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At least 1,477 records · Page 82

Model selection and signal extraction using Gaussian Process regression

We present a novel computational approach for extracting localized signals from smooth background distributions. We focus on datasets that can be naturally presented as binned integer counts, demonstrating our procedure on the CERN open dataset with the Higgs boson signature, from the ATLAS collaboration at the Large Hadron Collider. Our approach is based on Gaussian Process (GP) regression — a powerful and flexible machine learning technique which has allowed us to model the background without specifying its functional form explicitly and separately measure the background and signal contributions in a robust and reproducible manner. Unlike functional fits, our GP-regression-based approach does not need to be constantly updated as more data becomes available. We discuss how to select the GP kernel type, considering trade-offs between kernel complexity and its ability to capture the features of the background distribution. We show that our GP framework can be used to detect the Higgs boson resonance in the data with more statistical significance than a polynomial fit specifically tailored to the dataset. Finally, we use Markov Chain Monte Carlo (MCMC) sampling to confirm the statistical significance of the extracted Higgs signature.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Simulation and Emulation of X-Ray Diffraction from Dynamic Compression Experiments

Many important aspects of the dynamic thermo-mechanical response of materials occur at the mesoscale, i.e. a physical scale of interactions smaller than what can be adequately described by homogenous behaviors, yet larger than the scale of the atomic lattice. Concurrent advancements in computational power, continuum theory, and experimental diagnostics are enabling unprecedented understanding of such interactions. However, we cannot develop a sufficient level of confidence in such mesoscale capability until the constitutive description of the underlying constituents is reliably representative of their actual physical behavior. Therefore, there is a strong need to combine experimental, modeling, and data-science techniques to validate models of the thermomechanical response of individual single crystals. One experimental diagnostic with high potential impact to shock physics and materials science is in-situ x-ray diffraction. This paper is primarily focused on simulation of x-ray diffraction in shock physics, but with an aim toward quantifying parametric uncertainty of simulation models. Here, we develop and demonstrate a data-science and model-driven approach to constrain the parameterization of continuum models of crystal lattice deformation associated with the shock response of crystalline materials. The framework is built around the connection between continuum hydrodynamic simulations of lattice deformation and a new Bragg diffraction simulation code, BarberShop. The dynamic deformation of a crystal lattice is modeled using the DiscoFlux model within an arbitrary Lagrangian-Eulerian hydrodynamic code, FLAG. These detailed continuum simulations of lattice deformation can be computationally slow, thus a statistical model is used to emulate the evolution of lattice deformation fields in time and across the considered model parameter space. Emulated lattice deformation fields can then be generated rapidly for any combination of physics model parameters. In turn, these fields can be fed into BarberShop to realize a rapid prediction of Bragg diffraction patterns associated with particular values of physics model parameters. The framework enables parameterization of the single crystal model to obtain Bragg diffraction patterns that most closely resemble a corresponding measurement. Furthermore, the framework naturally provides sensitivities of the lattice deformation to the physics parameters. We highlight the utility of this framework through the application to a synthetic closed-loop inverse problem leading to the parameterization of a single crystal material model. As a model problem, we consider the dynamic response of the energetic molecular crystal, cyclotrimethylenetrinitramine (or RDX), under dynamic compression induced by simulated flyer plate impact experiments.

36 MATERIALS SCIENCE↗

Non-stationary precipitation design standards for stormwater infrastructure modernization at USAF installations

The resilience of defense infrastructure systems to a changing climate is critical for national security. Climate induced recurrent flooding is already impacting over 20 U.S. Air Force installations, underscoring the urgency of revisiting precipitation standards and stormwater infrastructure design. Despite growing scientific knowledge and an expanding set of tools for updating outdated precipitation standards based on the assumption of climate stationarity, the adoption of climate informed analyses remain limited in practice. This study utilizes an existing framework to update Intensity (or Depth)-Duration-Frequency (DDF) curves using an ensemble of future climate projections. Change factors in precipitation estimates are derived and applied to six USAF installations across the U.S. The analysis is further extended to evaluate the implications of climate-informed DDFs on stormwater infrastructure performance and flood analysis at Tyndall AFB. Results indicate that the current design precipitation estimates are likely to become obsolete in all six USAF bases by the end of the century. The wide range of change factors across 32 GCM ensembles highlights the need to integrate uncertainty and evolving scientific data into infrastructure planning. The study also finds that the impacts of a changing climate vary spatially and temporally, emphasizing the value of localized analysis for infrastructure decision-making. The work advances ongoing DoD and societal efforts to implement adaptation strategies aimed at enhancing infrastructure resilience.

Intensity-duration-frequency curves↗

Developing a digital twin framework for remotely monitoring nuclear reactor facilities

A digital twin must seek to represent all applicable functional components of the system of interest. Different expertise is required for understanding the physical system being modeled than the skills needed for transforming those models into a functional digital twin through physics modeling, machine learning analysis, and visualization. The diversity of knowledge requires a multi-disciplinary team to ensure all system details are captured. Team members also need a method to verify that the data they generate within their domain can be effectively communicated to professionals in other fields. To address this challenge, this work provides an approach for developing a digital twin framework to remotely monitoring nuclear facilities. Through this, general knowledge of the framework is presented along with two examples to solidify the process. The AGN-201 digital twin and microreactor digital twins provide varying levels of complexity in a potential nuclear facility, where common threads are identified and lessons learned are provided. The goal of this research is to aid future researchers by providing a formula for a successful digital twin and in turn reducing the development time of nuclear system digital twins, specifically for remote monitoring.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

A Comprehensive Monte Carlo Framework for Jet-Quenching

This article presents the motivation for developing a comprehensive modeling framework in which different models and parameter inputs can be compared and evaluated for a large range of jet-quenching observables measured in relativistic heavy-ion collisions at RHIC and the LHC. Here, the concept of a framework is discussed within the context of recent efforts by the JET Collaboration, the authors of JEWEL, and the JETSCAPE collaborations. The framework ingredients for each of these approaches is presented with a sample of important results from each. The role of advanced statistical tools in comparing models to data is also discussed, along with the need for a more detailed accounting of correlated errors in experimental results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Orientation of acetic acid hydrogen bonded to acetate terminated TiO 2 (110)

Acetic acid is a common pollutant for which photocatalytic degradation over titania provides a mitigating strategy. Knowledge of the bonding of acetate/acetic acid to this substrate is needed to aid interpretation of the photocatalytic data. In this work we use ambient pressure near edge X-ray absorption fine structure to measure the coverage and geometry of acetate in the TiO 2 (110) contact layer as well as acetic acid in an additional layer. A saturation coverage of 0.5 monolayers in both layers is found up to an acetic acid pressure of 10 -1 Torr at 266 K. The geometry of acetate appears to be unchanged by adsorption of an additional layer of acetic acid, with the contact layer involving a majority acetate species bidentate bonded to neighboring Ti 5c sites and a minority acetate species bonded in a perpendicular geometry. Acetic acid has a similar geometry dictated by hydrogen bonding to the contact layer as well as the substrate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Transfer learning as a method to reproduce high-fidelity non-local thermodynamic equilibrium opacities in simulations

Simulations of high-energy density physics often need non-local thermodynamic equilibrium opacity data. These data, however, are expensive to produce at relatively low fidelity. It is even more so at high fidelity such that the opacity calculations can contribute 95 % of the total computation time. This proportion can even reach large proportions. Neural networks can be used to replace the standard calculations of low-fidelity data, and the neural networks can be trained to reproduce artificial, high-fidelity opacity spectra. In this work, it is demonstrated that a novel neural network architecture trained to reproduce high-fidelity krypton spectra through transfer learning can be used in simulations. Further, it is demonstrated that this can be done while achieving a relative per cent error of the peak radiative temperature of the hohlraum of approximately 1 % to 4 % while achieving a 19.4 $\times$ speed up.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Crystal structure and polymorphic forms of auranofin revisited

Auranofin, initially developed as a treatment for rheumatoid arthritis, is currently under extensive investigation as a potential drug for various conditions, including cancer, bacterial infections, and parasitic infections. The compound is a known inhibitor of thioredoxin reductase (TXNRD1) and related selenoproteins. Although preliminary studies on the auranofin crystal polymorphism exist, and a low-quality crystal structure has been reported, a comprehensive crystallographic characterization remains unexplored. Utilizing X-ray crystallography techniques, we conducted detailed structural analysis of auranofin and compared our findings with related organogold compounds. Implementation of Hirshfeld atom refinement (HAR) enabled a more accurate hydrogen atom positioning in the structure. The crystal packing reveals a layered arrangement stabilised by numerous weak hydrogen bonds and dispersive interactions. Notably, our attempts to reproduce the previously reported polymorphic form of auranofin, purportedly more water-soluble, were unsuccessful despite following published protocols. To our knowledge, this is the first study describing a “disappearing polymorph” phenomenon of any pharmaceutically relevant transition metal coordination compound. Our findings may have significant implications for medicinal chemistry and pharmacology of coordination complexes, suggesting the need for systematic revision of historical crystallographic data in this field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Templates of expected measurement uncertainties for prompt fission neutron spectra

In this paper, we provide templates of uncertainty sources expected to appear for three measurement types of prompt fission neutron spectra (PFNS): (1) shape measurements, (2) clean-ratio shape, that is the monitor PFNS are measured in nearly exactly the same surrounding as the PFNS of interest, and (3) indirect ratios, where the detector efficiency is backed out from PFNS monitor measurements. Information is also listed that is needed to faithfully include PFNS in nuclear data evaluations to guide experimenters on how to best report data and metadata for their measurements. These templates also suggest a typical range of pertinent uncertainty values and their correlations in case realistic uncertainties cannot be estimated from information on the measurement itself. The templates were based on a literature review, information found in EXFOR for 252 Cf, 235, 238 U, and 239 Pu PFNS, and enhanced by expertise from experimenters contributing to these PFNS templates.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Local measurements of the spatial magnetic field distribution in a z-pinch plasma during and near stagnation using polarization spectroscopy

We present here the detailed measurements of radial distribution of the magnetic field in a gas-puff z-pinch plasma at the final stages of the implosion phase and at stagnation. While the measurements are chordal, the radial distribution of different charge states was utilized to measure the magnetic field locally for certain radii, so that unlike chordal measurements in general, the magnetic field radial distribution was obtained with no need for the Abel inversion of the data. The distribution was measured using the Zeeman effect via a novel spectroscopic technique, at several axial locations, and demonstrates striking features such as the peak field remaining at a radius much larger than the stagnation radius at all times. Furthermore, while the distribution observed is sometimes monotonic with respect to the radius, it is often not, a behavior that can be linked to 2D features in the plasma column resulting from the Rayleigh–Taylor instability. The current flowing through the stagnating plasma was found to be a small fraction of the total current, resulting in clearly insufficient magnetic pressure to balance the plasma pressure at stagnation. The magnetic field data, taken over several axial positions, are used to obtain the true inductance in the imploding plasma for the first time; it is found that the data cannot explain the current turnover at stagnation. A simulation with the MACH2-Tabular Collisional-Radiative Equilibrium magnetohydrodynamics code in the r–z plane shows that the peak of the magnetic field pinches to a much smaller radius than is observed in the spectroscopic data. Furthermore, the depth of the computed current turnover at stagnation is smaller than the measured one. The two observed features of a radially extended magnetic field at stagnation together with a deep current turnover are a challenge to match in simulations. Various calculations and estimates of the inductive and resistive load voltages are examined to ascertain if they are responsible for the observed current notch. Lastly, the results demonstrate that the knowledge of the true inductance in the driven load requires such magnetic-field-distribution measurements and that imaging data or electrical measurements are insufficient.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Measurement of L-shell emission from mid-Z targets under non-LTE conditions using Transmission Grating Spectrometer and DANTE power diagnostics

In this work, we present the measurement of L-band emission from buried Sc/V targets in experiments performed at the OMEGA laser facility. The goal of these experiments was to study non-local thermodynamic equilibrium plasmas and benchmark atomic physics codes. The L-band emission was measured simultaneously by the time resolved DANTE power diagnostic and the recently fielded time integrated Soreq-Transmission Grating Spectrometer (TGS) diagnostic. The TGS measurement was used to support the spectral reconstruction process needed for the unfolding of the DANTE data. The Soreq-TGS diagnostic allows for broadband spectral measurement in the 120 eV–2000 eV spectral band, covering L- and M-shell emission of mid- and high-Z elements, with spectral resolution λ/Δλ = 8–30 and accuracy better than 25%. The Soreq-TGS diagnostic is compatible with ten-inch-manipulator platforms and can be used for a wide variety of high energy density physics, laboratory astrophysics, and inertial confinement fusion experiments.

Fraenkel, M. (ORCID:0000000291939544)↗

Microcalorimeter measurement of x-ray spectra from a high-temperature magnetically confined plasma

A NASA-built x-ray microcalorimeter spectrometer has been installed on the MST facility at the Wisconsin Plasma Physics Laboratory and has recorded x-ray photons emitted by impurity ions of aluminum in a majority deuterium plasma. Much of the x-ray microcalorimeter development has been driven by the needs of astrophysics missions, where imaging arrays with few-eV spectral resolution are required. The goal of our project is to adapt these single-photon-counting microcalorimeters for magnetic fusion energy research and demonstrate the value of such measurements for fusion science. Microcalorimeter spectrometers combine the best characteristics of the x-ray instrumentation currently available on fusion devices: high spectral resolution similar to an x-ray crystal spectrometer and the broadband coverage of an x-ray pulse height analysis system. Fusion experiments are increasingly employing high-Z plasma-facing components and require measurement of the concentration of all impurity ion species in the plasma. This diagnostic has the capability to satisfy this need for multi-species impurity ion data and will also contribute to measurements of impurity ion temperature and flow velocity, Zeff, and electron density. Here, we introduce x-ray microcalorimeter detectors and discuss the diagnostic capability for magnetic fusion energy experiments. We describe our experimental setup and spectrometer operation approach at MST, and we present the results from an initial measurement campaign.

Eckart, M. E. (ORCID:0000000338945889)↗

CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities

As more distributed energy resources become part of the demand-side infrastructure, quantifying their energy flexibility on a community scale is crucial. CityLearn v1 provided an environment for benchmarking control algorithms. However, there is no standardized environment utilizing realistic building-stock datasets for distributed energy resource control benchmarking without co-simulation or third-party frameworks. CityLearn v2 extends CityLearn v1 by providing a stand-alone simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create grid-interactive communities for resilient, multi-agent, and objective control of distributed energy resources with dynamic occupant feedback. While the v1 environment used pre-simulated building thermal loads, the v2 environment uses data-driven thermal dynamics and eliminates the need for co-simulation with building energy performance software. This work details the v2 environment and provides application examples that use reinforcement learning control to manage battery energy storage system, vehicle-to-grid control, and thermal comfort during heat pump power modulation.

Nweye, Kingsley↗

Advances in prediction of tokamak experiments with theory-based models

The successful validation of theory-based models of transport, MHD stability, heating and current drive, with tokamak measurements over the last 20 years, has laid the foundation for a new era where these models can be routinely used in a "predict first" approach to design and predict the outcomes of experiments on tokamaks today. The capability to predict the plasma confinement and core profiles with a quantified uncertainty, based on a multi-machine, international, database of experience, will provide confidence that a proposed discharge will remain within the operational limits of the tokamak. Developing this predictive capability for the first generation of burning plasma devices, beginning with ITER, and progressing to tokamak demonstration reactors, is a critical mission of fusion energy research. Major advances have been made implementing this predict first methodology on today's tokamaks. In this work, an overview of several of these recent advances will be presented, providing the integrated modeling foundations of the experimental successes. The first steps to include boundary plasmas, and tokamak control systems, have been made. A commitment to predicting experiments as part of the planning process is needed in order to collect predictive accuracy data and evolve the models and software into a robust whole discharge pulse design simulator.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Signatures of multiple charge excitations in resonant inelastic x-ray scattering spectra of metals

In this work, we study how multiple charge excitations appear in the resonant inelastic x-ray scattering (RIXS) spectra of metals. Typically, the focus is upon single excitations in the problem, the plasmons and electron-hole pairs, while the multiexcitation processes are neglected. However, at small momentum transfer the multiexcitation contributions may dominate the signal and one needs to understand how to interpret the data in such cases. In particular, we demonstrate how to “decode” the total multiexcitation intensity and extract the plasmon dispersion. Furthermore, while our calculations are based on the random phase approximation for jellium, which does not allow us to obtain quantitatively precise results in the entire region of parameters, we expect them to capture semiqualitatively all features expected for charged Fermi-liquid states, including universal and singular properties of the RIXS spectra.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Robust Matrix Completion State Estimation in Distribution Systems

Due to the insufficient measurements in the distribution system state estimation (DSSE), full observability and redundant measurements are difficult to achieve without using the pseudo measurements. The matrix completion state estimation (MCSE) combines the matrix completion and power system model to estimate voltage by exploring the low-rank characteristics of the matrix. This paper proposes a robust matrix completion state estimation (RMCSE) to estimate the voltage in a distribution system under a low-observability condition. Tradition state estimation weighted least squares (WLS) method requires full observability to calculate the states and needs redundant measurements to proceed a bad data detection. The proposed method improves the robustness of the MCSE to bad data by minimizing the rank of the matrix and measurements residual with different weights. It can estimate the system state in a low-observability system and has robust estimates without the bad data detection process in the face of multiple bad data. The method is numerically evaluated on the IEEE 33-node radial distribution system. The estimation performance and robustness of RMCSE are compared with the WLS with the largest normalized residual bad data identification (WLS-LNR), and the MCSE.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Power Grid Contingency Analysis with Machine Learning: A Brief Survey and Prospects

We briefly review previous applications of machine learning (ML) in power grid analyses and introduce our ongoing effort toward developing a generative-adversarial (GA) model for fast and reliable grid contingency analyses. According to our review, the persisting limitation of traditional ML techniques in grid analyses is the need for an exhaustive amount of training data for model generalization and accurate predictions. GA models overcome this limitation by first learning true data distribution from a small training set, from which new samples assimilating true data are generated with some variations. Subsequently, GA models can transfer learn or super-generalize with increased accuracy, that is, accurately predict n - (k + 2) contingencies from a small n - k training set and generated n - (k + 1) data. The joint effort between Idaho National Lab and Florida State University strives to develop a zero-shot and deep learning-based contingency analysis tool, named Smart Contingency Analysis Neural Network (SCANN), by leveraging the aforementioned advantages of GA models. The basic architecture of SCANN stems from the Latent Encoding of Atypical Perturbations network combined with an adversarial network, and it is designed to generate imbalanced power flow data from learned true data distributions for prediction purposes. Here we also introduce the abstract concept of resilience-chaos plots, a new resilience characterization tool proposed to complement SCANN by aiding in the assessment of large amounts of high-order contingency predictions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Velocity Profiling of a Gas–Solid Fluidized Bed Using Electrical Capacitance Volume Tomography

In this study, a method of producing velocity profile maps from electrical capacitance volume tomography (ECVT) measurements by reconstructing displacement from measured changes in capacitance is developed and applied to fluidized bed systems. The mapping of the reconstruction leverages the gradient of the sensitivity distribution of the ECVT sensor to circumvent the need for image cross correlation techniques. Experimental data of both bubbling and slugging fluidized beds are collected in a cold flow model. Adaptation of the technique is discussed in detail, and velocity profiles are obtained for a range of gas flow rates. The produced velocity maps are compared against the established methods of cross correlation and against empirical correlations from the literature and are found to agree well in tracking slug and bubble velocity. The exception is when the tracked object is large relative to the ECVT sensor dimensions, a scenario that can be avoided through proper sensor design. The quantities of average velocity, momentum, and solid and gas volume fraction are derived from the image and velocity profiles. The results demonstrate and extend the power of ECVT as a measurement tool for the study and monitoring of gas–solid fluidized beds by providing a computationally cheaper alternative to 3-D cross correlation for deriving velocity profiles.

47 OTHER INSTRUMENTATION↗