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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 217 records · Page 12

Next generation of the self-consistent and environment-dependent Hamiltonian: Applications to various boron allotropes from zero- to three-dimensional structures

An upgrade of the previous self-consistent and environment-dependent linear combination of atomic orbitals Hamiltonian (referred as SCED-LCAO) has been developed. This improved version of the semi-empirical SCED-LCAO Hamiltonian, in addition to the inclusion of self-consistent determination of charge redistribution, multi-center interactions, and modeling of electron-electron correlation, has taken into account the effect excited on the orbitals due to the atomic aggregation. This important upgrade has been subjected to a stringent test, the construction of the SCED-LCAO Hamiltonian for boron. It was shown that the Hamiltonian for boron has successfully characterized the electron deficiency of boron and captured the complex chemical bonding in various boron allotropes, including the planar and quasi-planar, the convex, the ring, the icosahedral, and the fullerene-like clusters, the two-dimensional monolayer sheets, and the bulk alpha boron, demonstrating its transferability, robustness, reliability, and predictive power. The molecular dynamics simulation scheme based on the Hamiltonian has been applied to explore the existence and the energetics of ∼230 compact boron clusters B{sub N} with N in the range from ∼100 to 768, including the random, the rhombohedral, and the spherical icosahedral structures. It was found that, energetically, clusters containing whole icosahedral B{sub 12} units are more stable for boron clusters of larger size (N > 200). The ease with which the simulations both at 0 K and finite temperatures were completed is a demonstration of the efficiency of the SCED-LCAO Hamiltonian.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

A Domain Decomposition Model Reduction Method for Linear Convection-Diffusion Equations with Random Coefficients

We developed a domain decomposition model reduction method for linear steady- state convection-diffusion equations with random coefficients. Of particular interest to this effort are the diffusion equation with random diffusivity and the convection-dominated transport equation with random velocity. We investigated two types of random fields, i.e., colored noises and discrete white noises, both of which can lead to high-dimensional parametric dependence in practice. The motivation is to exploit domain decomposition to reduce the parametric dimension of local problems in subdomains, such that an entire parametric map can be approximated with a small number of expensive partial differential equation (PDE) simulations. The new method combines domain decomposition with model reduction and sparse polynomial approximation, so as to simultaneously handle the high dimensionality and irregular behavior of the PDEs under consideration. Here, the advantages of our method lie in three aspects: (i) online-offline decomposition, i.e., the online cost is independent of the size of the triangle mesh; (ii) sparse approximation of operators involving nonaffine high-dimensional random fields; (iii) an effective strategy to capture irregular behaviors, e.g., sharp transitions of the PDE solutions. Two numerical examples are provided to demonstrate the advantageous performance of our method.

97 MATHEMATICS AND COMPUTING↗

SU-E-T-143: Automatic PBS Treatment Planning for Prostate Cancer

Purpose: This study presents a novel idea of using automated planning technique for parallel-opposed prostate plans with pencil beam scanning proton therapy. Methods: A random 5 clinical prostate patients treated with parallel-opposed modulated-scanning proton beam in our proton center were selected in this study. All of the 5 cases were re-planned with in-house developed python scripts integrated into RayStation 4.0 clinical version. This automatic tool was designed to perform patient DICOM data import, external structure contours, CT density assignment, prescription to PTV assignment, beam placement, optimization, and dose calculation. Results: The mean time to generate a complete treatment plan was 1 minute per plan. For the automatically generated plans, 5 out of 5 plans (100%) were deemed clinically acceptable by one experienced radiation oncologist. In addition, the automated plans were overall dosimetrically equivalent to the clinical plans when scored for target coverage, rectum and bladder dose. Mann-Whitney U tests did not show significant dosimetric differences for normal tissue structures between the clinical and automated plans. Conclusion: We have developed a robust and automated method for fully inversed planned paralleled-opposed prostate proton treatment planning. This efficient method can be readily integrated into clinical practice. The tool produces clinically acceptable plans using only manually contoured anatomic structures and simulation CT data set. We anticipate that this tool will not only improve patient access to high-quality proton treatment by simplifying the planning process and will also reduce the effort and cost of incorporating more advanced planning into clinical practice in the near future.

60 APPLIED LIFE SCIENCES↗

A comparison of methods for representing sparsely sampled random quantities.

This report discusses the treatment of uncertainties stemming from relatively few samples of random quantities. The importance of this topic extends beyond experimental data uncertainty to situations involving uncertainty in model calibration, validation, and prediction. With very sparse data samples it is not practical to have a goal of accurately estimating the underlying probability density function (PDF). Rather, a pragmatic goal is that the uncertainty representation should be conservative so as to bound a specified percentile range of the actual PDF, say the range between 0.025 and .975 percentiles, with reasonable reliability. A second, opposing objective is that the representation not be overly conservative; that it minimally over-estimate the desired percentile range of the actual PDF. The presence of the two opposing objectives makes the sparse-data uncertainty representation problem interesting and difficult. In this report, five uncertainty representation techniques are characterized for their performance on twenty-one test problems (over thousands of trials for each problem) according to these two opposing objectives and other performance measures. Two of the methods, statistical Tolerance Intervals and a kernel density approach specifically developed for handling sparse data, exhibit significantly better overall performance than the others.

Romero, Vicente Jose↗

Radio-flaring Ultracool Dwarf Population Synthesis

Over a dozen ultracool dwarfs (UCDs), low-mass objects of spectral types ≥M7, are known to be sources of radio flares. These typically several-minutes-long radio bursts can be up to 100% circularly polarized and have high brightness temperatures, consistent with coherent emission via the electron cyclotron maser operating in approximately kilogauss magnetic fields. Recently, the statistical properties of the bulk physical parameters that describe these UCDs have become described adequately enough to permit synthesis of the population of radio-flaring objects. For the first time, I construct a Monte Carlo simulator to model the population of these radio-flaring UCDs. This simulator is powered by Intel Secure Key (ISK), a new processor technology that uses a local entropy source to improve random number generation that has heretofore been used to improve cryptography. The results from this simulator indicate that only ∼5% of radio-flaring UCDs within the local interstellar neighborhood (<25 pc away) have been discovered. I discuss a number of scenarios that may explain this radio-flaring fraction and suggest that the observed behavior is likely a result of several factors. The performance of ISK as compared to other pseudorandom number generators is also evaluated, and its potential utility for other astrophysical codes is briefly described.

79 ASTRONOMY AND ASTROPHYSICS↗

Numerical simulation of the nonlinear response of composite plates under combined thermal and acoustic loading

A time-domain study of the random response of a laminated plate subjected to combined acoustic and thermal loads is carried out. The features of this problem also include given uniform static inplane forces. The formulation takes into consideration a possible initial imperfection in the flatness of the plate. High decibel sound pressure levels along with high thermal gradients across thickness drive the plate response into nonlinear regimes. This calls for the analysis to use von Karman large deflection strain-displacement relationships. A finite element model that combines the von Karman strains with the first-order shear deformation plate theory is developed. The development of the analytical model can accommodate an anisotropic composite laminate built up of uniformly thick layers of orthotropic, linearly elastic laminae. The global system of finite element equations is then reduced to a modal system of equations. Numerical simulation using a single-step algorithm in the time-domain is then carried out to solve for the modal coordinates. Nonlinear algebraic equations within each time-step are solved by the Newton-Raphson method. The random gaussian filtered white noise load is generated using Monte Carlo simulation. The acoustic pressure distribution over the plate is capable of accounting for a grazing incidence wavefront. Numerical results are presented to study a variety of cases.

Mei, Chuh↗

X-ray microtomography shows pore structure and tortuosity in alkali-activated binders

Durability of alkali-activated binders is of vital importance in their commercial application, and depends strongly on microstructure and pore network characteristics. X-ray microtomography ({mu}CT) offers, for the first time, direct insight into microstructural and pore structure characteristics in three dimensions. Here, {mu}CT is performed on a set of sodium metasilicate-activated fly ash/slag blends, using a synchrotron beamline instrument. Segmentation of the samples into pore and solid regions is then conducted, and pore tortuosity is calculated by a random walker method. Segmented porosity and diffusion tortuosity are correlated, and vary as a function of slag content (slag addition reduces porosity and increases tortuosity), and sample age (extended curing gives lower porosity and higher tortuosity). This is particularly notable for samples with {>=} 50% slag content, where a space-filling calcium (alumino)silicate hydrate gel provides porosity reductions which are not observed for the sodium aluminosilicate ('geopolymer') gels which do not chemically bind water of hydration.

36 MATERIALS SCIENCE↗

Explaining TeV cosmic-ray anisotropies with non-diffusive cosmic-ray propagation

Constraining the behavior of cosmic ray data observed at Earth requires a precise understanding of how the cosmic rays propagate in the interstellar medium. The interstellar medium is not homogeneous; although turbulent magnetic fields dominate over large scales, small coherent regions of magnetic field exist on scales relevant to particle propagation in the nearby Galaxy. Guided propagation through a coherent field is significantly different from random particle diffusion and could be the explanation of spatial anisotropies in the observed cosmic rays. We present a Monte Carlo code to propagate cosmic particle through realistic magnetic field structures. We discuss the details of the model as well as some preliminary studies which indicate that coherent magnetic structures are important effects in local cosmic-ray propagation, increasing the flux of cosmic rays by over two orders of magnitude at anisotropic locations on the sky. Furthermore, the features induced by coherent magnetic structure could be the cause of the observed TeV cosmic-ray anisotropy.

79 ASTRONOMY AND ASTROPHYSICS↗

Liquid-state polaron theory of the hydrated electron revisited

The quantum path integral/classical liquid-state theory of Chandler and co-workers, created to describe an excess electron in solvent, is re-examined for the hydrated electron. The portion that models electron-water density correlations is replaced by two equations: the range optimized random phase approximation (RO-RPA), and the Donley, Rajasekaran, and Liu (DRL) approximation to the “two-chain” equation, both shown previously to describe accurately the static structure and thermodynamics of strongly charged polyelectrolyte solutions. The static equilibrium properties of the hydrated electron are analyzed using five different electron-water pseudopotentials. The theory is then compared with data from mixed quantum/classical Monte Carlo and molecular dynamics simulations using these same pseudopotentials. It is found that the predictions of the RO-RPA and DRL-based polaron theories are similar and improve upon previous theory, with values for almost all properties analyzed in reasonable quantitative agreement with the available simulation data. Also, it is found using the Larsen, Glover, and Schwartz pseudopotential that the theories give values for the solvation free energy that are at least three times larger than that from experiment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

On the Coupling Time of the Heat-Bath Process for the Fortuin–Kasteleyn Random–Cluster Model

We consider the coupling from the past implementation of the random–cluster heat-bath process, and study its random running time, or coupling time. We focus on hypercubic lattices embedded on tori, in dimensions one to three, with cluster fugacity at least one. We make a number of conjectures regarding the asymptotic behaviour of the coupling time, motivated by rigorous results in one dimension and Monte Carlo simulations in dimensions two and three. Amongst our findings, we observe that, for generic parameter values, the distribution of the appropriately standardized coupling time converges to a Gumbel distribution, and that the standard deviation of the coupling time is asymptotic to an explicit universal constant multiple of the relaxation time. Perhaps surprisingly, we observe these results to hold both off criticality, where the coupling time closely mimics the coupon collector’s problem, and also at the critical point, provided the cluster fugacity is below the value at which the transition becomes discontinuous. Finally, we consider analogous questions for the single-spin Ising heat-bath process.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

SU-E-J-74: Dosimetric Advantages of Adaptive Radiotherapy for Head and Neck Cancer Are Confirmed with Weekly CBCT Images

Purpose: Our previous study showed that weekly dose monitoring using cone-beam CT (CBCT) images can guide the timing and need for adaptive re-planning during the treatment of head and neck (HN) cancer. Here we aim to confirm the dosimetric improvement of adaptive radiotherapy (ART) using weekly CBCTs. Methods: We randomly selected seven HN patients treated with ART due to noticeable anatomic changes. Twenty weekly images acquired during the second treatment course were included. These CBCTs were aligned with both the initial and re-planning simulation CTs according to the clinical shifts. Daily doses were re-calculated for both the initial and adaptive plans. Contours of the tumor and organs-at-risk (OARs) were manually delineated by a physician on the re-planning CT and then were transferred to the CBCTs for plan evaluation. Contour modifications were made based on the daily anatomic changes observed on CBCTs. All patients were treated with 70Gy to the primary tumor and 56Gy to the elective lymph nodes. Results: Volumetric changes of the tumor (range — 43.9%∼+15.9%) were observed. The average D99 to the primary tumor was (70.1±2.0)Gy (range 62.2∼72.5Gy) for the adaptive plan and (66.0±5.5)Gy (range 50.9∼70.7Gy) for the initial plan(p<<0.01). The average D99 to the elective neck was (56.3±1.3)Gy (range 52.8∼59.2Gy) for the adaptive plan and (52.4±7.0)Gy (range 37.7∼58.6Gy) for the initial plan(p=0.01). The parotid decreased in volume during the treatment course (range 7.3%∼42.2%). The average D-mean to the spared parotid decreased by 15.3% (p=0.002) for the adaptive plan when compared to the original. With ART, 4 out of 7 patients experienced better sparing of the spinal cord (D-max reduced by 2.5%∼10.2%) and the oral cavity (D-mean reduced by 3.5%∼20.1%). Conclusion: Weekly CBCT dosimetry confirms that ART is an effective method to accommodate on-treatment anatomic changes. In select patients, tumor coverage and OAR sparing may be improved with ART. Research is funded by Siemens.

60 APPLIED LIFE SCIENCES↗

A finite-time exponent for random Ehrenfest gas

We consider the motion of a system of free particles moving on a plane with regular hard polygonal scatterers arranged in a random manner. Calling this the Ehrenfest gas, which is known to have a zero Lyapunov exponent, we propose a finite-time exponent to characterize its dynamics. As the number of sides of the polygon goes to infinity, when polygon tends to a circle, we recover the usual Lyapunov exponent for the Lorentz gas from the exponent proposed here. To obtain this result, we generalize the reflection law of a beam of rays incident on a polygonal scatterer in a way that the formula for the circular scatterer is recovered in the limit of infinite number of vertices. Thus, chaos emerges from pseudochaos in an appropriate limit. - Highlights: • We present a finite-time exponent for particles moving in a plane containing polygonal scatterers. • The exponent found recovers the Lyapunov exponent in the limit of the polygon becoming a circle. • Our findings unify pseudointegrable and chaotic scattering via a generalized collision rule. • Stretch and fold:shuffle and cut :: Lyapunov:finite-time exponent :: fluid:granular mixing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization

Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R{sup 2} = 0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q{sup 2}{sub ext} = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential. - Highlights: • It was compiled the largest publicly-available skin permeability dataset. • Predictive QSAR models were developed for skin permeability. • No concordance between skin sensitization and skin permeability has been found. • Structural rules for optimizing sensitization and penetration were established.

60 APPLIED LIFE SCIENCES↗

An advanced understanding of the specific effects of xylan and surface lignin contents on enzymatic hydrolysis of lignocellulosic biomass

A deep understanding of biomass recalcitrance has been hampered by the intricate and heterogeneous nature of pretreated biomass substrates obtained from random deconstruction methods. In this study, we established a unique methodology based on chemical pulping principles to create "reference substrates" with intact cellulose fibers and controlled morphological and chemical properties that enable us to investigate the individual effect of xylan, bulk, and surface lignin content on enzymatic hydrolysis. We also developed and demonstrated an X-ray photoelectron spectroscopy (XPS) technique for quantifying surface lignin content on biomass substrates. The results from this study show that, apart from its hindrance effect, xylan can facilitate cellulose fibril swelling and thus create more accessible surface area, which improves enzyme and substrate interactions. Surface lignin has a significant impact on enzyme adsorption kinetics and hydrolysis rate. Advanced understanding of xylan, bulk, and surface lignin effects provides critical information for an effective biomass conversion process.

biomass↗

Machine learning assisted phase and size-controlled synthesis of iron oxide particles

Synthesis of iron oxides with specific phases and particle sizes is a crucial challenge in various fields, including materials science, energy storage, biomedical applications, environmental science, and earth science. However, despite significant advances in this area, much of the current palette of particle outcomes has been based on time-consuming trial-and-error exploration of synthesis conditions. The present study was designed to explore a very different approach to 1) predict the outcome of synthesis from specified reaction parameters based on using machine learning (ML) techniques, and 2) correlate sets of parameters to obtain products with desired outcomes by a newly designed recommendation algorithm. To achieve this, four ML algorithms were tested, namely random forest, logistic regression, support vector machine, and k-nearest neighbor. Among the models, random forest outperformed the others, attaining 96% and 81% accuracy when predicting the phase and size of iron oxide particles in the test dataset. Surprisingly, the permutation feature importance analysis revealed that volume, which may strongly relate to pressure, was one of the important features, along with precursor concentration, pH, temperature, and time, influencing the phase and size of iron oxide particles during synthesis. To verify the robustness of the random forest models, prediction and experimental results were compared based on 24 randomly generated methods in additive and non-additive systems not included in the datasets. The predictions of product phase and particle size from the models agreed well with the experimental results. Furthermore, a searching and ranking algorithm was developed to recommend potential synthesis parameters for obtaining iron oxide products with the desired phase and particle size from previous studies in the dataset. Furthermore, this study lays the foundation for a closed-loop approach in materials synthesis and preparation, beginning with suggesting potential reaction parameters from the dataset and predicting potential outcomes, followed by conducting experiments and analyses, and ultimately enriching the dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Stochastic AC optimal power flow: A data-driven approach

There is an emerging need for efficient solutions to stochastic AC Optimal Power Flow (AC-OPF) to ensure optimal and reliable grid operations in the presence of increasing demand and generation uncertainty. Herein this paper presents a highly scalable data-driven algorithm for stochastic AC-OPF that has extremely low sample requirement. The novelty behind the algorithm’s performance involves an iterative scenario design approach that merges information regarding constraint violations in the system with data-driven sparse regression. Compared to conventional methods with random scenario sampling, our approach is able to provide feasible operating points for realistic systems with much lower sample requirements. Furthermore, multiple sub-tasks in our approach can be easily paralleled and based on historical data to enhance its performance and application. We demonstrate the computational improvements of our approach through simulations on different test cases in the IEEE PES PGLib-OPF benchmark library.

42 ENGINEERING↗

Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet

The majority of research on efficient and scalable algorithms in computational science and engineering has focused on the forward problem : given parameter inputs, solve the governing equations to determine output quantities of interest. In contrast, in this paper, we consider the broader question: given a (large-scale) model containing uncertain parameters, (possibly) noisy observational data, and a prediction quantity of interest, how do we construct efficient and scalable algorithms to (1) infer the model parameters from the data (the deterministic inverse problem ), (2) quantify the uncertainty in the inferred parameters (the Bayesian inference problem), and (3) propagate the resulting uncertain parameters through the model to issue predictions with quantified uncertainties (the forward uncertainty propagation problem )? We present efficient and scalable algorithms for this end-to-end, data-to-prediction process under the Gaussian approximation and in the context of modeling the flow of the Antarctic ice sheet and its effect on loss of grounded ice to the ocean. The ice is modeled as a viscous, incompressible, creeping, shear-thinning fluid. The observational data come from satellite measurements of surface ice flow velocity, and the uncertain parameter field to be inferred is the basal sliding parameter, represented by a heterogeneous coefficient in a Robin boundary condition at the base of the ice sheet. The prediction quantity of interest is the present-day ice mass flux from the Antarctic continent to the ocean. We show that the work required for executing this data-to-prediction process—measured in number of forward (and adjoint) ice sheet model solves—is independent of the state dimension, parameter dimension, data dimension, and the number of processor cores. The key to achieving this dimension independence is to exploit the fact that, despite their large size, the observational data typically provide only sparse information on model parameters. This property can be exploited to construct a low rank approximation of the linearized parameter-to-observable map via randomized SVD methods and adjoint-based actions of Hessians of the data misfit functional.

54 ENVIRONMENTAL SCIENCES↗

Spatial and temporal prediction of radiation dose rates near Fukushima Daiichi Nuclear Power Plant

In this paper, we have developed a methodology to estimate the spatiotemporal distribution of radiation air dose rates around the Fukushima Daiichi Nuclear Power Plant (FDNPP). In our exploratory data analysis, we found that (1) the temporal evolution of dose rates is composed of a log-linear decay trend and fluctuations of air dose rates that are spatially correlated among adjacent monitoring posts; and (2) the slope of the log-linear environmental decay trend can be represented as a function of the apparent initial dose rates, coordinate position, land-use type, and soil type. From these observations, we first estimated the log-linear decay trend at each location based on these predictors, using the random forest method. We then developed a modified Kalman filter coupled with a Gaussian process model to estimate the dose-rate time series at a given location and time. We applied this method to the Fukushima evacuation zone (as of March 2017), which included 17 monitoring post locations (with monitoring datasets collected between 2014 and 2018) and generated a time series of dose-rate maps. Our results show that this approach allows us to produce accurate spatial and temporal predictions of radiation dose-rate maps using limited spatiotemporal measurements.

61 RADIATION PROTECTION AND DOSIMETRY↗