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At least 991 records · Page 55

The motions of clusters and group of galaxies

The distributions of peculiar velocities of rich clusters and of groups of galaxies are investigated for different cosmological models and are compared with observations. Four cosmological models are studied: standard cold dark matter (CDM) (omega = 1); low-density CDM (omega = 0.3); hot dark matter (HDM) (omega = 1); and primeval baryonic isocurvature (PBI) (omega = 0.3). All models are normalized to the microwave background fluctuations observed by Cosmic Background Explorer (COBE). We find that rich clusters of galaxies exhibit a Maxwellian distribution of peculiar velocities in all models, as expected from a Gaussian initial density field. The clusters appear to be fundamental and efficient tracers of the large-scale velocity field. The cluster three-dimensional velocity distribution typically peaks at v approximately 600 km/s and extends to high cluster velocities of v approximately 2000 km/s. The low-density CDM model exhibits somewhat lower velocities: it peaks at approximately 400 km/s and extends to approximately 1200 km/s. Approximately 10% (approximately 1% for low-density CDM) of all model rich clusters move with high peculiar velocities of V greater than or = 10(exp 3) km/s. The highest velocity clusters frequently originate in dense superclusters. The model velocity distributions of rich clusters are compared with the model velocity distributions of small groups of galaxies, and of the total matter. The group velocity distribution is, in general, similar to the velocity distribution of the rich clusters. The matter velocity distribution is similar to that of the rich clusters for the omega = 0.3 models; these models exhibit Maxwellian velocity distributions for clusters, for groups, and for matter that are all similar to one another. The mass distribution in the omega = 1 models, however, exhibits a longer tail of high velocities than do the clusters. This high-velocity tail originates mostly from the high velocities that exist within rich clusters. The model velocity distributions of groups and clusters of galaxies are compared with observations. The data exhibit a larger high-velocity tail, to v(sub r) greater than or = 2000 km/s, than is seen in the model simulations (except for HDM). Because of the large observational uncertainties, however, the data are consistent at a approximately 1-3 sigma level with the model predictions and with a Gaussian initial density field. Accurate observations of cluster peculiar velocities, especially at the high-velocity tail, shoud provide powerful constraints on the cosmological models.

Bahcall, Neta A.↗

Fully‐Printed Ion Sensor Arrays for Measuring Agricultural Nitrogen and Potassium Concentrations Using Nernstian and AI Models

Abstract The chemical composition of growing media is a key factor for plant growth, impacting agricultural yield and sustainability. However, there is a lack of affordable chemical sensors for ubiquitous nutrient ion monitoring in agricultural applications. This work investigates using fully printed ion‐sensor arrays to measure the concentrations of nitrate, ammonium, and potassium in mixed‐electrolyte media. Ion sensor arrays composed of nitrate, ammonium, and potassium ion‐selective electrodes and a printed silver‐silver chloride (Ag/AgCl) reference electrode are fabricated and characterized in aqueous solutions in a range of concentrations that encompass what is typical for agricultural growing media (0.01 m m –1 m ). The sensors are also tested in mixed‐electrolyte solutions of NaNO 3 , NH 4 Cl, and KCl of varying concentrations, and the recorded potentials are input into Nernstian and artificial neural network models to compare the prediction accuracy of the models against ground truth. The artificial neural network models demonstrated higher accuracy over the Nernstian model, and the model using only ion‐sensor inputs is 7.5% more accurate than the Nernstian model under the same conditions. By enabling more precise and efficient fertilizer application, these sensor arrays coupled to computational models can help increase crop yields, optimize resource use, and reduce environmental impact.

Goodrich, Payton [University of California Berkele↗

Estimation of hydraulic conductivity in a watershed using sparse multi-source data via Gaussian process regression and Bayesian experimental design

Enhanced water management systems depend on accurate estimation of subsurface hydraulic properties. However, geologic formations can vary significantly, so information from a single source (e.g., widely spaced boreholes) is insufficient in characterizing subsurface aquifer properties. Therefore, multiple sources of information are needed to complement the hydrogeology understanding of a region. Here, this study presents a numerical framework in which information from different measurement sources is combined to characterize the 3D random field in a multi-fidelity prediction model. Coupled with the model, a Bayesian experimental design was used to determine the best future sampling locations. The Upper Sangamon watershed in east-central Illinois was selected as the case study site, where the multi-fidelity Gaussian process model was used to estimate the hydraulic conductivity in the region of interest. Multi-source observation data were obtained from electrical resistivity and borehole pumping tests. The accuracy of the model prediction is dependent on the locations and the distribution of both high- and low-fidelity data. Furthermore, the multi-fidelity model was compared with the single-fidelity model. The uncertainties and confidence in the measurements and parameter estimates were quantified and used to design future cycles of data collection to further improve the confidence intervals.

54 ENVIRONMENTAL SCIENCES↗

Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms

Here in this work we present a non-intrusive model reduction method to learn low-dimensional models of dynamical systems with non-polynomial nonlinear terms that are spatially local and that are given in analytic form. In contrast to state-of-the-art model reduction methods that are intrusive and thus require full knowledge of the governing equations and the operators of a full model of the discretized dynamical system, the proposed approach requires only the non-polynomial terms in analytic form and learns the rest of the dynamics from snapshots computed with a potentially black-box full-model solver. The proposed method learns operators for the linear and polynomially nonlinear dynamics via a least-squares problem, where the given non-polynomial terms are incorporated on the right-hand side. The least-squares problem is linear and thus can be solved efficiently in practice. The proposed method is demonstrated on three problems governed by partial differential equations, namely the diffusion–reaction Chafee–Infante model, a tubular reactor model for reactive flows, and a batch-chromatography model that describes a chemical separation process. The numerical results provide evidence that the proposed approach learns reduced models that achieve comparable accuracy as models constructed with state-of-the-art intrusive model reduction methods that require full knowledge of the governing equations.

42 ENGINEERING↗

Mechanisms of radiation-induced segregation around He bubbles in a Fe-Cr-Ni crystal

We report that Fe-15Cr-15Ni (wt.%) austenitic stainless steel was irradiated with 200 keV 4 He + ions to a fluence of 5 × 10 16 /cm 2 at 500 °C. Radiation-induced segregation (RIS) at He bubbles in the irradiated steel was measured using Energy Dispersive X-ray Spectroscopy (EDS) coupled with Scanning Transmission Electron Microscopy (STEM). The RIS measurements exhibited an enrichment of Ni and a depletion of Cr and Fe at the bubbles. To determine the underlying mechanisms of the RIS, the prediction of the Marwick model was compared with the RIS measurements. The model with parametric input in accordance with the Inverse Kirkendall effect (IKE), i.e. a preferential flux of elements associated with vacancy flux, was inadequate in explaining the observed amount of segregation of Fe, Cr and Ni. More specifically, a considerably lower ratio of the concentration gradient between Cr and Fe, $\left(\frac{∇Cr}{∇Fe}\right)$, of 0.53 was obtained from the RIS measurements versus the model-predicted value of 2.3, indicating an overestimation of Cr depletion at the bubbles by the Marwick model. To explain this difference between the model prediction and the RIS measurements, in addition to the Cr depletion at the bubbles by IKE due to the fastest diffusion via vacancy flux, a preferential interstitial flux of Cr from the matrix to the bubbles is discussed as a possible concurrently operating mechanism.

36 MATERIALS SCIENCE↗

Impact of Precipitation Parameters on the Specific Surface Area of PuO 2

Controlling the properties of PuO 2 through processing is of vital importance to environmental transport and fate, production of nuclear fuels, nuclear forensic analyses, stockpile stewardship, and storage of nuclear wastes applications. A number of processing conditions have been identified to control final product properties, including specific surface area (SSA), residual carbon content, adsorption of volatile species, morphology, and particle size. In this paper, a novel approach is developed for the prediction of PuO 2 SSA via the synthetic route of Pu(IV) oxalate precipitation followed by calcination. The proposed model utilizes multivariate regression methodology and leave one out formalism to link Savannah River Site (SRS) precipitation and calcination production data to the SSA of the final product. A comparison among the models provides insight into the accuracy and ability to identify variations amongst the processing data. Additionally, the models may also be used to fit new data outside of the parameters explored in a production facility. Finally, the trained model was compared to a similarly trained conventional model form to illustrate the influence of precipitation parameters on the prediction of the final SSA. The models presented here attempt to provide new methods for more accurate prediction of the PuO 2 product properties in a production scale environment for key environmental and nuclear applications.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments

Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips.

58 GEOSCIENCES↗

Molecular model of J-aggregated pseudoisocyanine fibers

Aggregated cyanines form ordered supramolecular structures with the potential to transport energy efficiently over long distances, a hallmark of photosynthetic light-harvesting complexes. In concentrated aqueous solution, pseudoisocyanine (PIC) spontaneously forms fibers with a chiral J-band red-shifted 1600 cm -1 from the monomeric 0–0 transition. A cryogenic transmission electron microscopy analysis of these fibers show an average fiber width of 2.89 nm, although the molecular-level structure of the aggregate is currently unknown. To determine a molecular model for these PIC fibers, the calculated spectra and dynamics using a Frenkel exciton model are compared to experiment. A chiral aggregate model in which the PIC monomers are neither parallel nor orthogonal to the long axis of the fiber is shown to replicate the experimental spectra most closely. This model can be physically realized by the sequential binding of PIC dimers and monomers to the ends of the fiber. Furthermore, these insights into the molecular aggregation model for aqueous PIC can also be applied to other similar cyanine-based supramolecular complexes with the potential for long-range energy transport, a key building block for the rational design of novel excitonic systems

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Noise-specific beating in the higher-level Ramsey curves of a transmon qubit

We report: in the higher levels of superconducting transmon devices and more generally charge sensitive devices, T 2 * measurements made in the presence of low-frequency time-correlated 1/f charge noise and quasiparticle-induced parity flips can give an underestimation of the total dephasing time. The charge variations manifest as beating patterns observed in the overlay of several Ramsey fringe curves and are reproduced with a phenomenological Ramsey curve model, which accounts for the charge variations. T 2 * dephasing times, which more accurately represent the total dephasing time, are obtained. The phenomenological model is compared with a Lindblad master equation model. Both models are found to be in agreement with one another and the experimental data. Finally, the phenomenological formulation enables a simple method in which the power spectral density for the low-frequency noise can be inferred from the overlay of several Ramsey curves.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Alpha Air Sample Counting Efficiency Versus Dust Loading: Evaluation of a Large Data Set

Dust loading on air sample filters is known to cause a loss of efficiency for direct counting of alpha activity on the filters, but the amount of dust loading and the correction factor needed to account for attenuated alpha particles is difficult to assess. In this paper, correction factors are developed by statistical analysis of a large database of air sample results for a uranium and plutonium processing facility at the Savannah River Site. As is typically the case, dust-loading data is not directly available, but sample volume is found to be a reasonable proxy measure; the amount of dust loading is inferred by a combination of the derived correction factors and a Monte Carlo model. The technique compares the distribution of activity ratios [beta/(beta + alpha)] by volume and applies a range of correction factors on the raw alpha count rate. The best-fit results with this method are compared with MCNP modeling of activity uniformly deposited in the dust and analytical laboratory results of digested filters. Finally, a linear fit is proposed to evenly-deposited alpha activity collected on filters with dust loading over a range of about 2 mg cm -2 to 1,000 mg cm -2 .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Asymptotic Freedom at the Berezinskii-Kosterlitz-Thouless Transition without Fine-Tuning Using a Qubit Regularization

We propose a two-dimensional hard-core loop-gas model as a way to regularize the asymptotically free massive continuum quantum field theory that emerges at the Berezinskii-Kosterlitz-Thouless transition. Without fine-tuning, our model can reproduce the universal step-scaling function of the classical lattice X Y model in the massive phase as we approach the phase transition. This is achieved by lowering the fugacity of Fock-vacuum sites in the loop-gas configuration space to zero in the thermodynamic limit. Some of the universal quantities at the Berezinskii-Kosterlitz-Thouless transition show smaller finite size effects in our model as compared to the traditional X Y model. Our model is a prime example of qubit regularization of an asymptotically free massive quantum field theory in Euclidean space-time and helps understand how asymptotic freedom can arise as a relevant perturbation at a decoupled fixed point without fine-tuning. Published by the American Physical Society 2024

Physics↗

DC-Connected Solar Plus Storage Modeling and Analysis for Behind-the-Meter Systems in the System Advisor Model

A detailed model for PV plus DC-connected batteries was developed. This model was compared to an existing ACconnected battery model in the System Advisor Model (SAM) tool using a hypothetical Honolulu residence with a PV plus storage system. The stand-alone PV system was shown to provide the most value; however due to the increased difficulty of PV systems being approved for grid interconnection in Hawaii, it is valuable to install an AC or DC connected battery to enable full PV self-consumption under a less restrictive interconnection option. In that case both battery systems enabled full PV selfconsumption at the residence, requiring no power to be exported to the grid. The DC-connected battery system captured energy from the PV array which would have also been clipped due to inverter limitations. For a PV system with DC/AC ratio of 1.23, the battery configurations produced net-present values within 1% of each other, but when the DC/AC ratio increased to 1.85, the DC-connected system increased the net-present value by 13%.

14 SOLAR ENERGY↗

SU-E-T-541: Measurement of CT Density Model Variations and the Impact On the Accuracy of Monte Carlo (MC) Dose Calculation in Stereotactic Body Radiation Therapy for Lung Cancer

Purpose: To measure the CT density model variations between different CT scanners used for treatment planning and impact on the accuracy of MC dose calculation in lung SBRT. Methods: A Gammex electron density phantom (RMI 465) was scanned on two 64-slice CT scanners (GE LightSpeed VCT64) and a 16-slice CT (Philips Brilliance Big Bore CT). All three scanners had been used to acquire CT for CyberKnife lung SBRT treatment planning. To minimize the influences of beam hardening and scatter for improving reproducibility, three scans were acquired with the phantom rotated 120° between scans. The mean CT HU of each density insert, averaged over the three scans, was used to build the CT density models. For 14 patient plans, repeat MC dose calculations were performed by using the scanner-specific CT density models and compared to a baseline CT density model in the base plans. All dose re-calculations were done using the same plan beam configurations and MUs. Comparisons of dosimetric parameters included PTV volume covered by prescription dose, mean PTV dose, V5 and V20 for lungs, and the maximum dose to the closest critical organ. Results: Up to 50.7 HU variations in CT density models were observed over the baseline CT density model. For 14 patient plans examined, maximum differences in MC dose re-calculations were less than 2% in 71.4% of the cases, less than 5% in 85.7% of the cases, and 5–10% for 14.3% of the cases. As all the base plans well exceeded the clinical objectives of target coverage and OAR sparing, none of the observed differences led to clinically significant concerns. Conclusion: Marked variations of CT density models were observed for three different CT scanners. Though the differences can cause up to 5–10% differences in MC dose calculations, it was found that they caused no clinically significant concerns.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Theoretical model for thin ferroelectric films and the multilayer structures based on them

A modified Weiss mean-field theory is used to study the dependence of the properties of a thin ferroelectric film on its thickness. The possibility of introducing gradient terms into the thermodynamic potential is analyzed using the calculus of variations. An integral equation is introduced to generalize the well-known Langevin equation to the case of the boundaries of a ferroelectric. An analysis of this equation leads to the existence of a transition layer at the interface between ferroelectrics or a ferroelectric and a dielectric. The permittivity of this layer is shown to depend on the electric field direction even if the ferroelectrics in contact are homogeneous. The results obtained in terms of the Weiss model are compared with the results of the models based on the correlation effect and the presence of a dielectric layer at the boundary of a ferroelectric and with experimental data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Methods and Usability Enhancements in Shift for Non-LWR Applications

Several development and analysis tasks were undertaken in FY21 under the Nuclear Energy Advanced Modeling and Simulation program to enhance modeling of non light–water reactors (LWRs) with Shift. Specifically, these efforts targeted enhancements for tristructural isotropic (TRISO) fuel modeling. A new Shift user interface was developed that allows for much better usability and ease of modeling for non-LWR problems and TRISO fuel. Performance studies were conducted using an HTR-10 fuel pebble model by comparing different geometry packages in Shift, KENO-VI, and Serpent. These studies showed that the new geometry package in Shift performs well compared to Serpent for TRISO fuel modeling with consistent tracking options between both packages. The studies also identified the most critical areas of improvement for more efficiently performing Monte Carlo transport on TRISO fuel models with Shift. Tally calculations in Shift were optimized for non-LWR cross section generation and depletion calculations, and areas for further optimization and accuracy improvements were identified. Finally, initial collaboration efforts were formed between Idaho National Laboratory, Argonne National Laboratory, and the Nuclear Regulatory Commission to use Shift for Comprehensive Reactor Analysis Bundle support.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Improving GCM Representation of Convective Cloud Microphysics by Using ARM Raman Lidar and Cloud Radar Observations

The overall objective of this Department of Energy (DOE) Atmospheric System Research (ASR) funded project is to improve the representation of convective cloud microphysics in global climate models (GCMs) and check it by comparing the model simulations with observations. We have derived the cloud ice water content by synthesizing ARM Raman lidar (RL) and cloud radar observations at the Atmospheric Radiation Measurement (ARM) sites. Noting that the simulated anvil clouds in terms of their macro- and micro-physical properties are sensitive to the parameterization of convective microphysical processes, observed ice water content in anvil clouds provides a useful constraint on these parameterizations. We have improved the convective microphysics parameterization scheme by (1) considering sedimentation for cloud ice crystals that do not fall in the original scheme, (2) applying a new terminal velocity parameterization that depends on the environmental conditions for convective snow, (3) adding a new hydrometeor category, “rimed ice,” to the original four-class (cloud liquid, cloud ice, rain, and snow) scheme, and (4) allowing convective clouds to detrain snow particles into stratiform clouds. We have examined the impact of improved convective cloud microphysics parameterization on the simulated global climate from GCM simulations.

54 ENVIRONMENTAL SCIENCES↗

A hydride model for solid and liquid cerium ejecta particles

Cerium ejecta particles form after a shock wave impacts a cerium plate with a rough surface or prescribed surface perturbation. When these particles are ejected into hydrogen or deuterium gas the material reacts exothermically and raises the temperature of the particle. Two models are developed to capture the heat and mass transfer that occurs during this process. One model assumes that for larger solid ejecta particles, the loss of mass is negligible while for smaller liquid ejecta particles, the loss of mass during reaction is accounted for. The loss of mass is assumed to occur from the hydride layer being extremely brittle and the fact that it will be relatively easy for the hydride material on the outside of the particle to go into tension, crack and flake off. The model is tested by performing simulations similar to the original experiments that motivated the development of these models and comparing the particle temperatures. While the model is able to capture some general features of the flowfield, inaccuracies clearly exist that must be addressed and the results underscore the need for more isolated experiments where fewer assumptions and physical processes are present simultaneously.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Particle Swarm Optimization of Dynamic Load Model Parameters in Large Systems

This paper considers two dynamic load models that are widely used in industry to account for induction motor behavior: CMLD and CLOD. These models must be parametrized for the specific utility system in a general way so that they can be used in planning studies and provide a conservative but realistic representation of load behavior. This study considers a measurement-based approach to tuning both models. The load modeling study compares the response of the tuned models to generic candidate models using historical events. This study considers one area-based subsystem to simplify the modeling approach and reduce the number of models required for simulations. Additionally, because dynamic load models often produce similar results for different sets of parameters, a sensitivity study was conducted to assess the parameter impacts on the voltage response. The sensitivity study covers the parameters that are tuned using event measurements. The process to estimate the parameters uses the particle-swarm optimization algorithm. Overall, the performance of the tuned model more accurately captures recovery voltage, delayed recovery, and settling voltage than its predecessor models while not being overly tuned so that it remains general for peak summer conditions.

dynamic load modeling↗