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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Learning stochastic dynamics and predicting emergent behavior using transformers

We show that a neural network originally designed for language processing can learn the dynamical rules of a stochastic system by observation of a single dynamical trajectory of the system, and can accurately predict its emergent behavior under conditions not observed during training. We consider a lattice model of active matter undergoing continuous-time Monte Carlo dynamics, simulated at a density at which its steady state comprises small, dispersed clusters. We train a neural network called a transformer on a single trajectory of the model. The transformer, which we show has the capacity to represent dynamical rules that are numerous and nonlocal, learns that the dynamics of this model consists of a small number of processes. Forward-propagated trajectories of the trained transformer, at densities not encountered during training, exhibit motility-induced phase separation and so predict the existence of a nonequilibrium phase transition. Transformers have the flexibility to learn dynamical rules from observation without explicit enumeration of rates or coarse-graining of configuration space, and so the procedure used here can be applied to a wide range of physical systems, including those with large and complex dynamical generators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Combined Influence of Rotation and Scrape-Off Layer Drifts on Recycling Asymmetries in Tokamak Plasmas

Coupled 2D fluid-kinetic simulations of a DIII-D high confinement tokamak plasma show that plasma rotation coupled with drift effects near the plasma edge play a significant role in the creation of the observed poloidal distribution of neutrals. It is observed that including either drift or rotation effects enhances particle flux at the inner target in the case of ion 𝐵×∇𝐵 drift toward the 𝑋-point. However, the particle flux asymmetry is significantly higher with the combination of drifts and rotation than either effect alone. The heightened particle flux asymmetry allows for improved simulation of the strong in-out asymmetry of the Lyman-𝛼 brightness profiles measured in the experiment. Enhancement of radial transport of parallel momentum changes the upstream scrape-off layer flow pattern, increasing the fraction of deuterium flux that reaches the inboard divertor entrance while lowering that which arrives at the outboard. In conclusion, this Letter indicates that by combining drifts, rotation, and viscous coupling, existing boundary plasma models can achieve a satisfactory agreement with experimentally measured neutral asymmetries.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Chemical order transitions within extended interfacial segregation zones in NbMoTaW

Interfacial segregation and chemical short-range ordering influence the behavior of grain boundaries in complex concentrated alloys. In this study, we use atomistic modeling of a NbMoTaW refractory complex concentrated alloy to provide insight into the interplay between these two phenomena. Hybrid Monte Carlo and molecular dynamics simulations are performed on columnar grain models to identify equilibrium grain boundary structures. Here our results reveal extended near-boundary segregation zones that are much larger than traditional segregation regions, which also exhibit chemical patterning that bridges the interfacial and grain interior regions. Furthermore, structural transitions pertaining to an A2-to-B2 transformation are observed within these extended segregation zones. Both grain size and temperature are found to significantly alter the widths of these regions. An analysis of chemical short-range order indicates that not all pairwise elemental interactions are affected by the presence of a grain boundary equally, as only a subset of elemental clustering types are more likely to reside near certain boundaries. The results emphasize the increased chemical complexity that is associated with near-boundary segregation zones and demonstrate the unique nature of interfacial segregation in complex concentrated alloys.

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

Uncertainty quantification of bank vegetation impacts on the flood flow field in the American River, California, using large‐eddy simulations

Bank vegetation plays a key role in both hydrodynamics and morphodynamics of natural rivers; however, these effects are often unaccounted for in the computational flow dynamics of natural waterways. Recent studies using the large‐eddy simulation (LES), however, have attempted to gain insights into the impacts of bank vegetation on the mean flow field of the natural rivers using a vegetation model, which applies a sink term to the momentum equations of motion. This approach accounts for the effects of the vegetation and provides a practical approach to account for the complex patches of bank vegetation in large‐scale rivers. To implement the vegetation model, a drag coefficient reflecting the overall resistance of vegetal structures to the flow is needed, but due to the lack of calibrated data and range of size, density and type of vegetation, this parameter can be a significant source of uncertainty in the model results. Here, in this study, we use uncertainty quantification (UQ) to investigate the hydrodynamics and bed shear results when a bank vegetation is incorporated in an LES model. To this end, we used the polynomial chaos expansion and Monte Carlo sampling techniques to determine the uncertainties associated with the drag coefficient in the vegetation model and from uncertainties in the bed roughness and inflow discharge. The UQ analysis provided spatially varying confidence levels for the spanwise and vertical distribution of velocity magnitude and for the bed shear stress distributions. In addition, Sobol indices were computed to indicate the relative influence that each parameter had on the overall uncertainty. In general, it was found that uncertainty in flow discharge was the dominant source of uncertainty; however, the drag coefficient in the vegetation model and the bed roughness parameter also made significant contribution to the uncertainty near the banks and bed, respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Λ Baryon Production in ν¯µ Interactions in the MicroBooNE Detector

The Cabibbo suppressed production of $\Lambda$ baryons in anti-neutrino interactions with nuclei is a rare process that is yet to be measured with a modern neutrino detector with automated reconstruction. The cross section for this process is sensitive to a number of unique nuclear effects, most notably the secondary interactions of the produced hyperon while attempting to escape from the nucleus. Other interactions within the nuclear remnant can impact the estimation of neutrino energy in oscillation measurements, and thus an accurate description of the nuclear environment is required. The strangeness violating hyperon production process is only available to anti-neutrinos. The model of this interaction is implemented into the NuWro neutrino interaction Monte Carlo simulation, and some predictions are presented, focusing on the role of nuclear effects. This model introduces a hyperon-nucleus potential, which calculations from hypernuclear theory permit to be strongly repulsive in th e case of $\Sigma$ baryons. The presence of this potential is found to sculpt the shape of the differential cross section in some variables. The MicroBooNE detector will be described, followed by a description of a measurement of the flux averaged, restricted phase space cross section of Cabibbo suppressed $\Lambda$ baryon production. A sophisticated event selection is employed, as a very large quantity of background neutrino interactions must be removed to perform the measurement with any sensitivity. This selection introduces some novel techniques such as the island finding method, and achieves a background reduction of $\sim 10^6$, with an efficiency of around 7\%. The calculation of the systematic uncertainties will be explained, including two procedures explored to handle sources of background with extremely poor simulation statistics: an in-situ constraint using data from sidebands, and a visual inspection of the data and simulation to remove the troublesome background events. The sensitivity to the $\Lambda$ baryon production cross section is calculated in the form of Bayesian posterior probability distributions, combining the systematic uncertainties with data and simulation statistical uncertainties. As a rare process, the statistical uncertainties are highly non-Gaussian, and the Bayesian approach is applied to include the full shapes of these uncertainties. Data corresponding to $2.2 \times 10^{20}$ protons on target of neutrino mode running and $4.9 \times 10^{20}$ protons on target of anti-neutrino running is analysed. When the data was unblinded, five $\Lambda$ production candidates were selected from the data, consistent with the MC simulation prediction of $5.3 \pm 1.1$ events. The final estimated cross section is $1.8^{+2.0}_{-1.6} \times 10^{-40}$cm$^2/$Ar when employing the sideband constraint procedure. A similar result of $2.0^{+2.2}_{-1.8} \times 10^{-40}$cm$^2/$Ar is obtained when performing the visual scan instead. The methods used in t his analysis are intended to be easily exported to other LArTPC detectors such as the Short Baseline Near Detector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Precision test of gauge/gravity duality in D0-brane matrix model at low temperature

We test the gauge/gravity duality between the matrix model and type IIA string theory at low temperatures with unprecedented accuracy. To this end, we perform lattice Monte Carlo simulations of the Berenstein-Maldacena-Nastase (BMN) matrix model, which is the one-parameter deformation of the Banks-Fischler-Shenker-Susskind (BFSS) matrix model, taking both the large N and continuum limits. We leverage the fact that sufficiently small flux parameters in the BMN matrix model have a negligible impact on the energy of the system while stabilizing the flat directions so that simulations at smaller N than in the BFSS matrix model are possible. Hence, we can perform a precision measurement of the large N continuum energy at the lowest temperatures to date. The energy is in perfect agreement with supergravity predictions including estimations of α'-corrections from previous simulations. At the lowest temperature where we can simulate efficiently (T = 0.25λ 1/3 , where λ is the ’t Hooft coupling), the difference in energy to the pure supergravity prediction is less than 10%. Furthermore, we can extract the coefficient of the 1/N 4 corrections at a fixed temperature with good accuracy, which was previously unknown.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Probabilistic R&D Portfolio Management Applied to Energy Storage

Here this paper proposes a methodology to analyze the potential of several alternative technologies in advancing a research and development portfolio. The methodology considers technology maturation, use case requirements, performance goals, cost projections, and existing research and development (R&D) efforts to predict the impact of various R&D investment tracks across several energy storage technologies. Technology maturation and cost projections are derived from a probabilistic Monte Carlo simulation, yielding results that incorporate uncertainty in modeling the impact of future R&D. This framework allows the researcher to perform a robust sensitivity analysis to uncover critical pathways and areas where investment will have a high impact.

25 ENERGY STORAGE↗
DOE OSTI2023

A TOPAS model for lens-based proton radiography

Abstract Objective. Proton Radiography can be used in conjunction with proton therapy for patient positioning, real-time estimates of stopping power, and adaptive therapy in regions with motion. The modeling capability shown here can be used to evaluate lens-based radiography as an instantaneous proton-based radiographic technique. The utilization of user-friendly Monte Carlo program TOPAS enables collaborators and other users to easily conduct medical- and therapy- based simulations of the Los Alamos Neutron Science Center (LANSCE). The resulting transport model is an open-source Monte Carlo package for simulations of proton and heavy ion therapy treatments and concurrent particle imaging. Approach. The four-quadrupole, magnetic lens system of the 800-MeV proton beamline at LANSCE is modeled in TOPAS. Several imaging and contrast objects were modelled to assess transmission at energies from 230–930 MeV and different levels of particle collimation. At different proton energies, the strength of the magnetic field was scaled according to βγ, the inverse product of particle relativistic velocity and particle momentum. Main results. Materials with high atomic number, Z, (gold, gallium, bone-equivalent) generated more contrast than materials with low-Z (water, lung-equivalent, adipose-equivalent). A 5-mrad collimator was beneficial for tissue-to-contrast agent contrast, while a 10-mrad collimator was best to distinguish between different high-Z materials. Assessment with a step-wedge phantom showed water-equivalent path length did not scale directly according to predicted values but could be mapped more accurately with calibration. Poor image quality was observed at low energies (230 MeV), but improved as proton energy increased, with sub-mm resolution at 630 MeV. Significance. Proton radiography becomes viable for shallow bone structures at 330 MeV, and for deeper structures at 630 MeV. Visibility improves with use of high-Z contrast agents. This modality may be particularly viable at carbon therapy centers with accelerators capable of delivering high energy protons and could be performed with carbon therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey

Uncertainty quantification (UQ) includes the characterization, integration, and propagation of uncertainties that result from stochastic variations and a lack of knowledge or data in the natural world. Monte Carlo (MC) method is a sampling-based approach that has widely used for quantification and propagation of uncertainties. However, the standard MC method is often time-consuming if the simulation-based model is computationally intensive. This article gives an overview of modern MC methods to address the existing challenges of the standard MC in the context of UQ. Specifically, multilevel Monte Carlo (MLMC) extending the concept of control variates achieves a significant reduction of the computational cost by performing most evaluations with low accuracy and corresponding low cost, and relatively few evaluations at high accuracy and corresponding high cost. Multifidelity Monte Carlo (MFMC) accelerates the convergence of standard Monte Carlo by generalizing the control variates with different models having varying fidelities and varying computational costs. Multimodel Monte Carlo method (MMMC), having a different setting of MLMC and MFMC, aims to address the issue of UQ and propagation when data for characterizing probability distributions are limited. Multimodel inference combined with importance sampling is proposed for quantifying and efficiently propagating the uncertainties resulting from small data sets. All of these three modern MC methods achieve a significant improvement of computational efficiency for probabilistic UQ, particularly uncertainty propagation. Finally, an algorithm summary and the corresponding code implementation are provided for each of the modern MC methods. The extension and application of these methods are discussed in detail.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A generalized approximate control variate framework for multifidelity uncertainty quantification

We describe and analyze a variance reduction approach for Monte Carlo (MC) sampling that accelerates the estimation of statistics of computationally expensive simulation models using an ensemble of models with lower cost. These lower cost models — which are typically lower fidelity with unknown statistics — are used to reduce the variance in statistical estimators relative to a MC estimator with equivalent cost. We derive the conditions under which our proposed approximate control variate framework recovers existing multifidelity variance reduction schemes as special cases. We demonstrate that existing recursive/nested strategies are suboptimal because they use the additional low-fidelity models only to efficiently estimate the unknown mean of the first low-fidelity model. As a result, they cannot achieve variance reduction beyond that of a control variate estimator that uses a single low-fidelity model with known mean. However, there often exists about an order-of-magnitude gap between the maximum achievable variance reduction using all low-fidelity models and that achieved by a single low-fidelity model with known mean. We show that our proposed approach can exploit this gap to achieve greater variance reduction by using non-recursive sampling schemes. The proposed strategy reduces the total cost of accurately estimating statistics, especially in cases where only low-fidelity simulation models are accessible for additional evaluations. Several analytic examples and an example with a hyperbolic PDE describing elastic wave propagation in heterogeneous media are used to illustrate the main features of the methodology.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

CORPSE model with litter decomposition parameters derived from the LIDET dataset

This is a version of the CORPSE model (Carbon, Organisms, Rhizosphere and Protection in the Soil Environment, Sulman et al. 2014) that uses litter decomposition parameters derived from a modified Monte Carlo simulation using the LIDET litter decomposition dataset (Long-term Intersite Decomposition Experiment Team, Harmon 2013). The code also includes the Baseline parameters, and the eight other best parameter sets identified in a modified Monte Carlo simulation. Related publication:Juice, S.M., Ridgeway, J.R., Hartman, M.D., Parton, W.J., Berardi, D.M., Sulman, B.N., Allen, K.E., & Brzostek, E.R. Reparameterizing litter decomposition using a simplified Monte Carlo method improves litter decay simulated by a microbial model and alters bioenergy soil carbon estimates. Description of files:The folder "Input Files" contains one folder for each LIDET site with data necessary to run the model. Note that "(site)" in the filenames below indicates where the LIDET site code appears (see Table 1 for site codes). Data streams include: CORPSE_full_spinup_litter.csv, CORPSE_full_spinup_rhizo.csv, CORPSE_full_spinup_bulk.csv, litterbag_init_100g_6spp.csv: initial C and N (kg C or N/m2) pool values for each soil layer, the litterbag_init_100_6spp.csv file is for the litterbag layer and is the same file for all sites. All initial C and N files have the same columns (Column - Description - Units) uFastC - Unprotected fast decomposing carbon - kg carbon/m2 uSlowC - Unprotected slow decomposing carbon - kg carbon/m2 uNecroC - Unprotected necromass carbon - kg carbon/m2 pFastC - Protected fast decomposing carbon - kg carbon/m2 pSlowC - Protected slow decomposing carbon - kg carbon/m2 pNecroC - Protected necromass carbon - kg carbon/m2 livingMicrobeC - Carbon in living microbial biomass - kg carbon/m2 uFastN - Unprotected fast decomposing nitrogen - kg nitrogen/m2 uSlowN - Unprotected slow decomposing nitrogen - kg nitrogen/m2 uNecroN - Unprotected necromass nitrogen - kg nitrogen/m2 pFastN - Protected fast decomposing nitrogen - kg nitrogen/m2 pSlowN - Protected slow decomposing nitrogen - kg nitrogen/m2 pNecroN - Protected necromass nitrogen - kg nitrogen/m2 inorganicN - Inorganic nitrogen - kg nitrogen/m2 CO2 - Carbon in carbon dioxide - kg carbon/m2 livingMicrobeN - Nitrogen in living microbial biomass - kg nitrogen/m2 soilT (site) DOY274start.csv: Average daily soil temperature (oC) interpolated from previously calculated monthly values used in DayCent LIDET simulations (Bonan et al., 2013). soilT (site) DOY274start.csv: Average daily soil volumetric water content (VWC) scalar interpolated from previously calculated monthly values used in DayCent LIDET simulations (Bonan et al., 2013). litter production.csv: Average daily litter production values for each site, data sources listed in Table S3 of related publication. litter (site) CN.csv: C:N ratio for each species from LIDET dataset (Table 2, Harmon 2013). (site).csv: Table indicating number of observations for each species decomposed at each site. Instructions: Save the model code ("CORPSE_LIDET.R") and "Input Files" folder in the same folder. Also make a folder for the model output (e.g., "results_Baseline") in the same folder. Set the working directory (setwd) in the model code to the folder with the files saved in step #1. Select the parameter set to use for the litter and litterbag compartments, comment out all other parameter sets. Run code. Output will be saved in the folder made in step 1. Output destination can be changed as necessary in code section called "Running the model." Table 1 LIDET sites and site codes used in model files. Site Code - Site AND - H.J. Andrews Experimental Forest BNZ - Bonanza Creek Experimental Forest BSF - Blodgett Research Forest CDR - Cedar Creek Natural History Area CPR - Central Plains Experimental Range HBR - Hubbard Brook Experimental Forest HFR - Harvard Forest JUN - Juneau KBS - Kellogg Biological Station KNZ - Konza Prairie Research Natural Area NWT - Niwot Ridge/Green Lakes Valley OLY - Olympic National Park OLY Conifer forest SEV - Sevilleta National Wildlife Refuge SMR - Santa Margarita Ecological Reserve UFL - University of Florida VCR - Virginia Coast Reserve Table 2 LIDET species and species codes used in model files (6 common species). Species - Species Code Sugar maple (Acer saccharum) - ACSA Drypetes (Drypetes glauca) - DRGL Red pine (Pinus resinosa) - PIRE Chestnut oak (Quercus prinus) - QUPR Western redcedar (Thuja plicata) - THPL Wheat (Triticum aestivum) - TRAE References:Bonan, G. B., Hartman, M. D., Parton, W. J., & Wieder, W. R. (2013). Evaluating litter decomposition in earth system models with long-term litterbag experiments: an example using the Community Land Model version 4 (CLM4). Global Change Biology, 19(3), 957-974. https://doi.org/https://doi.org/10.1111/gcb.12031 Harmon, M. (2013). LTER Intersite Fine Litter Decomposition Experiment (LIDET), 1990 to 2002. Long-Term Ecological Research. Forest Science Data Bank, Corvallis, OR. [Data set]. Accessed http://andlter.forestry.oregonstate.edu/data/abstract.aspx?dbcode=TD023. https://doi.org/10.6073/pasta/f35f56bea52d78b6a1ecf1952b4889c5. Sulman, B. N., Phillips, R. P., Oishi, A. C., Shevliakova, E., & Pacala, S. W. (2014). Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nature Climate Change, 4, 1099 - 1102. https://doi.org/10.1038/nclimate2436

Juice, Stephanie↗
DOE OSTI2021

Evolution of microstructures in radiation fields using a coupled binary-collision Monte Carlo phase field approach

The simulation of radiation effects in materials broadly falls into two categories. At short time and length scales lies the modeling of primary radiation damage, such as point defect creation, energy deposition, and ballistic mixing. This is followed by the modeling at longer time scales of thermally activated microstructure evolution and defect reactions, such as recombination, clustering, and coarsening. The binary collision Monte Carlo method is an established, numerically efficient method for the computation of primary radiation damage. Conversely, the phase field method is a state of the art method for microstructure evolution on longer time and length scales. Here we present a concurrent coupling of these two methods, overcoming the difference between the discrete object Monte Carlo paradigm for primary radiation damage and the continuum field variable approach for microstructure evolution. The coupling is bidirectional, in which the microstructure evolution in the MOOSE finite element frame- work provides the spatial scattering data set for the charged particle trans- port and receives point defect, mass transport, and heat source terms from the simulated collision cascades that contribute to the field variable evolution. The concurrent coupling scheme is implemented in the code Magpie and demonstrated by investigating patterning for a model irradiated immiscible binary alloy. The results from the coupled binary collision Monte Carlo/phase field simulations reproduce the results of analytical models for phase separation, phase mixing, and patterning, supporting the approach and indicating its utility for modeling real materials systems.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Development of CTF modeling of interfacial drag, wall shear, and interfacial heat transfer for bubbly and annular-mist flow regimes

This milestone report discusses improvements to CTF closure models for modeling of bubbly and annular mist two-phase flow conditions, which are important for accurate modeling and simulation for boiling water reactor (BWR) conditions. Models that were improved include the two-phase wall shear, wall boiling, and phase mass and heat transfer. A new flow regime map was implemented to improve on the prediction of transition to annular flow, which was generally largely over-predicted by the legacy CTF flow regime map. The annular mist interfacial drag models were also reviewed and new models were proposed for implementation into the code. In addition to implementing new models, an activity was performed to calibrate these new models to experimental data using a Markov Chain Monte Carlo technique, thus further improving their accuracy. Finally, new experiments were implemented into the CTF validation matrix which add more two-phase pressure drop and void measurements for the assessment of CTF for modeling of BWRs. The results show that the Chisholm model, which was implemented and then calibrated, results in significantly improved comparison against the Riso two-phase pressure drop data. Furthermore, improvements in the implicit heat transfer and wall boiling models resulted in good agreement with the void measurement data.

42 ENGINEERING↗
DOE OSTI2022

Induced seismicity potential based on probabilistic geomechanics for the San Juan Basin CarbonSAFE project

Although geologic carbon sequestration projects have yet to induce — or may never induce — a damaging earthquake, experiences from other deep injection industries such as hydraulic fracturing, enhanced geothermal systems, and saltwater disposal suggest that effective quantitative seismic risk assessment is necessary for deep saline carbon capture and sequestration (CCS) projects. One such imminent CCS project is the San Juan Basin CarbonSAFE Phase III program. We use Monte Carlo probabilistic geomechanical analyses combined with observations of the geologic and operational parameters of the San Juan Basin site and suggest that this project is of low-induced seismic risk. The primary analysis is split into four sections. First, we assess the literature for faults and past seismicity, and at least five faulting scenarios are directly relevant. Second, we develop and calibrate an integrated earth model for the project site. Third, we perform Monte Carlo simulations that consider reasonable uncertainties of the geomechanical parameters. Of five tested faulting scenarios, only one presents high Coulomb failure functions, but fourth, we determine the risk to be low based on the combined lack of historical seismicity, the geologic framework of the faulting scenario, and the presence of saltwater injection at the same depth as the proposed supercritical carbon dioxide injection. The most sensitive parameters in the geomechanical calculations are the fault dip and the coefficient of friction. The least sensitive are the fault strike and the orientation of the maximum horizontal principal stress.

58 GEOSCIENCES↗
DOE OSTI2022

Dielectric strength of noble and quenched gases for high pressure time projection chambers

Abstract Dielectric breakdown strength is one of the critical performance metrics for pure gases and gas mixtures used in large, high pressure gas time projection chambers. In this paper we experimentally study dielectric breakdown strengths of several important time projection chamber working gases and gas-phase insulators over the pressure range 100 mbar to 10 bar, and gap sizes ranging from 0.1 to 10 mm. Gases characterized include argon, xenon, CO $$_2$$ 2 , CF $$_4$$ 4 , and mixtures 90-10 argon-CH $$_4$$ 4 , 90-10 argon-CO $$_2$$ 2 and 99-1 argon-CF $$_4$$ 4 . We develop a theoretical model for high voltage breakdown based on microphysical simulations that use electron swarm Monte Carlo results as input to Townsend- and Meek-like discharge criteria. This model is shown to be highly predictive at high pressure, out-performing traditional Paschen–Townsend and Meek–Raether models significantly. At lower pressure-times-distance, the Townsend-like model is an excellent description for noble gases whereas the Meek-like model provides a highly accurate prediction for insulating gases.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Active learning with multifidelity modeling for efficient rare event simulation

Here, while multifidelity modeling provides a cost-effective way to conduct uncertainty quantification with computationally expensive models, much greater efficiency can be achieved by adaptively deciding the number of required high-fidelity (HF) simulations, depending on the type and complexity of the problem and the desired accuracy in the results. We propose a framework for active learning with multifidelity modeling emphasizing the efficient estimation of rare events. Our framework works by fusing a low-fidelity (LF) prediction with an HF-inferred correction, filtering the corrected LF prediction to decide whether to call the high-fidelity model, and for enhanced subsequent accuracy, adapting the correction for the LF prediction after every HF model call. The framework does not make any assumptions as to the LF model type or its correlations with the HF model. In addition, for improved robustness when estimating smaller failure probabilities, we propose using dynamic active learning functions that decide when to call the HF model. We demonstrate our framework using several academic case studies (including some high-dimensional problems) and two finite element model case studies: estimating Navier-Stokes velocities using the Stokes approximation and estimating stresses in a transversely isotropic model subjected to displacements via a coarsely meshed isotropic model. Across these case studies, not only did the proposed framework estimate the failure probabilities accurately, but compared with either Monte Carlo or a standard variance reduction method, it also required only a small fraction of the calls to the HF model.

42 ENGINEERING↗
DOE OSTI2021

Model Light Curves for Type Ib and Ic Supernovae

Using the Monte Carlo code SEDONA, multiband photometry and spectra are calculated for supernovae derived from stripped helium stars with presupernova masses of 2.2 to 10.0 M ⊙ . The models are representative of evolution in close binaries and have previously been exploded using a parameterized one-dimensional model for neutrino transport. A subset, those with presupernova masses in the range of 2.2–5.6 M⊙, have many properties in common with observed Type Ib and Ic supernovae, including a median ejected mass near 2 M ⊙ , explosion energies near 1 × 10 51 erg, typical 56 Ni masses of 0.07–0.09 M ⊙ , peak times of about 20 days, and a narrow range for the V - R color index 10 days post-V-maximum near 0.3 mag. The median peak bolometric luminosity, near 10 42.3 erg s -1 , is fainter, however, than several observational tabulations, and the brightest explosion has a bolometric luminosity of only 10 42.50 erg s -1 . The brightest absolute B, V, and R magnitudes at peak are -17.2, -17.8, and -18.0. These limits are fainter than some allegedly typical Type Ib and Ic supernovae and could reflect problems in our models or in the observational analysis. Helium stars with lower and higher masses also produce interesting transients that may have been observed, including fast, faint, blue transients and long, red, faint Type Ic supernovae. New models are specifically presented for SN 2007Y, SN 2007gr, SN 2009jf, LSQ 13abf, SN 2008D, and SN 2010X.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Phase diagram of a distorted kagome antiferromagnet and application to Y-kapellasite

Abstract We investigate the magnetism of a previously unexplored distorted spin-1/2 kagome model consisting of three symmetry-inequivalent nearest-neighbor antiferromagnetic Heisenberg couplings J ⬡ , J , and $$J^{\prime}$$ J ′ , and uncover a rich ground state phase diagram even at the classical level. Using analytical arguments and numerical techniques we identify a collinear $$\overrightarrow{Q}=0$$ Q → = 0 magnetic phase, two unusual non-collinear coplanar $$\overrightarrow{Q}=(1/3,1/3)$$ Q → = ( 1 / 3 , 1 / 3 ) phases and a classical spin liquid phase with a degenerate manifold of non-coplanar ground states, resembling the jammed spin liquid phase found in the context of a bond-disordered kagome antiferromagnet. We further show with density functional theory calculations that the recently synthesized Y-kapellasite Y 3 Cu 9 (OH) 19 Cl 8 is a realization of this model and predict its ground state to lie in the region of $$\overrightarrow{Q}=(1/3,1/3)$$ Q → = ( 1 / 3 , 1 / 3 ) order, which remains stable even after the inclusion of quantum fluctuation effects within variational Monte Carlo and pseudofermion functional renormalization group. The presented model opens a new direction in the study of kagome antiferromagnets.

36 MATERIALS SCIENCE↗