Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Inference mechanisms”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Analytic solutions for Asay foil trajectories with implications for ejecta source models and mass measurements

We consider the trajectory of an Asay foil ejecta diagnostic for scenarios where ejecta are produced at a singly shocked planar surface and fly ballistically through a perfect vacuum to the sensor. We do so by building upon a previously established mathematical framework derived for the analytic study of stationary sensors. First, we derive the momentum conservation equation for the problem, in a form amenable to accelerating sensors, in terms of a generic ejecta source model. The result is an integrodifferential equation of motion for the foil trajectory. This equation yields an easily calculable closed-form implicit solution for the foil trajectory in instant-production scenarios. From there, we derive a boundary condition that particle velocity distributions must satisfy if their associated foil trajectories are to exhibit a smooth initial acceleration, as occurs in some experiments. This condition is identical to one derived previously from a consideration of piezoelectric voltage data obtained in similar experiments. We also compare techniques for inferring accumulated ejecta masses from foil trajectories, first by deriving the exact solution, and then by quantifying the error imposed by a frequently used approximate solution (both subject to the assumption of instantaneous ejecta production). Finally, we examine the common practice of presenting inferred cumulative ejecta masses as a function of implied ejecta velocity, establishing the conditions under which this methodology is most meaningful.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Solving the Orszag–Tang vortex magnetohydrodynamics problem with physics-constrained convolutional neural networks

We study the 2D Orszag–Tang vortex magnetohydrodynamics (MHD) problem through the use of physics-constrained convolutional neural networks (PCNNs) for forecasting the density, ρ, and the magnetic field, B, as well as the prediction of B given the velocity field v of the fluid. In addition to translation equivariance from the convolutional architecture, other physics constraints were embedded: absence of magnetic monopoles, non-negativity of ρ, use of only relevant variables, and the periodic boundary conditions of the problem. The use of only relevant variables and the hard constraint of non-negative ρ were found to facilitate learning greatly. The divergenceless condition ∇·B=0 was implemented as a hard constraint up to machine precision through the use of a magnetic potential to define B=∇×A. Residual networks and data augmentation were also used to improve performance. This allowed for some of the residual models to function as surrogate models and provide reasonably accurate simulations. For the prediction task, the PCNNs were evaluated against a physics-informed neural network, which had the ideal MHD induction equation as a soft constraint. Several models were able to generate highly accurate fields, which are visually almost indistinguishable and have low mean squared error. Only methods with built-in hard constraints produced physical fields with ∇·B=0. The use of PCNNs for MHD has the potential to produce physically consistent real-time simulations to serve as virtual diagnostics in cases where inferences must be made with limited observables.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Inference, Prediction, & Entropy-Rate Estimation of Continuous-Time, Discrete-Event Processes

Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground. However, a much broader class of discrete-event processes operates in continuous-time. Here, we provide new methods for inferring, predicting, and estimating them. The methods rely on an extension of Bayesian structural inference that takes advantage of neural network’s universal approximation power. Based on experiments with complex synthetic data, the methods are competitive with the state-of-the-art for prediction and entropy-rate estimation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Dynamic flow stress of pure polycrystalline aluminum: Pressure-shear plate impact experiments and extension of dislocation-based modeling to large strains

The dynamic thermo-mechanical behavior of pure aluminum has attracted renewed interest lately due to experimental observations of an anomalous increase in Hugoniot Elastic Limit (HEL) at incipient plasticity and elevated temperatures in polycrystalline pure metals. In context of current dislocation-mediated plasticity models for metals, this increase in dynamic strength is indicative of a transition in the rate controlling mechanism for dislocation glide from being thermally assisted to phonon-drag restricted due to increase in phonon viscosity at elevated temperatures. Though these studies have helped to shed light on these important mechanisms operative in FCC metals at incipient plasticity, the extent to which they contribute to flow stress, particularly at larger strains, remains unclear. Here, we address these questions through a combined experimental and modeling effort focused on investigating the evolution of dynamic flow stress in polycrystalline aluminum using experimental data gathered from a series of combined pressure-and-shear plate impact (PSPI) experiments designed to reveal the flow stress of pure aluminum at strain rates ~ 10 5 /s, plastic strains of up to 40% and temperatures ranging from room to 866 K. In all cases, the flow stress of aluminum, as inferred from the measured transverse particle velocity histories at the free surface of an elastic tungsten carbide target plate, reveals saturation with increasing plastic strains at stress levels that decrease with increasing test temperatures. Numerical simulations are performed to correlate the experimentally observed temperature and strain rate dependence of flow stress at small and large plastic strains using the Austin-McDowell dislocation-mediated plasticity model parametrized to normal plate impact experiments conducted in an earlier study by Zaretsky and Kanel (2012). Extensions to the model are made to better represent dynamic behavior of pure aluminum at larger plastic strains as observed in elevated temperature split Hopkinson Pressure bar (SPHB) experiments of Samanta (1971) and Lindholm and Yeakly (1965), and the combined pressure-and-shear plate-impact experiments conducted in the present study. The main theoretical extension to the Austin-McDowell model made in this paper is the introduction of a new rate- and temperature- dependent dynamic recovery function, which can potentially allow for an effective reduction in the rate of accumulation of dislocations at large plastic strains. The numerical predictions of the revised and re-calibrated plasticity model are brought into agreement with the experimental observations, correlating sufficiently well with dynamic yield stress at incipient plasticity and the flow stress levels at larger plastic strains, plastic strain rates in the range 10 3 – 10 6 /s, and elevated temperatures up to near melt. The model, in concert with the experimental measurements, suggests that at incipient plasticity the rate governing mechanism for plastic flow is phonon-drag restricted dislocation glide, whereas, at higher magnitudes of plastic strain, it transitions to stress-assisted thermally activated glide. The transition between these two rate governing mechanisms is controlled by the evolution of dislocations throughout the deformation process.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Role of Surface Hydroxyls and Lattice Oxygen in Governing Selectivity and Restructuring During Acetic Acid Conversion on Fe 3 O 4 (001)

Understanding the reactivity of carboxylic acids on metal oxide surfaces is critical for elucidating ketonization mechanisms relevant to biomass upgrading. Here, we investigate the adsorption and thermal decomposition of acetic acid (CH 3 COOH) on Fe 3 O 4 (001) using scanning tunneling microscopy (STM), temperature-programmed reaction spectroscopy (TPRS), and X-ray photoelectron spectroscopy (XPS). At room temperature, acetic acid adsorbs dissociatively to form ordered bidentate acetate (CH 3 COO) overlayer that lifts the (√2 × √2)R 45° surface reconstruction. TPRS reveals ketene (CH 2 CO) as the dominant product, along with CO, CO 2 , and H 2 O, the latter evolving via a Mars–van Krevelen (MvK) mechanism. Isotopic labeling shows preferential CO 2 formation from the carboxyl carbon and a more balanced CO/CO 2 ratio from the methyl carbon, suggesting distinct oxidation pathways. STM imaging reveals embedded acetate intermediates filling surface oxygen vacancies created in MvK steps. Upon product formation completion (~700 K), extensive surface etching is observed, with pits elongated along the octahedral Fe rows. Approximately 20% of the surface oxygen is removed, consistent with vacancy formation stoichiometry inferred from product distributions. These findings demonstrate that carboxylate-induced restructuring of Fe 3 O 4 (001) involves both surface healing and reduction processes, offering mechanistic insights relevant to ketonization and broader carboxylic acid chemistry on metal oxides.

acetic acid↗
DOE OSTI2022

Uranium sorption to organic matter and long-term accumulation in a pristine alpine wetland

Understanding the controls on uranium (U) mobility in the environment is key to improve the management of sites contaminated by U mining activities. Previous research has shown that natural or engineered wetlands are particularly able to scavenge high amounts of U(VI) and U(IV) under noncrystalline forms. However, questions remain on the respective roles of sorption and reduction processes in the removal of U from running waters in wetlands, as well as on the long-term stability of U storage. Here, we performed a series of geochemical, isotopic ($δ$ 238 U, ( 234 U/ 238 U)), microscopic (SEM-EDXS, EPMA) and spectroscopic (µ-XRF, µ-XAS, XANES and EXAFS at the U L 3 and M 4 -edges and Fe K-edge) investigations to determine the modes of U accumulation and assess U mobility in a natural exceptionally U-enriched (up to 5000 µg/g) wetland on the shore of Lake Nègre (Mediterranean Alps, France). Uranium (VI) was largely dominant in the two studied soil cores, except a few samples containing as much as ~50 % U(IV). At the particle scale, U is associated to a variety of organic constituents of the soil matrix with a homogenous oxidation state. Bulk EXAFS spectroscopy at the U L 3 -edge shows that U is mostly mononuclear, with dominant monodentate binding to organic moieties (C neighbors at ~3.45 Å). An additional minor fraction of U under polymeric forms is inferred from wavelet (CCWT) analysis of the EXAFS data. These observations are reinforced by 1 M bicarbonate extractions that result in the dissolution of 82–96 % of total U, including putative polymeric species. At the wetland scale, similar or slightly fractionated isotopic ratios ($δ$ 238 U) between the wetland-feeding creek waters and the wetland soils are observed, supporting the idea that U(VI) sorption on organic matter is the primary U scavenging mechanism. Furthermore, it indicates that partial U(VI) reduction to U(IV) occurs as a second step, after sorption. Analysis of U decay chain disequilibria in the cores as a function of depth suggests that U accumulation in this wetland has lasted for several thousand years. In conclusion, we propose that the wetland acts as an active reactor where U has been massively accumulating for ~14500 years, especially as U(VI) forms associated to organic matter and is further partly exported to the lake through soil erosion.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2020

Unifying Advective and Diffusive Descriptions of Bedform Pumping in the Benthic Biolayer of Streams

Abstract Many water quality and ecosystem functions performed by streams occur in the benthic biolayer, the biologically active upper (~5 cm) layer of the streambed. Solute transport through the benthic biolayer is facilitated by bedform pumping, a physical process in which dynamic and static pressure variations over the surface of stationary bedforms (e.g., ripples and dunes) drive flow across the sediment‐water interface. In this paper we derive two predictive modeling frameworks, one advective and the other diffusive, for solute transport through the benthic biolayer by bedform pumping. Both frameworks closely reproduce patterns and rates of bedform pumping previously measured in the laboratory, provided that the diffusion model's dispersion coefficient declines exponentially with depth. They are also functionally equivalent, such that parameter sets inferred from the 2D advective model can be applied to the 1D diffusive model, and vice versa. The functional equivalence and complementary strengths of these two models expand the range of questions that can be answered, for example, by adopting the 2D advective model to study the effects of geomorphic processes (such as bedform adjustments to land use change) on flow‐dependent processes and the 1D diffusive model to study problems where multiple transport mechanisms combine (such as bedform pumping and turbulent diffusion). By unifying 2D advective and 1D diffusive descriptions of bedform pumping, our analytical results provide a straightforward and computationally efficient approach for predicting, and better understanding, solute transport in the benthic biolayer of streams and coastal sediments.

Grant, Stanley B.↗
DOE OSTI2025

Announcing the Biomedical Data Translator: Initial Public Release

ABSTRACT The growing availability of biomedical data offers vast potential to improve human health, but the complexity and lack of integration of these datasets often limit their utility. To address this, the Biomedical Data Translator Consortium has developed an open‐source knowledge graph–based system—Translator—designed to integrate, harmonize, and make inferences over diverse biomedical data sources. We announce here Translator's initial public release and provide an overview of its architecture, standards, user interface, and core features. Translator employs a scalable, federated, knowledge graph framework for the integration of clinical, genomic, pharmacological, and other biomedical knowledge sources, enabling query retrieval, inference, and hypothesis generation. Translator's user interface is designed to support the exploration of knowledge relationships and the generation of insights, without requiring deep technical expertise and gradually revealing more detailed evidence, provenance, and confidence information, as needed by a given user. To demonstrate Translator's application and impact, we highlight features of the user interface in the context of three real‐world use cases: suggesting potential therapeutics for patients with rare disease; explaining the mechanism of action of a pipeline drug; and screening and validating drug candidates in a model organism. We discuss strengths and limitations of reasoning within a largely federated system and the need for rich concept modeling and deep provenance tracking. Finally, we outline future directions for enhancing Translator's functionality and expanding its data sources. Translator represents a significant step forward in making complex biomedical knowledge more accessible and actionable, aiming to accelerate translational research and improve patient care.

Research & Experimental Medicine↗
DOE OSTI2024

Estimating scalar turbulent fluxes with slow-response sensors in the stable atmospheric boundary layer

Conventional and recently developed approaches for estimating turbulent scalar fluxes under stable atmospheric conditions are evaluated, with a focus on gases for which fast sensors are not readily available. First, the relaxed eddy accumulation (REA) classical approach and a recently proposed mixing length parameterization, labeled A22, are tested against eddy-covariance computations. Using high-frequency measurements collected from two contrasting sites (the frozen tundra near Utqiaġvik, Alaska, and a sparsely vegetated grassland in Wendell, Idaho, during winter), it is shown that the REA and A22 models outperform the conventional Monin–Obukhov similarity theory (MOST) utilized widely to infer fluxes from mean gradients. Second, scenarios where slow trace gas sensors are the only viable option in field measurements are investigated using digital filtering applied to fast-response sensors to simulate their slow-response counterparts. With a filtered scalar signal, the observed filtered eddy-covariance fluxes are referred to here as large-eddy-covariance (LEC) fluxes. A virtual eddy accumulation (VEA) approach, akin to the REA model but not requiring a mechanical apparatus to separate the gas flows, is also formulated and tested. A22 outperforms VEA and LEC in predicting the observed unfiltered (total) eddy-covariance (EC) fluxes; however, VEA can still capture the LEC fluxes well. This finding motivates the introduction of a sensor response time correction into the VEA formulation to offset the effect of sensor filtering on the underestimated net averaged fluxes. The only needed parameter for this correction is the mean velocity at the instrument height, a surrogate of the advective timescale. The VEA approach is very suitable and simple to use with gas sensors of intermediate speed (∼ 0.5 to 1 Hz) and with conventional open- or closed-path setups.

58 GEOSCIENCES↗
DOE OSTI2020

Optimal experimental design for prediction based on push-forward probability measures

Incorporating experimental data is essential for increasing the credibility of simulation-aided decision making and design. This paper presents a method which uses a computational model to guide the optimal acquisition of experimental data to produce data-informed predictions of quantities of interest (QoI). Many strategies for optimal experimental design (OED) select data that maximize some utility that measures the reduction in uncertainty of uncertain model parameters, for example the expected information gain between prior and posterior distributions of these parameters. In this paper, we seek to maximize the expected information gained from the pushforward of an initial (prior) density to the push-forward of the updated (posterior) density through the parameter-to-prediction map. The formulation presented is based upon the solution of a specific class of stochastic inverse problems which seeks a probability density that is consistent with the model and the data in the sense that the push-forward of this density through the parameter-to-observable map matches a target density on the observable data. While this stochastic inverse problem forms the mathematical basis for our approach, we develop a one-step algorithm, focused on push-forward probability measures, that leverages inference-for-prediction to bypass constructing the solution to the stochastic inverse problem. A number of numerical results are presented to demonstrate the utility of this optimal experimental design for prediction and facilitate comparison of our approach with traditional OED.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applications

Physics-informed neural networks (PINNs) are effective in solving inverse problems based on differential and integro-differential equations with sparse, noisy, unstructured, and multifidelity data. PINNs incorporate all available information, including governing equations (reflecting physical laws), initial-boundary conditions, and observations of quantities of interest, into a loss function to be minimized, thus recasting the original problem into an optimization problem. In this paper, we extend PINNs to parameter and function inference for integral equations such as nonlocal Poisson and nonlocal turbulence models, and we refer to them as nonlocal PINNs (nPINNs). The contribution of the paper is three-fold. First, we propose a unified nonlocal Laplace operator, which converges to the classical Laplacian as one of the operator parameters, the nonlocal interaction radius δ goes to zero, and to the fractional Laplacian as δ goes to infinity. This universal operator forms a super-set of classical Laplacian and fractional Laplacian operators and, thus, has the potential to fit a broad spectrum of data sets. We provide theoretical convergence rates with respect to δ and verify them via numerical experiments. Second, we use nPINNs to estimate the two parameters, δ and α, characterizing the kernel of the unified operator. The strong non-convexity of the loss function yielding multiple (good) local minima reveals the occurrence of the operator mimicking phenomenon, that is, different pairs of estimated parameters could produce multiple solutions of comparable accuracy. Third, we propose another nonlocal operator with spatially variable order α(γ), which is more suitable for modeling turbulent Couette flow. Our results show that nPINNs can jointly infer this function as well as δ. More importantly, these parameters exhibit a universal behavior with respect to the Reynolds number, a finding that contributes to our understanding of nonlocal interactions in wall-bounded turbulence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Interstitial hydrogen enhances the mobility of some grain boundaries in tungsten

Segregation of interstitials at a grain boundary (GB) is known to generally lower its mobility. This phenomenon, called 'solute-drag', has important ramifications on the process of recrystallization and microstructural evolution. Here, we present predictions from molecular dynamics (MD) simulations which demonstrate that interstitial hydrogen in tungsten can in fact increase the mobility of some GBs which exhibit shear coupling. Assuming a disconnection-based mechanism, activation energies and pre-factors for disconnection nucleation are predicted from simulations of shear-coupled motion. In GBs where enhanced mobility is predicted, interstitial H reduces both the activation energy and the pre-factor for disconnection nucleation, thus effectively increasing the mobility. For GBs with diminished mobility, MD predicts that presence of interstitial H reduces the pre-factor and, in some cases, increases the activation energy. The reduction in the activation energy inferred from MD simulations are confirmed by nudged elastic band calculations. Temperature-dependent structural transitions are observed for some GBs, and the effect of interstitial H is found to change with the changes in structure. The effect of interstitial H is predicted to be complex and highly variable, providing some plausible explanations for experimental observations on the recrystallization of tungsten in presence of H-loaded plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Generic Hilbert space fragmentation in Kogut-Susskind lattice gauge theories

At the heart of quantum many-body physics lies the understanding of mechanisms that avoid quantum thermalization in an isolated system quenched far from equilibrium. A prominent example is Hilbert space fragmentation, which has recently emerged as an ergodicity-breaking mechanism in constrained spin models. Here, we show that Kogut-Susskind formulations of lattice gauge theories in d+1D (d spatial and one temporal dimensions) give rise to Hilbert space fragmentation, and discuss possible implications for understanding continuum physics. Lastly, our findings not only prove that lattice gauge theories are a natural platform for Hilbert space fragmentation, they also serve as a guide to the conditions under which these models can be faithfully used to infer the thermalization properties of quantum chromodynamics.

Eigenstate thermalization↗
DOE OSTI2022

Environmental controls on the light use efficiency of terrestrial gross primary production

Abstract Gross primary production (GPP) by terrestrial ecosystems is a key quantity in the global carbon cycle. The instantaneous controls of leaf‐level photosynthesis are well established, but there is still no consensus on the mechanisms by which canopy‐level GPP depends on spatial and temporal variation in the environment. The standard model of photosynthesis provides a robust mechanistic representation for C 3 species; however, additional assumptions are required to “scale up” from leaf to canopy. As a consequence, competing models make inconsistent predictions about how GPP will respond to continuing environmental change. This problem is addressed here by means of an empirical analysis of the light use efficiency (LUE) of GPP inferred from eddy covariance carbon dioxide flux measurements, in situ measurements of photosynthetically active radiation (PAR), and remotely sensed estimates of the fraction of PAR (fAPAR) absorbed by the vegetation canopy. Focusing on LUE allows potential drivers of GPP to be separated from its overriding dependence on light. GPP data from over 100 sites, collated over 20 years and located in a range of biomes and climate zones, were extracted from the FLUXNET2015 database and combined with remotely sensed fAPAR data to estimate daily LUE. Daytime air temperature, vapor pressure deficit, diffuse fraction of solar radiation, and soil moisture were shown to be salient predictors of LUE in a generalized linear mixed‐effects model. The same model design was fitted to site‐based LUE estimates generated by 16 terrestrial ecosystem models. The published models showed wide variation in the shape, the strength, and even the sign of the environmental effects on modeled LUE. These findings highlight important model deficiencies and suggest a need to progress beyond simple “goodness of fit” comparisons of inferred and predicted carbon fluxes toward an approach focused on the functional responses of the underlying dependencies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The heliospheric ambipolar potential inferred from sunward-propagating halo electrons

ABSTRACT We provide evidence that the sunward-propagating half of the solar wind electron halo distribution evolves without scattering in the inner heliosphere. We assume the particles conserve their total energy and magnetic moment, and perform a ‘Liouville mapping’ on electron pitch angle distributions measured by the Parker Solar Probe SPAN-E instrument. Namely, we show that the distributions are consistent with Liouville’s theorem if an appropriate interplanetary potential is chosen. This potential, an outcome of our fitting method, is compared against the radial profiles of proton bulk flow energy. We find that the inferred potential is responsible for nearly 100 per cent of the proton acceleration in the solar wind at heliocentric distances 0.18-0.79 AU. These observations combine to form a coherent physical picture: the same interplanetary potential accounts for the acceleration of the solar wind protons as well as the evolution of the electron halo. In this picture the halo is formed from a sunward-propagating population that originates somewhere in the outer heliosphere by a yet-unknown mechanism.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Gene targets for engineering osmotolerance in Caldicellulosiruptor bescii

Abstract Background Caldicellulosiruptor bescii , a promising biocatalyst being developed for use in consolidated bioprocessing of lignocellulosic materials to ethanol, grows poorly and has reduced conversion at elevated medium osmolarities. Increasing tolerance to elevated fermentation osmolarities is desired to enable performance necessary of a consolidated bioprocessing (CBP) biocatalyst. Results Two strains of C. bescii showing growth phenotypes in elevated osmolarity conditions were identified. The first strain, ORCB001, carried a deletion of the FapR fatty acid biosynthesis and malonyl-CoA metabolism repressor and had a severe growth defect when grown in high-osmolarity conditions—introduced as the addition of either ethanol, NaCl, glycerol, or glucose to growth media. The second strain, ORCB002, displayed a growth rate over three times higher than its genetic parent when grown in high-osmolarity medium. Unexpectedly, a genetic complement ORCB002 exhibited improved growth, failing to revert the observed phenotype, and suggesting that mutations other than the deleted transcription factor (the fruR/cra gene) are responsible for the growth phenotype observed in ORCB002. Genome resequencing identified several other genomic alterations (three deleted regions, three substitution mutations, one silent mutation, and one frameshift mutation), which may be responsible for the observed increase in osmolarity tolerance in the fruR / cra -deficient strain, including a substitution mutation in dnaK , a gene previously implicated in osmoresistance in bacteria. Differential expression analysis and transcription factor binding site inference indicates that FapR negatively regulates malonyl-CoA and fatty acid biosynthesis, as it does in many other bacteria. FruR/Cra regulates neighboring fructose metabolism genes, as well as other genes in global manner. Conclusions Two systems able to effect tolerance to elevated osmolarities in C. bescii are identified. The first is fatty acid biosynthesis. The other is likely the result of one or more unintended, secondary mutations present in another transcription factor deletion strain. Though the locus/loci and mechanism(s) responsible remain unknown, candidate mutations are identified, including a mutation in the dnaK chaperone coding sequence. These results illustrate both the promise of targeted regulatory manipulation for osmotolerance (in the case of fapR ) and the challenges (in the case of fruR/cra ).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Design and Characterization of a Lens-Coupled System for Dynamic X-Ray Diffraction

X-ray diffraction (XRD) is a necessary technique for understanding states of materials under static and dynamic loading conditions. The higher-pressure Equation of State (EOS) of many materials can only be explored via shock or ramp compression at temperatures and pressures of interest. While static XRD work has yielded EOS measurements in the 100 - 200 GPa regime, dynamic X-ray diffraction (DXRD) can explore EOS phases in the TPa regime, which closely resembles inner-core planetary conditions. DXRD hinges on the ability to measure the exact phase or phase change of a material while under dynamic loading conditions. Macroscopic diagnostic systems (e.g. velocimetry and pyrometry) can infer a phase change but not identify the specific phase entered by a material. While microscopic (atomic-level) diagnostic systems (e.g. DXRD) have been designed and implemented in Department of Energy’s (DOE) National Laboratories complex, the unique nature of Sandia National Laboratories’ Pulsed Power Facility (Z Machine) prohibits the use of such devices. The destructive nature of Z experiments presents a challenge to data capture and retrieval. Furthermore there are electromagnetic interference, X-ray background, and mechanical constraints to consider. Thus, a multi-part X-ray diagnostic for use on the Z Machine and Z-Beamlet Laser system has been designed and analyzed. Portions of this new DYnamic SCintillator Optic (DYSCO) have been built, tested and fielded. A data analysis software has been written. Finally, the radiance profile of the DYSCO’s scintillator has been characterized through experiments performed at the University of Arizona.

36 MATERIALS SCIENCE↗