Search NASA⌕ Search

SEARCH · Search NASA

Results for “Yield Modeling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Climate impacts on global agriculture emerge earlier in new generation of climate and crop models

Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project’s Global Gridded Crop Model Intercomparison based on the Coupled Model Intercomparison Project Phase 5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new twenty-first-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to -6% (SSP126) and from +1% to -24% (SSP585)—explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts consistently occurs earlier in the new projections—before 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

Jagermeyr, Jonas↗
DOE OSTI2020

System-Level Optimization to Improve Biofuel Potential via Genetic Engineering and Hydrothermal Liquefaction

The economic success of biofuels and bioproducts depends on system-level optimization including biomass production and conversion. Hydrothermal liquefaction (HTL) can convert wet biomass such as microalgae into a biofuel intermediate (BFI) under elevated temperatures and pressure. An understanding of the impacts of biomass composition on BFI yield and quality can inform genetic engineering strategies in the improvement of biochemical composition for biofuel production. In this work, wild type cyanobacterium Synechocystis sp. PCC 6803 biomass was doped with various common cellular storage compounds in lab-scale HTL experiments. Doping with glycogen or polyhydroxybutyrate (PHB) significantly reduced BFI yields, while doping with triglycerides (TAG) or medium chain-length polyhydroxyalkanoate (mcl-PHA) increased BFI yield and quality. In light of these observations, a genetically engineered Synechocystis strain deficient in glycogen biosynthesis was cultivated to produce biomass for HTL, leading to a 17% increase in BFI yield. In addition, we built a multiphase component additivity (MCA) model that can predict BFI yield and quality with PHAs in the biomass. This work demonstrates an effective strategy to integrate strain development with downstream biomass conversion to maximize biofuel yield, with lessons applicable to microalgae as well as other biomass.

09 BIOMASS FUELS↗
DOE OSTI2022

Comparing Model Representations of Physiological Limits on Transpiration at a Semi-arid Ponderosa Pine Site

Mechanistic representations of biogeochemical processes in ecosystem models are rapidly advancing, requiring advancements in model evaluation approaches. Here we quantify multiple aspects of model functional performance to evaluate improved process representations in ecosystem models. We compare semi-empirical stomatal models with hydraulic constraints against more mechanistic representations of stomatal and hydraulic functioning at a semi-arid pine site using a suite of metrics and analytical tools. We find that models generally perform similarly under unstressed conditions, but performance diverges under atmospheric and soil drought. The more empirical models better capture synergistic information flows between soil water potential and vapor pressure deficit to transpiration, while the more mechanistic models are overly deterministic. Although models can be parameterized to yield similar functional performance, alternate parameterizations could not overcome structural model constraints that underestimate the unique information contained in soil water potential about transpiration. Additionally, both multilayer canopy and big-leaf models were unable to capture the magnitude of canopy temperature divergence from air temperature, and we demonstrate that errors in leaf temperature can propagate to considerable error in simulated transpiration. This study demonstrates the value of merging underutilized observational data streams with emerging analytical tools to characterize ecosystem function and discriminate among model process representations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Discrete photon implementation for plasma simulations

The self-produced light emission from pulsed plasma discharges and its impact on plasma development are challenging to characterize through simulation and modeling, chiefly due to the large number of radiating species and limited computer memory. Often, photo-processes, such as photo-ionization or photo-emission of electrons, are implemented through over-simplifying approximations or neglected altogether. Here, a method applicable to plasma simulations is implemented in a Particle-in-Cell /Monte Carlo Collision model, which is capable of discretely tracking photons and their corresponding wavelengths. Combined with the appropriate cross sections or quantum yields, a wavelength dependent model for photo-ionization or photo-emission may be implemented. Additionally, by resolving the wavelengths of each photon, an emission spectrum for a region of interest may be generated. Simulations for a pure nitrogen environment reveal that the calculated emission profile of the second positive system agrees well with the experimental spectrum of a pulsed, nanosecond discharge in the same spectral region.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

Searches for New Symmetries in pp Collisions with the Razor Kinematic Variables at $\mathbf{\sqrt{s}} =$ 7 TeV

The construction and LHC phenomenology of the razor variables M R , an event-by-event indicator of the heavy particle mass scale, and R, a dimensionless variable related to the transverse momentum imbalance of events and missing transverse energy, are presented. The variables are used in the analysis of the first proton-proton collisions dataset at CMS (35 pb -1 ) in a search for superpartners of the quarks and gluons, targeting indirect hints of dark matter candidates in the context of supersymmetric theoretical frameworks. The analysis produced the highest sensitivity results for SUSY to date and extended the LHC reach far beyond the previous Tevatron results. A generalized inclusive search is subsequently presented for new heavy particle pairs produced in √s = 7 TeV proton-proton collisions at the LHC using 4.7±0.1 fb -1 of integrated luminosity from the second LHC run of 2011. The selected events are analyzed in the 2D razor-space of MR and R and the analysis is performed in 12 tiers of all-hadronic, single and double leptons final states in the presence and absence of b-quarks, probing the third generation sector using the event heavy- flavor content. The search is sensitive to generic supersymmetry models with minimal assumptions about the superpartner decay chains. No excess is observed in the number or shape of event yields relative to Standard Model predictions. Exclusion limits are derived in the CMSSM framework with gluino masses up to 800 GeV and squark masses up to 1.35 TeV excluded at 95% confidence level, depending on the model parameters. The results are also interpreted for a collection of simplified models, in which gluinos are excluded with masses as large as 1.1 TeV, for small neutralino masses, and the first-two generation squarks, stops and sbottoms are excluded for masses up to about 800, 425 and 400 GeV, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Novel Microbial Routes to Synthesize Industrially Significant Precursor Compounds

Ethylene is the most widely employed organic precursor compound in industry. The potential to impact ethylene formation via recently discovered microbial processes is tenable using plentiful CO2 feedstocks. The overall long-term objective of this project was to develop an industrially compatible microbial process to synthesize ethylene in high yields. The key objective of this project was to fully define and initially characterized a recently discovered and genetically regulated anaerobic pathway to produce high levels of ethylene called the Dihydroxyacetone Phosphate - Ethylene Pathway in phototrophic bacteria. This was addressed through the following specific aims: 1. Fully probe the catalytic potential of all enzymes of the DHAP ethylene pathway and determine the regulatory mechanism of DHAP-ethylene pathway gene expression. 2. Discover effective and active ethylene enzymes encoded in cultured and uncultured organisms from anoxic environments. 3.Model the thermodynamics and kinetics of ethylene synthetic pathways to guide engineering efforts in integrating best performing DHAP-ethylene pathway enzymes into model bacteria chassis for enhance ethylene yields. Through this project we discovered the initially missing genetic and enzyme component of the DHAP-ethylene pathway that directly synthesized ethylene and other important industrial compounds like methane and ethane from specific substrates. We uncovered and partially characterized a nitrogenase-like reductase that functions in DHAP-ethylene pathway specifically and in methionine synthesis in general. This nitrogenase-like system is called the Methylthio-Alkane Reductase (MAR) for its ability to cleave volatile organic sulfur compounds into methanethiol (CH3-SH) for methionine synthesis and a hydrocarbon byproduct. Key to the DHAP-ethylene pathway, MAR is the essential enzyme that cleaves 2-methylthioethanol (CH3-S-CH2-CH2-OH) into ethylene. Coordinately, we uncovered that the MAR genes and genes associated with conversion of methanethiol (CH3-SH) to methionine are under genetic control of a LysR Type Transcriptional Regulator called SalR, whose activity is dependent upon the amount of sulfate available to the cell. When sulfate as the preferred sulfur source for cell growth drops below 200 micromolar, SalR become active for expressing the MAR and methionine biosynthesis genes to enable the cell to grow from volatile organic sulfur compounds and make ethylene. Metabolic thermos-kinetic modeling revealed that these MAR reactions for ethylene and other hydrocarbon production are highly thermodynamically favorable and are one of the largest driving forces for ethylene production by the DHAP-ethylene pathway for high ethylene yields. Modeling also indicated that a key aldolase and to a lesser extent an isomerase of the DHAP-ethylene pathway for production of the ethylene precursor, 2-methylthioethanol, also would increase ethylene yields. Through metagenomic mining and gene synthesis by the JGI DNA synthesis program, over 500 aldolase and isomerase homologs were synthesized and screened. From this, variants were uncovered with substantially higher activity that increased ethylene yields 5-fold via the aldolase reaction and 1.5-fold via the isomerase reaction. Each of these elements that increase ethylene production were integrated together via plasmid under appropriate gene promoter elements in the phototrophic bacterium, Rhodospirillum rubrum, resulting in at least 3 orders of magnitude increase in ethylene yield from carbon dioxide feedstock.

10 SYNTHETIC FUELS↗
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 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↗