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At least 109 records · Page 6

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DOE OSTI2024

Spectrum and extension of the inverse-Compton emission of the Crab Nebula from a combined Fermi -LAT and H.E.S.S. analysis

The Crab Nebula is a unique laboratory for studying the acceleration of electrons and positrons through their non-thermal radiation. Observations of very-high-energy γ rays from the Crab Nebula have provided important constraints for modelling its broadband emission. We present the first fully self-consistent analysis of the Crab Nebula’s γ-ray emission between 1 GeV and ∼100 TeV, that is, over five orders of magnitude in energy. Using the open-source software package GAMMAPY, we combined 11.4 yr of data from the Fermi Large Area Telescope and 80 h of High Energy Stereoscopic System (H.E.S.S.) data at the event level and provide a measurement of the spatial extension of the nebula and its energy spectrum. We find evidence for a shrinking of the nebula with increasing γ-ray energy. Furthermore, we fitted several phenomenological models to the measured data, finding that none of them can fully describe the spatial extension and the spectral energy distribution at the same time. Especially the extension measured at TeV energies appears too large when compared to the X-ray emission. Our measurements probe the structure of the magnetic field between the pulsar wind termination shock and the dust torus, and we conclude that the magnetic field strength decreases with increasing distance from the pulsar. We complement our study with a careful assessment of systematic uncertainties.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Sulfonated Aromatic Polypentenamers: Scope of the Acetyl Sulfate Reaction

A precision polymer featuring one phenyl pendant at every fifth carbon on a polyethylene backbone was modified to varying degrees of sulfonation (DS = 33–98%) using acetyl sulfate with standard conditions by varying reaction times (up to 48 h). This soft, homogeneous method allows for evenly distributed phenylsulfonic acid groups with minimal side reactions and results in light brown, transparent materials with glass transition temperatures of 33–78 °C that increase with DS and parent polymer molar mass. The DS was accurately determined through well-resolved 1 H NMR (DSHNMR) signals of the regularly spaced phenyl groups and corroborated by inverse-gated decoupling 13 C NMR analysis. These analyses were compared to a series of other commonly accepted methods for DS determination including titration and elemental analysis (EA). The DS by titration (DS titr ) also increased with reaction time but was consistently lower than those determined by NMR. Significant discrepancies between theoretical and measured sulfur mass were observed for several samples through EA. Qualitative methods, such as infrared spectroscopy and thermal analyses, were also performed and correlated well with the DS determined by NMR. Here, this study presents a promising method to achieve a complete scope of homogeneous sulfonation from mild reaction conditions and presents a case to determine DS through multiple methods, with spectroscopic analyses, such as NMR, being potentially the most valuable.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

The Role of Non-Local Effects on Surface Sensible Heat Flux Under Different Types of Thermal Structures Over the Arctic Sea-Ice Surface

The effects of atmospheric thermal structure on the surface energy flux are poorly understood over the Arctic sea-ice surface. Here, we explore the mechanism of sensible heat exchange under different types of thermal structures over the Arctic sea-ice surface by using data collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition. The quadrant analysis indicates that strong surface temperature inversions below 100 m enhance non-local effects on the positive (upward) sensible heat flux ($\overline{w'θ'}$) through entrainment of large eddies from the convective boundary layer aloft. However, strong surface inversions restrict the contributions of large eddies to the negative (downward) $\overline{w'θ'}$ due to intensified surface stability. By inspecting the existing parameterization schemes, we found that the European Center for Medium-Range Weather Forecasts Integrated Forecasting System scheme fails to predict the impacts of non-local processes on the positive $\overline{w'θ'}$, and an adjustment term to correct the bias of parameterized $\overline{w'θ'}$ is proposed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Structural disorder-driven topological phase transition in noncentrosymmetric BiTeI

In this work, we investigate using local structural disorder to induce a topologically nontrivial phase in a solid state system. Using first-principles calculations, we introduce structural disorder in the trivial insulator BiTeI and observe the emergence of a topological insulating phase. By modifying the bonding environments, the crystal-field splitting is enhanced, with spin-orbit interactions producing a band inversion in the bulk electronic structure. Analysis of the Wannier charge centers and the surface electronic structure reveals a strong topological insulator with Dirac surface states. Finally, we propose a prescription for inducing topological states from disorder in crystalline materials. Understanding how local environments produce topological phases is a key step for predicting disordered and amorphous topological materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Final Search for Short-Baseline Neutrino Oscillations with the PROSPECT-I Detector at HFIR

The PROSPECT experiment is designed to perform precise searches for antineutrino disappearance at short distances (7–9 m) from compact nuclear reactor cores. This Letter reports results from a new neutrino oscillation analysis performed using the complete data sample from the PROSPECT-I detector operated at the High Flux Isotope Reactor in 2018. The analysis uses a multiperiod selection of inverse beta decay neutrino interactions with reduced backgrounds and enhanced statistical power to set limits on electron neutrino disappearance caused by mixing with sterile neutrinos with 0.2–20 eV 2 mass splittings. Inverse beta decay positron energy spectra from six different reactor-detector distance ranges are found to be statistically consistent with one another, as would be expected in the absence of sterile neutrino oscillations. The data excludes at 95% confidence level the existence of sterile neutrinos in regions above 3 eV 2 previously unexplored by terrestrial experiments, including all space below 10 eV 2 suggested by the recently strengthened Gallium Anomaly. The best-fit point of the Neutrino-4 reactor experiment’s claimed observation of short-baseline oscillation is ruled out at more than 5 standard deviations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Laser-Plasma Spatiotemporal Cyanide Spectroscopy and Applications

This article reports new measurements of laser-induced plasma hypersonic expansion measurements of diatomic molecular cyanide (CN). Focused, high-peak-power 1064 nm Q-switched radiation of the order of 1 TW/cm 2 generated optical breakdown plasma in a cell containing a 1:1 molar gas mixture of N 2 and CO 2 at a fixed pressure of 1.1 × 10 5 Pascal and in a 100 mL/min flow of the mixture. Line-of-sight (LOS) analysis of recorded molecular spectra indicated the outgoing shockwave at expansion speeds well in excess of Mach 5. Spectra of atomic carbon confirmed increased electron density near the shockwave, and, equally, molecular CN spectra revealed higher excitation temperature near the shockwave. Results were consistent with corresponding high-speed shadowgraphs obtained by visualization with an effective shutter speed of 5 nanoseconds. In addition, LOS analysis and the application of integral inversion techniques allow inferences about the spatiotemporal plasma distribution.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Recent Progress in U-10Mo Mechanical and Thermophysical Property Characterization

Results are presented on the mechanical and thermophysical property characterization of U-10Mo as part of the MP-1 qualification campaign. The thermal diffusivity, specific heat and thermal conductivity of as fabricated U-10Mo fuel foils and plates were measured using the thermal conductivity microscope (TCM), differential scanning calorimeter (DSC) and laser flash analyzer (LFA) all of which located in a shielded glove box – thermal property cell (TPC). A novel inverse method based on finite element analysis was developed to evaluate the thermal diffusivity of composite/layered materials via the LFA technique such as the 5-lyered U-10Mo monolithic fuel mini-plates.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Recent Progress in U-10Mo Mechanical and Thermophysical Property Characterization -- Poster

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Parameter estimation for X-ray scattering analysis with Hamiltonian Markov Chain Monte Carlo

Bayesian-inference-based approaches, in particular the random-walk Markov Chain Monte Carlo (MCMC) method, have received much attention recently for X-ray scattering analysis. Hamiltonian MCMC, a state-of-the-art development in the field of MCMC, has become popular in recent years. It utilizes Hamiltonian dynamics for indirect but much more efficient drawings of the model parameters. We described the principle of the Hamiltonian MCMC for inversion problems in X-ray scattering analysis by estimating high-dimensional models for several motivating scenarios in small-angle X-ray scattering, reflectivity, and X-ray fluorescence holography. Hamiltonian MCMC with appropriate preconditioning can deliver superior performance over the random-walk MCMC, and thus can be used as an efficient tool for the statistical analysis of the parameter distributions, as well as model predictions and confidence analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

How reliable is distribution of relaxation times (DRT) analysis? A dual regression-classification perspective on DRT estimation, interpretation, and accuracy

The distribution of relaxation times (DRT) has gained increasing attention and adoption in recent years as a versatile method for analyzing electrochemical impedance spectroscopy (EIS) data obtained from complex devices like fuel cells, electrolyzers, and batteries. The DRT deconvolutes the impedance without a priori specification of a generative model, which is especially useful for interpretation and model selection when the governing principles of the system under study are not fully understood. However, DRT estimation is an ill-posed inversion problem that must be addressed with a subjective choice of regularization and tuning, which leaves substantial risk of misleading interpretations of EIS data. In this work, we suggest a new classification view of the DRT inversion to clarify DRT estimation and interpretation. We introduce a dual regression-classification framework that unifies the classification and regression views of the DRT inversion with wide-reaching implications for DRT analysis. The dual framework is employed to demonstrate a new kind of DRT inversion algorithm and develop novel evaluation metrics that capture previously ignored aspects of DRT accuracy. These approaches are applied to both synthetic data and experimental spectra collected from a protonic ceramic fuel cell and a lithium-ion battery to illustrate their broad utility. The dual inversion algorithm shows promising performance for accurate DRT estimation and autonomous model identification, while the dual evaluation approach produces metrics that meaningfully assess the strengths and risks of DRT algorithms. Here this work provides valuable insight for both practical application of the DRT to experimental data and further development of EIS analysis methods.

36 MATERIALS SCIENCE↗

DOE OSTI2025

ForceFinder

SAND2025-11750O ForceFinder extends the Structural Dynamics Python Libraries (SDynPy) with comprehensive tools for inverse source estimation (ISE) tasks via frequency response function (FRF) matrix inversion. The software is designed for transfer path analysis and multiple-input/multiple-output (MIMO) vibration control problems. It allows users to estimate sources through various algorithms, from the basic Moore-Penrose pseudo-inverse to statistical learning methods such as Tikhonov regularization via an L-curve and elastic net regularization via an information criterion. ForceFinder uses an object-oriented framework, where all components of the ISE problem—such as FRFs, responses, and transformations—are stored in a "SourcePathReceiver" object. This software can be applied to any noise and vibration problem. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Carter, Steven [Sandia National Lab. (SNL-CA), Liv↗

DOE OSTI2023

Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado

Abstract. Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model calibration by directly estimating parameters from observations. However, the increasing computational demand for running the state-of-the-art hydrological model limits sufficient ensemble runs for its calibration. In this work, we present a novel knowledge-informed deep learning method that can efficiently conduct the calibration using a few hundred realizations. The method involves two steps. First, we determine decisive model parameters from a complete parameter set based on the mutual information (MI) between model responses and each parameter computed by a limited number of realizations (∼50). Second, we perform more ensemble runs (e.g., several hundred) to generate the training sets for the inverse mapping, which selects informative model responses for estimating each parameter using MI-based parameter sensitivity. We applied this new DL-based method to calibrate a process-based integrated hydrological model, the Advanced Terrestrial Simulator (ATS), at Coal Creek Watershed, CO. The calibration is performed against observed stream discharge (Q) and remotely sensed evapotranspiration (ET) from the water year 2017 to 2019. Preliminary MI analysis on 50 realizations resulted in a down-selection of 7 out of 14 ATS model parameters. Then, we performed a complete MI analysis on 396 realizations and constructed the inverse mapping from informative responses to each of the selected parameters using a deep neural network. Compared with calibration using observations covering all time steps, the new inverse mapping improves parameter estimations, thus enhancing the performance of ATS forward model runs. The Nash–Sutcliffe efficiency (NSE) of streamflow predictions increases from 0.53 to 0.8 when calibrating against Q alone. Using ET observations, on the other hand, does not show much improvement on the performance of ATS modeling mainly due to both the uncertainty of the remotely sensed product and the insufficient coverage of the model ET ensemble in capturing the observation. By using observed Q only, we further performed a multiyear analysis and show that Q is best simulated (NSE > 0.8) by including in the calibration the dry-year flow dynamics that show more sensitivity to subsurface characteristics than the other wet years. Moreover, when continuing the forward runs till the end of 2021, the calibrated models show similar simulation performances during this evaluation period as the calibration period, demonstrating the ability of the estimated parameters in capturing climate sensitivity. Our success highlights the importance of leveraging data-driven knowledge in DL-assisted hydrological model calibration.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Mallat Scattering Transformation based surrogate for Magnetohydrodynamics

Abstract A Machine and Deep Learning (MLDL) methodology is developed and applied to give a high fidelity, fast surrogate for 2D resistive MagnetoHydroDynamic (MHD) simulations of Magnetic Liner Inertial Fusion (MagLIF) implosions. The resistive MHD code is used to generate an ensemble of implosions with different liner aspect ratios, initial gas preheat temperatures (that is, different adiabats), and different liner perturbations. The liner density and magnetic field as functions of x , y , and z were generated. The Mallat Scattering Transformation (MST) is taken of the logarithm of both fields and a Principal Components Analysis (PCA) is done on the logarithm of the MST of both fields. The fields are projected onto the PCA vectors and a small number of these PCA vector components are kept. Singular Value Decompositions of the cross correlation of the input parameters to the output logarithm of the MST of the fields, and of the cross correlation of the SVD vector components to the PCA vector components are done. This allows the identification of the PCA vectors vis-a-vis the input parameters. Finally, a Multi Layer Perceptron (MLP) neural network with ReLU activation and a simple three layer encoder/decoder architecture is trained on this dataset to predict the PCA vector components of the fields as a function of time. Details of the implosion, stagnation, and the disassembly are well captured. Examination of the PCA vectors and a permutation importance analysis of the MLP show definitive evidence of an inverse turbulent cascade into a dipole emergent behavior. The orientation of the dipole is set by the initial liner perturbation. The analysis is repeated with a version of the MST which includes phase, called Wavelet Phase Harmonics (WPH). While WPH do not give the physical insight of the MST, they can and are inverted to give field configurations as a function of time, including field-to-field correlations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Correction and verification of x-ray imaging crystal spectrometer analysis on Wendelstein 7-X through x-ray ray tracing

X-ray ray tracing is used to develop ion-temperature corrections for the analysis of the X-ray Imaging Crystal Spectrometer (XICS) used at Wendelstein 7-X (W7-X) and perform verification on the analysis methods. The XICS is a powerful diagnostic able to measure ion-temperature, electron-temperature, plasma flow, and impurity charge state densities. While these systems are relatively simple in design, accurate characterization of the instrumental response and validation of analysis techniques are difficult to perform experimentally due to the requirement of extended x-ray sources. For this reason, a ray tracing model has been developed that allows characterization of the spectrometer and verification of the analysis methods while fully considering the real geometry of the XICS system and W7-X plasma. Through the use of ray tracing, several important corrections have been found that must be accounted for in order to accurately reconstruct the ion-temperature profiles. The sources of these corrections are described along with their effect on the analyzed profiles. The implemented corrections stem from three effects: (1) effect of sub-pixel intensity distribution during de-curving and spatial binning, (2) effect of sub-pixel intensity distribution during forward model evaluation and generation of residuals, and (3) effect of defocus and spherical aberrations on the instrumental response. Possible improvements to the forward model and analysis procedures are explored, along with a discussion of trade-offs in terms of computational complexity. Finally, the accuracy of the tomographic inversion technique in stellarator geometry is investigated, providing for the first time a verification exercise for inversion accuracy in stellarator geometry and a complete XICS analysis tool-chain.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Baycal

Bayesian Model Calibration (BayCal) toolkit is a software plugin for Risk Analysis Virtual Environment (RAVEN) framework, arming at inversely quantifying the uncertainties associated with simulation model parameters based on available experiment data. BayCal seeks statistical inference of the uncertain input parameters that are consistent with the available measurement data or observed data. The unique feature of BayCal is the capability to be linked with RAVEN to build corresponding calibration workflows for complex multi-physics simulations. In addition to be able to use the machine learning capability of RAVEN to significantly reduce the computational cost of expensive simulation models, another distinctive feature of BayCal is the capability to deal with high-dimensional correlated model outputs, such as time series observations at multiple locations, via principal component analysis (PCA) technique.

Wang, Congjian↗

DOE OSTI2022

Basin and Range Investigation for Developing Geothermal Energy: Exploration Data

This data package includes exploration material from the Basin & Range Investigation for Developing Geothermal Energy [in Hidden Systems] project (BRIDGE), which is part of a broader initiative to advance the exploration of hidden geothermal resources in the Basin & Range Province of the western U.S. Data modalities include a helicopter-borne time-domain electromagnetic survey, magnetotellurics, 2-meter temperature measurements, ground-based gravity and legacy aeromagnetic surveys, geochemistry, geologic mapping, LiDAR analysis, 3D models, associated geospatial data, and a bibliography of existing data and references utilized in prospect characterization and conceptual modeling. Key files are in CSV, Geosoft, and Geotools formats. Please refer to READMEs for dataset-specific information. Where applicable, acquisition data and inversion models for a particular prospect or area of interest are organized separately. This BRIDGE data package is the product of a collaboration led by Sandia National Laboratories with partners from Geologica Geothermal Group, Inc., the U.S. Navy Geothermal Program Office, and consultants Steven Sewell (Australis Geoscience Ltd) and William Cumming (Cumming Geoscience). The project's areas of interest (AOIs) are based off priority areas of interest in the southwestern portion of the Nevada Play Fairway map, distribution across tectonic provinces, accessibility, and the project team's extensive experience in the region. AOIs cover about a dozen basins that include unexplored prospects, partially explored prospects, and some developed analogue resources that provide validation cases. Many unexplored and partially explored prospects are on U.S. Department of Defense (DoD) land, though adjacent lands are included as well.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

SANDD: A directional antineutrino detector with segmented 6 Li-doped pulse-shape-sensitive plastic scintillator

We present a characterization of a small (9-liter) and mobile 0.1% 6 Li-doped pulse-shape-sensitive plastic scintillator antineutrino detector called SANDD (Segmented AntiNeutrino Directional Detector), constructed for the purpose of near-field reactor monitoring with sensitivity to antineutrino direction. SANDD comprises three different types of module. A detailed Monte Carlo simulation code was developed to match and validate the performance of each of the three modules. The combined model was then used to produce a prediction of the performance of the entire detector. Analysis cuts were established to isolate antineutrino inverse beta decay events while rejecting large fraction of backgrounds. The neutron and positron detection efficiencies are estimated to be 34.8% and 80.2%, respectively, while the coincidence detection efficiency is estimated to be 71.7%, resulting in inverse beta decay detection efficiency of 20.0% ± 0.2%(stat.) ± 2.1%(syst.). Finally, the predicted directional sensitivity of SANDD produces an uncertainty of 20° in the azimuthal direction per 100 detected antineutrino events.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Seismic response to paleo-sand dunes in the Nugget Sandstone Formation, southwestern Wyoming

We have analyzed a 3D seismic survey acquired for a carbon sequestration project on top of the Moxa Arch in southwestern Wyoming. We observed a zone of discontinuous reflectors on vertical slices of seismic amplitude volume, whereas, the northwest–southeast lineations were observed on the time slices. We performed a seismic to well tie that suggested that the lineations occur within the Nugget Sandstone. The Nugget Sandstone is an eolian sandstone deposit of Early Jurassic age, deposited as a subtropical dune field, and equivalent to the Navajo Sandstone of southwestern Utah. Petrophysical analysis indicates that the Nugget Sandstone is dominated by clean sandstone (70%–80%), whereas evaporites, including halite and anhydrite, are present in certain zones. Previous outcrop studies on the Navajo Sandstone indicate the wind direction to be predominantly northeast–southwest. Seismic attributes, including coherence and curvature, displayed on stratal slices within the Nugget Sandstone interval indicate the presence of lineations in the northwest–southeast direction with irregular spacing. These lineations are approximately perpendicular to the inferred dominant wind direction. We computed the dominant wind direction from the average azimuth of the lineations as seen on the curvature attribute in the Nugget Sandstone interval. Geological feature: Eolian sand dunes with interdunal evaporites Seismic appearance: Parallel lineations with irregular spacing on seismic attribute horizon slices Alternative interpretations: Canyons at continental slopes; slope failures Features with a similar appearance: Marine bars; contourites Formation: The Nugget Sandstone — equivalent to the Navajo Sandstone Age: Early Jurassic Location: Moxa Arch, Wyoming Seismic data: Obtained by the University of Wyoming with U.S. DOE funding Contributors: Dhruv Agrawal, Brady Lujan, Sumit Verma, Shuvajit Bhattacharya, and Subhashis Mallick Analysis tools: Coherence and curvature attributes; seismic inversion; petrophysical inversion

Geochemistry & Geophysics↗