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DOE OSTI2019

Simple analytical model for fitting QENS data from liquids

The advent of new generation of neutron spectrometers for quasielastic scattering measurements has demonstrated the need for multi-component (at least two-component) fitting of data collected from liquids. The narrowest component is associated with long-range translational diffusion, whereas the broader component originates from localized center-of-mass motions in the transient cage of the nearest neighbors. Intra-particle dynamics (e.g., of molecular side groups) may give rise to additional components besides the center-of-mass motions. We present a simple analytical model for two-component fits of quasielastic neutron scattering data collected from liquids. Here, the use of such models is optional (albeit helpful) in the traditional approach, where fits are performed sequentially and independently for the data collected at separate Q values, but imperative in the global fitting, where the data are fitted simultaneously at all Q values, which may be necessary to rationalize the data from increasingly complex systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Sampling Low-Dimensional Markovian Dynamics for Preasymptotically Recovering Reduced Models from Data with Operator Inference

This work introduces a method for learning low-dimensional models from data of high-dimensional black-box dynamical systems. The novelty is that the learned models are exactly the reduced models that are traditionally constructed with classical projection-based model reduction techniques. Thus, the proposed approach learns models that are guaranteed to have the well-studied properties of reduced models known from model reduction, without requiring full knowledge of the governing equations and without requiring the operators of the high-dimensional systems. The key ingredient is a new data sampling scheme to obtain re-projected trajectories of high-dimensional systems that correspond to Markovian dynamics in low-dimensional subspaces. The exact recovery of reduced models from these re-projected trajectories is guaranteed pre-asymptotically under certain conditions for finite amounts of data and for a large class of systems with polynomial nonlinear terms. Numerical results demonstrate that the low-dimensional models learned with the proposed approach match reduced models from traditional model reduction up to numerical errors in practice. In conclusion, the numerical results further indicate that low-dimensional models fitted to re-projected trajectories are predictive even in situations where models fitted to trajectories without re-projection are inaccurate and unstable.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model

Purpose: A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. Methods: The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods—3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration—are used to establish shape correspondence. Results: The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. Conclusions: The proposed automatic approach achieves robust, accurate, and fast liver segmentation for 3D CTce datasets. The AdaBoost voxel classifier can detect liver area quickly without errors and provides sufficient liver shape information for model initialization. The AdaBoost profile classifier achieves sufficient accuracy and greatly decreases segmentation time. These results show that the proposed segmentation method achieves a level of accuracy comparable to that of state-of-the-art automatic methods based on ASM.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Spitzer observations of dust emission from H II regions in the Large Magellanic Cloud

Massive stars can alter physical conditions and properties of their ambient interstellar dust grains via radiative heating and shocks. The H II regions in the Large Magellanic Cloud (LMC) offer ideal sites to study the stellar energy feedback effects on dust because stars can be resolved, and the galaxy's nearly face-on orientation allows us to unambiguously associate H II regions with their ionizing massive stars. The Spitzer Space Telescope survey of the LMC provides multi-wavelength (3.6-160 μm) photometric data of all H II regions. To investigate the evolution of dust properties around massive stars, we have analyzed spatially resolved IR dust emission from two classical H II regions (N63 and N180) and two simple superbubbles (N70 and N144) in the LMC. We produce photometric spectral energy distributions (SEDs) of numerous small subregions for each region based on its stellar distributions and nebular morphologies. We use DustEM dust emission model fits to characterize the dust properties. Color-color diagrams and model fits are compared with the radiation field (estimated from photometric and spectroscopic surveys). Strong radial variations of SEDs can be seen throughout the regions, reflecting the available radiative heating. Emission from very small grains drastically increases at locations where the radiation field is the highest, while polycyclic aromatic hydrocarbons (PAHs) appear to be destroyed. PAH emission is the strongest in the presence of molecular clouds, provided that the radiation field is low.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Constraint-dependent CTOA determination for stable ductile crack growth

The crack tip opening angle (CTOA) has been used as a reliable fracture toughness to characterize stable crack growth for thin-wall structures in low-constraint conditions. Recently, it is found that CTOA can also be utilized as a robust fracture parameter to describe the arrest fracture toughness for gas transmission pipelines in ductile steels. For better understanding the concept of CTOA, this paper presents a brief review on the CTOA definitions and its test methods. And then, four CTOA models are developed for determining the CTOA toughness using conventional single edge notched bend (SENB) specimens, including the load–displacement linear fit model, the logarithmic load–displacement linear fit model, the stable tearing energy model, and the J-differentiation model. Here fracture test data from SENB specimens in A285 carbon steel are employed to evaluate the proposed CTOA models. The results show that these CTOA models can determine either identical or comparable critical CTOA values for stable ductile crack growth using the SENB specimens. A constraint-corrected CTOA resistance curve and a constraint-corrected critical CTOA equation are also obtained for A285 carbon steel.

42 ENGINEERING↗

DOE OSTI2014

Testing MONDian dark matter with galactic rotation curves

MONDian dark matter (MDM) is a new form of dark matter quantum that naturally accounts for Milgrom's scaling, usually associated with modified Newtonian dynamics (MOND), and theoretically behaves like cold dark matter (CDM) at cluster and cosmic scales. In this paper, we provide the first observational test of MDM by fitting rotation curves to a sample of 30 local spiral galaxies (z ≈ 0.003). For comparison, we also fit the galactic rotation curves using MOND and CDM. We find that all three models fit the data well. The rotation curves predicted by MDM and MOND are virtually indistinguishable over the range of observed radii (∼1 to 30 kpc). The best-fit MDM and CDM density profiles are compared. We also compare with MDM the dark matter density profiles arising from MOND if Milgrom's formula is interpreted as Newtonian gravity with an extra source term instead of as a modification of inertia. We find that discrepancies between MDM and MOND will occur near the center of a typical spiral galaxy. In these regions, instead of continuing to rise sharply, the MDM mass density turns over and drops as we approach the center of the galaxy. Our results show that MDM, which restricts the nature of the dark matter quantum by accounting for Milgrom's scaling, accurately reproduces observed rotation curves.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Improving Empirical Magnetic Field Models by Fitting to In Situ Data Using an Optimized Parameter Approach

A method for comparing and optimizing the accuracy of empirical magnetic field models using in situ magnetic field measurements is presented in this paper. The optimization method minimizes a cost function—τ—that explicitly includes both a magnitude and an angular term. A time span of 21 days, including periods of mild and intense geomagnetic activity, was used for this analysis. A comparison between five magnetic field models (T96, T01S, T02, TS04, and TS07) widely used by the community demonstrated that the T02 model was, on average, the most accurate when driven by the standard model input parameters. The optimization procedure, performed in all models except TS07, generally improved the results when compared to unoptimized versions of the models. Additionally, using more satellites in the optimization procedure produces more accurate results. This procedure reduces the number of large errors in the model, that is, it reduces the number of outliers in the error distribution. The TS04 model shows the most accurate results after the optimization in terms of both the magnitude and direction, when using at least six satellites in the fitting. It gave a smaller error than its unoptimized counterpart 57.3% of the time and outperformed the best unoptimized model (T02) 56.2% of the time. Its median percentage error in |B| was reduced from 4.54% to 3.84%. Finally, the difference among the models analyzed, when compared in terms of the median of the error distributions, is not very large. However, the unoptimized models can have very large errors, which are much reduced after the optimization.

58 GEOSCIENCES↗

DOE OSTI2018

An evaluation of equilibrium conditions and temperature-dependent speciation in a laser-produced air plasma

A laser-induced air breakdown plasma is a dynamical system and its fundamental parameters change significantly during its lifetime. The temporal evolution of the spectral features from an air plasma shows emission from bremsstrahlung followed by discrete electronic transitions from ions, atoms, and rotational and vibrations bands from molecules. The molecular band emission from air plasma typically appears at times ≳5 μs and persists for 100s of μs. Here, an evaluation is made on the temperature evolution and speciation in a laser-produced air plasma. The air plasmas are generated by focusing 1064 nm, 6 ns pulses from an Nd:YAG laser. High-resolution emission spectra of various molecules such as N 2 , N 2 + , CN, OH, NH, NO are acquired for fits to spectral models. Fitting of atomic and molecular emission features permits tracking of the air spark temperature evolution from 1 to 200 µs. Though the excitation and molecular temperatures show good overlap at times ≲10 μs, the molecular temperatures obtained from different species show a discontinuity at times ~ 30 μs and this is related to shock collapse and subsequent changes in hydrodynamics and chemistry of the plume. The fitting of multiple species in broadband spectra has permitted calculation of the relative concentrations of various molecules as a function of temperature that provides insight into the air spark speciation. Finally, the measured mole fractions from laser-induced air plasma are within a factor of 2 - 6 of the values estimated by speciation model under the assumption of thermal and chemical equilibrium across the temperature ranges studied and probable reasons for the observed deviation are described.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Efficient flexible characterization of quantum processors with nested error models

We present a simple and powerful technique for finding a good error model for a quantum processor. The technique iteratively tests a nested sequence of models against data obtained from the processor, and keeps track of the best-fit model and its wildcard error (a metric of the amount of unmodeled error) at each step. Each best-fit model, along with a quantification of its unmodeled error, constitutes a characterization of the processor. We explain how quantum processor models can be compared with experimental data and to each other. We demonstrate the technique by using it to characterize a simulated noisy two-qubit processor.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Effect of Temperature and Hydrophilic Ratio on the Structure of Poly( N -vinylcaprolactam)- block -poly(dimethylsiloxane)- block -poly( N -vinylcaprolactam) Polymersomes

Nanosized polymeric vesicles (polymersomes) assembled from ABA triblock copolymers of poly(N-vinylcaprolactam)-poly(dimethylsiloxane)-poly(N-vinylcaprolactam) (PVCL–PDMS–PVCL) are a promising platform for biomedical applications, as the temperature-responsiveness of the PVCL blocks enables reversible vesicle shrinkage and permeability of the polymersome shell at elevated temperatures. Herein, we explore the effects of molecular weight, polymer block weight ratios, and temperature on the structure of these polymersomes via electron microscopy, dynamic light scattering, small angle neutron scattering (SANS), and all-atom molecular dynamics methods. We show that the shell structure and overall size of the polymersome can be tuned by varying the hydrophilic (PVCL) weight fraction of the polymer: at room temperature, polymers of smaller hydrophilic ratios form larger vesicles that have thinner shells, whereas polymers with higher PVCL content exhibit interchain aggregation of PVCL blocks within the polymersome shell above 50 °C. Model fitting and model-free analysis of the SANS data reveals that increasing the mass ratio of PVCL to the total copolymer weight from 0.3 to 0.56 reduces the temperature-induced change in vesicle diameter by a factor of 3, while simultaneously increasing the change in shell thickness by a factor of 1.5. Finally, by analysis of the shell structures and overall size of polymersomes with various PVCL weight ratios and those without temperature-dependent polymer components, we bring into focus the mechanism of temperature-triggered drug release reported in a previous study. This work provides new fundamental perspectives on temperature-responsive polymersomes and elucidates important structure–property relationships of their constituent polymers.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Multiwavelength analysis of annihilating dark matter as the origin of the gamma-ray emission from M31

Indirect detection of dark matter (DM) by multiwavelength astronomical observations provides a promising avenue for probing the particle nature of DM. In the case of DM consisting of weakly interacting massive particles (WIMPs), self-annihilation ultimately produces various observable products including e ± pairs and gamma rays. The gamma rays can be detected directly, while the e ± pairs can be detected by radio emission from synchrotron radiation or X rays and soft gamma rays from inverse Compton scattering. An intriguing region to search for astrophysical signs of DM is the Galactic center (GC) of the Milky Way, due in part to an observed excess of gamma rays that could be DM. A recent observation by the Fermi-LAT collaboration of a similar excess in the central region of the Andromeda galaxy (M31) leads us to explore the possibility of a DM-induced signal there as well. We use the RX-DMFIT tool to perform a multifrequency analysis of potential DM annihilation emissions in M31. We consider WIMP particle models consistent with the GC excess and calculate the expected emission across the electromagnetic spectrum in comparison with available observational data from M31. We find that the particle models that best fit the M31 excess favor lower masses than the GC excess. The best-fitting models are for a $b\bar{b}$ final state with M χ = 11 GeV and $\langle$σv$\rangle$ = 2.6 × 10 -26 cm 3 s -1 , as well as an evenly mixed $\bar{b}$b/τ + τ - final state with M χ = 5.8 GeV and $\langle$σv$\rangle$ = 2.03 × 10 -26 cm 3 s -1 . For conservative estimates of the diffusion and magnetic field models the expected radio emissions appear to be in tension with currently available data in the central region of M31, although this constraint has a fairly strong dependence on the values chosen for parameters describing the magnetic field strength and geometry.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Glass formulation and composition optimization with property models: A review

Abstract Glass is a versatile material with a remarkable history and many practical applications. It plays a critical role in our everyday lives, the advancement of science, and the development of many technologies. The Edisonian type trial‐and‐error method was commonly used for conventional design of glass compositions, which was time‐consuming and costly. With the urgent need to develop new glass compositions for technology applications rapidly, it has become necessary to develop precise property models with predictive powers using large databases and efficient formulation approaches. This paper reviews the design of glass compositions using these analytical and numerical models of composition–structure–property relations of glasses, some based on large databases and machine learning approaches. Aspects of data collection, model fitting, feature extraction, model evaluation, and uncertainty quantification will be covered. Furthermore, advances in the glass optimization framework and available tools are summarized with examples. The outlook and perspective for further glass property model development and formulation approaches are discussed.

Lu, Xiaonan↗

DOE OSTI2020

The effect of enrofloxacin on enteric Escherichia coli : Fitting a mathematical model to in vivo data

Antimicrobial drugs administered systemically may cause the emergence and dissemination of antimicrobial resistance among enteric bacteria. To develop logical, research-based recommendations for food animal veterinarians, we must understand how to maximize antimicrobial drug efficacy while minimizing risk of antimicrobial resistance. Our objective is to evaluate the effect of two approved dosing regimens of enrofloxacin (a single high dose or three low doses) on Escherichia coli in cattle. We look specifically at bacteria above and below the epidemiological cutoff (ECOFF), above which the bacteria are likely to have an acquired or mutational resistance to enrofloxacin. We developed a differential equation model for the antimicrobial drug concentrations in plasma and colon, and bacteria populations in the feces. The model was fit to animal data of drug concentrations in the plasma and colon obtained using ultrafiltration probes. Fecal E. coli counts and minimum inhibitory concentrations were measured for the week after receiving the antimicrobial drug. We predict that the antimicrobial susceptibility of the bacteria above the ECOFF pre-treatment strongly affects the composition of the bacteria following treatment. Faster removal of the antimicrobial drugs from the colon throughout the study leads to improved clearance of bacteria above the ECOFF in the low dose regimen. If we assume a fitness cost is associated with bacteria above the ECOFF, the increased fitness costs leads to reduction of bacteria above the ECOFF in the low dose study. These results suggest the initial E. coli susceptibility is a strong indicator of how steers respond to antimicrobial drug treatment.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Use of a New Analytical Model To Match Production Data and Identify Opportunities To Maximize Well Productivity in the Tuscaloosa Marine Shale Reservoir

The low productivity of the oil wells in the Tuscaloosa Marine Shale (TMS) Trend, located in Louisiana and Mississippi, is a mystery. Production data from 55 wells in the TMS Trend were analyzed to identify possible means of enhancing well productivity. The -1/2 slope in a log–log plot for reservoir linear flow (RLF) was observed for some wells, but not all, and the -1 slope for boundary–dominated flow (BDF) has not been seen in the past 5 years of production. The behavior of the TMS wells is attributed to the ultralow permeability of the TMS matrix and the decline of fracture conductivity, both of which delay the BDF. On the basis of the concept of the distance of investigation, derived from the radius–of–investigation concept, the matrix permeability of the seven TMS wells was estimated to be between 34 and 65 nd, with an average of 51 nd. A new mathematical model for production decline of multifractured horizontal wells was developed taking into consideration the time–dependent fracture conductivity during the BDF period. This model fits TMS–well production data, with an average error of 6.12% and an R2 value of 0.96. Assuming constant matrix permeability, fitting the new model to the production data in the BDF period gives a rate of fracture–conductivity decline of between 0.20 and 0.74% per month. The new mathematical model reveals opportunities to optimize well completion to enhance well productivity in the TMS Trend. For a given amount of fracture proppant allocated to a well, the shortest possible fracture spacing should be used to maximize well productivity. If the number of fractures is fixed as a constraint of well completion design, well productivity is inversely proportional to the square root of fracture spacing (i.e., if the fracture spacing is shortened by fourfold, well productivity is expected to double). If the horizontal wellbore length is fixed as a constraint of well completion, well productivity is inversely proportional to fracture spacing (i.e., if the fracture spacing is shortened by 50%, well productivity is expected to double).

Engineering↗

DOE OSTI2019

Oxidation of Matrix Material in Helium with Varied Moisture Content

Exploration of the oxidation behavior of matrix material from tristructural-isotropic (TRISO) coated particle fuel elements in moisture environments provides critical data needed to predict the material’s response during off-normal accident scenarios. Near-representative, matrix-only samples were fabricated to facilitate oxidation testing in varied moisture concentrations. Oxidation testing was completed over a range of temperatures and oxidizing environments to obtain the relevant oxidation kinetics parameters and empirical oxidation rates necessary to better predict and understand matrix oxidation behavior at relevant conditions. The acquired oxidations rates were fit to established models for steam oxidation of nuclear graphite, and data were acquired following an established process. The data showed good agreement over conditions typical of the kinetic regime, and they showed a departure from the kinetic model fit at elevated temperatures and steam partial pressures in helium carrier gas. The departure was confirmed to be due to the experimental conditions being beyond the kinetic regime based on destructive analysis.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Linear point and sound horizon as purely geometric standard rulers

The baryon acoustic oscillations feature (BAO) imprinted in the clustering correlation function is known to furnish us cosmic distance determinations that are independent of the cosmological-background model and the primordial perturbation parameters. These measurements can be accomplished rigorously by means of the purely geometric BAO methods. To date two different purely geometric BAO approaches have been proposed. The first exploits the linear-point standard ruler. The second, called correlation-function model-fitting, exploits the sound-horizon standard ruler. A key difference between them is that, when estimated from clustering data, the linear point makes use of a cosmological-model-independent procedure to extract the ratio of the ruler to the cosmic distance, while the correlation-function model-fitting relies on a phenomenological cosmological model for the correlation function. Nevertheless the two rulers need to be precisely defined independently of any specific observable (e.g., the BAO). We define the linear point and sound horizon and we fully characterize and compare the two rulers’ cosmological-parameter dependence. We find that they are both geometrical (i.e., independent of the primordial cosmological parameters) within the required accuracy, and that they have the same parameter dependence for a wide range of parameter values. We estimate the rulers’ best-fit values and errors, given the cosmological constraints obtained by the Planck Satellite team from their measurements of the cosmic microwave background temperature and polarization anisotropies. We do this for three different cosmological models encompassed by the purely geometric BAO methods. In each case we find that the relative errors of the two rulers coincide and they are insensitive to the assumed cosmological model. Interestingly both the linear point and the sound horizon shift by 0.5σ when we do not fix the spatial geometry to be flat in Λ CDM. Finally, this points toward a sensitivity of the rulers to different cosmological models when they are estimated from the cosmic microwave background.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

The inside-out growth of the most massive galaxies at 0.3 < z < 0.9

We study the surface brightness profiles of a sample of brightest cluster galaxies (BCGs) with 0.3 < z < 0.9. The BCGs are selected from the first Red-sequence Cluster Survey and an X-ray cluster survey. The surface brightness profiles of the BCGs are measured using HST ACS images, and the majority of them can be well modeled by a single Sérsic profile with a typical Sérsic index n ∼ 6 and a half-light radius ∼30 kpc. Although the single Sérsic model fits the profiles well, we argue that the systematics in the sky background measurement and the coupling between the model parameters make the comparison of the best-fit model parameters ambiguous. Direct comparison of the BCG profiles, on the other hand, has revealed an inside-out growth for these most massive galaxies: as the mass of a BCG increases, the central mass density of the galaxy increases slowly (ρ{sub 1kpc}∝M{sub ∗}{sup 0.39}), while the slope of the outer profile grows continuously shallower (α{sub r{sup 1}{sup /}{sup 4}}∝M{sub ∗}{sup −2.5}). Such a fashion of growth continues down to the less massive early-type galaxies (ETGs) as a smooth function of galaxy mass, without apparent distinction between BCGs and non-BCGs. For the very massive ETGs and BCGs, the slope of the Kormendy relation starts to trace the slope of the surface brightness profiles and becomes insensitive to subtle profile evolution. These results are generally consistent with dry mergers being the major driver of the mass growth for BCGs and massive ETGs. We also find strong correlations between the richness of clusters and the properties of BCGs: the more massive the clusters are, the more massive the BCGs (M{sub bcg}{sup ∗}∝M{sub clusters}{sup 0.6}) and the shallower their surface brightness profiles. After taking into account the bias in the cluster samples, we find the masses of the BCGs have grown by at least a factor of 1.5 from z = 0.5 to z = 0, in contrast to the previous findings of no evolution. Such an evolution validates the expectation from the ΛCDM model.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

A no-scale inflationary model to fit them all

The magnitude of B-mode polarization in the cosmic microwave background as measured by BICEP2 favours models of chaotic inflation with a quadratic m{sup 2} φ{sup 2}/2 potential, whereas data from the Planck satellite favour a small value of the tensor-to-scalar perturbation ratio r that is highly consistent with the Starobinsky R +R{sup 2} model. Reality may lie somewhere between these two scenarios. In this paper we propose a minimal two-field no-scale supergravity model that interpolates between quadratic and Starobinsky-like inflation as limiting cases, while retaining the successful prediction n{sub s} ≅ 0.96.

79 ASTRONOMY AND ASTROPHYSICS↗