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At least 577 records · Page 32

bmdrc: Python package for quantifying phenotypes from chemical exposures with benchmark dose modeling

Though chemical exposures are known to potentially have negative impacts on health, including contributing to chronic diseases such as cancer, the quantitative contribution of risk is not fully understood for every chemical. A commonly used approach to quantify levels of risk is to measure the proportion of organisms (such as a total number of zebrafish on a plate or mice in a cage) with abnormal behavioral responses or morphology at increasing concentrations of chemical exposure. A particular challenge with processing the proportional data from these assays is the appropriate estimation of chemical concentration levels that result in malformations or acute toxicity, as these values typically vary between experimental measurements. The recommended approach by the Environmental Protection Agency (EPA) is to fit benchmark dose curves with specific filters and model fitting steps, which are crucial to properly processing the proportional data. Several tools exist for the fitting of benchmark dose response curves, but none are standalone Python libraries built to process both morphological and behavioral data as proportions with all the EPA recommended filters, filter parameters, models, and model parameters. Thus, here we present the benchmark dose response curve (bmdrc) Python library, which was built to closely follow these EPA guidelines with helpful visualizations of filters and fitted model curves, and reports for reproducibility purposes. bmdrc is open-source and has demonstrated utility as a support package to an existing web portal for information on chemicals (https://srp.pnnl.gov). Our package will support any toxicology analysis where the response is a proportional value at increasing levels of a concentration of a chemical or chemical mixture.

Superfund↗

Projecting Power Converter Specific Power Through 2050 for Aerospace Applications

In order to analyze the potential fuel burn benefit from the electrification of aircraft powertrains, it is important to quantify the amount of weight that will be added to the aircraft for each additional component of the electric powertrain. This paper provides a projection of the specific power and efficiency of power converters, (AC-DC, DC-AC, or DC-DC), through the year 2050. Data was first collected on state of the art power converters in multiple application areas, creating a power converter database. Relevant specific powers were added to a set of historical data from 1976-2020, and then three different logistic curves were fit through the historical data to represent S-curve shaped growth through the year 2050. The three curves were differentiated by conservative, nominal, and aggressive assumptions for the year in which the logistic curve begins to bend down towards slower growth. With a 30% knockdown factor accounting for the additional weight required for a high altitude converter, projections range from the aggressive specific power projection of 52.9 kW/kg in 2050 to a much more conservative specific power of 12 kW/kg in which growth is limited due to certifiability concerns. Little historical data was found on converter efficiencies to project efficiency based on historical trends. Projections are based on expert opinion on yearly decreases in converter losses. 2050 projections range from 0.987 to 0.997.

fuel burn↗

Ultraviolet extinction to 10.8 inverse microns

UV extinction curves that represent observed variations i diffuse clouds (Zeta Oph, Xi Per, and Sigma Sco) have been derived by combining IUE and Voyager data and have been modeled by a multicomponent size distribution of silicate and carbonaceous giants. We have used theoretical model atmospheres as our reddening-free standards, and, in the case of Zeta Oph, also compare with the use of a stellar standard. An attempt was made to classify the UV spectra via theoretical spectral indices, but still missing line opacity appears to limit the use of this method at the present. The modeling includes five possible grain ingrediants, largge (0.015-0.25 micrometers) and small (0.005 micrometers) silicate grains, similarly large and small carbonaceous grains, and polycyclic aromatic hydrocarbons (PAHs). The observed extinction curves are fitted by the model via nonlinear Chi-square-minimization, varying the fractional amount of cosmic Si and C that are locked up in each igredient. The observed variation from 'steep' to 'flat' UV extinction curves is found to correspond to a modest model variation in the lower limit of the power-law size distribution of the large grains. Graphite is found to be necessary grain ingredient that cannot be replaced by the laboratory amorphous carbon materials. Carbonaceous mantles on the larger silicate grains provide no good fits to the observed curves. Using absorption properties of neutral laboratory PAHs limits PAHs to a few percent of the cosmic C abundance for acceptable fits that also give values for R(sub V) close to the observed values.

Aannestad, Per A.↗

Development of Digital Twin Predictive Model for PWR Components: Updates on Multi Times Series Temperature Prediction Using Recurrent Neural Network, DMW Fatigue Tests, System Level Thermal-Mechanical-Stress Analysis

The long-term operation (LTO) of nuclear power plant (NPP) beyond their original design life of 40 years, can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Frequent NDE requirement may lead to frequent shutdown of the NPP. This in turn can lead to power outage and additional NDE-inspection-cost related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing more digital approach such as by using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. Towards this goal Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program is working on the development of a DT framework that can be used for real time environmental fatigue prediction of reactor components. The DT framework is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics such as finite element (FE) based modeling tools. Towards this overall goal, following are some of the major contributions made during the FY21: 1) Multiple 82/182 dissimilar metal weld (DMW) specimens (both solid-weld and joint-weld representing the actual reactor multi-metal nozzles) were fatigue tested. The resulting fatigue lives were compared to the NUREG-6909 based best-fit and design fatigue curves. Additionally, the results of 52/152 DMW fatigue specimens (which were recently tested at Republic of Korea under the sponsorship of International Nuclear Energy Research Initiative - INERI program) were compared to the NUREG-6909 based best-fit and design fatigue curves. From the comparison of 82/182 and 52/152 DMW test data with NUREG-6909 best-fit curve, most of the reported test data fall way away from the NUREG-6909 suggested best-fit or mean curve. The NUREG-6909 suggested best-fit curve is the best-fit curve of austenitic stainless steel and due to lack of enough data on Nickel-based welds, this is currently being used for predicting the life of Nickel-alloy-based welded components. However, the above observation may require higher scaling factor (e.g., ASME suggested factor of 20 on cycles rather than the current NUREG-6909 suggested factor of 12 on cycles) for scaling the austenitic-stainless-steel best-fit-curve for estimating the design or safe-life of a welded component. Accordingly, for example, if a DMW component experience a strain amplitude of 0.6% the PWR-water life of the component would be 52 cycles instead of 85 cycles. However, more DMW tests are required to further ascertain the above-mentioned observations. 2) A system level CAD and finite element model were developed which consists of reactor pressure vessel (RPV), part of steam generator (SG), part of pressurizer (PRZ), hot leg (HL), and surge line (SL). This is with detailed nozzle geometry and thermal-mechanical material properties of different metals to simulate realistic thermal-mechanical stress under connected system global thermal-mechanical boundary conditions. 3) Different system level heat transfer analyses were performed with estimation of relevant heat transfer coefficients. The resulting data were used in subsequent system level thermal-mechanical stress analysis and for generating spatial-temporal training and validation data for a system level digital-twin based temperature predictor. Transient heat transfer analyses were performed considering thermal boundary condition under design-basis (DB) loading and EDF (Électricité de France) data-based grid-load-following (EDF-GLF) loading cycles. 4) System level thermal-mechanical stress analysis was performed for identifying damage-prone hotspots and for future extension of the model for cyclic state prediction. From the system-level model simulation under DB loading cycle it is found that HL and the SL nozzle that connect to the HL can experience significant stress and strain and could be one of the weakest links in the overall reactor coolant system (RCS). 5) An AI/ML based DT model was developed for multi-time-series temperature prediction at any inside/outside thickness locations of PWR pressure boundary components. This is by using Recurrent-neural-network (RNN) and keras machine learning libraries. The RNN model was validated against two laboratory test-based data sets with one obtained through ANL’s in-air fatigue test system and other through PWR-water test loop. The experimentally validated DT model further validated against FE model results to predict thermal scarification related spatialtemporal temperatures at random locations of a component. The well validated DT model was then used for demonstrating spatial-temporal temperature prediction under 100+ years of reactor operation subjected to combined DB, EDF-GLF and randomized grid-load-following (RANDOMGLF) loading Cycles. The expert-elicitation DT model framework was developed assuming field/input/process measurements can be available from a few existing plant sensors and can readily be used by the NPP operators. The above temperature prediction model will feed to the next-step stress analysis model based on which the life of a component can be predicted in realtime, which is one of our future works.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Simultaneous Optical Transmission Spectroscopy of a Terrestrial, Habitable-zone Exoplanet with Two Ground-based Multiobject Spectrographs

Investigating the atmospheres of rocky exoplanets is key to performing comparative planetology between these worlds and the terrestrial planets that reside in the inner solar system. Terrestrial exoplanet atmospheres exhibit weak signals, and attempting to detect them pushes at the boundaries of what is possible for current instrumentation. We focus on the habitable-zone terrestrial exoplanet LHS 1140b. Given its 25-day orbital period and 2 hr transit duration, capturing transits of LHS 1140b is challenging. We observed two transits of this object, approximately 1 yr apart, which yielded four data sets thanks to our simultaneous use of the IMACS and LDSS3C multiobject spectrographs mounted on the twin Magellan telescopes at Las Campanas Observatory. We present a jointly fit white light curve, as well as jointly fit 20 nm wavelength-binned light curves from which we construct a transmission spectrum. Binning the joint white light-curve residuals to 3-minute time bins gives an rms of 145 ppm; binning down to 10-minute time bins gives an rms of 77 ppm. Our median uncertainty in R{sub p}{sup 2}/R{sub s}{sup 2} in the 20 nm wavelength bins is 260 ppm, and we achieve an average precision of 1.3× the photon noise when fitting the wavelength-binned light curves with a Gaussian process regression. Our precision on R{sub p}{sup 2}/R{sub s}{sup 2} is a factor of four larger than the feature amplitudes of a clear, hydrogen-dominated atmosphere, meaning that we are not able to test realistic models of LHS 1140b’s atmosphere. The techniques and caveats presented here are applicable to the growing sample of terrestrial worlds in the Transiting Exoplanet Survey Satellite era, as well as to the upcoming generation of ground-based giant segmented mirror telescopes.

79 ASTRONOMY AND ASTROPHYSICS↗

A calibration model for a stellar photometer using a SEC vidicon.

Description of a SEC vidicon response model that was used to calculate intensities of stars in the ultraviolet. The tube was used only as a stellar filter photometer. The model proposed is applicable for point sources. Experiments with both curve- or surface-fitting techniques and with extrapolation-interpolation procedures were conducted. The curve-fitting techniques were not successful, and the target gain was calculated with the aid of interpolation techniques.

Deutschman, W. A.↗

Probabilistic Multi-Factor Interaction Model for Evaluating Continuous Smooth Curves

The Multi-Factor Interaction Model (MFIM) is used to evaluate the divot weight (foam weight ejected) from launch external tanks. The MFIM has sufficient degrees of freedom to evaluate a large number of factors that may contribute to the divot ejection. It also accommodates all interactions by its product form. Each factor has an exponent that satisfies only two points the initial and final points. They are selected so that the MFIM will generate a common curve to fit the individual point data. The results show that the approach used generates the continuous curve.

Chamis, Christos C.↗

Introducing a New Spitzer Master BLISS Map to Remove the Instrument Systematic Phase-curve-parameter Degeneracy, as Demonstrated by a Reanalysis of the 4.5 μm WASP-43b Phase Curve

While Spitzer Infrared Array Camera (IRAC) systematics are generally well understood, each data set can provide its own challenges that continue to teach us about the underlying functional form of these systematics. Multiple groups have analyzed the phase curves of WASP-43b with varying detrending techniques, each obtaining different results. In this work, we take another look at WASP-43b while exploring the degenerate relation between Bilinearly Interpolated Subpixel Sensitivity (BLISS) mapping, point response function (PRF)–FWHM detrending, and phase curve parameters. We find that there is a strong correlation between the detrending parameters in the two models, and best-fit phase curve amplitudes vary strongly when the data are temporally binned. To remove this degeneracy, we present a new Gaussian centroided intrapixel sensitivity map (hereafter fixed sensitivity map), generated using 3,712,830 exposures spanning 5 yr, for a variety of aperture sizes at 4.5 μm. We find evidence for time variability in the sensitivity at 3.6 μm and do not generate a visit-independent map for this channel. With the fixed 4.5 μm intrapixel sensitivity map, the best fits for WASP-43b no longer vary strongly with bin size and PRF–FWHM detrending is no longer required to remove correlated noise. For data sets that do not fall completely within the sweet spot, temporal binning should not be used in the analysis of Spitzer phase curves. We confirm nightside emission for WASP-43b with a disk-integrated nightside temperature of 806 ± 48 K at 4.5 μm. The 4.5 μm maps are available at github.com/kevin218/POET.

79 ASTRONOMY AND ASTROPHYSICS↗

A light synthesis study of W Ursae Majoris

A common set of Roche model parameters produces an accurate fit to I, V, and B data for W Ursae Majoris. The R data are defective. Two pairs of cool photospheric starspots are adopted in producing the fit. The model also adopts the cross-correlation radial velocity obtained by McLean and satisfactorily eliminates evidence, cited in the literature, for a third body in the system. Changing starspot longitudes, while maintaining fixed angular diameter, latitude, and temperature contrast from the adjacent photosphere, can simulate all of the types of light curve instability recorded in historical W Ursae Majoris light curves. Although the theoretical curve accurately fits primary minimum in U, the theoretical curve is too shallow at secondary minimum. A hot secondary explanation of W-type light curves, rather than starspots on an otherwise barotropic photosphere is argued.

Linnell, Albert P.↗

Boundary-fitted coordinates for regions with highly curved boundaries and reentrant boundaries

A procedure was developed using the differential equation approach for generating boundary fitted coordinates for regions with highly curved boundaries as well as reentrant boundaries, such as those encountered in breaking surface waves. The resulting coordinates are nearly orthogonal and can provide adequate resolution even in the reentrant region. Consistent treatment of end boundaries and the use of a systematic initialization scheme and advanced implicit numerical solution techniques make the procedure highly efficient. The method developed for implicit enforcement of the periodicity boundary condition should be beneficial in the analysis of turbomachinery flow applications.

Ghia, U.↗

Code for multi-shape Gaussian process (GP) fitting with uncertainty quantification (UQ)

This is code associated with the publication “Nonparametric Multi-shape Modeling with Uncertainty Quantification,” authored by Hengrui Luo (Lawrence Berkeley National Laboratory) and Justin Strait (Los Alamos National Laboratory). The code is used to fit multiple-output Gaussian process (GP) models of planar closed curves to collections of ordered point sets, allowing for flexible nonlinear prediction of the underlying curve under dense or sparse point set samplings and with or without noise, as well as tractable uncertainty quantification. To do this, we employ use of a periodic kernel to account for the nonlinear input space of closed curves, and combine with coregionalization models to account for dependence both (i) between curve coordinates, and (ii) between pairs of curves. Functions in the code are capable of fitting these models, as well as performing additional tasks with the fitted curves such as (a) shape registration and alignment, (b) shape averaging, and (c) fitting for curve sub-populations / clusters.

Strait, Justin↗

Saturn Ring Data Analysis and Thermal Modeling

CIRS, VIMS, UVIS, and ISS (Cassini's Composite Infrared Specrtometer, Visual and Infrared Mapping Spectrometer, Ultra Violet Imaging Spectrometer and Imaging Science Subsystem, respectively), have each operated in a multidimensional observation space and have acquired scans of the lit and unlit rings at multiple phase angles. To better understand physical and dynamical ring particle parametric dependence, we co-registered profiles from these three instruments, taken at a wide range of wavelengths, from ultraviolet through the thermal infrared, to associate changes in ring particle temperature with changes in observed brightness, specifically with albedos inferred by ISS, UVIS and VIMS. We work in a parameter space where the solar elevation range is constrained to 12 deg - 14 deg and the chosen radial region is the B3 region of the B ring; this region is the most optically thick region in Saturn's rings. From this compilation of multiple wavelength data, we construct and fit phase curves and color ratios using independent dynamical thermal models for ring structure and overplot Saturn, Saturn ring, and Solar spectra. Analysis of phase curve construction and color ratios reveals thermal emission to fall within the extrema of the ISS bandwidth and a geometrical dependence of reddening on phase angle, respectively. Analysis of spectra reveals Cassini CIRS Saturn spectra dominate Cassini CIRS B3 Ring Spectra from 19 to 1000 microns, while Earth-based B Ring Spectrum dominates Earth-based Saturn Spectrum from 0.4 to 4 microns. From our fits we test out dynamical thermal models; from the phase curves we derive ring albedos and non-lambertian properties of the ring particle surfaces; and from the color ratios we examine multiple scattering within the regolith of ring particles.

Saturn's rings↗

Efficient analysis of small-angle scattering curves for large biomolecular assemblies using Monte Carlo methods

Structure elucidation from small-angle scattering curves of large biomolecular assemblies is notoriously challenging. This is because the simulation of high-resolution features in the structure of large macromolecular assemblies, such as de novo protein assemblies, is computationally demanding when it needs to cover a broad range of length scales. Conventional methods, such as the numerical approximation to the Debye equation or the use of spherical harmonics, do not scale well as the size of the assembly increases, which limits their application to small structures (e.g. individual proteins). This work explores the effectiveness of a Monte Carlo method to simulate and fit scattering curves for large biomolecular assemblies spanning over ranges covering atomic and molecular detail (e.g. spacing and orientation of proteins in an assembly) as well as large-scale (hundreds of nanometres) features. Owing to its speed and scalability, it can be combined with a fitting algorithm to extract structural features from experimental small-angle scattering curves in biomolecular assemblies that are otherwise intractable for interpretation. This work first demonstrates the effectiveness of the tool using experimental small-angle X-ray scattering (SAXS) data from tile-like proteins that assemble into 1D tube-like macromolecular structures. Here, the diameter distribution of tubes is extracted from SAXS fits, and this is quantitatively compared with distributions from electron microscopy. SAXS data are also obtained from 2D sheet-like protein assemblies, and the proposed method is used to quantify structural features such as the separation distance between protein building blocks and the flexing of the sheet. An open-source implementation of the methodology is provided for use in a broad range of biological systems involving multi-scale scattering analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Ultraviolet observations of NGC 205

Low-resolution IUE observations of the dwarf elliptical galaxy NGC 205 show that the UV spectral energy distribution of the galaxy is relatively flat. Spectra centered on the nucleus and on a region north of the nucleus show evidence of recent 'bursts' of star formation which contribute strongly to the UV spectral energy distribution. The UV spectra are fit with a composite spectrum based on a Miller-Scalo initial mass function, an underlying older population and an extinction based on a SMC-like extinction curve. This fit implies that the total mass of young stars in the galaxy is about 700,000 solar mass, which can be compared to the total mass of Population II stars in the galaxy of about 100 million solar mass.

Wilcots, Eric M.↗

Light-Curve Modelling Constraints on the Obliquities and Aspect Angles of the Young Fermi Pulsars

In more than four years of observation the Large Area Telescope on board the Fermi satellite has identified pulsed gamma-ray emission from more than 80 young or middle-aged pulsars, in most cases providing light curves with high statistics. Fitting the observed profiles with geometrical models can provide estimates of the magnetic obliquity alpha and of the line of sight angle zeta, yielding estimates of the radiation beaming factor and radiated luminosity. Using different gamma-ray emission geometries (Polar Cap, Slot Gap, Outer Gap, One Pole Caustic) and core plus cone geometries for the radio emission, we fit gamma-ray light curves for 76 young or middle-aged pulsars and we jointly fit their gamma-ray plus radio light curves when possible. We find that a joint radio plus gamma-ray fit strategy is important to obtain (alpha, zeta) estimates that can explain simultaneously detectable radio and gamma-ray emission: when the radio emission is available, the inclusion of the radio light curve in the fit leads to important changes in the (alpha, gamma) solutions. The most pronounced changes are observed for Outer Gap and One Pole Caustic models for which the gamma-ray only fit leads to underestimated alpha or zeta when the solution is found to the left or to the right of the main alpha-zeta plane diagonal respectively. The intermediate-to-high altitude magnetosphere models, Slot Gap, Outer Gap, and One pole Caustic, are favored in explaining the observations. We find no apparent evolution of α on a time scale of 106 years. For all emission geometries our derived gamma-ray beaming factors are generally less than one and do not significantly evolve with the spin-down power. A more pronounced beaming factor vs. spin-down power correlation is observed for Slot Gap model and radio-quiet pulsars and for the Outer Gap model and radio-loud pulsars. The beaming factor distributions exhibit a large dispersion that is less pronounced for the Slot Gap case and that decreases from radio-quiet to radio-loud solutions. For all models, the correlation between gamma-ray luminosity and spin-down power is consistent with a square root dependence. The gamma-ray luminosities obtained by using the beaming factors estimated in the framework of each model do not exceed the spin-down power. This suggests that assuming a beaming factor of one for all objects, as done in other studies, likely overestimates the real values. The data show a relation between the pulsar spectral characteristics and the width of the accelerator gap. The relation obtained in the case of the Slot Gap model is consistent with the theoretical prediction.

neutron aEuro" pulsars:↗

Rapid fitting of particle cascade development data from X-ray film densitometry measurements

A semiautomatic method of fitting transition curves to X-ray film optical density measurements of electromagnetic particle cascades is described. Several hundred singly and multiple interacting cosmic ray events from the JACEE 8 balloon flights were analyzed using this procedure. In addition to greatly increased speed compared to the previous manual method, the semiautomatic method offers increased accuracy through maximum likelihood fitting.

Roberts, E.↗

Multiwavelength Modeling of Nove Atmospheres

LMC 1988 #1 was a slow, CO type, dust forming classical nova. It was the first extragalactic nova to be observed with the IUE satellite. We have successfully fitted observed ultraviolet and optical spectra of LMC 1988 #1 taken within the first two months of its outburst (when the atmosphere was still optically thick) with synthetic spectra computed using PHOENIX nova model atmospheres. The synthetic spectra reproduce most of the features seen in the spectra and provide V band magnitudes consistent with the observed light curve. The fits are improved by increasing the CNO abundances to 10 times the solar values. The bolometric luminosity of LMC 1988 #1 was approximately constant at 2 x 10(exp 38) ergs per second at a distance of 47.3 kpc for the first 2 months of the outburst until the formation of the dust shell.

Huschildt, P. H.↗

Data reduction using cubic rational B-splines

A geometric method is proposed for fitting rational cubic B-spline curves to data that represent smooth curves including intersection or silhouette lines. The algorithm is based on the convex hull and the variation diminishing properties of Bezier/B-spline curves. The algorithm has the following structure: it tries to fit one Bezier segment to the entire data set and if it is impossible it subdivides the data set and reconsiders the subset. After accepting the subset the algorithm tries to find the longest run of points within a tolerance and then approximates this set with a Bezier cubic segment. The algorithm uses this procedure repeatedly to the rest of the data points until all points are fitted. It is concluded that the algorithm delivers fitting curves which approximate the data with high accuracy even in cases with large tolerances.

Chou, Jin J.↗