Uncertainty Quantification for Equation of State using Heterogeneous Data Sources
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36 MATERIALS SCIENCE↗
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Uncertainty Quantification for a TNT Equation of State Including the Kinetic Burn Model
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36 MATERIALS SCIENCE↗
Accurate quantification of lattice temperature dynamics from ultrafast electron diffraction of single-crystal films using dynamical scattering simulations
In ultrafast electron diffraction (UED) experiments, accurate retrieval of time-resolved structural parameters, such as atomic coordinates and thermal displacement parameters, requires an accurate scattering model. Unfortunately, kinematical models are often inaccurate even for relativistic electron probes, especially for dense, oriented single crystals where strong channeling and multiple scattering effects are present. This article introduces and demonstrates dynamical scattering models tailored for quantitative analysis of UED experiments performed on single-crystal films. As a case study, we examine ultrafast laser heating of single-crystal gold films. Comparison of kinematical and dynamical models reveals the strong effects of dynamical scattering within nm-scale films and their dependence on sample topography and probe kinetic energy. Applying to UED experiments on an 11 nm thick film using 750 keV electron probe pulses, the dynamical models provide a tenfold improvement over a comparable kinematical model in matching the measured UED patterns. Also, the retrieved lattice temperature rise is in very good agreement with predictions based on previously measured optical constants of gold, whereas fitting the Debye–Waller factor retrieves values that are more than three times lower. Altogether, these results show the importance of a dynamical scattering theory for quantitative analysis of UED and demonstrate models that can be practically applied to single-crystal materials and heterostructures.
36 MATERIALS SCIENCE↗
Quantification of MagLIF morphology using the Mallat scattering transformation
The morphology of the stagnated plasma resulting from magnetized liner inertial fusion is measured by imaging the self-emission x rays coming from the multi-keV plasma. Equivalent diagnostic responses can be generated by integrated radiation-magnetohydrodynamic (rad-MHD) simulations from programs such as HYDRA and GORGON. There have been only limited quantitative ways to compare the image morphology, that is the texture, of simulations and experiments. We have developed a metric of image morphology based on the Mallat scattering transformation (MST), a transformation that has proved to be effective at distinguishing textures, sounds, and written characters. This metric is designed, demonstrated, and refined by classifying ensembles (i.e., classes) of synthetic stagnation images and by regressing an ensemble of synthetic stagnation images to the morphology (i.e., model) parameters used to generate the synthetic images. We use this metric to quantitatively compare simulations to experimental images, experimental images to each other, and to estimate the morphological parameters of the experimental images with uncertainty. This coordinate space has proved to be very adept at doing a sophisticated relative background subtraction in the MST space. This was needed to compare the experimental self-emission images to the rad-MHD simulation images.
Glinsky, Michael E. (ORCID:0000000324933326)↗
Reconstruction and uncertainty quantification of lattice Hamiltonian model parameters from observations of microscopic degrees of freedom
The emergence of scanning probe and electron beam imaging techniques has allowed quantitative studies of atomic structure and minute details of electronic and vibrational structure on the level of individual atomic units. These microscopic descriptors, in turn, can be associated with local symmetry breaking phenomena, representing the stochastic manifestation of the underpinning generative physical model. In this work, we explore the reconstruction of exchange integrals in the Hamiltonian for a lattice model with two competing interactions from observations of microscopic degrees of freedom and establish the uncertainties and reliability of such analysis in a broad parameter-temperature space. In contrast to other approaches, we specifically specify a loss function inherent to thermodynamic systems and utilize it to estimate uncertainty in simulated realizations of different models. As an ancillary task, we develop a machine learning approach based on histogram clustering to predict phase diagrams efficiently using a reduced descriptor space. We further demonstrate that reconstruction is possible well above the phase transition and in the regions of parameter space when the macroscopic ground state of the system is poorly defined due to frustrated interactions. This suggests that this approach can be applied to the traditionally complex problems of condensed matter physics such as ferroelectric relaxors and morphotropic phase boundary systems, spin and cluster glasses, and quantum systems once the local descriptors linked to the relevant physical behaviors are known.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Experimental quantification of the impact of heterogeneous mix on thermonuclear burn
In inertial confinement fusion, deuterium–tritium (DT) fuel is brought to densities and temperatures where fusion ignition occurs. However, mixing of the ablator material into the fuel may prevent ignition by diluting and cooling the fuel. MARBLE experiments at the National Ignition Facility provide new insight into how mixing affects thermonuclear burn. These experiments use laser-driven capsules containing deuterated plastic foam and tritium gas. Embedded within the foam are voids of known sizes and locations, which control the degree of heterogeneity of the fuel. Initially, the reactants are separated, with tritium concentrated in the voids and deuterium in the foam. During the implosion, mixing occurs between the foam and gas materials, leading to DT fusion reactions in the mixed region. Here, it is shown that by measuring the ratios of DT and deuterium–deuterium neutron yields for different macropore sizes and gas compositions, the effects of mix heterogeneity on thermonuclear burn may be quantified, supporting an improved understanding of these effects.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Quantification of electron correlation for approximate quantum calculations
State-of-the-art many-body wave function techniques rely on heuristics to achieve high accuracy at an attainable computational cost to solve the many-body Schrödinger equation. By far, the most common property used to assess accuracy has been the total energy; however, total energies do not give a complete picture of electron correlation. In this work, we assess the von Neumann entropy of the one-particle reduced density matrix (1-RDM) to compare selected configuration interaction (CI), coupled cluster, variational Monte Carlo, and fixed-node diffusion Monte Carlo for benchmark hydrogen chains. A new algorithm, the circle reject method, is presented, which improves the efficiency of evaluating the von Neumann entropy using quantum Monte Carlo by several orders of magnitude. The von Neumann entropy of the 1-RDM and the eigenvalues of the 1-RDM are shown to distinguish between the dynamic correlation introduced by the Jastrow and the static correlation introduced by determinants with large weights, confirming some of the lore in the field concerning the difference between the selected CI and Slater–Jastrow wave functions.
Chemistry↗
Quantification and purification of isotopic contamination at the ReAccelerator of the Facility for Rare Isotope Beams
At the ReAccelerator within the Facility for Rare Isotope Beams, a combination of an interchangeable aluminum foil and a silicon detector was developed to quantify isobaric contamination in rare isotope beams. The device is simple to operate and is now used routinely. In this article, we describe the system and show an application of the device to determine the level of contamination of an Si-32 rare isotope beam by stable S-32. In addition, we describe how the new diagnostic device helped confirm an enhancement of the beam purity prior to beam delivery to experiments.
Instruments & Instrumentation↗
Multi-frame x-ray radiography and image tracking for quantification of expansion in laser-driven tin ejecta microjets
One regime of experimental particle-laden flow study involves ejecta microjets—often defined as a stream of micrometer-scale particles generated through shock interaction with a non-uniform surface and generally travel above 1 km/s. In order to capture the change in characteristics as a function of propagation time, we apply a multi-frame x-ray radiography platform to observe and track the jet transport dynamics. A synchrotron x-ray source allows us to perform quantitative analyses and comparisons between the eight images captured by the imaging system. Observation of a single jet through time allows the use of a cross correlation algorithm to independently track various regions within the jet and quantify the jet expansion over time using normalized area and normalized areal density values. Through a comparison with the calculated values of ballistic transport, these findings show less expansion than expected for ballistically transporting particles. In conclusion, this work combines multi-frame synchrotron radiography with image tracking to establish a foundation for future studies on jet transport and particle interaction dynamics.
Sun, Yuchen [Lawrence Livermore National Laborator↗
Full-stack Quantification of Variability in Predicting Ion Transport Properties using Machine-learned Interatomic Potentials
Machine-learned interatomic potentials (MLIPs) have become the state-of-the-art for performing accurate, scalable molecular dynamics (MD) simulations. It is therefore crucial to understand and quantify the reliability of MLIPs for downstream property predictions. Uncertainty in predicted properties can arise from limitations in first-principles training data, intrinsic MLIP model errors in representing the data, and the statistical noise introduced during subsequent MD simulations. Using ion transport in Li7P3S11 as a case study, we systematically assess the impact of training set size and selection, neural network stochasticity, and MD sampling statistics on predicted diffusivity and activation energy. We find that when using equivariant MLIP architectures with standard MD protocols, uncertainty arising from MD sampling dominates over model-induced errors. In contrast, MLIP errors relative to the underlying first-principles data are consistently minor. Given this, there are two main routes to improving the accuracy of predictions based on MLIP potentials: adopting higher accuracy reference data generation methods, and improving the MD sampling statistics.
36 MATERIALS SCIENCE↗
Single-cell visualization and quantification of trace metals in Chlamydomonas lysosome-related organelles
Significance Transition metals are of crucial importance for primary productivity; their scarcity limits crop yield in agriculture and carbon sequestration on a global scale. Copper (Cu), iron (Fe), and manganese (Mn) are among the most important trace elements that enable the redox chemistry in oxygenic photosynthesis. The single-celled, eukaryotic green alga Chlamydomonas reinhardtii is a choice experimental system for studying trace metal homeostasis in the context of phototrophy, offering all the advantages of a classical microbial system with a well-characterized photosystem and trace metal metabolism machinery of relevance to plants. This project identifies and differentiates different trace metal storage sites in Chlamydomonas and uncovers the dynamics of trace metal storage and mobilization in situations of fluctuating resources.
54 ENVIRONMENTAL SCIENCES↗
Uncertainty quantification of unconfined spill fire data by coupling Monte Carlo and artificial neural networks
Not Available
Nuclear Science & Technology↗
Uncertainty Quantification of the 1-D SFR Thermal Stratification Model via the Latin Hypercube Sampling Monte Carlo Method
Not provided.
Nuclear Science & Technology↗
Validation and Uncertainty Quantification for Two-Phase Natural Circulation Flows Using TRACE Code
Not provided.
Nuclear Science & Technology↗
Convolutional Non-Homogeneous Poisson Process and its Application to Wildfire Ignition Risk Quantification for Power Delivery Networks
To quantify wildfire ignition risks on power delivery networks, the current practice predominantly relies on the empirically calculated fire danger indices, which may not well capture the effects of dynamically changing environmental factors. This article proposes a spatio-temporal point process model, known as the Convolutional Non-homogeneous Poisson Process (cNHPP), and applies the model to quantify wildfire ignition risks for power delivery networks. The proposed model captures both the current (i.e., instantaneous) and cumulative (i.e., historical) effects of key environmental processes (i.e., covariates) on wildfire risks, as well as the spatio-temporal dependency among different segments of the power delivery network. The computation and interpretation of the intensity function are thoroughly investigated. We apply the proposed approach to estimate wildfire ignition risks on major transmission lines in California, using historical fire data, meteorological and vegetation data obtained from the National Oceanic and Atmospheric Administration and National Aeronautics and Space Administration. Here, a comprehensive comparison study is performed to show the applicability and predictive capability of the proposed approach.
Non-homogeneous Poisson Process↗
Uncertainty Quantification of the Decay Gamma Source in Mesh-Based Shutdown Dose Rate Calculations
Not provided.
Nuclear Science & Technology↗
Confirmation and Quantification of Gas Flow into Capsules
Inertial confinement fusion experiments require a fuel filled target. At the National Ignition Facility (NIF), these targets are filled through a capsule fill tube assembly (CFTA). While fabricating these assemblies, it is possible to plug the fill tube with glue, which would render the CFTA and eventual target unusable. Historically, this plugging was first detectable in a finished target after considerable resources had been expended. Herein, this paper presents a method for not only detecting a plug in the fill tube before the CFTA is assembled into a target, but also characterizing gas flow through the fill tube, which can help NIF operations prepare to expend each target.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗