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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Extinction in the Star Cluster SAI 113 and Galactic Structure in Carina

Photometric CCD UB VI {sub C} photometry obtained for 4860 stars surrounding the embedded southern cluster SAI 113 (Skiff 8) is used to examine the reddening in the field and derive the distance to the cluster and nearby van Genderen 1. Spectroscopic color excesses for bright cluster stars, photometric reddenings for A3 dwarfs, and dereddening of cluster stars imply that the reddening and extinction laws match results derived for other young clusters in Carina: E{sub U-B}/E{sub B-V}≃0.64 and R{sub V}≃4. SAI 113 displays features that may be linked to a history of dynamical interactions among member stars: possible circumstellar reddening and rapid rotation of late B-type members, ringlike features in star density, and a compact core, with most stars distributed randomly across the field. The group van Genderen 1 resembles a stellar asterism, with potential members distributed randomly across the field. Distances of 3.90 ± 0.19 kpc and 2.49 ± 0.09 kpc are derived for SAI 113 and van Genderen 1, respectively, with variable reddenings E{sub B-V} ranging from 0.84 to 1.29 and 0.23 to 1.28. The SRC variables CK Car and EV Car may be outlying members of van Genderen 1, thereby of use for calibrating the period–luminosity relation for pulsating M supergiants. More importantly, the anomalous reddening and extinction evident in Carina and nearby regions of the Galactic plane in the fourth quadrant impact the mapping of spiral structure from young open clusters. The distribution of spiral arms in the fourth quadrant may be significantly different from how it is often portrayed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Assessment of the baseline scenario at q 95 ~ 3 for ITER

The International Tokamak Physics Activity topical group on integrated operational scenarios has compiled a database of stationary H-mode discharges at q 95 ~ 3 from AUG, C-Mod, DIII-D, JET and JT-60U, for both carbon wall and high-Z metal wall experiments with ~3300 entries. The analyses focus on discharges that are stationary for ≥5 thermal energy confinement times to evaluate the baseline scenario proposed for ITER at 15 MA for achieving its goals of Q = 10, fusion power of 500 MW at normalized pressure, β N = 1.8 and normalized confinement as predicted by the standard H-mode scaling, H 98y2 = 1. With the data restricted to stationary H-modes at q 95 ~ 3, the database shows significant variation of thermal energy confinement compared to the standard H-mode scaling (IPB98(y,2)) in dimensionless form. The data show similar scaling with normalized gyro-radius, but more favorable scaling towards lower collision frequency and more favorable scaling with plasma beta. Using all the engineering variables employed in IPB98(y,2), results in an overfit due to correlations among the data. Furthermore, there are significant residual trends in the confinement for plasma current, device size, loss power, and in particular for the plasma density. Significant differences between results obtained for devices with a carbon wall and high-Z metal wall are observed in the data, with data from carbon wall devices providing a larger operating space, encompassing ITER parameters or even exceeding them. H-modes in high-Z metal wall devices have, so-far, not accessed conditions at low collision frequencies, have lower normalized confinement (H 98y2 ~ 0.8–0.9) at low input power or beta, achieving H 98y2 ~ 1.0 only at input powers two times the L- to H-mode transition scaling predictions and at β N ~ 2.0. Hence, only the best H-modes with high-Z metal walls reach ITER baseline performance requirements.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

TCAD-Machine Learning Enabled TID Compact Model Development for Commercial SiC MOSFET

We propose a TCAD (Technology Computer Aided Design)-machine learning coupled approach that combines a TCAD tool (Charon), optimization/uncertainty quantification tool (Dakota), surrogate models, and Bayesian learning capabilities. The coupling approach is used for accurate modeling and calibration of total ionizing dose (TID) induced threshold voltage (V th ) shifts in Commercial-Off-The-Shelf (COTS) semiconductor devices and to develop physics-informed TID compact models. This versatile approach is applied to model the TID effect in an exemplar COTS 3.3 kV SiC power MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor). With the Charon-Dakota coupling, we can determine key device geometry and doping values based on device physics, which are difficult to obtain or not available for COTS devices but important for TCAD simulation; additionally, we can efficiently generate thousands of simulation results in a large parameter space, which makes it possible to develop data-driven surrogate models and perform Bayesian calibration. Utilizing the full tool-coupling approach, we achieve calibrated TCAD simulation models that accurately capture the average TID-induced V th shifts behavior with total doses and V th shifts saturation at high doses as observed in experimental data. More importantly, the calibrated TCAD simulations are obtained with determined TID model parameters (e.g., hole trap density and capture cross section) values that contain well quantified uncertainties. Furthermore, we can isolate and quantify the noises that are not captured by the TCAD models but exist in the measured data due to measurements and devices variabilities. Lastly, the calibrated surrogate models are used to develop physics-informed TID compact models. The method is generalizable to other devices and/or radiation conditions with few modifications and can provide well-determined uncertainties.

COTS↗
DOE OSTI2019

Opacity: A window into High Energy Density Plasma Physics

Opacity is a fundamental property of matter and knowledge of it is required to understand the transport of radiation through a material. Exact knowledge of opacity is crucial in many fields such as astrophysics, which recently produced a white paper on the current state of astrophysical opacities [1], plasma physics, including inertial confinement fusion (ICF) and magnetic confinement fusion, high energy density physics (HEDP), the semiconductor industry [2] and stockpile stewardship. In industry, the accurate knowledge of the opacity of tin is important for extreme ultraviolet (EUV) lithography and the development of processors with smaller components. In astrophysics, Cepheid variable stars are used as “standard candles” for determining the distance to objects. Opacities of these stars are required to understand the relationship between their periodicity, luminosity and distance. Our sun is the most studied star and provides the basis of our understanding for other stars. Yet in significant ways the sun is poorly understood. In recent years, there has been a debate over the abundances of heavy elements (Z > 2) in the solar interior. Current solar atmosphere models [3] find a significantly lower abundance for C, N, and O compared to models used roughly a decade ago. Although solar evolution models have matured, a discrepancy still exists between spectroscopic solar models (which require opacities) with updated abundances, and solar properties inferred from helioseismology and neutrinos [4]. Despite its necessity, opacity is not solved, and is an open and active area of research needed for improving our understanding of plasma physics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Integrating Contaminant Source Indicators, Water Quality Measures, and Ecotoxicity to Characterize Contaminant Mixtures and Per- and Polyfluoroalkyl Substance (PFAS) Variability in an Urban Watershed

Thousands of chemical contaminants threaten watersheds but are time and cost prohibitive to monitor. Identifying their sources, transport, and ecological risk is limited in heterogeneous urban watersheds. We present an integrative watershed approach using source-specific indicator compounds, common water quality measures, and ecotoxicity assays to examine the distribution of contaminant mixtures in an urbanized watershed. Indicator compound concentrations were temporally and spatially distributed for treated/untreated sewage (sucralose, artificial sweetener), road runoff (diphenyl-guanidine [DPG] and 6PPD-quinone [6PPD-Q], automobile tire additives), and lawncare runoff (aminomethanephosphonic acid (AMPA), major degradant of the herbicide glyphosate). Sucralose was predominately sourced from treated wastewater; measurable concentrations in tributaries indicated raw sewage inputs. DPG and 6PPD-Q concentrations correlated to road density during base flow and were elevated during stormflow. AMPA was measurable spring through fall, especially where lawns were dense. When specific sources dominated flow, water quality measures correlated with wastewater (sulfate, potassium, chloride, and sodium) and road runoff (chromium and lead) indicators. The limited behavioral toxicity observed in exposed zebrafish (Danio rerio) (18%) was not well explained by source-indicators. PFAS concentrations were highly variable spatially but not well explained by our source-specific indicator compounds. Here, more costly compound-specific monitoring may be necessary when multiple sources exist or when unexpected toxicity trends occur.

computer simulations↗
DOE OSTI2018

Toward a Theory of the Evolution of Perturbations of the Dark Matter Density in the Very Early Universe

We investigate the influence of primordial baryonic matter in the form of cosmic plasma on the evolution of perturbations in dark matter considered as a gas of weakly interacting massive particles (WIMPs). Nonstationary equations of state of the WIMP gas for both its ideal model and its real model are proposed along with the state parameter of the gas in the form of a power-law dependence on time. A description of the appearance in the WIMP gas of seed perturbations is given which takes account of the influence of the primordial cosmic plasma and the variability of the state parameter of dark matter. Regimes of the evolution of perturbations of non-baryonic matter are found, describing their growth for various characteristics of the internal energy of the WIMP gas. The size and mass of the seed perturbations in the WIMP gas are estimated.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A Multi-Fidelity Gaussian Process Regression Method for Probabilistic Wind Farm Power Curve Estimation

Accurate estimation of the power curve for wind turbines or wind farms is crucial to ensure their efficient operation and management. However, conventional methods for power curve estimation rely either on expensive and infrequent measurements or on low-quality numerical simulations. Moreover, the majority of previous studies on power curve estimation for wind turbines or wind farms focused on deterministic estimation, which provides a point estimate of the relationship between wind speed and power generation. Nevertheless, the deterministic approach fails to consider the inherent uncertainty associated with wind energy production resulting from varying turbine characteristics. This can lead to inaccurate power generation estimation and suboptimal decisions regarding energy management. In this paper, a kernel density estimation (KDE) based Multi-Fidelity Gaussian Process Regression (MFGPR) model is proposed to fuse theoretical power curve data and the ground true measurements to create a mapping of wind speed and wind power. By conducting a case study on an actual wind farm in China, the efficacy of the proposed MFGPR model was demonstrated in characterizing the variability of wind power. The probabilistic MFGPR model was also able to generate confidence intervals that encompassed the measured power, thereby improving the accuracy and confidence in wind power estimation or wind resource assessment. Overall, the proposed MFGPR model offers a reliable approach to integrate high-fidelity ground measurements and theoretical power curve data, resulting in precise wind resource assessment and power estimation.

Gaussian process regression↗
DOE OSTI2022

Using Cosmic Ray Muons to Assess Geological Characteristics in the Subsurface

Cosmic rays are energetic nuclei and elementary particles that originate from stars and intergalactic events. The interaction of these particles with the upper atmosphere produces a wide range of secondary particles that reach the surface of the earth, of which muons are the most prominent. With enough energy, muons can travel up to a few kilometers beneath the surface of the earth before being stopped completely. The terrestrial muon flux profile and associated zenith angle can be utilized to determine geological characteristics of a location (e.g., rock overburden and density) without having to use conventional methods such as boreholes. This work uses a low-power plastic scintillator-based muon detection system as a prototype for this non-destructive geological assay methodology. Four custom designed 102 cm x 51 cm x 5 cm plastic scintillation panels are used to realize two orthogonal detection planes. Optical photons from each scintillation panel are read using OnSemi J-Series 4x4 silicon photomultiplier (SiPM) arrays in conjunction with preamplifiers. Simultaneous triggers between detectors from two planes indicate a coincidence event which is recorded using the QuarkNet data acquisition system (DAQ) from Fermi National Accelerator Laboratory. A custom detector holder was designed to securely mount the detection system and rotate the panels along the zenith to collect data at variable angles. In order to quantify the systematic uncertainties associated with the detector, such as energy depositions and angular resolution of the detector design, a Monte Carlo (MC) simulation using Geant4 is being developed. Cosmic ray flux prediction will be included in the project by adding the CORSIKA MC code to the simulation toolchain. Simulated and experimental data will drive the development and validation of a reconstruction algorithm that, upon completion, is expected to predict average overburden and rock density. Extended detector exposure to muons can be used as a means to understand changes in the surrounding environment like rock porosity. On the experimental front, muons will initially be measured at the surface, establishing the baseline flux. This is followed by recording the muon flux at variable depths and zenith angles, where the data will be used by the reconstruction algorithm to predict the overburden. The result will be benchmarked against geological surveys. The measured flux data will also be used to benchmark independent and established models. Successful proof-of-concept demonstration of this technology can open doors for long term non-invasive geological monitoring. The detector design, experimental methodology, and the benchmarking efforts are detailed in this work.

Gadey, Harish Reddy↗
DOE OSTI2019

Pelleting of Pine and Switchgrass Blends: Effect of Process Variables and Blend Ratio on the Pellet Quality and Energy Consumption

The blending of woody and herbaceous biomass can influence pellet quality and the energy consumption of the process. This work aims to understand the pelleting characteristics of 2-inch top-pine residue blended with switchgrass at high moisture content. The process variables tested are blend moisture content, length-to-diameter (L/D) ratio in the pellet die, and the blend ratio. A flat die pellet mill was also used in this study. The pine and switchgrass blend ratios that were tested include: (1) 25% 2-inch top pine residue with 75% switchgrass; (2) 50% 2-inch top pine residue with 50% switchgrass; and (3) 75% 2-inch top pine residue with 25% switchgrass. The pelleting process conditions tested included the L/D ratio in the pellet die (i.e., 1.5 to 2.6) and the blend moisture content (20 to 30%, w.b.). Analysis of experimental data indicated that blending 25% switchgrass with 75% 2-inch top pine residue and 50% switchgrass with 50% 2-inch top pine residue resulted in pellets with a bulk density of > 550 kg/m3 and durability of > 95%. Optimization of the response surface models developed for process conditions in terms of product properties indicated that a higher L/D ratio of 2.6 and a lower blend-moisture content of 20% (w.b.) maximized bulk density and durability. Higher pine in the blends improved the pellet durability and reduced energy consumption.

09 BIOMASS FUELS↗
DOE OSTI2018

Defects and 3D structural inhomogeneity in electron beam additively manufactured Inconel 718

Highlights: • A protocol for 3D characterization of structure and defects in additively manufactured EBM 718 is presented. • Lack of fusion defects disrupt local microstructure, creating isolated regions of equiaxed grains. • Clustering of micron-scale nitrides is attributed to a particle rafting process. • Normalized size-number density relationship of carbides matches that of industrial ingots. • Removal of porosity via post-processing will not correct microstructural inhomogeneities created by lack of fusion defects. - Abstract: Material structure and defects created during additive manufacturing contribute to the variability in mechanical properties of these parts, limiting their use for critical applications. Three-dimensional crystallographic, structural, and chemical information in electron beam melted Inconel 718 was collected from the sub-micron to the millimeter scale using the TriBeam tomography system. The relationship of defects to the surrounding microstructure has been studied in detail. Lack of fusion defects created sharp discontinuities in the grain morphology that extended several build layers beyond the layer where the defect originated. Nitrides and carbides in the size range of 1.25 μm to 10 μm were observed throughout the volume, with pronounced nitride clustering. The similarities between solidification in additive manufacturing and traditional manufacturing processes are demonstrated by the comparable size and distribution of carbides measured for industrial-scale samples of 718. Defects created during additive manufacturing cannot be easily remedied via standard post-processing techniques, and will likely diminish the mechanical performance of additively manufactured parts.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep learning-enabled probing of irradiation-induced defects in time-series micrographs

Modeling time-series data with convolutional neural networks (CNNs) requires building a model to learn in batches as opposed to training sequentially. Coupling CNNs with in situ or operando techniques opens the possibility of accurately segmenting dynamic reactions and mass transport phenomena to understand how materials behave under the conditions in which they are used. In this article, in situ ion irradiation transmission electron microscopy (TEM) images are used as inputs into the CNN to assess the defect generation rate, defect cluster density, and saturation of defects. We then use the output segmentation maps to correlate with conventional TEM micrographs to assess the model’s ability to detail nanoscale interactions. Next, we discuss the implications of preprocessing and hyperparameters on model variability, accuracy when expanded to other datasets, and the role of regularization when controlling model variance. Ultimately, we eliminate human bias when extrapolating physical metrics, speed up analysis time, decouple reactions that happen at 100 ms intervals, and deploy models that are both accurate and transferable to similar experiments.

Burns, Kory (ORCID:0000000158019909)↗
DOE OSTI2021

ATOMIC Simulations and Experimental Data for CaCO3 Mixtures

This data consists of simulations and experimental measurements of laser-induced breakdown spectroscopy (LIBS). The simulations are produced by ATOMIC, a general purpose plasma modeling and kinetics code that has been designed to compute emission (or absorption) spectra from plasmas [1] and are used to develop a statistical characterization of matrix effects. Our overall suite of simulations includes contains several sets of simulations: training and validation sets of simulations for three and four element mixtures of calcium, carbon, oxygen, and nitrogen (included to account for atmosphere) along with simulations of the individual elements. The 4-element simulations include the mixture of all four elements mentioned and for each of the four individual elements. The 3-element simulations include output for the mixture of calcium, carbon, oxygen and for these three individual elements. The training data were produced using a 600-run design, shown in Figure 1, that varies input parameters temperature (T), electron density (Ne), and proportion of the elements calcium, carbon, oxygen, and nitrogen (Ca; C; O; N) for the 4 element output. The 3-element output includes all parameters except for the proportion of nitrogen. The element proportions (all the variables but T and Ne) sum to one and are unused in the single-element simulations. The validation data was produced with a 80-run design shown in Figure 2. The training and validation simulation outputs for the 4-element simulations for the mixture and for the single element calcium are shown as sample simulations in Figures 3 and 4 respectively. The simulations produce spectra over a range of 190nm - 950nm that roughly mimics the range collected by the SciAps Z-300 LIBS instrument that was used for the experimental data. The measured spectra for a CaCO3 (which may include contribution from Earth's atmosphere) in the experiment is shown in in Figure 5. All files are kept in directories whose names indicate the elemental composition (CaCO3, Ca, C, O, or N), number of elements (3 or 4), and purpose (training, which is not labeled in the file name, or validation) with file names numbered to indicate the line in the design files used to produce the simulation. The designs are provided as text files with names indicating their purpose. The experimental data is provided as a CSV file. [1] J Colgan, EJ Judge, DP Kilcrease, and JE Barefield II. Ab-initio modeling of an iron laser-induced plasma: Comparison between theoretical and experimental atomic emission spectra. Spectrochimica Acta Part B: Atomic Spectroscopy, 97:65{73}, 2014.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

The Mechanism of Electron Injection and Acceleration in Transrelativistic Reconnection

Electron acceleration during magnetic reconnection is thought to play a key role in time-variable high-energy emission from astrophysical systems. By means of particle-in-cell simulations of transrelativistic reconnection, we investigate electron injection and acceleration mechanisms in low-β electron–proton plasmas. We set up a diversity of density and field structures (e.g., X-points and plasmoids) by varying the guide field strength and choosing whether to trigger reconnection or let it spontaneously evolve. We show that the number of X-points and plasmoids controls the efficiency of electron acceleration, with more X-points leading to a higher efficiency. Using on-the-fly acceleration diagnostics, we also show that the nonideal electric fields associated with X-points play a critical role in the first stages of electron acceleration. As a further diagnostic, we include two populations of test particles that selectively experience only certain components of electric fields. We find that the out-of-plane component of the parallel electric field determines the hardness of the high-energy tail of the electron energy distribution. These results further our understanding of electron acceleration in this regime of magnetic reconnection and have implications for realistic models of black hole accretion flows.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Issue Summary of INL Phase IV Transient Results for IAEA CRP on HTGR UAM Benchmark

This report details the Parallel and Highly Innovative Simulation for Idaho National Laboratory (INL) Code System (PHISICS)/Reactor Excursions and Leak Analysis Program (RELAP5)-3D results obtained for the transient core exercises defined for Phase IV of the International Atomic Energy Agency (IAEA) Coordinated Research Project (CRP) on high-temperature gas cooled reactor (HTGR) uncertainty analysis in modeling (UAM). The Phase III models and results are linked to the earlier Standardized Computer Analyses for Licensing Evaluation (SCALE)/Sampler/New ESC-based Weighting Transport (NEWT) data generated for the lattice physics (lattice) stage Phase I of the CRP. The focus of this report is the Uncertainty/Sensitivity Assessment (U/SA) of the prismatic modular high-temperature gas cooled reactor (MHTGR)-350 design, and specifically for Exercises IV-1 and IV-2 of the benchmark: the Control Rod Withdrawal (CRW) and Pressurised Loss of Cooling (PLOFC) events. The statistical U/SA methodology is implemented and demonstrated using the RAVEN code, based on perturbed cross-section libraries obtained from the SCALE/Sampler sequence. Uncertainties in nuclear data (cross-sections and the average number of neutrons produced per fission, 235U[¯v ]) lead to standard deviations (uncertainties of one s) of approximately 0.5% in the core eigenvalues of the MHTGR-350 and core models. For the coupled neutronics/thermal fluid model, local power density uncertainties up to 3.6% were observed in the colder regions of the core, while the local maximum fuel temperature uncertainties reached 1.5% for the models that included thermal fluid uncertainties. The addition of thermal fluid uncertainties dominated the impacts of nuclear data uncertainties in all cases. The main contributors to uncertainties in the power density and fuel temperatures during the transients were uncertainties in the reactor operating conditions (total power, inlet mass flow rate and inlet gas temperature). Variations in the bypass flows did not have significant impact on any of the output variables. For the nuclear data uncertainties it was found that the 235U(¯v ) / 235U(¯v ) covariance produced the largest sensitivities in terms of its impact on the eigenvalue and peak reactor power. It was also observed that the impact of any nuclear data uncertainties on the maximum fuel temperature was much less significant that the impact on eigenvalue and power. Another important finding was that although the use of eight or more energy groups is recommended for best-estimate HTGR simulation, two-group models produced acceptable uncertainty and sensitivity results for most FOMs. Since the statistical U/SA methodology is computationally expensive, and most transient solver requirements will scale directly with the number of energy groups, two energy groups could be used by HTGR developers during the early stages of design when larger uncertainty margins can be tolerated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Evolution of oxygen and stratification and their relationship in the North Pacific Ocean in CMIP6 Earth system models

Abstract. This study examines the linkages between the upper-ocean (0–200 m) oxygen (O2) content and stratification in the North Pacific Ocean using four Earth system models (ESMs), an ocean hindcast simulation, and an ocean reanalysis. The trends and variability in oceanic O2 content are driven by the imbalance between physical supply and biological demand. Physical supply is primarily controlled by ocean ventilation, which is responsible for the transport of O2-rich surface waters to the subsurface. Isopycnic potential vorticity (IPV), a quasi-conservative tracer proportional to density stratification that can be evaluated from temperature and salinity measurements, is used herein as a dynamical proxy for ocean ventilation. The predictability potential of the IPV field is evaluated through its information entropy. The results highlight a strong O2–IPV connection and somewhat higher (as compared to the rest of the basin) predictability potential for IPV across the tropical Pacific, where the El Niño–Southern Oscillation occurs. This pattern of higher predictability and strong anticorrelation between O2 and stratification is robust across multiple models and datasets. In contrast, IPV at mid-latitudes has low predictability potential and its center of action differs from that of O2. In addition, the locations of extreme events or hotspots may or may not differ between the two fields, with a strong model dependency, which persists in future projections. On the one hand, these results suggest that it may be possible to monitor ocean O2 in the tropical Pacific based on a few observational sites co-located with the more abundant IPV measurements; on the other, they lead us to question the robustness of the IPV–O2 relationship in the extratropics. The proposed framework helps to characterize and interpret O2 variability in relation to physical variability and may be especially useful in the analysis of new observation-based data products derived from the BGC-Argo float array in combination with the traditional but far more abundant Argo data.

Novi, Lyuba↗
DOE OSTI2017

A Method to Measure the Unbiased Decorrelation Timescale of the AGN Variable Signal from Structure Functions

A simple, model-independent method to quantify the stochastic variability of active galactic nuclei (AGNs) is the structure function (SF) analysis. If the SF for the timescales shorter than the decorrelation timescale τ is a single power law and for the longer ones becomes flat (i.e., white noise), then the auto-correlation function (ACF) of the signal can have the form of the power exponential (PE). We show that the signal decorrelation timescale can be measured directly from the SF as the timescale matching the amplitude 0.795 of the flat SF part (at long timescales), and only then is the measurement independent of the ACF PE power. Typically, the timescale has been measured at an arbitrarily fixed SF amplitude, but as we prove, this approach provides biased results, because the AGN SF/power spectral density slopes, and thus the ACF shape, are not constant and depend on either the AGN luminosity and/or the black hole mass. In particular, we show that using such a method for the simulated SFs that includes a combination of empirically known dependencies between the AGN luminosity L and both the SF amplitude and the PE power, and having no intrinsic τ–L dependence, produces a fake τ∝L{sup κ} relation with 0.3≲κ≲0.6, which otherwise is expected from theoretical works (κ≡0.5). Our method provides an alternative means for analyzing AGN variability to the standard SF fitting. The caveats, for both methods, are that the light curves must be sufficiently long (with a several year rest frame) and the ensemble SF assumes AGNs to have the same underlying variability process.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Complex Fracture Nucleation and Evolution with Nonlocal Elastodynamics

A mechanical model is introduced for predicting the initiation and evolution of complex fracture patterns without the need for a damage variable or law. The model, a continuum variant of Newton’s second law, uses integral rather than partial differential operators where the region of integration is over finite domain. The force interaction is derived from a novel nonconvex strain energy density function, resulting in a nonmonotonic material model. The resulting equation of motion is proved to be mathematically well-posed. The model has the capacity to simulate nucleation and growth of multiple, mutually interacting dynamic fractures. In the limit of zero region of integration, the model reproduces the classic Griffith model of brittle fracture. As a result, the simplicity of the formulation avoids the need for supplemental kinetic relations that dictate crack growth or the need for an explicit damage evolution law.

36 MATERIALS SCIENCE↗