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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Quantification of probabilistic ignition thresholds of polymer-bonded explosives with microstructure defects

Microscopic defects such as voids and cracks in an energetic material significantly influence its shock sensitivity. Currently, there is a lack of systematic and quantitative study of the effects of cracks both experimentally and computationally, although significant work has been done on voids. We present an approach for quantifying the effects of intragranular and interfacial cracks in polymer-bonded explosives (PBXs) via mesoscale simulations that explicitly account for such defects. With this method, the ignition thresholds corresponding to any given level of ignition probability and, conversely, the ignition probability corresponding to any loading condition (i.e., ignition probability maps) are predicted for PBX 9404 containing different levels of initial grain cracking or interfacial debonding. James relations are utilized to express the predicted thresholds and ignition probabilities. It is found that defects lower the ignition thresholds and cause the material to be more sensitive. This effect of defects on shock sensitivity diminishes as the shock load intensity increases. Furthermore, the sensitivity differences are rooted in energy dissipation and the consequent hotspot development. The spatial preference in hotspot distribution is studied and quantified using a parameter called the defect preference ratio (r pref ). Analyses reveal that defects play an important role in the development of hotspots and thus have a strong influence on the ignition thresholds. The findings are in qualitative agreement with reported trends in experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Multi-fidelity uncertainty quantification strategies for large-scale multiphysics applications: PSAAP II particle-based solar energy receiver

The study of complex systems is often based on computationally intensive, high-fidelity, simulations. To build confidence or improve the prediction accuracy of such simulations, the impact of uncertainties in model inputs, or even the structure of the models, on the quantities of interest must be measured. This, however, requires a computational budget that is a possibly large multiple of the cost of a single simulation, and thus may become infeasible for expensive simulation models featuring a large number of uncertain inputs and highly non-linear behavior. Therefore, this work explores multi-fidelity strategies to accelerate the estimation of the effect of uncertainties. The main idea behind multi-fidelity methods is to utilize cheaper, lower-fidelity models – than the intended high-fidelity, expensive model of the problem – to generate a baseline solution that together with relatively small number of high-fidelity simulations can lead to accurate predictions. The methods are briefly presented, and their performance assessed on an irradiated particle-laden turbulent flow case related to Stanford’s PSAAP II particle-based solar energy receiver.

Physics↗
DOE OSTI2018

Note: Plasma optical emission spectroscopy for water vapor quantification and detection during vacuum drying process

In this work, a methodology involving plasma optical emission spectroscopy driven by a direct current (dc) plasma source is developed to quantify water vapor concentration in a gaseous stream. The experimental setup consists of a dc driven low-pressure plasma cell in which the emission from the plasma discharge is measured by using an optical emission spectrometer. The emission from Hα at 656.2 nm—the first transition in the Balmer series, was found to be the most sensitive to the water vapor concentration in the gas stream. Consistent linear trends of the emission signals with respect to variation in concentration of water are observed for multiple combinations of operating parameters. This method has been applied to a vacuum drying process of a mock nuclear fuel assembly to quantify the concentration of water vapor during the drying process.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

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↗
DOE OSTI2020

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)↗
DOE OSTI2020

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↗
DOE OSTI2022

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↗
DOE OSTI2022

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↗
DOE OSTI2023

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↗
DOE OSTI2024

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↗
DOE OSTI2026

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↗
DOE OSTI2021

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↗