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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Progenitors and explosion properties of supernova remnants hosting central compact objects: II. A global systematic study with a comparison to nucleosynthesis models

ABSTRACT Core-collapse explosions of massive stars leave behind neutron stars, with a known diversity that includes the ‘Central Compact Objects’ (CCOs). Typified by the neutron star discovered near the centre of the Cas A supernova remnant (SNR), CCOs have been observed to shine only in X-rays. To address their supernova progenitors, we perform a systematic study of SNRs that contain a CCO and display X-ray emission from their shock-heated ejecta. We make use of X-ray data primarily using the Chandra X-ray observatory, complemented with XMM–Newton. This study uses a systematic approach to the analysis of each SNR aimed at addressing the supernova progenitor as well as the explosion properties (energy and ambient density). After fitting for the ejecta abundances estimated from a spatially resolved spectroscopic study, we compare the data to six nucleosynthesis models making predictions on supernova ejecta yields in core-collapse explosions. We find that the explosion models commonly used by the astrophysics community do not match the ejecta yields for any of the SNRs, suggesting additional physics, for example multidimensional explosion models or updated progenitor structures, are required. Overall we find low-mass (≤25 solar masses) progenitors among the massive stars population and low-energy explosions (<1051 ergs). We discuss degeneracies in our model fitting, particularly how altering the explosion energy affects the estimate of the progenitor mass. Our systematic study highlights the need for improving on the theoretical models for nucleosynthesis predictions as well as for sensitive, high-resolution spectroscopy observations to be acquired with next generation X-ray missions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Stopping Force Analysis of 235 U Elemental Fission Product Yields for $E_n$ = 0.11–92.4 MeV

We report most evaluated elemental fission product yield distributions are not experimentally measured. Instead, the majority of evaluated distributions are based on analytic expressions of the Zp-model for relevant cumulative yields. Here we report independent elemental fission product yield distributions of a 235 U target for incident neutron energies ranging from 0.11 MeV through 92.4 MeV. Atomic numbers are calculated by an approach that combines a 2E analysis with a stopping force analysis method, developed within this paper. These analyses are applied to more than 6.1 × 10 6 fission fragment ionization tracks captured within the NIFFTE (Neutron Induced Fission Fragment Tracking Experiment) collaboration fission time projection chamber (fissionTPC). A 3-Z resolution was obtained with the fissionTPC spatial and energy resolutions. Tabulated results are presented for the atomic yield and experimentally derived Zp values as a function of pre-neutron-emission fragment masses for the complete range of incident neutron energies. The stopping-force-derived Zp values tend to support the unchanged charge distribution theory within uncertainty.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Validation of Geant4 fragmentation for Heavy Ion Therapy

12 C ion therapy has had growing interest in recent years for its excellent dose conformity. However at therapeutic energies, which can be as high as 400 MeV/u, carbon ions produce secondary fragments. For an incident 400 MeV/u 12 C ion beam, ~70% of the beam will undergo fragmentation before the Bragg Peak. The dosimetric and radiobiological impact of these fragments must be accurately characterised, as it can result in increasing the risk of secondary cancer for the patient as well as altering the relative biological effectiveness. Here, this work investigates the accuracy of three different nuclear fragmentation models available in the Monte Carlo Toolkit Geant4, the Binary Intranuclear Cascade (BIC), the Quantum Molecular Dynamics (QMD) and the Liege Intranuclear Cascade (INCL++). The models were benchmarked against experimental data for a pristine 400 MeV/u 12 C beam incident upon a water phantom, including fragment yield, angular and energy distribution. For fragment yields the three alternative models agreed between ~5 and ~35% with experimental measurements, the QMD using the “Frag” option gave the best agreement for lighter fragments but had reduced agreement for larger fragments. For angular distributions INCL++ was seen to provide the best agreement among the models for all elements with the exception of Hydrogen, while BIC and QMD was seen to produce broader distributions compared to experiment. BIC and QMD performed similar to one another for kinetic energy distributions while INCL++ suffered from producing lower energy distributions compared to the other models and experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Tripled yield in direct-drive laser fusion through statistical modelling

Accurate predictive models are essential to the design of high-performance, laser-fusion experiments. Here, we report the development of a promising statistical approach that has been used to quantitatively predict and design several cryogenic deuterium-tritium (DT) implosions on the OMEGA Laser System, leading to the tripling of the fusion yield to its highest value for direct-drive laser fusion of DT-layered targets. When scaled to National Ignition Facility laser energies of 1.9 MJ, these targets are predicted to produce a fusion energy output of ~500 kJ—several times larger than current fusion yields achieved at that facility. Future applications of this approach will guide the exploration of the vast parameter space for the conditions required for thermonuclear ignition and expose gaps in our understanding of laser-fusion physics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Estimating Field-Level Perennial Bioenergy Grass Biomass Yields Using the Normalized Difference Red-Edge Index and Linear Regression Analysis for Central Virginia, USA

We investigated the indicative power of the normalized difference red-edge index (NDRE) for estimating field-level perennial bioenergy grass biomass yields utilizing Sentinel-2 imagery and a linear regression model as a rapid, cost-effective method for biomass yield estimations for bioenergy. We used 2019 data from three study sites containing mature perennial bioenergy grass stands in central Virginia, USA. Of the simulated daily NDRE values based on the temporally weighted averaging of two temporal neighbors, we found the strongest index–yield correlation on 11 August (R = 0.85). We estimated the perennial bioenergy grass biomass yields for (1) all sites using the data pooled from the three sites (all-site estimation) and (2) each site using the data pooled from the other two sites (cross-site estimation). The estimated field-level perennial bioenergy grass biomass yields strongly correlated with the recorded yields (average R2 = 0.76), with a root mean square error (RMSE) of 1.5 Mg/ha and a mean absolute error (MAE) of 1.2 Mg/ha for the all-site estimation. For the cross-site estimation, the site with diverse perennial grass types had the weakest correlation (R2 = 0.44) of the sites, indicating a difficulty in accounting for heterogeneous index–yield relationships in a single model. In addition to identifying a strong indicative power of the NDRE for estimating the overall perennial bioenergy grass biomass yields at a field level, the findings from this study call for an analysis across multiple perennial grasses and a comparison using multiple sites to understand (1) if the indicative power of the index shifts from the biomass of the specific perennial bioenergy grass type to the overall biomass during the growing season and (2) the level of perennial bioenergy grass heterogeneity that may hinder the remotely sensed biomass yield estimation using a single model.

09 BIOMASS FUELS↗
DOE OSTI2016

View factor modeling of sputter-deposition on micron-scale-architectured surfaces exposed to plasma

The sputter-deposition on surfaces exposed to plasma plays an important role in the erosion behavior and overall performance of a wide range of plasma devices. Plasma models in the low density, low energy plasma regime typically neglect micron-scale surface feature effects on the net sputter yield and erosion rate. The model discussed in this paper captures such surface architecture effects via a computationally efficient view factor model. The model compares well with experimental measurements of argon ion sputter yield from a nickel surface with a triangle wave geometry with peak heights in the hundreds of microns range. Further analysis with the model shows that increasing the surface pitch angle beyond about 45° can lead to significant decreases in the normalized net sputter yield for all simulated ion incident energies (i.e., 75, 100, 200, and 400 eV) for both smooth and roughened surfaces. At higher incident energies, smooth triangular surfaces exhibit a nonmonotonic trend in the normalized net sputter yield with surface pitch angle with a maximum yield above unity over a range of intermediate angles. The resulting increased erosion rate occurs because increased sputter yield due to the local ion incidence angle outweighs increased deposition due to the sputterant angular distribution. The model also compares well with experimentally observed radial expansion of protuberances (measuring tens of microns) in a nano-rod field exposed to an argon beam. The model captures the coalescence of sputterants at the protuberance sites and accurately illustrates the structure's expansion due to deposition from surrounding sputtering surfaces; these capabilities will be used for future studies into more complex surface architectures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

In Situ Micro-Pillar Compression to Examine Radiation-Induced Hardening Mechanisms of FeCrAl Alloys

Here, the effects of 5 MeV Fe 2+ ion irradiation at 300°C on the microstructure evolution and deformation behavior of a FeCrAl C26M alloy are presented. It has been found that dislocation loop density increases an order of magnitude from 1 dpa to 16 dpa irradiations, whereas, the dislocation loop size saturates with increasing damage. Micropillars, 600 nm in diameter and 1.3 µm in height, were fabricated and compressed inside grains with <001>, <011> and <111> crystallographic orientations, respectively. {112} <111> has been identified as the primary slip system in both unirradiated and irradiated alloy. The increase in yield stress after irradiation is observed with measurable variation along <001> and <011> vs. along <111>. By applying the Orowan dispersed barrier model, the increase of yield stress is found mainly due to the slip resistance of radiation generated defect loops. Detailed transmission electron microscopy (TEM) studies were performed to quantify the Burgers vector and the distribution of irradiation induced dislocations at elevated strains. It is revealed that localized shear instability is caused by avalanche slip events of ½<111> dislocations gliding out of tested pillars. Simultaneously, a large number of sessile/immobile <100> dislocations formed in the vicinity of slip band, leading to the hardening at elevated strains.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Improving maize growth processes in the community land model: Implementation and evaluation

Earth system models (ESMs) are essential tools to study the impacts of historical and future climate on regional and global food production, as well as to assess the effectiveness of possible adaptations and their potential feedback to climate. Several current ESMs have the capabilities to simulate crop growth. However, some critical crop growth processes (e.g. flowering and other reproductive processes) and their responses to environmental extremes (e.g. heat stress) are not yet represented in most of these models. In this paper, an improved maize growth model was implemented in the Community Land Model version 4.5 (CLM4.5) by modifying the maize planting scheme, incorporating the phenology scheme adopted from the APSIM model (Agricultural Production Systems sIMulator), adding a new carbon allocation scheme into CLM4.5, and improving the estimation of canopy structure parameters including leaf area index (LAI) and canopy height. Unique features of the new model (CLM-APSIM) include more detailed phenology stages, an explicit implementation of the impacts of various abiotic environmental stresses (including nitrogen, water, temperature and heat stresses) on maize phenology and carbon allocation, as well as an explicit simulation of grain number. Evaluation of results at 7 AmeriFlux sites located in the US Corn Belt show that the CLM-APSIM model performs better than the original CLM4.5 in simulating phenology (LAI and canopy height), surface fluxes including gross primary production (GPP), net ecosystem exchange (NEE), latent heat (LH), and sensible heat (SH), and especially in simulating the biomass partition and maize yield. The CLM-APSIM model corrects a serious deficiency in CLM4.5-related to CLM4.5’s underestimation of aboveground biomass (i.e. overestimation of belowground biomass) and overestimation of Harvest Index, which lead to a reasonable yield estimation with wrong mechanisms. Moreover, 13-year simulation results from 2001 to 2013 at the three Mead sites (US-Ne1, Ne2 and Ne3) show that the CLM-APSIM model can more accurately reproduce maize yield responses to growing season climate (temperature and precipitation) than the original CLM4.5 when benchmarked with the site-based observations and USDA county-level survey statistics. The CLM-APSIM model is thus more suitable than its predecessor models in terms of simulating abiotic environmental stresses on maize yield. This new model provides an improved tool to attribute maize yield change to various processes under historical and future climate, as well as to assess and design effective climate adaptation strategies for sustainable agricultural production.

Peng, Bin↗
DOE OSTI2017

Predictive modeling to de-risk bio-based manufacturing by adapting to variability in lignocellulosic biomass supply

Commercial-scale bio-refineries are designed to process 2000 tons/day of single lignocellulosic biomass. Several geographical areas in the United States generate diverse feedstocks that, when combined, can be substantial for bio-based manufacturing. Blending multiple feedstocks is a strategy being investigated to expand bio-based manufacturing outside Corn Belt. Here in this study, we developed a model to predict continuous envelopes of biomass blends that are optimal for a given pretreatment condition to achieve a predetermined sugar yield or vice versa. For example, our model predicted more than 60% glucose yield can be achieved by treating an equal part blend of energy cane, corn stover, and switchgrass with alkali pretreatment at 120 °C for 14.8 h. Finally, by using ionic liquid to pretreat an equal part blend of the biomass feedstocks at 160 °C for 2.2 h, we achieved 87.6% glucose yield. Such a predictive model can potentially overcome dependence on a single feedstock.

09 BIOMASS FUELS↗
DOE OSTI2011

Compositionally controlled metal-insulator transition in Tl{sub 2-x}In{sub x}TeO{sub 6}

Tl{sub 2}TeO{sub 6} and In{sub 2}TeO{sub 6} are both known to crystallize in the Na{sub 2}SiF{sub 6}-type structure. We find Tl{sub 2}TeO{sub 6} is metallic, whereas In{sub 2}TeO{sub 6} is an insulator. We have prepared a complete Tl{sub 2-x}In{sub x}TeO{sub 6} series in a search for a compositionally controlled metal-insulator transition that might be expected if a complete solid solution can be obtained. Unit cell edges and volume vary monotonically with no indication of a miscibility gap. The metal-insulator transition occurs at an x value of about 1.4, which can be rationalized on a percolation model. No superconductivity could be detected down to 5 K. -- Graphical abstract: A complete solid solution between Tl{sub 2}TeO{sub 6} and In{sub 2}TeO{sub 6} is formed. A compositionally controlled metal-insulator transition occurs in Tl{sub 2-x}In{sub x}TeO{sub 6} at an x value of about 1.5. No superconductivity could be detected down to 5 K. Display Omitted Research highlights: {yields} A complete solid solution between Tl{sub 2}TeO{sub 6} and In{sub 2}TeO{sub 6} is formed. {yields} A compositionally controlled metal-insulator transition occurs in Tl{sub 2-x}In{sub x}TeO{sub 6} at an x value of about 1.5, which can be rationalized on a percolation model. {yields} No superconductivity could be detected down to 5 K.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Identification of crystal plasticity model parameters by multi-objective optimization integrating microstructural evolution and mechanical data

Crystal plasticity models evolve a polycrystalline yield surface using meso-scale descriptions of deformation mechanisms. The activation of deformation mechanisms is governed by crystallography and a set of model parameters, which are typically calibrated through the fitting of mechanical data such as stress–strain curves and elastic lattice strains. Microstructural data such as phase fractions and texture evolution are used for verifying crystal plasticity parameters. In this study, we use a multi-objective genetic algorithm to identify hardening parameters from flow stress curves with an option to incorporate texture into the optimization approach. Robust, generalized objective functions are developed and used to identify sets of parameters pertaining to dislocation density-based hardening laws in visco-plastic and elasto-plastic self-consistent (VPSC and EPSC) homogenization models. First, the parameters are identified for pure Nb directly from texture using an objective function based on generalized spherical harmonics. Since texture evolution is driven by the relative contribution of active slip systems, the parameters governing the evolution of slip resistance ratios can be recovered from fitting discrete textures at a series of strains. Next, a comprehensive set of load reversal data for dual phase (DP) 780 steel is used to fit a hardening law and a back-stress law in EPSC. Finally, parameters pertaining to a complex hardening law for the evolution of slip and twinning in pure α-Ti are identified. Remarkably, using texture as an objective in combination with stress–strain objectives constrains the model of Ti to fully reproduce not only stress–strain and texture evolution but also hierarchical twinning measurements as a function of initial grain size and texture. Furthermore, given an appropriate model fit to representative experimental texture evolution, underlying twin volume fractions contributing to texture evolution can be predicted.

42 ENGINEERING↗
DOE OSTI2019

Using a machine learning approach to determine the space group of a structure from the atomic pair distribution function

Here a method is presented for predicting the space group of a structure given a calculated or measured atomic pair distribution function (PDF) from that structure. The method utilizes machine learning models trained on more than 100 000 PDFs calculated from structures in the 45 most heavily represented space groups. In particular, a convolutional neural network (CNN) model is presented which yields a promising result in that it correctly identifies the space group among the top-6 estimates 91.9% of the time. The CNN model also successfully identifies space groups for 12 out of 15 experimental PDFs. Interesting aspects of the failed estimates are discussed, which indicate that the CNN is failing in similar ways as conventional indexing algorithms applied to conventional powder diffraction data. This preliminary success of the CNN model shows the possibility of model-independent assessment of PDF data on a wide class of materials.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

EVALUATING SYSTEMATIC DEPENDENCIES OF TYPE Ia SUPERNOVAE: THE INFLUENCE OF CENTRAL DENSITY

We present a study exploring a systematic effect on the brightness of Type Ia supernovae using numerical models that assume the single-degenerate paradigm. Our investigation varied the central density of the progenitor white dwarf at flame ignition, and considered its impact on the explosion yield, particularly the production and distribution of radioactive {sup 56}Ni, which powers the light curve. We performed a suite of two-dimensional simulations with randomized initial conditions, allowing us to characterize the statistical trends that we present. The simulations indicate that the production of Fe-group material is statistically independent of progenitor central density, but the mass of stable Fe-group isotopes is tightly correlated with central density, with a decrease in the production of {sup 56}Ni at higher central densities. These results imply that progenitors with higher central densities produce dimmer events. We provide details of the post-explosion distribution of {sup 56}Ni in the models, including the lack of a consistent centrally located deficit of {sup 56}Ni, which may be compared to observed remnants. By performing a self-consistent extrapolation of our model yields and considering the main-sequence lifetime of the progenitor star and the elapsed time between the formation of the white dwarf and the onset of accretion, we develop a brightness-age relation that improves our prediction of the expected trend for single degenerates and we compare this relation with observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

A Bayesian inversion framework for yield and height-of-burst/depth-of-burial for near-surface explosions

A Bayesian inversion framework is presented to estimate the yield of an explosion and height-of-burst/depth-of-burial (HOB/DOB) using seismic and air pressure data. This is accomplished by first calibrating the parameters in the forward models that relate the observations to the yield and HOB/DOB and then using the calibrated model to estimate yield and HOB/DOB associated with a new set of seismic and air pressure observations. The MCMC algorithms required to perform these steps are outlined, and the results with real data are shown. Finally, an extension is proposed for a case when clustering in the seismic displacement occurs as a function of different types of rock and other factors.

58 GEOSCIENCES↗
DOE OSTI2022

Transfer learning driven design optimization for inertial confinement fusion

Transfer learning is a promising approach to create predictive models that incorporate simulation and experimental data into a common framework. In this technique, a neural network is first trained on a large database of simulations and then partially retrained on sparse sets of experimental data to adjust predictions to be more consistent with reality. Previously, this technique has been used to create predictive models of Omega [Humbird et al., IEEE Trans. Plasma Sci. 48, 61–70 (2019)] and NIF [Humbird et al., Phys. Plasmas 28, 042709 (2021); Kustowski et al., Mach. Learn. 3, 015035 (2022)] inertial confinement fusion (ICF) experiments that are more accurate than simulations alone. Here, in this work, we conduct a transfer learning driven hypothetical ICF campaign in which the goal is to maximize experimental neutron yield via Bayesian optimization. The transfer learning model achieves yields within 5% of the maximum achievable yield in a modest-sized design space in fewer than 20 experiments. Furthermore, we demonstrate that this method is more efficient at optimizing designs than traditional model calibration techniques commonly employed in ICF design. Such an approach to ICF design could enable robust optimization of experimental performance under uncertainty.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Different to the core: The pre-supernova structures of massive single and binary-stripped stars

The majority of massive stars live in binary or multiple systems and will interact with a companion during their lifetimes, which helps to explain the observed diversity of core-collapse supernovae. Donor stars in binary systems can lose most of their hydrogen-rich envelopes through mass transfer. As a result, not only are the surface properties affected, but so is the core structure. However, most calculations of the core-collapse properties of massive stars rely on single-star models. We present a systematic study of the difference between the pre-supernova structures of single stars and stars of the same initial mass (11–21 M ⊙ ) that have been stripped due to stable post-main-sequence mass transfer at solar metallicity. We present the pre-supernova core composition with novel diagrams that give an intuitive representation of the isotope distribution. As shown in previous studies, at the edge of the carbon-oxygen core, the binary-stripped star models contain an extended gradient of carbon, oxygen, and neon. This layer remains until core collapse and is more extended in mass for higher initial stellar masses. It originates from the receding of the convective helium core during core helium burning in binary-stripped stars, which does not occur in single-star models. We find that this same evolutionary phase leads to systematic differences in the final density and nuclear energy generation profiles. Binary-stripped star models have systematically higher total masses of carbon at the moment of core collapse compared to single-star models, which likely results in systematically different supernova yields. In about half of our models, the silicon-burning and oxygen-rich layers merge after core silicon burning. We discuss the implications of our findings for the “explodability”, supernova observations, and nucleosynthesis of these stars. Our models are publicly available and can be readily used as input for detailed supernova simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Bose-Einstein condensation phenomenology in systems with repulsive interactions

The role of repulsive interactions in statistical systems of Bose particles is investigated. Three different phenomenological frameworks are considered: A mean-field model, an excluded volume model, and a model with a medium-dependent effective mass. All three models are tuned to yield similar equations of state, with only minor deviations from the ideal Bose gas at small chemical potentials. Our analysis indicates, however, that these models lead to qualitatively different results for the Bose-Einstein condensation phenomenon. We discuss the different aspects of this phenomenon, namely, an onset of the Bose-Einstein condensation, particle number fluctuations, and a behavior of the Bose condensate. The obtained results can be helpful for interpreting the lattice QCD data at small temperature and large isospin chemical potential and the data on multiple pion production in high-energy nuclear collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Modeling and Validation of Irradiation Damage in Ni-based Alloys for Long-Term LWR Applications (Final Report)

As light water reactor (LWR) plant lives are extended, the need for predictive modeling tools for materials degradation increase in order to ensure safe operation and plan for component replacements. The Grizzly code, which is built on the MOOSE multi-physics simulation environment, is being developed for exactly this purpose. To extend Grizzly to include capabilities for modeling Ni-based alloys, we propose a US/UK integrated program to address thermal and irradiation-induced transformations mechanisms of Alloys 690 and 625. Alloy 690 is widely used in existing LWR plants due to its superior stress corrosion cracking (SCC) resistance compared to Alloy 600. Alloy 625 is used in more limited applications but offers the benefits of both high strength (in the aged condition) and corrosion/SCC resistance. Research has shown that both alloys can undergo phase changes due to thermal or irradiation exposure. In the precipitation-hardened condition, Alloy 625 “softens” during neutron irradiation as the strengthening precipitates decompose and metastable precipitates form. However, the nature and rates of these transformations as a function of exposure conditions are not well understood. Similarly, the effects of these thermal and irradiation-induced microstructural changes on mechanical properties require evaluation. The proposed program combines thermal and irradiation experiments, mechanical testing, microstructural characterization using state-of-the art analytical techniques, atomistic modeling, micro-and macro-scale modeling via Grizzly. This multi-pronged approach yields several benefits: 1) atomistic scale models capture the physics of the mechanisms of phase transformations, which can be generalized to other systems, 2) the modeling results (from atomistic to continuum) will be validated experimentally to ensure the predictive aspect, 3) the microstructure/property relationships captured with mechanical testing provides information at the engineering scale that is needed to validate the integration of the proposed new capability into Grizzly

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗