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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Orbital Decay of Double White Dwarfs: Beyond Gravitational-wave Radiation Effects

The traditional description of the orbital evolution of compact-object binaries, like double white dwarfs (DWDs), assumes that the system is driven only by gravitational-wave (GW) radiation. However, the high magnetic fields with intensities of up to gigagausses measured in WDs alert a potential role of the electromagnetic (EM) emission in the evolution of DWDs. We evaluate the orbital dynamics of DWDs under the effects of GW radiation, tidal synchronization, and EM emission by a unipolar inductor generated by the magnetic primary and the relative motion of the nonmagnetic secondary. We show that the EM emission can affect the orbital dynamics for magnetic fields larger than megagausses. We applied the model to two known DWDs, SDSS J0651+2844 and ZTF J1539+5027, for which the GW radiation alone does not fully account for the measured orbital decay rate. We obtain upper limits to the primary's magnetic field strength, over which the EM emission causes an orbital decay faster than observed. The contribution of tidal locking and the EM emission is comparable, and together they can contribute up to 20% to the measured orbital decay rate. We show that the gravitational waveform for a DWD modeled as purely driven by GWs and including tidal interactions and EM emission can have large relative dephasing detectable in the mHz regime of frequencies relevant for space-based detectors like LISA. Therefore, including physics besides GW radiation in the waveform templates is essential to calibrate the GW detectors using known sources, e.g., ZTF J1539+5027, and to infer binary parameters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

TbasCO: trait-based comparative ‘omics identifies ecosystem-level and niche-differentiating adaptations of an engineered microbiome

A grand challenge in microbial ecology is disentangling the traits of individual populations within complex communities. Various cultivation-independent approaches have been used to infer traits based on the presence of marker genes. However, marker genes are not linked to traits with complete fidelity, nor do they capture important attributes, such as the timing of gene expression or coordination among traits. To address this, we present an approach for assessing the trait landscape of microbial communities by statistically defining a trait attribute as a shared transcriptional pattern across multiple organisms. Leveraging the KEGG pathway database as a trait library and the Enhanced Biological Phosphorus Removal (EBPR) model microbial ecosystem, we demonstrate that a majority (65%) of traits present in 10 or more genomes have niche-differentiating expression attributes. For example, while many genomes containing high-affinity phosphorus transporter pstABCS display a canonical attribute (e.g. up-regulation under phosphorus starvation), we identified another attribute shared by many genomes where transcription was highest under high phosphorus conditions. Taken together, we provide a novel framework for unravelling the functional dynamics of uncultivated microorganisms by assigning trait-attributes through genome-resolved time-series metatranscriptomics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.

42 ENGINEERING↗
DOE OSTI2025

Virtual to Physical: Reinforcement Learning to Optimize SNS Particle Accelerator Controls

Complex accelerators must have control systems that can handle dynamic nonlinear environments. This makes traditional control methods unsuitable as they can struggle to adapt to these uncertainties. This provides an ideal environment for reinforcement learning algorithms as they are adaptable and generalizable. We present a reinforcement learning pipeline that can effectively handle the dynamics of a complex accelerator. We test and prove our pipelines capabilities on multiple environments including the Spallation Neutron Source (SNS) and the Beam Test Facility (BTF) at Oakridge National Lab (ORNL). Due to the limited time available to train an online algorithm like reinforcement learning on a real accelerator, we utilize a virtual twin accelerator (VIRAC) developed by ORNL to pretrain the policy and show its ability to converge in the virtual environment. We then test the adaptability of the pretrained RL model by applying it on the real accelerator and comparing the results. Utilizing our Scientific Optimization and Controls Toolkit (SOCT) and open-source standards such as Gymnasium we create and solve for a MEBT orbit correction problem in the SNS and an emittance maximization problem in the BTF. We show how Twin Delayed Deep Deterministic Policy Gradient (TD3) can solve this optimization environment in the virtual accelerator and transfer this policy onto the real accelerator for inference and model retraining. We show how reinforcement learning can be utilized as a control system for complex accelerators and provide a model pipeline for how an implementation performs and can be adapted to new accelerator control problems.

Kasparian, Armen [Thomas Jefferson National Accele↗
DOE OSTI2022

Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series: Supporting Data

This dataset consists of soil temperature time series that were used to estimate soil thermal diffusivity and its uncertainty trough the probabilistic modelling approach developed and presented in the article named "Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series" and published in Earth Surface Dynamics. There are two compressed (.zip) files that contains synthetic (Synthetic_soiltemp_Teller.zip) and field (Field_soiltemp_Teller.zip) data. There is one MATLAB file that requires MATLAB to execute but any text editor can open it. The Synthetic_soiltemp_Teller.zip file includes 5 comma-delimited data files (.csv) each of which contains soil temperature time series generated through forward modeling (i.e., heat-conduction process in a heterogeneous medium using an explicit finite difference method) to mimic various types of temperature gradients, trend and fluctuations. These synthetic soil temperatures were used to investigate the impact of different environmental conditions on the uncertainty of thermal diffusivity estimates. The Field_soiltemp_Teller.zip file contains 28 comma-delimited data files (.csv) out of which (a) 27 files includes soil temperature time series recorded from 27 temperature probes located in a site along Teller Road about 40 km northwest of Nome, Alaska (64.72°N, 165.94°W), (b) one includes the name and coordinates of the 27 probes. These field soil temperatures were used to infer soil thermal diffusivity at numerous locations and depths in a discontinuous permafrost environment, and to evaluate the links between the estimated soil thermal diffusivity values and soil physical properties. The comma-delimited data files (.csv) of the synthetic and field soil temperature time series includes date and time (UTC) in the first column and soil temperature from 5 cm below the ground surface to 1.05 m depth (with 5 or 10 cm spacing between sensors) in the other columns. The measurements were acquired every 15 minutes. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Quantum-Based Approach to Predict Primary Radiation Damage in Polymeric Networks

Initial atomistic-level radiation damage in chemically reactive materials is thought to induce reaction cascades that can result in undesirable degradation of macroscale properties. Ensembles of quantum-based molecular dynamics (QMD) simulations can accurately predict these cascades, but extracting chemical insights from the many underlying trajectories is a labor-intensive process that can require substantial a priori intuition. We develop here a general and automated graph-based approach to extract all chemically distinct structures sampled in QMD simulations and apply our approach to predict primary radiation damage of polydimethylsiloxane (PDMS), the main constituent of silicones. A postprocessing protocol is developed to identify underlying polymer backbone structures as connected components in QMD trajectories. Furthermore, these backbones form a repository of radiation-damaged structures. A scheme for extracting and updating a library of isomorphically distinct structures is proposed to identify the spanning set and aid chemical interpretation of the repository. The analyses are applied to ensembles of cascade QMD simulations in which the four element types in PDMS are selectively excited in primary knock-on atom events. Our approach reveals a much higher degree of combinatorial complexity in this system than was inferred through radiolysis experiments. Probabilities are extracted for radiation-induced network changes including formation of branch points, carbon linkages, cycles, bond scissions, and carbon uptake into the Si–O siloxane backbone network. The general analysis framework presented here is readily extendable to modeling chemical degradation of other polymers and molecular materials and provides a basis for future quantum-informed multiscale modeling of radiation damage.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Dark matter haloes of massive elliptical galaxies at z ∼ 0.2 are well described by the Navarro–Frenk–White profile

ABSTRACT We investigate the internal structure of elliptical galaxies at z ∼ 0.2 from a joint lensing–dynamics analysis. We model Hubble Space Telescope images of a sample of 23 galaxy–galaxy lenses selected from the Sloan Lens ACS (SLACS) survey. Whereas the original SLACS analysis estimated the logarithmic slopes by combining the kinematics with the imaging data, we estimate the logarithmic slopes only from the imaging data. We find that the distribution of the lensing-only logarithmic slopes has a median 2.08c ± 0.03 and intrinsic scatter 0.13 ± 0.02, consistent with the original SLACS analysis. We combine the lensing constraints with the stellar kinematics and weak lensing measurements, and constrain the amount of adiabatic contraction in the dark matter (DM) haloes. We find that the DM haloes are well described by a standard Navarro–Frenk–White halo with no contraction on average for both of a constant stellar mass-to-light ratio (M/L) model and a stellar M/L gradient model. For the M/L gradient model, we find that most galaxies are consistent with no M/L gradient. Comparison of our inferred stellar masses with those obtained from the stellar population synthesis method supports a heavy initial mass function (IMF) such as the Salpeter IMF. We discuss our results in the context of previous observations and simulations, and argue that our result is consistent with a scenario in which active galactic nucleus feedback counteracts the baryonic-cooling-driven contraction in the DM haloes.

Shajib, Anowar J.↗
DOE OSTI2026

A Comprehensive Comparison of Building Height Products at a Global Level

The built environment continues to expand due to increasing human populations with some projections indicating additional growth of 2.5 billion persons by 2050. Buildings are a key component of the built environment and information on building height and urban morphology in general can help to better understand and address a host of development and urban planning issues including energy consumption, urbanization, population dynamics, urban heat islands, environmental degradation, and others. To date, several approaches have been developed to address the need for mapping building height at scale. These approaches can be grouped into two main categories: 1) directly measure height from remote sensing products or 2) indirectly estimate height from footprint morphology features at the building-by-building level. Due to a number of these global height datasets now being available for consumption and use across domains, there is a critical need to understand the differences displayed between these products and how the use of these may influence the outcome when applied. In this analysis, we compare and evaluate 5 global building height datasets: Global Human Settlement, World Settlement Footprint 3D, Global Building Morphology dataset, three-dimensional Global Building Footprints, and an approach developed at Oak Ridge National Laboratory (ORNL). Our analysis shows that, across 5 cities at a global scale with high-quality building height reference data, the approach developed by ORNL of inferring height at the building-by-building level is more accurate and consistent. This was evidenced by the ORNL product improving upon the baseline in all 5 cities, the only product to do so. These findings will aid researchers in choosing a global building height product that best meets their needs and provides global availability, consistency, and accuracy.

42 ENGINEERING↗
DOE OSTI2023

Detection of open cluster rotation fields from Gaia EDR3 proper motions

Context: Most stars from in groups which with time disperse, building the field population of their host galaxy. In the Milky Way, open clusters have been continuously forming in the disk up to the present time, providing it with stars spanning a broad range of ages and masses. Observations of the details of cluster dissolution are, however, scarce. One of the main difficulties is obtaining a detailed characterisation of the internal cluster kinematics, which requires very high-quality proper motions. For open clusters, which are typically loose groups with tens to hundreds of members, there is the additional difficulty of inferring kinematic structures from sparse and irregular distributions of stars. Aims: Here, we aim to analyse internal stellar kinematics of open clusters, and identify rotation, expansion, or contraction patterns. Methods: We use Gaia Early Data Release 3 (EDR3) astrometry and integrated nested Laplace approximations to perform vector-field inference and create spatio-kinematic maps of 1237 open clusters. The sample is composed of clusters for which individual stellar memberships were already known, thus minimising contamination from field stars in the velocity maps. Projection effects were corrected using EDR3 data complemented with radial velocities from Gaia Data Release 2 and other surveys. Results: We report the detection of rotation patterns in eight open clusters. Nine additional clusters display possible rotation signs. We also observe 14 expanding clusters, with 15 other objects showing possible expansion patterns. Contraction is evident in two clusters, with one additional cluster presenting a more uncertain detection. In total, 53 clusters are found to display kinematic structures. Within these, elongated spatial distributions suggesting tidal tails are found in five clusters. These results indicate that the approach developed here can recover kinematic patterns from noisy vector fields, as those from astrometric measurements of open clusters or other stellar or galactic populations, thus offering a powerful probe for exploring the internal kinematics and dynamics of these types of objects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

The charge reduction rate for multiply charged polymer ions via ion–ion recombination at atmospheric pressure

The charge reduction of multiply charged macromolecular ions via recombination with small ions in the gas phase is commonly employed to modulate the charge on macromolecules prior to mass spectrometric and mobility analyses. We employ a recently developed continuum-Molecular Dynamics (MD) calculation approach to determine the recombination rate coefficient of multiply charged (1 to 7 excess positive charged) polyethylene glycol ions (mass of 4600 Da) with smaller singly charged anions, modeled as NO 2 – ions. The continuum-MD approach accounts explicitly for the influence of the background gas on the recombination process, accounts explicitly for ion translational, vibrational, and rotational motion, and enables recombination rate coefficient calculation in nitrogen near atmospheric pressure, wherein neither low pressure nor high pressure recombination theories are strictly applicable. Continuum-MD simulations yield recombination rate coefficients near 3.9 × 10 –14 m 3 s –1 for singly charged ions, increasing to 3.0 × 10 –11 m 3 s –1 for the +7 ion. Pre-existing collision rate coefficient expressions for rigid ions are found to be within a factor of 2–5 of calculations for all charge states, but their use requires knowledge of an appropriate collision distance, which is not well-defined for flexible polymer ions. Continuum-MD-inferred rate coefficients are incorporated into a model of charge reduction, and the charge state distribution versus anion concentration determined with it is compared to charge reduction measurements made via atmospheric pressure differential mobility analysis. Furthermore, good agreement is observed between simulations and experiments; although results are extremely sensitive to the recombination rate coefficients, experimental results are bound by models utilizing rates within a factor of 2 (0.5–2.0×) of the continuum-MD rates.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Organic matter sulfurization and organic carbon burial in the Mesoproterozoic

Throughout the Proterozoic Era, sedimentary organic carbon burial helped set the pace of global oxygenation and acted as a major modulator of atmospheric CO 2 and climate. Although Proterozoic rocks generally contain low concentrations of organic matter (OM), there are key exceptions to this rule, including the relatively OM-rich Arctic Bay shales from Baffin Island, Canada (Bylot Supergroup, Borden Basin, ~1.05 Ga). The mechanisms driving elevated OM concentrations in these and other Proterozoic shales remain poorly understood. In the Mesozoic and Cenozoic, organic matter sulfurization can be a major driver of enhanced OM burial across a range of redox conditions comparable to those inferred for many Proterozoic environments. Therefore, in this study, we evaluate the role of sulfurization in driving OM preservation in the Mesoproterozoic Borden Basin and discuss its relevance to Proterozoic systems in general. We present the first evidence for syngenetic-to-early-diagenetic OM sulfurization in a Proterozoic basin, which begins to fill a several-billion-year gap in our record of organic S across Earth history. We find that OM sulfurization was particularly extensive in shales from a relatively shallow-water section (Alpha River) but less extensive in shales deposited in deeper water (Shale Valley), which is consistent with models that infer sulfidic ‘wedges’ or O 2 -minimum-zone-type structures on shelf margins at least intermittently at this time. At the shallower site, organic S and pyrite are similarly 34 S-depleted and thus likely formed at roughly the same time near the sediment–water interface under conditions previously interpreted to have been ferruginous to intermittently sulfidic. In contrast, at the deeper-water site, large S-isotope differences between pyrite and organic S along with low apparent OM sulfurization intensities indicate that pyrite formation was favored over OM sulfurization during early sedimentation under variable but primarily ferruginous conditions. Although Mesoproterozoic biomass can be substantially sulfurized, indicators of sulfurization intensity are not correlated with OM concentrations, and therefore sulfurization does not appear to have been the primary driver of enhanced OM concentrations in Arctic Bay Formation shales. The link between sulfurization and total OM preservation may have been modulated during the deposition of Arctic Bay Formation shales by differences in iron availability, nutrient cycling, and particle dynamics in the Mesoproterozoic.

58 GEOSCIENCES↗
DOE OSTI2024

The Effect of the Velocity Distribution on Kilonova Emission

Abstract The electromagnetic emission from the nonrelativistic ejecta launched in neutron star mergers (either dynamically or through a disk wind) has the potential to probe both the total mass and composition of this ejecta. These observations are crucial in understanding the role of these mergers in the production of r -process elements in the Universe. However, many properties of the ejecta can alter the light curves and we must both identify which properties play a role in shaping this emission and understand the effects these properties have on the emission before we can use observations to place strong constraints on the amount of r -process elements produced in the merger. This paper focuses on understanding the effect of the velocity distribution (amount of mass moving at different velocities) for lanthanide-rich ejecta on the light curves and spectra. The simulations use distributions guided by recent calculations of disk outflows and compare the velocity-distribution effects to those of ejecta mass, velocity, and composition. Our comparisons show that uncertainties in the velocity distribution can lead to a factor of 2–4 uncertainties in the inferred ejecta mass based on peak infrared luminosities. We also show that early-time UV or optical observations may be able to constrain the velocity distribution, reducing the uncertainty in the ejecta mass.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The Effect of the Velocity Distribution on Kilonova Emission

The electromagnetic emission from the nonrelativistic ejecta launched in neutron star mergers (either dynamically or through a disk wind) has the potential to probe both the total mass and composition of this ejecta. These observations are crucial in understanding the role of these mergers in the production of r-process elements in the Universe. However, many properties of the ejecta can alter the light curves and we must both identify which properties play a role in shaping this emission and understand the effects these properties have on the emission before we can use observations to place strong constraints on the amount of r-process elements produced in the merger. This paper focuses on understanding the effect of the velocity distribution (amount of mass moving at different velocities) for lanthanide-rich ejecta on the light curves and spectra. The simulations use distributions guided by recent calculations of disk outflows and compare the velocity-distribution effects to those of ejecta mass, velocity, and composition. Our comparisons show that uncertainties in the velocity distribution can lead to a factor of 2–4 uncertainties in the inferred ejecta mass based on peak infrared luminosities. We also show that early-time UV or optical observations may be able to constrain the velocity distribution, reducing the uncertainty in the ejecta mass.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Physics-based reward driven image analysis in microscopy

The rise of electron microscopy has expanded our ability to acquire nanometer and atomically resolved images of complex materials. The resulting vast datasets are typically analyzed by human operators, an intrinsically challenging process due to the multiple possible analysis steps and the corresponding need to build and optimize complex analysis workflows. We present a methodology based on the concept of a Reward Function coupled with Bayesian Optimization, to optimize image analysis workflows dynamically. The Reward Function is engineered to closely align with the experimental objectives and broader context and is quantifiable upon completion of the analysis. Here, cross-section, high-angle annular dark field (HAADF) images of ion-irradiated (Y, Dy)Ba 2 Cu 3 O 7–δ thin-films were used as a model system. The reward functions were formed based on the expected materials density and atomic spacings and used to drive multi-objective optimization of the classical Laplacian-of-Gaussian (LoG) method. These results can be benchmarked against the DCNN segmentation. This optimized LoG* compares favorably against DCNN in the presence of the additional noise. We further extend the reward function approach towards the identification of partially-disordered regions, creating a physics-driven reward function and action space of high-dimensional clustering. We pose that with correct definition, the reward function approach allows real-time optimization of complex analysis workflows at much higher speeds and lower computational costs than classical DCNN-based inference, ensuring the attainment of results that are both precise and aligned with the human-defined objectives.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning

We demonstrate the first live, autonomous control over neutron diffraction experiments by developing and deploying ANDiE: the autonomous neutron diffraction explorer. Neutron scattering is a unique and versatile characterization technique for probing the magnetic structure and behavior of materials. However, instruments at neutron scattering facilities in the world is limited, and instruments at such facilities are perennially oversubscribed. We demonstrate a significant reduction in experimental time required for neutron diffraction experiments by implementation of autonomous navigation of measurement parameter space through machine learning. Prior scientific knowledge and Bayesian active learning are used to dynamically steer the sequence of measurements. We show that ANDiE can experimentally determine the magnetic ordering transition of both MnO and Fe 1.09 Te all while providing a fivefold enhancement in measurement efficiency. Furthermore, in a hypothesis testing post-processing step, ANDiE can determine transition behavior from a set of possible physical models. ANDiE's active learning approach is broadly applicable to a variety of neutron-based experiments and can open the door for neutron scattering as a tool of accelerated materials discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Time-resolved velocity and ion sound speed measurements from simultaneous bow shock imaging and inductive probe measurements

We present a technique to measure the time-resolved velocity and ion sound speed in magnetized, supersonic high-energy-density plasmas. We place an inductive (“b-dot”) probe in a supersonic pulsed-power-driven plasma flow and measure the magnetic field advected by the plasma. As the magnetic Reynolds number is large (R M > 10), the plasma flow advects a magnetic field proportional to the current at the load. This enables us to estimate the flow velocity as a function of time from the delay between the current at the load and the signal at the probe. The supersonic flow also generates a hydrodynamic bow shock around the probe, the structure of which depends on the upstream sonic Mach number. By imaging the shock around the probe with a Mach–Zehnder interferometer, we determine the upstream Mach number from the shock Mach angle, which we then use to determine the ion sound speed from the known upstream velocity. We use the sound speed to infer the value of Z̄T e , where Z̄ is the average ionization and T e is the electron temperature. We use this diagnostic to measure the time-resolved velocity and sound speed of a supersonic (M S ~ 8), super-Alfvénic (M A ~ 2) aluminum plasma generated during the ablation stage of an exploding wire array on the Magpie generator (1.4 MA, 250 ns). The velocity and Z̄T e measurements agree well with the optical Thompson scattering measurements reported in the literature and with 3D resistive magnetohydrodynamic simulations in GORGON.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Spectroscopic Characterization of Plasmoid Properties During Pellet Fueling in W7-X

This study utilizes a spectroscopic approach to investigate the properties of plasmoids that are formed during the process of cryogenic hydrogen pellet fueling in the Wendelstein 7-X (W7-X) stellarator. An analysis of the Balmer series emissions was conducted using a diagnostic that was installed during the 2024 operational campaign. Electron temperature, density, and plasma beta ( β ) values can be inferred from the emissions of radiation from the ablation plasmoid. These values are essential for validating pellet ablation models and, in the future, optimizing fueling strategies in steady-state fusion devices.

Cryogenic hydrogen pellets↗
DOE OSTI2021

Bayesian estimations of orientation distribution functions from small-angle scattering enable direct prediction of mechanical stress in anisotropic materials

Properties of soft materials are influenced by their anisotropic structuring under nonequilibrium fields. Although anisotropic structure-property relationships have been extensively explored theoretically, comparison to experiments requires determination of the microstructural orientation probability distribution function (OPDF) of microstructural elements. Small angle scattering (SAS) measurements encode information about the OPDF, but tools to navigate this connection are incomplete. Here, we develop and validate an explicit framework to link arbitrary OPDFs to SAS measurements. Specifically, we propose, validate, and apply a method, maximum a posteriori scattering inference (MAPSI), whereby the OPDF may be obtained from SAS measurements using a Bayesian estimation method. Using this method, we obtain estimates of the full 3D OPDF for two model semidilute fd-virus (rodlike) dispersions at concentrations that are approximately equal to and twice the overlap concentration. From the OPDF, we calculate its second and fourth moments and compare these to predictions for a dilute suspension of rigid rods and to a recent theory for semidilute suspensions. Finally, we use both the theoretical and measured moments to calculate the stress, both for dilute and semidilute suspensions. These predictions are not only compared to each other, but also to measured values of the shear stress, and point to new insights into the behavior of suspensions of highly elongated particles in the transition between dilute and semidilute behavior. We also use this new framework to provide perspective on the connection between scalar parameterizations of scattering and the OPDF that have frequently been used in the past. Here, the new tools developed in this work provide an unprecedented path toward experimental validation of dynamical theories of rodlike colloids and polymers, and for measurement of nonequilibrium structures and stresses of other complex fluids and soft materials with SAS.

36 MATERIALS SCIENCE↗