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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Strong Hilbert space fragmentation and fractons from subsystem and higher-form symmetries

Here, we introduce a route to Hilbert space fragmentation in high dimensions leveraging the group-word formalism. We show that taking strongly fragmented models in one dimension and “lifting” to higher dimensions using subsystem symmetries can yield strongly fragmented dynamics in higher dimensions, with subdimensional (e.g., lineonic) excitations. This provides a route to higher-dimensional strong fragmentation, and also a route to fractonic behavior. Meanwhile, lifting one-dimensional fragmented models to higher dimensions using higher-form symmetries yields models with topologically robust fragmentation. In three or more spatial dimensions, one can also “mix and match” subsystem and higher-form symmetries, leading to canonical fracton models such as X cube. We speculate that this approach could also yield a route to non-Abelian fractons. These constructions unify a number of phenomena that have been discussed in the literature, as well as furnishing models with unique properties.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Low-dose high-resolution $\mathrm{TOF}$-$\mathrm{PET}$ using ionization-activated multi-state low-$\mathrm{Z}$ detector media

We propose PET scanners using low atomic number media that undergo a persistent local change of state along the paths of the Compton recoil electrons. Measurement of the individual scattering locations and angles, deposited energies, and recoil electron directions allows using the kinematical constraints of the 2-body Compton scattering process to perform a statistical time-ordering of the scatterings, with a high probability of precisely identifying where the gamma first interacted in the detector. In these cases the Line-of-Response is measured with high resolution, determined by the underlying physics processes and not the detector segmentation. There are multiple such media that act through different mechanisms. As an example in which the change of state is quantum-mechanical through a change in molecular configuration, rather than thermodynamic, as in a bubble chamber, we present simulations of a two-state photoswitchable organic dye, a ‘Switchillator’, that is activated to a fluorescent-capable state by the ionization of the recoil electrons. The activated state is persistent, and can be optically excited multiple times to image individual activated molecules. Energy resolution is provided by counting the activated molecules. Location along the LOR is implemented by large-area time-of-flight MCP-PMT photodetectors with single photon time resolution in the tens of ps and sub-mm spatial resolution. Simulations indicate a large reduction of dose.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Using active matter to introduce spatial heterogeneity to the susceptible infected recovered model of epidemic spreading

Abstract The widely used susceptible-infected-recovered (S-I-R) epidemic model assumes a uniform, well-mixed population, and incorporation of spatial heterogeneities remains a major challenge. Understanding failures of the mixing assumption is important for designing effective disease mitigation approaches. We combine a run-and-tumble self-propelled active matter system with an S-I-R model to capture the effects of spatial disorder. Working in the motility-induced phase separation regime both with and without quenched disorder, we find two epidemic regimes. For low transmissibility, quenched disorder lowers the frequency of epidemics and increases their average duration. For high transmissibility, the epidemic spreads as a front and the epidemic curves are less sensitive to quenched disorder; however, within this regime it is possible for quenched disorder to enhance the contagion by creating regions of higher particle densities. We discuss how this system could be realized using artificial swimmers with mobile optical traps operated on a feedback loop.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Climate Dynamics Preceding Summer Forest Fires in California and the Extreme Case of 2018

Recent record-breaking wildfire seasons in California prompt an investigation into the climate patterns that typically precede anomalous summer burned forest area. Using burned-area data from the U.S. Forest Service’s Monitoring Trends in Burn Severity (MTBS) product and climate data from the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) over 1984–2018, relationships between the interannual variability of antecedent climate anomalies and July California burned area are spatially and temporally characterized. Lag correlations show that antecedent high vapor pressure deficit (VPD), high temperatures, frequent extreme high temperature days, low precipitation, high subsidence, high geopotential height, low soil moisture, and low snowpack and snowmelt anomalies all correlate significantly with July California burned area as far back as the January before the fire season. Seasonal regression maps indicate that a global midlatitude atmospheric wave train in late winter is associated with anomalous July California burned area. July 2018, a year with especially high burned area, was to some extent consistent with the general patterns revealed by the regressions: low winter precipitation and high spring VPD preceded the extreme burned area. However, geopotential height anomaly patterns were distinct from those in the regressions. Extreme July heat likely contributed to the extent of the fires ignited that month, even though extreme July temperatures do not historically significantly correlate with July burned area. While the 2018 antecedent climate conditions were typical of a high-burned-area year, they were not extreme, demonstrating the likely limits of statistical prediction of extreme fire seasons and the need for individual case studies of extreme years.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Charge conservation and higher moments of charge fluctuations

Higher moments of distributions of net charge and baryon number in heavy-ion collisions have been proposed as signals of fundamental QCD phase transitions. In order to better understand background processes for these observables, models are presented which enable one to gauge the effects of local charge conservation, decays of resonances and clusters, Bose symmetrization, and volume fluctuations. Monte Carlo methods for generating samplings of particles consistent with local charge conservation are presented and are followed by a review of simple analytic models involving a single type of charge with a constant experimental efficiency. The main model consists of thermal emission superimposed onto a simple parametrization of collective flow, known as a blast wave, with emission being consistent with individual canonical ensembles. The spatial extent of local charge conservation is parameterized by the size and extent over which charge is conserved. Here, the sensitivity of third- and fourth-order moments, skewness and kurtosis, to these parameters, and to beam energy and baryon density is explored. Comparisons with STAR data show that a significant part of the observed non-Poissonian fluctuations in net-proton fluctuations are explained by charge and baryon-number conservation, but that measurements of the STAR collaboration for fluctuations of net electric charge significantly differ from expectations of the models presented here.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Lagrangian large eddy simulations via physics-informed machine learning

High-Reynolds number homogeneous isotropic turbulence (HIT) is fully described within the Navier–Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as large eddy simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about effects of subgrid scales on the resolved scales. Here, we take an alternative approach and design LES heuristics stated in terms of Lagrangian particles moving with the flow. Our Lagrangian LES, thus L-LES, is described by equations generalizing the weakly compressible smoothed particle hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from direct numerical simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The subgrid-scale contributions are modeled separately with physical constraints to account for the effects from unresolved scales. We build the resulting model under the differentiable programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our L-LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Spectral Energy Distributions for 258 Local Volume Galaxies

We present model spectral energy distribution (SED) fits to ultraviolet/optical/infrared observations for the 258 nearby galaxies in the Local Volume Legacy survey, a sample dominated by lower-luminosity dwarf irregular systems. The data for each galaxy include up to 26 spatially-integrated broadband and narrowband fluxes from the Galaxy Evolution Explorer, Spitzer Space Telescope, and Infrared Astronomical Satellite space-based platforms, and from the Sloan Digital Sky Survey, Two Micron All Sky Survey, and other ground-based efforts. The CIGALE SED fitting package is employed using a delayed star-formation history with an optional late burst or quenching episode to constrain 11 different free parameters that characterize the properties of each galaxy's stellar and dust emission, with the overriding constraint that the ultraviolet/optical emission absorbed by interstellar dust grains is emitted in equal energy portions at infrared wavelengths. The main results are: (i) 94% of the SED fits yield reduced χ 2 values less than 3; (ii) the modeled stellar masses agree with those derived from 3.6 μm-based measures with a scatter of 0.07 dex; (iii) for a typical galaxy in the sample the SED-derived star-formation rate averaged over the past 100 Myr is about 88% of the value derived from standard hybrid indicators on similar timescales; and (iv) there is a statistically significant inverse relation between the stellar mass fraction appearing in the late burst and the total stellar mass. These results build upon prior SED modeling efforts in the local volume and lay the groundwork for future studies of more distant low-metallicity galaxies with JWST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Antarctic ice sheet model comparison with uncurated geological constraints shows that higher spatial resolution improves deglacial reconstructions

Accurately reconstructing past changes to the shape and volume of the Antarctic ice sheet relies on the use of physically based and thus internally consistent ice sheet modeling, benchmarked against spatially limited geologic data. The challenge in model benchmarking against geologic data is diagnosing whether model-data misfits are the result of an inadequate model, inherently noisy or biased geologic data, and/or incorrect association between modeled quantities and geologic observations. In this work we address this challenge by (i) the development and use of a new model-data evaluation framework applied to an uncurated data set of geologic constraints, and (ii) nested high-spatial-resolution modeling designed to test the hypothesis that model resolution is an important limitation in matching geologic data. While previous approaches to model benchmarking employed highly curated datasets, our approach applies an automated screening and quality control algorithm to an uncurated public dataset of geochronological observations (specifically, cosmogenic-nuclide exposure-age measurements from glacial deposits in ice-free areas). This optimizes data utilization by including more geological constraints, reduces potential interpretive bias, and allows unsupervised assimilation of new data as they are collected. We also incorporate a nested model framework in which high-resolution domains are downscaled from a continent-wide ice sheet model. We highlight the application of this framework by applying these methods to a small ensemble of deglacial ice-sheet model simulations, and demonstrate that the nested approach improves the ability of model simulations to match exposure age data collected from areas of complex topography and ice flow. We develop a range of diagnostic model-data comparison metrics to provide more insight into model performance than possible from a single-valued misfit statistic, showing that different metrics capture different aspects of ice sheet deflation.

Geosciences↗
DOE OSTI2023

Code for the manuscript "Lagrangian Large Eddy Simulations vis Physics-informed Machine Learning"

We would like to disclose a code based on Python/Pytorch, in which we implement the data-driven physics-informed machine learning of Lagrangian large eddy simulation that is described in the manuscript "Lagrangian Large Eddy Simulations via Physics-Informed Machine Learning ". High-Reynolds number Homogeneous Isotropic Turbulence is fully described within the Navier-Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as Large Eddy Simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about the effects of sub-grid scales on the resolved scales. Here, we take an alternative approach and design novel LES heuristics stated in terms of Lagrangian particles moving with the flow. Our \emph{Lagrangian LES}, thus L-LES, is described by equations generalizing the weakly compressible Smoothed Particle Hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from Direct Numerical Simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The sub-grid scale contributions are modeled separately with physical constraints to account for the effects from un-resolved scales. We build the resulting model under the Differentiable Programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our Lagrangian LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.

Tian, Yifeng↗
DOE OSTI2023

The cosmic web around the Coma cluster from constrained cosmological simulations

Galaxy clusters in the Universe occupy the important position of nodes of the cosmic web. They are connected among them by filaments, elongated structures composed of dark matter, galaxies, and gas. The connection of galaxy clusters to filaments is important, as it is related to the process of matter accretion onto the former. For this reason, investigating the connections to the cosmic web of massive clusters, especially well-known ones for which a lot of information is available, is a hot topic in astrophysics. In a previous work, we performed an analysis of the filament connections of the Coma cluster of galaxies, as detected from the observed galaxy distribution. In this work we resort to a numerical simulation whose initial conditions are constrained to reproduce the local Universe, including the region of the Coma cluster to interpret our observations in an evolutionary context. We detect the filaments connected to the simulated Coma cluster and perform an accurate comparison with the cosmic web configuration we detect in observations. We perform an analysis of the halos’ spatial and velocity distributions close to the filaments in the cluster outskirts. We conclude that, although not significantly larger than the average, the flux of accreting matter on the simulated Coma cluster is significantly more collimated close to the filaments with respect to the general isotropic accretion flux. This paper is the first example of such a result and the first installment in a series of publications which will explore the build-up of the Coma cluster system in connection to the filaments of the cosmic web as a function of redshift.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Remote Sensing of Tundra Ecosystems Using High Spectral Resolution Reflectance: Opportunities and Challenges

Abstract Observing the environment in the vast regions of Earth through remote sensing platforms provides the tools to measure ecological dynamics. The Arctic tundra biome, one of the largest inaccessible terrestrial biomes on Earth, requires remote sensing across multiple spatial and temporal scales, from towers to satellites, particularly those equipped for imaging spectroscopy (IS). We describe a rationale for using IS derived from advances in our understanding of Arctic tundra vegetation communities and their interaction with the environment. To best leverage ongoing and forthcoming IS resources, including National Aeronautics and Space Administration’s Surface Biology and Geology mission, we identify a series of opportunities and challenges based on intrinsic spectral dimensionality analysis and a review of current data and literature that illustrates the unique attributes of the Arctic tundra biome. These opportunities and challenges include thematic vegetation mapping, complicated by low‐stature plants and very fine‐scale surface composition heterogeneity; development of scalable algorithms for retrieval of canopy and leaf traits; nuanced variation in vegetation growth and composition that complicates detection of long‐term trends; and rapid phenological changes across brief growing seasons that may go undetected due to low revisit frequency or be obscured by snow cover and clouds. We recommend improvements to future field campaigns and satellite missions, advocating for research that combines multi‐scale spectroscopy, from lab studies to satellites that enable frequent and continuous long‐term monitoring, to inform statistical and biophysical approaches to model vegetation dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Initial Atmospheric Transport of Particles

A new version of the Initial Atmospheric Transport (IAT) model has been developed. This report fully characterizes the new model. IAT is designed to model heat release into the atmosphere that contains contaminated particles. The heat release forms a buoyant puff or plume. Buoyancy is quickly dissipated in 10's to 100's of seconds through drag and entrainment of ambient air. The final location of contaminant particles in the atmosphere after dissipation of abnormal heat is an important input to long term transportation codes such as the National Oceanic and Atmospheric Administration's (NOAA) Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) code. Ultimately the settling of contaminants after long term transport provides a basis for estimating health consequences. In comparison to the old version, the new model includes explicit simulation of particles, newly formulated equations of motion for a rising puff, and a new plume model. The plume model releases many instances of a single puff along a single trajectory in a way that balances energy released over a longer period than would be appropriate for a single puff. The puff model has additions for vorticity, turbulence, virtual mass, drag, and entrainment. The particle model leads to direct interfacing between IAT and the long-term transport code HYSPLIT. Previously, particle locations were given a pattern based on IAT's puff trajectory with no particle simulation. Now, particles are explicitly simulated, and 3-D spatial distributions of particles are output based on final positions. Ballistic particles are thrown outward at high velocity from explosive events and tend to hit the ground before the end of the simulation. Non-ballistic particles tend to get pulled into the rising puff s ring of vorticity with some escaping to ambient conditions as buoyant energy dissipates. A new model verification and validation effort was made for the new plume model. The Wallops Island Solid Rocket Propellant (SRP) fire plume was extracted to a 3-D transient dataset. After calibration, the model performs better than previous single puff attempts. Even so, error levels in the process used to estimate location and ambient weather conditions approach 30%. This leads to the conclusion that unplanned photogrammetry evaluations of the IAT model are useful but not sufficiently accurate to quantify model parameters. Like previous attempts, the vertical motion entrainment parameters come out much too high in comparison to values for other studies in the literature. It is suspected that a new model of shear entrainment would alleviate this problem. Unfortunately, IAT already has more calibration parameters than the data comparisons thus far can resolve with statistical certainty. This underscores the need to verify and validate the IAT model using detailed CFD studies that allow ideal statistical comparison with the IAT model for hundreds of cases. Even so, validation by comparison to carefully planned experiments (i.e. with acceptable error levels) is also necessary because the dynamics of entrainment are at a sub-grid level for CFD. Though better validation and verification are most important, many new enhancements are still envisioned for future IAT development. These include particle rain out due to condensation and agglomeration of water, direct connection to the North American Mesoscale (NAM) weather dataset, dynamic weather conditions, including ambient particle entrainment into the puff, and detrainment at the puff to ambient mixing interface. In general, including detailed aerosol dynamics is the greatest coming challenge which will require an entire new sub-model in IAT. This is very important if contaminant particles change in such a way that they increase or decrease in their potential harm to the environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Velocity Map Asymmetry of Ionized Gas in MaNGA. I. The Catalog and General Properties

The SDSS-IV MaNGA survey has measured two-dimensional maps of emission-line velocities for a statistically powerful sample of nearby galaxies. The asymmetric features of these kinematics maps reflect the nonrotational component of a galaxy’s internal motion of ionized gas. In this study, we present a catalog of kinematic asymmetry measurements of the H α velocity map of a sample of 5353 MaNGA galaxies. Based on this catalog, we find that “special” galaxies (e.g., merging galaxies, barred galaxies, and active galactic nucleus host galaxies) contain more galaxies with highly asymmetric velocity maps. However, we notice that more than half of galaxies with high kinematic asymmetry in our sample are quite “regular.” For those “regular” galaxies, kinematic asymmetry shows a significant anticorrelation with stellar mass at log M * < 9.7 , while such a trend becomes very weak at log M * > 9.7 . Moreover, at a given stellar mass, the kinematic asymmetry shows weak correlations with photometric morphology, star formation rate, and environment, while it is independent of H i gas content. We also have quantified the observational effects in the kinematic asymmetry measurement. We find that both the signal-to-noise ratio of H α flux and disk inclination angle contribute to the measures of kinematic asymmetry, while the physical spatial resolution is an irrelevant factor inside the MaNGA redshift coverage.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Modeling the Functional Forms of Grid Disturbances

This report introduces a functional form that may be used to quantitatively predict the impacts of new grid tools and changing system qualities on the likelihoods, durations, and depths of various grid disturbances. Each disturbance scenario is modeled to have three component stages—avoidance, reactance and recovery, which together parametrically estimate one disturbance’s impacts. The modeled scenario is then placed and replicated within an analysis period to represent the likelihood or frequency of the scenario and its consequent impacts. Whereas analysts have struggled to define and apply metrics for grid resilience, the functional form introduced by this report shares units of measurement with accepted grid-status measures (e.g., numbers of customers currently experiencing a service outage). Furthermore, the integrated and averaged functional form over an analysis period provides a meaningful normalized performance metric (e.g., customer outage minutes per year) that is ultimately independent of the duration of the period of. The approach may be applied similarly regardless of the severity or frequency of the disturbances that are being analyzed. Because metrics can be chosen to be identical in both the hypothetical future and the actual historical past, the historical past eventually becomes the test of the future predictions, at least in a statistical sense. The authors originally developed this approach to facilitate analysis of the effects of transactive energy (TE) systems effects on electric power grid resilience. TE systems invite energy suppliers and consumers to actively collaborate toward the discovery of, and their responses to, the locational value of energy. The findings from this process are often embodied as energy prices, the dynamics of which indicate the locational value of energy and can further represent important grid service needs. While some academic papers claim to quantify the value of a specific TE system design toward grid resilience, the answer, in general, has been elusive. Not only do multiple and conflicting definitions of resilience and reliability exist, but countless TE systems are being invented. We conclude the following: (1) The ideal analysis should harmonize rather than differentiate resilience and reliability. Therefore, this report uses the more general term disturbance whenever the overloaded terms resilience and reliability can be avoided. (2) The effectiveness of TE systems must be mapped to underlying qualities of a TE system, thereby avoiding presumptions that every TE design offers similar advantages. The authors seek to evaluate the parametric effects of TE system qualities (e.g., spatial granularity, granularity of time steps, length of future prediction horizon) on avoiding, reacting to, and recovering from grid disturbances. Furthermore, any advantages (or disadvantages) must be fairly compared with the many alternative tools, systems, and strategies that might offer comparable benefits.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Assessing the Influence of Climate on the Spatial Pattern of West Nile Virus Incidence in the United States

West Nile virus (WNV) is the leading cause of mosquito-borne disease in humans in the United States. Since the introduction of the disease in 1999, incidence levels have stabilized in many regions, allowing for analysis of climate conditions that shape the spatial structure of disease incidence. Our goal was to identify the seasonal climate variables that influence the spatial extent and magnitude of WNV incidence in humans. We developed a predictive model of contemporary mean annual WNV incidence using U.S. county-level case reports from 2005 to 2019 and seasonally averaged climate variables. We used a random forest model that had an out-of-sample model performance of R 2 =0.61. Our model accurately captured the V-shaped area of higher WNV incidence that extends from states on the Canadian border south through the middle of the Great Plains. It also captured a region of moderate WNV incidence in the southern Mississippi Valley. The highest levels of WNV incidence were in regions with dry and cold winters and wet and mild summers. The random forest model classified counties with average winter precipitation levels <23.3 mm/month as having incidence levels over 11 times greater than those of counties that are wetter. Among the climate predictors, winter precipitation, fall precipitation, and winter temperature were the three most important predictive variables. We consider which aspects of the WNV transmission cycle climate conditions may benefit the most and argued that dry and cold winters are climate conditions optimal for the mosquito species key to amplifying WNV transmission. Our statistical model may be useful in projecting shifts in WNV risk in response to climate change.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Constraining the phase shift of relativistic species in DESI BAOs

In the early Universe, neutrinos decouple quickly from the primordial plasma and propagate without further interactions. The impact of free-streaming neutrinos is to create a temporal shift in the gravitational potential that impacts the acoustic waves known as baryon acoustic oscillations (BAOs), resulting in a non-linear spatial shift in the Fourier-space BAO signal. In this work, we make use of and extend upon an existing methodology to measure the phase shift amplitude $\beta _{\phi }$ and apply it to the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) BAOs with an anisotropic BAO fitting pipeline. We validate the fitting methodology by testing the pipeline with two publicly available fitting codes applied to highly precise cubic box simulations and realistic simulations representative of the DESI DR1 data. We find further study towards the methods used in fitting the BAO signal will be necessary to ensure accurate constraints on $\beta _{\phi }$ in future DESI data releases. Using DESI DR1, we present individual measurements of the anisotropic BAO distortion parameters and the $\beta _{\phi }$ for the different tracers, and additionally a combined fit to $\beta _{\phi }$ resulting in $\beta _{\phi } = 2.7 \pm 1.7$. After including a prior on the distortion parameters from constraints using Planck we find $\beta _{\phi } = 2.7^{+0.60}_{-0.67}$ suggesting $\beta _{\phi } > 0$ at 4.3$\sigma$ significance. This result may hint at a phase shift that is not purely sourced from the standard model expectation for $N_{\rm {eff}}$ or could be a upwards statistical fluctuation in the measured $\beta _{\phi }$; this result relaxes in models with additional freedom beyond Lambda-cold dark matter.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Emergent disorder and mechanical memory in periodic metamaterials

Ordered mechanical systems typically have one or only a few stable rest configurations, and hence are not considered useful for encoding memory. Multistable and history-dependent responses usually emerge from quenched disorder, for example in amorphous solids or crumpled sheets. In contrast, due to geometric frustration, periodic magnetic systems can create their own disorder and espouse an extensive manifold of quasi-degenerate configurations. Inspired by the topological structure of frustrated artificial spin ices, we introduce an approach to design ordered, periodic mechanical metamaterials that exhibit an extensive set of spatially disordered states. While our design exploits the correspondence between frustration in magnetism and incompatibility in meta-mechanics, our mechanical systems encompass continuous degrees of freedom, and thus generalize their magnetic counterparts. We show how such systems exhibit non-Abelian and history-dependent responses, as their state can depend on the order in which external manipulations were applied. We demonstrate how this richness of the dynamics enables to recognize, from a static measurement of the final state, the sequence of operations that an extended system underwent. Thus, multistability and potential to perform computation emerge from geometric frustration in ordered mechanical lattices that create their own disorder.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Nanotomography for Quantitative 3D Particle Reconstruction

Particulates are ubiquitous across fuel cycle operations and carry critical information about particle formation, processing, and potential proliferation-related activities. Traditional analytical techniques, including micro-Raman spectroscopy and standard electron microscopy, are often limited in spatial resolution or dimensionality, particularly when used to examine metallic or submicron-scale features. Understanding particle morphology, phase distribution, and internal porosity is essential for constraining formation conditions, thermodynamic environments, and material transport behavior. In this report, we demonstrate the application of plasma focused ion beam nanotomography to reconstruct micron-scale particulates at nanoscale resolution. Using high-resolution backscattered electron imaging and Avizo software, we obtained 3D reconstructions that enabled quantitative analysis of particle morphology, phase composition, and internal voids. Representative examples include a Ta particle with a large central void and a composite particle with embedded tetrahedral crystalline structures. These reconstructions reveal structural and compositional details that are inaccessible through conventional 2D imaging. The results demonstrate that nanotomography provides both qualitative and quantitative insights into particle formation and behavior. Using nanotomography, porosity and phase distributions can be quantified to inform models of particle density, transport, and solidification conditions. Beyond technical insights, the workflow developed here establishes a transferable capability for analyzing heterogeneous particles and has potential applications in bulk materials studies via x-ray computed tomography or other volumetric imaging modalities. Ongoing efforts are focused on optimizing the workflow to process multiple particles simultaneously, increasing throughput and statistical robustness. Overall, this work illustrates the power of nanotomography as a tool for connecting particulate morphology to formation mechanisms, composition, and transport, thereby strengthening analytical capabilities for nuclear forensics, fuel cycle analysis, and related scientific investigations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗