optimal Design of Experiments

This article describes PyOED, a highly extensible scientific package that enables developing and testing model-constrained optimal experimental design (OED) for inverse problems. Specifically, PyOED aims to be a comprehensive Python toolkit for model-constrained OED. The package targets scientists and researchers interested in understanding the details of OED formulations and approaches. It is also meant to enable researchers to experiment with standard and innovative OED technologies with a wide range of test problems (e.g., simulation models). OED, inverse problems (e.g., Bayesian inversion), and data assimilation (DA) are closely related research fields, and their formulations overlap significantly. Thus, PyOED is continuously being expanded with a plethora of Bayesian inversion, DA, and OED methods as well as new scientific simulation models, observation error models, and observation operators. These pieces are added such that they can be permuted to enable testing OED methods in various settings of varying complexities. The PyOED core is completely written in Python and utilizes the inherent object-oriented capabilities; however, the current version of PyOED is meant to be extensible rather than scalable. Specifically, PyOED is developed to “enable rapid development and benchmarking of OED methods with minimal coding effort and to maximize code reutilization.” This article provides a brief description of the PyOED layout and philosophy and provides a set of exemplary test cases and tutorials to demonstrate the potential of the package.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

CO 2 storage site characterization using ensemble-based approaches with deep generative models

Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO 2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO 2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.

42 ENGINEERING↗
DOE OSTI2024

The Collaborative Seismic Earth Model: Generation 2

Geological interpretations, earthquake source inversions and ground motion modeling, among other applications, require models that jointly resolve crustal and mantle structure. With the second generation of the Collaborative Seismic Earth Model (CSEM2), we present a global multi-resolution tomographic Earth model that serves this purpose. The model evolves through successive regional- and global-scale refinements. While the first generation aggregated regional models, with this study, we ensure consistency between all individual submodels, resulting in a model that accurately explains wave propagation across scales. Recent regional tomographic models were incorporated, comprising continental-scale inversions for Asia and Africa, as well as regional inversions for the Western US, Central Andes, Iran, and Southeast Asia. Across all regional refinements, over 793,000 source-receiver pairs contributed. Moreover, the long-wavelength Earth model (LOWE) introduces large-scale structures outside of pre-existing local refinements. A full-waveform inversion for global anisotropic P-and S-wave speed structure over a total of 194 iterations with a minimum period of 50 s on a large data set of 1 hr of waveform data from 2,423 earthquakes and over 6 million source-receiver pairs ensures that regional updates in the crust and uppermost mantle translate into updates of deeper, global-scale structure. To test the performance of CSEM2, we evaluate waveform fits between observed and synthetic seismograms at 50 s for an independent data set on the global scale, and on the regional scale for lower periods. We accurately simulate waveforms within and across regional refinements, maintaining the original resolution of the submodels embedded in the global framework.

58 GEOSCIENCES↗
DOE OSTI2024

Origin of spin-driven ferroelectricity and effect of external pressure on the complex magnetism of the 6⁢ H perovskite Ba 3 ⁢Ho⁢Ru 2⁢ O 9

The compound Ba 3 HoRu 2 O 9 magnetically orders at 50 K (T N1 ), followed by another complex magnetic ordering at 10.2 K (T N2 ). The second magnetic phase transition was characterized by the coexistence of two competing magnetic ground states associated with two different magnetic wave vectors (K 1 = 0.5 0 0 and K 2 = 0.25 0.25 0). The multiferroicity and magnetoelectric coupling were predicted below T N2 in these 4d-based materials. Here, in this work, we have discussed the origin of spin-driven ferroelectricity, which is not known yet, and the nature of magnetoelectric domains. We have investigated the compound through time-of-flight neutron diffraction, synchrotron x-ray diffraction (XRD), ac susceptibility, frequency-dependent complex dielectric spectroscopy, and dc magnetization under external pressure. We have demonstrated that the noncollinear structure involving two different magnetic ions, Ru (4d) and Ho (4f), breaks the spatial inversion symmetry via inverse Dzyaloshinskii-Moriya (DM) interaction through strong 4d-4f magnetic correlation, which shifts the oxygen atoms and results in nonzero polarization. Such an observation of inverse DM interaction from two different magnetic ions which cause ferroelectricity is rarely observed. The stronger spin-orbit coupling of 4d orbital might play a major role in creating DM interaction of noncollinear spins. We have systematically studied the spin and dipolar dynamics, which exhibit intriguing behavior with shorter coherence lengths of second magnetic phase associated with the k 2 wave vector. The results manifest the development of finite-size magnetoelectric domains instead of true long-range ordering, which justifies the experimentally obtained low value of ferroelectric polarization. The lattice parameters and volume show a sharp anomaly at T N2 obtained by analyzing the temperature-dependence XRD, which is consistent with the ferroelectric transition, predicting a noncentrosymmetric space group, $P\bar{6}2c$, for this compound. Furthermore, we have investigated the effect of external pressure on this complex magnetism. The result reveals an enhancement of ordering temperature by the application of external pressure (~1.6 K/GPa). The external pressure might favor stabilizing the magnetic ground state associated with second magnetic phase. Our study shows an unconventional mechanism of spin-driven ferroelectricity involving inverse DM interaction between Ru (4d) and Ho (4f) magnetic ions due to strong 4d-4f cross coupling.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Orbital Ingredients and Persistent Dirac Surface State for the Topological Band Structure in FeTe 0.55 Se 0.45

FeTe 0.55 Se 0.45 (FTS) occupies a special spot in modern condensed matter physics at the intersections of electron correlation, topology, and unconventional superconductivity. The bulk electronic structure of FTS is predicted to be topologically nontrivial due to the band inversion between the d x z and p z bands along Γ − Z . However, there remain debates in both the authenticity of the Dirac surface states (DSSs) and the experimental deviations of band structure from the theoretical band inversion picture. Here we resolve these debates through a comprehensive angle-resolved photoemission spectroscopy investigation. We first observe a persistent DSS independent of k z . Then, by comparing FTS with FeSe, which has no band inversion along Γ − Z , we identify the spectral weight fingerprint of both the presence of the p z band and the inversion between the d x z and p z bands. Furthermore, we propose a renormalization scheme for the band structure under the framework of a tight-binding model preserving crystal symmetry. Our results highlight the significant influence of correlation on modifying the band structure and make a strong case for the existence of topological band structure in this unconventional superconductor. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Regularizing INR with Diffusion Prior for Self-Supervised 3D Reconstruction OF Neutron Computed Tomography Data

Recently, generative diffusion priors have made huge strides as inverse problem solvers, including the ability to be adapted for inference on out-of-distribution data. Concurrently, implicit neural representations (INRs) have emerged as fast and lightweight inverse imaging solvers that are amenable to hybrid approaches that combine learned priors with traditional inverse problem formulations. In this paper, we present a diffusive computed tomography (CT) inversion framework for regularizing INRs called Diffusive INR (DINR), designed to enable high-quality reconstruction from sparse-view neutron CT. Pretrained purely on synthetic data, DINR is evaluated on simulated and experimentally obtained observations of concrete microstructures, where traditional reconstruction methods suffer substantial degradation when the number of views is reduced. Our approach delivers superior performance, reduces reconstruction artifacts, and achieves gains in PSNR and SSIM, enabling accurate micro-structural characterization even under extreme data limitations compared to state-of-the-art sparse-view reconstruction techniques.

Hossain, Maliha [ORNL]↗
DOE OSTI2024

Modeling Microwave-Enhanced Chemical Vapor Infiltration Process for Preventing Premature Pore Closure

The chemical vapor infiltration (CVI) process involves infiltrating a porous preform with reacting gases that undergo chemical transformation at high temperatures to deposit the ceramic phase within the pores, ultimately leading to a dense composite. The conventional CVI process in composite manufacturing needs to follow an isothermal approach to minimize temperature differences between the external and internal surfaces of the preform, ensuring that reactive gases infiltrate internal pores before external surfaces seal. Here, this study addresses the challenge of premature pore closure in CVI processes through microwave heating. A frequency-domain microwave solver is developed in OpenFOAM to investigate volumetric heating mechanisms within the preform. Through numerical studies, we demonstrate the capability of microwave heating of creating an inside-out temperature inversion. This inversion accelerates reactions proximal to the preform center, effectively mitigating the risk of premature external pore closure and ensuring uniform densification. The results reveal a significant enhancement in temperature inversion when high-permittivity reflectors are incorporated to generate resonant waves. This microwave heating strategy is then coupled with high-fidelity direct numerical simulation (DNS) of reacting flow, enabling the analysis of resulting densification processes. The DNS includes detailed chemistry and realistic diffusion coefficients. The numerical results can be used to estimate the impact of microwave-induced temperature inversion on densification in productions.

42 ENGINEERING↗
DOE OSTI2024

Modeling Microwave-Enhanced Chemical Vapor Infiltration Process for Preventing Premature Pore Closure

The chemical vapor infiltration (CVI) process involves infiltrating a porous preform with reacting gases that undergo chemical transformation at high temperatures to deposit the ceramic phase within the pores, ultimately leading to a dense composite. The conventional CVI process in composite manufacturing needs to follow an isothermal approach to minimize temperature differences between the external and internal surfaces of the preform, ensuring that reactive gases infiltrate internal pores before external surfaces seal. This study addresses the challenge of premature pore closure in CVI processes through microwave heating. A frequency-domain microwave solver is developed in Open-FOAM to investigate volumetric heating mechanisms within the preform. Through numerical studies, we demonstrate the capability of microwave heating of creating an inside-out temperature inversion. This inversion accelerates reactions proximal to the preform center, effectively mitigating the risk of premature external pore closure and ensuring uniform densification. The results reveal a significant enhancement in temperature inversion when high-permittivity reflectors are incorporated to generate resonant waves. This microwave heating strategy is then coupled with high-fidelity direct numerical simulation (DNS) of reacting flow, enabling the analysis of resulting densification processes. The DNS simulation includes detailed chemistry and realistic diffusion coefficients. The numerical results can be used to estimate the impact of microwave-induced temperature inversion on densification in productions.

Ge, Wenjun↗
DOE OSTI2024

Using Median Point in Keeling Plot to Reduce the Uncertainty of the Isotopic Composition of Evapotranspiration

Abstract The isotopic composition of evapotranspirationδ ET is a crucial parameter in isotope-based evapotranspiration (ET) partitioning and moisture recycling studies. The Keeling plot method is the most prevalent method to calculateδ ET , though it contains large extrapolated uncertainties from the least squares regression. Traditional Keeling regression uses the mean point of individual measurements. Here, a modified Keeling plot framework was proposed using the median point of individual measurements. We tested theδ ET uncertainty using the mean point [σ ET (mean)] and median point [σ ET (median)]. Multiple resolutions of input and output data from six independent sites were used to test the performance of the two methods. Theσ ET (mean) would be greater thanσ ET (median) when the mean value of inverse vapor concentration () is greater than the median value of inverse vapor concentration []. When applying the filter ofr 2 > 0.8, around 70% ofσ ET (mean) was greater thanσ ET (median). This phenomenon might be due to the normality of the vapor concentrationC υ producing the asymmetric distribution of 1/C υ . The median method could perform significantly better than the mean method when inputting high-resolution measurements (e.g., 1 Hz) and when the water vapor concentrationC υ is relatively low. Compared to the mean method, applying the median method could on average reduce 6.88% of ET partitioning uncertainties and could on average reduce 9.00% of moisture recycling uncertainties. This study provided a new insight of the Keeling plot method and emphasized handling model output uncertainty from multiple perspectives instead of only from input parameters.

Meteorology & Atmospheric Sciences↗
DOE OSTI2026

Field and Model Data Associated with the Manuscript “Drivers of Streamflow Intermittency in Humid Regions: 1. Evaluating Above- and Below-ground Controls of Flow Persistence in a Forested Catchment”

This package contains field data, modeling files, and scripts supporting the investigation of the drivers of streamflow intermittency in a forested catchment. It includes the field data collected from electrical resistivity tomography (ERT) surveys, ground penetrating radar (GPR), continuous self-potential (SP) monitoring, electromagnetic (EM) imaging, groundwater and stilling well. In addition, it contains the data and results of the coupled water- and electrical-flow model developed using the COMSOL Multiphysics and Advanced Terrestrial Simulator (ATS), as well as software files and Jupyter notebooks used to process the data and generate figures in the manuscript submitted for peer review. The data archive is organized in the following directories: 1) Climate Includes hourly precipitation and daily evapotranspiration time series (2024 – 2025) provided as CSV files, alongside a text file detailing dataset units. 2) Coupled_model Contains two subfolders: Synthetic and Field_Application subfolder. Synthetic subfolder contains the ATS XML input script (can be opened using any code editor) for the four synthetic hydrological cases tested (Connected and gaining, Connected and losing, Disconnected and losing, and dry stream). It also includes other experimental cases to test the influence of precipitation and concentration gradient. For each synthetic case, the flow model simulation is executed using the ATS XML scripts and the included Python script (generate_data_set.py) to convert ATS output to COMSOL-ready input. COMSOL Multiphysics template (.mph can be opened with the commercial software COMSOL and requires a license) is executed using the ATS output data to simulate the potential field. It also includes the Synthetic_model_plot.ipynb (can be opened using any code editor) to visualize the SP result and generate manuscript figures. The data subfolder contains mesh files to run both the ATS (.exo and .stl files can be viewed using Paraview; .h5 files can be opened using HDFView software and h5py Python package) and COMSOL models. Field_Application subfolder contains two subfolders: ES_MDA_inversion and Final_Model. ES_MDA_inversion contains the Python script (.py can be opened using any code editor) and SP observation data used to run the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) inversion sequence to get the optimal model parameters. The Final_model subfolder contains the ATS XML input scripts, data files, output data for the two SP sites. The same workflow steps outlined for the Synthetic subfolder apply here. It also contains the Jupyter notebook (Plot_final_calib.ipynb) to visualize the results of the modeled SP, stream-groundwater exchange and moisture content. 3) Discharge Includes the electrical conductivity (EC) time series (provided as CSV files) from salt slug injections. It also includes the Jupyter notebook (Discharge_process.ipynyb) used to estimate discharge. All discharge measurements collated into rating_curve_processed.csv 4) EM Contains the CSV file of the EM data from the DUALEM-42, including spatial coordinates (x, y, z), apparent conductivity, and in-phase measurements at 2 m coil separations for horizontal coplanar (HCP) and perpendicular (PRP) geometries. 5) ERT Contains raw resistivity data (provided as CSV files), spatial location of each of the electrodes (provided as CSV files), and files used for the resistivity inversion (.resipy can be opened with the open-source ResIPy software). 6) GPR Includes GPR field datasets collected at 100 MHz and 250 MHz antenna frequencies, along with the processing/interpretation project file (GPR_process.gpz can be viewed using EKKO_Project 6, a commercial software by Sensors & Software that requires a license). 7) Slug_test Includes the slug test data at all the groundwater wells provided as CSV files, as well as the Jupyter notebook (Slug_test.ipynb) for calculating hydraulic conductivity. 8) SP Contains the SP data collected in field at the two SP sites (one in the perennial reach and the other in the intermittent reach), provided as DAT files. 9) Well_data Contains two subfolders: 1) Raw, which provides unprocessed pressure, electrical conductivity and temperature timeseries downloaded from the loggers in all the groundwater and stilling wells, and 2) Processed, which contains sorted, QA/QC timeseries data for each well. The data archive also contains data_process.ipynb, a Jupyter notebook used for field data analysis and generating figures (plotting well, SP, climate, and discharge data, as well as calculating head gradient at sites with nested groundwater wells). It also includes DTW.ipynb, a Jupyter notebook containing the code for the dynamic time warping (DTW) with sliding window to evaluate SP signal synchronicity.

ATS↗
DOE OSTI2026

The Influence of Shallow Subsurface Properties on Particle Motion in Acoustic-Seismic Coupling

Atmospheric acoustic waves transmit energy into the solid Earth through air-to-ground coupling. These waves are recorded by seismic sensors and provide insight into both atmospheric phenomena and subsurface properties. Interpreting these signals is often challenging because they are modulated by subsurface structure and the incidence angle of the acoustic wave. This study examines acoustic--seismic coupling generated by the 2012 Camp Minden Explosion, which was recorded by hundreds of seismoacoustic stations. We apply a novel technique to quantify the seismic particle motion, model coupled waves with a propagator matrix approach, and apply a Bayesian inversion to infer properties of the shallow subsurface. Our analysis reveals that prograde motion is widespread and focused in low shear-wave velocity regions, such as the Mississippi Embayment, and retrograde motion is more common in higher shear-velocity areas. Inversion results at some stations produce plausible subsurface models with strong waveform fits, while inversions at other sites are less successful. These results indicate prograde particle motion in air-to-ground coupled waves is more prevalent than previously recognized and may serve as a diagnostic for shallow velocity structure. Our comprehensive modeling and inversion framework provides a potential method to extract layered near-surface properties from acoustic-seismic coupling observations.

58 GEOSCIENCES↗
DOE OSTI2025

Air temperature and precipitation constraining the modelled wetland methane emissions in a boreal region in northern Europe

Wetland methane responses to temperature and precipitation are studied in a boreal wetland-rich region in northern Europe using ecosystem process models. Six ecosystem models (JSBACH-HIMMELI, LPX-Bern, LPJ-GUESS, JULES, CLM4.5, and CLM5) are compared to multi-model means of ecosystem models and atmospheric inversions from the Global Carbon Project and upscaled eddy covariance flux results for their temperature and precipitation responses and seasonal cycles of the regional fluxes. Two models with contrasting response patterns, LPX-Bern and JSBACH-HIMMELI, are used as priors in atmospheric inversions with Carbon Tracker Europe–CH4 (CTE-CH4) in order to find out how the assimilation of atmospheric concentration data changes the flux estimates and how this alters the interpretation of the flux responses to temperature and precipitation. Inversion moves wetland emissions of both models towards co-limitation by temperature and precipitation. Between 2000 and 2018, periods of high temperature and/or high precipitation often resulted in increased emissions. However, the dry summer of 2018 did not result in increased emissions despite the high temperatures. The process models show strong temperature and strong precipitation responses for the region (51 %–91 % of the variance explained by both). The month with the highest emissions varies from May to September among the models. However, multi-model means, inversions, and upscaled eddy covariance flux observations agree on the month of maximum emissions and are co-limited by temperature and precipitation. The setup of different emission components (peatland emissions, mineral land fluxes) has an important role in building up the response patterns. Considering the significant differences among the models, it is essential to pay more attention to the regional representation of wet and dry mineral soils and periodic flooding which contribute to the seasonality and magnitude of methane fluxes. The realistic representation of temperature dependence of the peat soil fluxes is also important. Furthermore, it is important to use process-based descriptions for both mineral and peat soil fluxes to simulate the flux responses to climate drivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

EPCAPE-PT-LANL Measurements: Humidified Cavity Attenuated Phase Shift Spectroscopy

Coastal cities offer a unique environment for studying aerosol-cloud interactions and the effects of urban emissions on cloud properties. As part of the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE), the Partitioning Thrust by Los Alamos National Laboratory (EPCAPE-PT-LANL) was conducted. Our campaign focused on measuring the optical and chemical properties of aerosols and their interactions within marine stratocumulus clouds in La Jolla, California. EPCAPE-PT-LANL enhances the primary goals of EPCAPE through innovative observations of vapor-phase transitions between aerosols and cloud droplets, the impact of black carbon on aerosol-cloud dynamics, and the effects of cloud processing on aerosol optical properties. Instrument: Humidified Cavity Attenuated Phase Shift Particulate Matter Single Scattering Albedo (H-CAPS-PMSSA, Aerodyne Inc) Data Notes: The scattering truncation correction was not applied to Bsca. The Bext needs no correction. Files: data_10sec_CAPS.csv, data_10min_CAPS.csv Header: - Bext_wet_CAPS_450nm[1/Mm]: Wet aerosol extinction coefficient measured at 450 nm by the CAPS, in inverse megameters (Mm⁻¹). - Bsca_wet_CAPS_450nm[1/Mm]: Wet aerosol scattering coefficient measured at 450 nm by the CAPS, in inverse megameters (Mm⁻¹). - Temp_wet_CAPS[K]: Temperature inside the wet CAPS measurement chamber, in Kelvin. - Bext_dry_CAPS_450nm[1/Mm]: Dry aerosol extinction coefficient measured at 450 nm by the CAPS, in inverse megameters (Mm⁻¹). - Bsca_dry_CAPS_450nm[1/Mm]: Dry aerosol scattering coefficient measured at 450 nm by the CAPS, in inverse megameters (Mm⁻¹). - Temp_dry_CAPS[K]: Temperature inside the dry CAPS measurement chamber, in Kelvin. - Wet_RH_preCAPS[%]: Relative humidity before entering the wet CAPS, in percent. - Wet_RH_postCAPS[%]: Relative humidity after exiting the wet CAPS, in percent. - Humidifier_RH_CAPS[%]: Relative humidity inside the humidifier used with the CAPS, in percent. - dualCAPS_inlet_RH[%]: Relative humidity at the inlet of the dual (wet/dry) CAPS setup, in percent. - Wet_Temp_preCAPS[C]: Temperature before entering the wet CAPS, in degrees Celsius. - Wet_Temp_postCAPS[C]: Temperature after exiting the wet CAPS, in degrees Celsius. - Humidifier_Temp[C]: Temperature inside the humidifier used with the CAPS, in degrees Celsius. - dualCAPS_inlet_Temp[C]: Temperature at the inlet of the dual (wet/dry) CAPS setup, in degrees Celsius. - Zero_dry_CAPS: 1 is a calibration check for the dry CAPS to ensure zero reading under filter air conditions. - Zero_wet_CAPS: 1 is a calibration check for the wet CAPS to ensure zero reading under filter air conditions. - CVI_Flag[bool]: A boolean flag indicating whether the Counterflow Virtual Impactor (CVI) was active (true) or inactive (false) during the measurement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Goal-oriented real-time Bayesian inference for linear autonomous dynamical systems with application to digital twins for tsunami early warning

We present a goal-oriented framework for constructing digital twins with the following properties: (1) they employ discretizations of high-fidelity partial differential equation (PDE) models governed by autonomous dynamical systems, leading to large-scale forward problems; (2) they solve a linear inverse problem to assimilate observational data to infer uncertain model components followed by a forward prediction of the evolving dynamics; and (3) the entire end-to-end, data-to-inference-to-prediction computation is carried out without approximation and in real time through a Bayesian framework that rigorously accounts for uncertainties. Several challenges must be overcome to realize this framework, including the large scale of the forward problem, the high dimensionality of the parameter space, and for a class of problems including those we target, the slow decay of the singular values of the parameter-to-observable map. Here we introduce a methodology to overcome these challenges by exploiting the autonomous structure of the forward model to decompose the solution of the inverse problem into a one-time-only offline phase in which the PDE model is solved a limited number of times (equal to the number of sensors), and an online phase that maps well onto GPUs and computes the parameter inference and prediction of quantities of interest in real time, given observational data. Our ultimate goal is to apply this framework to construct digital twins for subduction zones, including Cascadia, to provide early warning for tsunamis generated by megathrust earthquakes. To this end, we demonstrate how our methodology can be used to employ seafloor pressure observations, along with the coupled acoustic–gravity wave equations, to infer the earthquake-induced spatiotemporal seafloor motion (discretized with $\mathscr{O}$ (10 9 ) parameters) and forward predict the tsunami propagation. We present results of an end-to-end inference, prediction, and uncertainty quantification for a representative test problem with $\mathscr{O}$ (10 8 ) inversion parameters for which goal-oriented Bayesian inference is accomplished exactly and in real time, that is, in a matter of seconds.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2024

Aligned yet large dipoles: a SMEFT study

We study a non-universal flavor scenario at the level of the Standard Model Effective Field Theory, according to which the matrix of Wilson coefficients cuW of an up-type electroweak quark dipole operator is aligned with the up-type Yukawa coupling. Such an alignment usually follows from the assumption of Minimal Flavor Violation (MFV), away from which we step by allowing the entries of cuW to be sizable along the first quark generations. A particular example, which we refer to as “inverse hierarchy MFV”, features Wilson coefficients inversely proportional to quark masses, and arises from BSM models respecting MFV and containing heavy fields that replicate the mass hierarchy of SM quarks. We then analyze the phenomenology driven by cuW at colliders and at lower-energy flavor experiments. We show that precision measurements of the process pp → Wh → γγℓν at FCC-hh could set an upper bound on |cuW| ≲ O (10 -2 )(Λ/TeV) 2 , with Λ the cutoff of the effective field theory. This bound is an order of magnitude stronger than the existing LHC bounds. Moreover, we estimate that Wh → b$\overline{b}$ℓν at HL-LHC could also give competitive bounds. In the low-energy regime, we consider bounds arising from rare kaon decays, which turn out to be loose, |c$_{uW}^{11}$ | < O (1)(Λ/TeV) 2 . We finally demonstrate that our flavor and operator assumptions can be derived from a weakly-coupled UV model, which we choose to simultaneously illustrate the UV origin of inverse hierarchy MFV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗