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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Curvature Energetics Determined by Alchemical Simulation on Four Topologically Distinct Lipid Phases

The relative curvature energetics of two lipids are tested using thermodynamic integration (TI) on four topologically distinct lipid phases. Simulations use TI to switch between choline headgroup lipids (POPC; that prefers to be flat) and ethanolamine headgroup lipids (POPE; that prefer, for example, the inner monolayer of vesicles). Here, the thermodynamical moving of the lipids between planar, inverse hexagonal (H II ), cubic (Q II ; Pn3m space group), and vesicle topologies reveals differences in material parameters that were previously challenging to access. The methodology allows for predictions of two important lipid material properties: the difference in POPC/POPE monolayer intrinsic curvature (ΔJ 0 ) and the difference in POPC/POPE monolayer Gaussian curvature modulus (Δ$\bar{κ_m}$), both of which are connected to the energetics of topological variation. Analysis of the TI data indicates that, consistent with previous experiment and simulation, the J 0 of POPE is more negative than POPC (ΔJ 0 = –0.018 ± 0.001 Å –1 ). The theoretical framework extracts significant differences in $\bar{κ_m}$ of which POPE is less negative than POPC by 2.0 to 4.0 kcal/mol. The range of these values is determined by considering subsets of the simulations, and disagreement between these subsets suggests separate mechanical parameters at very high curvature. Finally, the fit of the TI data to the model indicates that the position of the pivotal plane of curvature is not constant across topologies at high curvature. Overall, the results offer insights into lipid material properties, the limits of a single HC model, and how to test them using simulation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

CANShield: Signal-based Intrusion Detection for Controller Area Networks

Modern vehicles rely on complex cyber-physical systems made up of hundreds of electronic control units (ECUs) connected through controller area network (CAN) buses. However, the CAN bus attack surface is increasing due to advanced features in automobiles, making it prone to injection attacks. The ordinary injection attacks disrupt the typical timing properties of the CAN data stream, and the rule-based intrusion detection systems (IDS) can easily detect them. However, advanced attackers can inject false data to the signal level, maintaining the regular pattern/frequency of the CAN messages. Such attacks can bypass the rule-based IDS or any anomaly-based IDS built on binary payload data. To make the vehicles robust against such intelligent attacks, we propose CANShield, a signal-based intrusion detection framework for the CAN bus that consists of three modules. A data preprocessing module handles the high-dimensional CAN data stream at the signal level and make them suitable for any machine learning model. A data analyzer module consists of multiple deep autoencoder networks, each analyzing the time series data from a different perspective. Finally, an attack detection module uses an ensemble method to make the final decision. Evaluation results on a standard signal-based dataset show the effectiveness of the CANShield in detecting five advanced attacks.

Shahriar, Md Hasan↗
DOE OSTI2023

Predicting magnetic properties of single-molecule magnets from self-interaction-free density-functional theory (Final Report)

In this project we investigated electronic structure of an intermediate-sized copper-based molecule and magnetic and hyperfine properties of several small non-magnetic and magnetic molecules including transition-metal elements by applying self-interaction corrections to density functional theory (DFT). In a sufficient number of cases, DFT-calculated electronic structure and magnetic properties of single-molecule magnets qualitatively differ from corresponding experimental data. This is partly due to self-interacting electrons within the DFT formalism. Recently, an efficient method to correct the self-interactions was proposed, i.e., Fermi-Lowdin prbital (FLO) based self-interaction corrected (SIC) methodology, within DFT. Henceforth, this method is referred to as FLO-SIC method which exists in FLOSIC code. We used this FLO-SIC method for our studies of electronic structure and magnetic and hyperfine properties of small magnetic molecules and non-magnetic molecules. Our study will provide insight into predictions of magnetic properties of single-molecule magnets where self-interaction corrections play a critical role. There are two components of this project. In the first work, we studied the electronic structure of a planar mononuclear Cu-based molecule in two oxidation states, using DFT with the FLO-SIC method. We chose this system because it is small enough and it includes a transition metal element. We found that the standard FLO-SIC method takes too much compute time even for the small transition-metal molecule and so we slightly modified the method in order to expedite the process. In the dianionic state, we found that the FLO-SIC spin density agrees quantitatively with accurate quantum chemistry methods, while DFT spin density without self-interaction corrections are severely deviated from the quantum chemistry methods. We also showed that the energy gap between the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) of the dianionic state is larger than that of the monoanionic state. This result is consistent with experimental data. In the second work, we investigated how the interaction between the electron spin and nuclear spin is affected by electron self-interactions within small non-magnetic and magnetic molecules. Such an interaction is called hyperfine interaction. For molecules without significant orbital angular momentum, the hyperfine interaction consists of Fermi contact and dipolar interaction terms. Since the Fermi contact term depends on electron spin density at the nuclear site, it would be highly affected by self-interaction corrections. Therefore, we calculated the hyperfine interaction for the small molecules using DFT with the slightly modified expedited FLO-SIC method which was obtained in the first work, and compared the results to experimental data and DFT calculations without self-interaction corrections. We found significant improvement of the Fermi contact term computed using the FLO-SIC method for small magnetic molecules. Overall, the first and second work provided positive outlook of application of the FLO-SIC method to magnetic molecules and systems including transition-metal elements.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Validation of the DESI-DR1 3x2-pt analysis: scale cut and shear ratio tests

Combined survey analyses of galaxy clustering and weak gravitational lensing (3x2-pt studies) will allow new and accurate tests of the standard cosmological model. However, careful validation is necessary to ensure that these cosmological constraints are not biased by uncertainties associated with the modelling of astrophysical or systematic effects. In this study we validate the combined 3x2-pt analysis of the Dark Energy Spectroscopic Instrument Data Release 1 (DESI-DR1) spectroscopic galaxy clustering and overlapping weak lensing datasets from the Kilo-Degree Survey (KiDS), the Dark Energy Survey (DES), and the Hyper-Suprime-Cam Survey (HSC). By propagating the modelling uncertainties associated with the non-linear matter power spectrum, non-linear galaxy bias and baryon feedback, we design scale cuts to ensure that measurements of the matter density and the amplitude of the matter power spectrum are biased by less than 30% of the statistical error. We also test the internal consistency of the data and weak lensing systematics by performing new measurements of the lensing shear ratio. We demonstrate that the DESI-DR1 shear ratios can be successfully fit by the same model used to describe cosmic shear correlations, and analyse the additional information that can be extracted about the source redshift distributions and intrinsic alignment parameters. This study serves as crucial preparation for the upcoming cosmological parameter analysis of these datasets.

Emas, N. [Swinburne U., Ctr. Astrophys. Supercompu↗
DOE OSTI2026

Deep learning–based digital twins for heat pumps

Heat pumps are effective cooling and heating appliances to save energy in buildings. However, traditional heat pump models are challenging to integrate with building demands in a co-simulation environment because of the nonlinear thermodynamics of refrigerants. Developing digital twin representatives for heat pumps capable of faster calculations with good accuracy is desirable. This study aimed to establish a generic deep learning–based digital twin for heat pumps with a large amount of high-fidelity data. Two refrigerants for two different heat pumps were considered: an air source heat pump with refrigerant R-410A, an air source heat pump with refrigerant CO 2 , a water source heat pump with refrigerant R-410A, and a water source heat pump with refrigerant CO 2 . Furthermore, results showed that the deep learning (long short-term memory) models effectively represented these four heat pumps as a digital twin: (a) accuracy for training and testing showed smaller than 0.02 for heating electricity and heating demands, and (b) the digital twins showed good consistency with original data for heating electricity and heating demands (root mean square errors of less than 0.12 W and 0.19 W, respectively). Therefore, deep learning–based heat pump models can be used in the co-simulation of building mechanical systems.

Air source heat pump↗
DOE OSTI2024

Search for Periodic Time Variations of the Solar 8 B Neutrino Flux between 1996 and 2018 in Super-Kamiokande

We report a search for time variations of the solar 8 B neutrino flux using 5804 live days of Super-Kamiokande data collected between May 31, 1996, and May 30, 2018. Super-Kamiokande measured the precise time of each solar neutrino interaction over 22 calendar years to search for solar neutrino flux modulations with unprecedented precision. Periodic modulations are searched for in a dataset comprising five-day interval solar neutrino flux measurements with a maximum likelihood method. We also applied the Lomb-Scargle method to this dataset to compare it with previous reports. The only significant modulation found is due to the elliptic orbit of the Earth around the Sun. The observed modulation is consistent with astronomical data: we measured an eccentricity of (1.53 ± 0.35)%, and a perihelion shift of (−1.5 ± 13.5) days.

astroparticle detectors↗
DOE OSTI2024

Should We Conserve Entropy or Energy when Computing CAPE with Mixed-Phase Precipitation Physics?

Abstract The rapidly increasing resolution of global atmospheric reanalysis and climate model datasets necessitates finding methods for computing convective available potential energy (CAPE) both efficiently and accurately. To this end, this article compares two common methods for computing CAPE which conserve either energy or entropy. Inaccuracies in these computations arise from both physical and numerical errors. For instance, computing CAPE with entropy conserved results in physical errors from nonequilibrium phase transitions but minimizes numerical errors because solutions are analytic at each height. In contrast, computing CAPE with energy conserved avoids these physical errors, but accumulates numerical errors that are grid-resolution-dependent because the numerical integration of a differential equation is required. Analysis of CAPE computed with large databases of soundings from the tropical Amazon and midlatitude storm environments shows that physical errors from the entropy method are typically 1%–3% as large as CAPE, which is comparable to the numerical errors from conserving energy with grid spacing of 25 and 250 m using explicit first-order and second-order integration schemes, respectively. Errors in entropy-based CAPE calculations are also insensitive to vertical grid spacing, in contrast to energy-based calculations whose error strongly scales with the grid spacing. It is shown that entropy-based methods are advantageous when intercomparing datasets with differing vertical resolution because they produce accurate and reasonably fast results that are insensitive to grid resolution, whereas a second-order energy-based method is advantageous when analyzing data with a consistent vertical resolution because of its superior computational efficiency. Significance Statement Convective available potential energy (CAPE) is a measure of instability in the atmosphere that helps forecasters and researchers understand when and where thunderstorms will form. The purpose of this article is to identify the most efficient and accurate methods for computing CAPE. Two methods are considered here, one that relates to the entropy (a measure of thermodynamic disorder) of an air parcel and one that relates to the energy of an air parcel. Results indicate that the entropy method is most accurate and insensitive to the resolution of the data used for the calculation (which can vary considerably), whereas the energy method uses the least computation time.

Peters, John M.↗
DOE OSTI2023

Extending Parsimonious Bayesian Inference

Parsimonious Bayesian inference is a theoretical framework for efficient data assimilation that seeks to balance increased consistency between predictions and training data against corresponding increases in model complexity. Within this framework, over-training is understood as optimization that encodes excessive information within model parameters while only achieving small improvements between predictions and training data. This project aims to develop practical methods of limiting excess model information during optimization. One key observation is that practical heuristics for parsimonious learning in high-dimensions must balance expressivity, i.e. the ability of the model to capture diverse predictions with only a few non-zero parameters, against discoverability, i.e. the ability to train the model with gradient-based optimization and drive parameters to low information states. As such, we developed logical activation functions that are able to adaptively approximate arbitrary truth tables that define Boolean logic operations within a probabilistic framework. These functions have demonstrated the ability to learn exclusive disjunction (XOR) and conditioned disjunction (if [condition] then [result_if_true] else [result_if_false]) within a single layer of a neural network. To efficiently exploit these activation functions to drive parsimonious learning required several other advances within the domain of variational inference. The most efficient form of complexity suppression is structured sparsification, driving most model parameters to zero while achieving the structural coherence among nonzeros needed for bandwidth reduction. Such models are not only far more efficient at suppressing information-theoretic complexity, they also reduce the other forms of complexity (computations, communication, storage, and the number of dependencies needed to evaluate predictions). Aiming to support enhanced sparsification, this project examined new approaches to high-dimensional variational inference that allow us to calibrate and control parameter uncertainty during optimization. By identifying which parameters can sustain sparsifying perturbations with little impact on prediction quality, we can develop better pruning strategies by framing them as approximate Bayesian inference. These advances also open paths to mitigate concerns with deploying advanced learning methods in resource-constrained environments, such as running models on power-limited or communication-limited devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Non-oscillatory no-scale inflation

We propose a non-oscillatory no-scale supergravity model of inflation (NO-NO inflation) in which the inflaton does not oscillate at the end of the inflationary era. Instead, the Universe is then dominated by the inflaton kinetic energy density (kination). During the transition from inflation to kination, the Universe preheats instantly through a coupling to Higgs-like fields. These rapidly annihilate and scatter into ultra-relativistic matter particles, which subsequently dominate the energy density, and reheating occurs at a temperature far above that of Big Bang Nucleosynthesis. After the electroweak transition, the inflaton enters a tracking phase as in some models of quintessential inflation. The model predictions for cosmic microwave background observables are consistent with Planck 2018 data, and the density of gravitational waves is below the upper bound from Big Bang Nucleosynthesis. Furthermore, we also find that the density of supersymmetric cold dark matter produced by gravitino decay is consistent with Planck 2018 data over the expected range of supersymmetric particle masses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

A Pd III Sulfate Dimer Initiates Rapid Methane Monofunctionalization by H Atom Abstraction

An electrogenerated Pd III 2 species in fuming sulfuric acid is competent for rapid and concurrent methane monohydroxylation to methyl bisulfate (CH 3 OSO 3 H) and methane sulfonation to methanesulfonic acid (CH 3 SO 3 H). In situ NMR at 50 °C is used to track methane transformation exclusively to CH 3 OSO 3 H and CH 3 SO 3 H at high conversions. Integrating a set of kinetic and computational studies, the mechanism of methane monofunctionalization by Pd III 2 is examined. Here, experimental rate laws and common kinetic isotope effects for CH 3 OSO 3 H and CH 3 SO 3 H formation suggest that both transformations proceed via a common rate-limiting C-H activation step. Introduction of O 2 or Pd II,III 2 suppresses CH 3 SO 3 H generation, indicating a radical chain sequence. Although the metal-metal bonded Pd III 2 complex is a net two-electron oxidant, our aggregate kinetic data point to a mechanistic model that features rate-limiting H atom abstraction by the Pd III 2 complex to generate a methyl radical intermediate. The CH 3 • intermediate then recombines with Pd II,III 2 to furnish a CH 3 Pd III 2 intermediate that reductively eliminates CH 3 OSO 3 H. Alternatively, the CH 3 intermediate can enter a chain reaction with SO 3 to generate CH 3 SO 3 H. DFT computations support the radical-based C-H activation by Pd III 2 and delineate H atom abstraction pathways with computed reaction barriers and kinetic isotope effects (KIEs) that are consistent with experimental data. These mechanistic investigations challenge the paradigm of electrophilic C-H activation and highlight H atom abstraction as a potent pathway for selective methane C-H oxidative functionalization at high reaction rates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Polymorphism and phase transitions in Na 2 U 2 O 7 from density functional perturbation theory

Polymorphism and phase transitions in sodium diuranate, Na 2 U 2 O 7 , are investigated with density functional perturbation theory (DFPT). Thermal properties of crystalline α-, β- and γ-Na 2 U 2 O 7 polymorphs are predicted from DFPT phonon calculations, i.e., the first time for the high-temperature γ-Na 2 U 2 O 7 phase (R$\bar{3}$ with combining macron]m symmetry). The standard molar isochoric heat capacities predicted within the quasi-harmonic approximation are C p (298.15K)= 219.4 and 220.9 J K -1 mol -1 mfor P21/a α-Na 2 U 2 O 7 and C2/m β-Na 2 U 2 O 7 , respectively. Gibbs free energy calculations reveal that α-Na 2 U 2 O 7 (P2 1 /a) and β-Na 2 U 2 O 7 (C2/m) are almost energetically degenerate at low temperature, with β-Na 2 U 2 O 7 becoming slightly more stable than α-Na 2 U 2 O 7 as temperature increases. Here these findings are consistent with XRD data showing a mixture of α and β phases after cooling of γ-Na 2 U 2 O 7 to room temperature and the observation of a sluggish α → β phase transition above ca. 600 K. A recently observed α-Na 2 U 2 O 7 structure with P21 symmetry is also shown to be metastable at low temperature. Based on Gibbs free energy, no direct β → γ solid-solid phase transition is predicted at high temperature, although some experiments reported the existence of such phase transition around 1348 K. This, along with recent experiments, suggests the occurrence of a multi-step process consisting of initial β-phase decomposition, followed by recrystallization into γ-phase as temperature increases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Cavity evolution and void swelling in dual ion irradiated tempered martensitic steels

Microstructural evolutions in two variants of the F82H 8 wt%Cr tempered martensitic steel under dual (He + plus Fe 3+ ) ion irradiations at 500 °C have been characterized by transmission electron microscopy. Emphasis is on understanding the combined effects of displacements per atom (dpa) and the helium/dpa ratio (He/dpa) on cavity evolution and void swelling (f v ). The dual ion database includes 231 alloy-dpa-He/dpa conditions for irradiations up to ~ 82 dpa and 3700 appm helium. Here, we hypothesize that the large “scatter” observed in the f v data is primarily due to local microstructural variations, especially those that shield regions at shallower depths from the effects of injected interstitial atoms, which would otherwise reduce f v . More generally, our hypothesis is that the highest f v data, at a specified dpa and He/dpa, provides the most appropriate estimate of swelling for fusion neutron irradiation conditions. The dose dependence of f v can be described by an incubation dpa i , marking the onset of significant void growth, and a post-incubation swelling rate, f v ’ = Δf v /Δdpa. Both dpa i and f v ’ decrease with increasing He/dpa. The f v ’ data are quantitatively consistent with a simple model for defect partitioning between evolving bubble, void, and dislocation sinks strengths and biases. Most notably, the model correctly predicts both the incubation dpa i , and the subsequent f v ’, in spite of the fact that they reflect somewhat different physics. Our analysis also suggests that defect recombination is modest at 500 °C. Along with other considerations, this supports the hypothesis that, even at high dpa rates, dual ion irradiations can be used to reasonably emulate void nucleation and growth for a range of fusion-relevant neutron irradiation service conditions. A companion paper incorporates the results of this study in a reduced-order, data-driven void swelling model.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Outdoor annual algae productivity improvements at the pre-pilot scale through crop rotation and pond operational management strategies

The Development of Integrated Screening, Cultivar Optimization, and Verification Research (DISCOVR) collaborative consortium operated pre-pilot scale outdoor ponds to deliver much-needed multi-year, long-term and consistent, algae cultivation data relevant to understanding the current state of technology in terms of expected seasonal algae biomass productivity. Over the course of four years from 2018 to 2021, twelve identical 4.2 m 2 mini-ponds were run in triplicate sets to test strains and operational strategies demonstrated in small-, indoor photobioreactors, in pursuit of increasing overall algae areal productivity and projected farm yield. Fourteen different cultivars derived from a strain screening pipeline were tested. Through deliberate seasonal crop rotation and improvements in operational strategies, annual biomass productivity increased from 11.6 to 17.6 g m -2 day -1 , a > 50% increase over the 2018 baseline. Both brackish and marine strains were included and four out of the fourteen strains consistently yielded high productivity across multiple years; brackish strains Monoraphidium minutum (26BAM) and Scenedesmus obliquus (UTEX393), and marine strains Tetraselmis striata (LANL1001) and Picochlorum celeri (TG2). These freely available datasets, which represent nearly complete annual daily coverage of cultivation metrics including weather, pond temperature and pH, nutrients, and productivity, are unique in the public domain and seek to fill agronomic and operational knowledge gaps to help in the eventual commercialization of algal biofuels and bioproducts.

09 BIOMASS FUELS↗
DOE OSTI2023

Development of a Benchmark Eddy Flux Evapotranspiration Dataset for Evaluation of Satellite-Driven Evapotranspiration Models Over the CONUS

A large sample of ground-based evapotranspiration (ET) measurements made in the United States, primarily from eddy covariance systems, were post-processed to produce a benchmark ET dataset. The dataset was produced primarily to support the intercomparison and evaluation of the OpenET satellite-based remote sensing ET (RSET) models and could also be used to evaluate ET data from other models and approaches. OpenET is a web-based service that makes field-delineated and pixel-level ET estimates from well-established RSET models readily available to water managers, agricultural producers, and the public. The benchmark dataset is composed of flux and meteorological data from a variety of providers covering native vegetation and agricultural settings. Flux footprint predictions were developed for each station and included static flux footprints developed based on average wind direction and speed, as well as dynamic hourly footprints that were generated with a physically based model of upwind source area. The two footprint prediction methods were rigorously compared to evaluate their relative spatial coverage. Data from all sources were post-processed in a consistent and reproducible manner including data handling, gap-filling, temporal aggregation, and energy balance closure correction. The resulting dataset included 243,048 daily and 5,284 monthly ET values from 194 stations, with all data falling between 1995 and 2021. We assessed average daily energy imbalance using 172 flux sites with a total of 193,021 days of data, finding that overall turbulent fluxes were understated by about 12% on average relative to available energy. Multiple linear regression analyses indicated that daily average latent energy flux may be typically understated slightly more than sensible heat flux. This dataset was developed to provide a consistent reference to support evaluation of RSET data being developed for a wide range of applications related to water accounting and water resources management at field to watershed scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A mechanistic, multiscale model for predicting Pd penetration in TRISO fuels using BISON

TRistructural ISOtropic (TRISO) particles use silicon carbide (SiC) as the primary structural member and barrier against metallic fission product (FP) release. palladiums (PDs), produced by fission in the fuel kernel, can diffuse to and chemically interact with the SiC layer, degrading its structural integrity and ability to contain radioactive FPs. Existing temperature-dependent correlations for predicting Pd penetration rely on experimental data with significant scatter due to varying conditions, potentially complicating ongoing fuel qualification and licensing efforts for advanced reactors that would subject TRISO fuels to operating conditions outside of those examined in the experiments. A mechanistic model of Pd production, transport, and reaction is developed in this work to better understand and predict PD attack of SiC in TRISO particles. molecular dynamicss (MDs) simulations are utilized to calculate the Pd diffusivity in SiC grain bulk and grain boundaries. A mesoscale phase-field diffusion model, informed by the MD diffusivities, is used to develop a reduced order model (ROM) for the effect of SiC microstructure on the Pd penetration rate. The engineering scale BISON model calculates Pd production and transport, utilizing the ROM to predict penetration rates consistent with experimental data. This novel mechanistic ROM captures the effect of temperature, microstructure, and irradiation history on Pd penetration. In conclusion, these new capabilities are expected to support ongoing qualification and licensing efforts associated with near-term TRISO-fueled reactor applications.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Investigating color screening in proton-nucleus collisions with complex potentials*

Abstract Color screening and parton inelastic scattering modify the heavy-quark antiquark potential in mediums consisting of particles from quantum chromodynamics (QCD), leading to the suppression of quarkonium production in relativistic heavy-ion collisions. Owing to the small charm/anti-charm ( ) pair production number in proton-nucleus ( pA ) collisions, the correlation between different pairs is negligible, which makes the Schrödinger equation viable for tracking the evolution of only one pair. We employ the time-dependent Schrödinger equation with an in-medium potential to study the evolution of charmonium wave functions in a hydrodynamic-like QCD medium produced in pA collisions. We explore different parametrizations of real and imaginary parts of the potential and calculate the nuclear modification factors ( ) of and in TeV energy p -Pb collisions at the Large Hadron Collider (LHC). Comparing strong and weak screening scenarios with experimental data in this approach, we arrive at the conclusion that color screening is weak at temperatures close to the deconfined phase transition. Moreover, the imaginary part of the potential is crucial in describing the experimental data, which is consistent with widely studied semi-classical approaches, where dissociation rates are essential.

Physics↗
DOE OSTI2025

The Dark Energy Camera All Data Everywhere cosmic shear project V: Constraints on cosmology and astrophysics from 270 million galaxies across 13,000 deg$^2$ of the sky

We present constraints on models of cosmology and astrophysics using cosmic shear data vectors from three datasets: the northern and southern Galactic cap of the Dark Energy Camera All Data Everywhere (DECADE) project, and the Dark Energy Survey (DES) Year 3. These data vectors combined consist of 270 million galaxies spread across 13,000 ${\rm deg}^2$ of the sky. We first extract constraints for $Λ$CDM cosmology and find $S_8= 0.805^{+0.019}_{-0.019}$ and $Ω_{\rm m} = 0.262^{+0.023}_{-0.036}$, which is consistent within $1.9 σ$ of constraints from the Planck satellite. Extending our analysis to dynamical dark energy models shows that lensing provides some (but still minor) improvements to existing constraints from supernovae and baryon acoustic oscillations. Finally, we study six different models for the impact of baryons on the matter power spectrum. We show the different models provide consistent constraints on baryon suppression, and associated cosmology, once the astrophysical priors are sufficiently wide. Current scale-cut approaches for mitigating baryon contamination result in a residual bias of $\approx 0.3σ$ in the $S_8, Ω_{\rm m}$ posterior. Using all scales with dedicated baryon modeling leads to negligible improvement as the new information is used solely to self-calibrate the baryon model on small scales. Additional non-lensing datasets, and/or calibrations of the baryon model, will be required to access the full statistical power of the lensing measurements. The combined dataset in this work represents the largest lensing dataset to date (most galaxies, largest area) and provides an apt testing ground for analyses of upcoming datasets from Stage IV surveys. The DECADE shear catalogs, data vectors, and likelihoods are made publicly available.

Anbajagane, D. [Chicago U., Astron. Astrophys. Ctr↗