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This thesis presents a study of the NuMI beam line intended to clarify how the particle trajectories through the focusing system and consequently the neutrino event yield are affected by the variation of the Horn Currents.
Various statistical techniques are discussed that can be used to assign a level of confidence in the prediction of models that depend on input data with known uncertainties and correlations. The particular techniques reviewed in this paper are: 1) random realizations of the input data using Monte-Carlo methods, 2) the construction of confidence intervals to assess the reliability of model predictions, and 3) resampling techniques to impose statistical constraints on the input data based on additional information. These techniques are illustrated with a calculation of the keff value, based on the 235 U(n, f) and 239 Pu (n, f) cross sections.
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Abstract not provided.
This final technical report describes the activities undertaken through Department of Energy, Office of Science, Advanced Scientific Computing Research Early Career award DE-SC-0019039, “Analyzing Multifaceted Scientific Data with Topological Analytics." This report summarizes contributions made toward the research of visualization, machine learning, and topological data analysis of complex simulation data.
We collected a time series of Russian River infiltration rates to parameterize stochastic model development of infiltration conditions as a function of bioclogging. The time series of infiltration were collected from the Russian River Riverbank Filtration site located in Sonoma County California. Infiltration datasets are shown in units of m/day and were obtained using a seepage meter in 2012. To address the combined effects of bioclogging and disconnection on infiltration, we developed numerical representations of bioclogging processes based on these datasets using a within a one-dimensional, variably saturated flow model representing losing-connected and losing-disconnected rivers. All models and bioclogging formulations were used to create synthetic test cases for bioclogging.This research was supported by the Jane Lewis Fellowship from the University of California, Berkeley, the Sonoma County Water Agency (SCWA), the Roy G. Post Foundation Scholarship, the U.S. Department of Energy, Office of Science Graduate Student Research (SCGSR) Program, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under award DE-AC02-05CH11231, and the UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany.
Amyloid fibrils are thought to grow by a two-step dock-lock mechanism. However, previous simulations of fibril formation (i) overlook the bi-molecular nature of the docking step and obtain rates with first-order units, or (ii) superimpose the docked and locked states when computing the potential of mean force for association and thereby muddle the docking and locking steps. Here, we developed a simple microkinetic model with separate locking and docking steps and with the appropriate concentration dependences for each step. We constructed a simple model comprised of chiral dumbbells that retains qualitative aspects of fibril formation. We used rare events methods to predict separate docking and locking rate constants for the model. The rate constants were embedded in the microkinetic model, with the microkinetic model embedded in a population balance model for “bottom-up” multiscale fibril growth rate predictions. These were compared to “top-down” results using simulation data with the same model and multiscale framework to obtain maximum likelihood estimates of the separate lock and dock rate constants. We used the same procedures to extract separate docking and locking rate constants from experimental fibril growth data. Our multiscale strategy, embedding rate theories, and kinetic models in conservation laws should help to extract docking and locking rate constants from experimental data or long molecular simulations with correct units and without compromising the molecular description.
We present the first results from a new technique for the prediction of Fe Kα profiles directly from general relativistic magnetohydrodynamic (GRMHD) simulations. Data from a GRMHD simulation are processed by a Monte Carlo global radiation transport code, which determines the X-ray flux irradiating the disk surface and the coronal electron temperature self-consistently. With that irradiating flux and the disk’s density structure drawn from the simulation, we determine the reprocessed Fe Kα emission from photoionization equilibrium and solution of the radiation transfer equation. We produce maps of the surface brightness of Fe Kα emission over the disk surface, which—for our example of a 10M{sub ⊙} Schwarzschild black hole accreting at 1% the Eddington value—rises steeply one gravitational radius outside the radius of the innermost stable circular orbit and then falls ∝r {sup −2} at larger radii. We explain these features of the Fe Kα radial surface brightness profile as consequences of the disk’s ionization structure and an extended coronal geometry, respectively. We also present the corresponding Fe Kα line profiles as would be seen by distant observers at several inclinations. Both the shapes of the line profiles and the equivalent widths of our predicted Kα lines are qualitatively similar to those typically observed from accreting black holes. Most importantly, this work represents a direct link between theory and observation: in a fully self-consistent way, we produce observable results—iron fluorescence line profiles—from the theory of black hole accretion with almost no phenomenological assumptions.
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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]. Our overall suite of simulations contains 12 sets of simulations: training and validation sets of simulations for each of two resolutions of each of sodium, copper, and mixtures of the two. The training data were produced using a 500-run design that varies input parameters temperature (T), electron density (Ne), proportion of sodium (pNa), and proportion of copper (pCu). The latter two variables sum to one and are unused in the single-element simulations. The validation data was produced with a 25-run design. The coarse simulations produce spectra over a range of 240nm - 880nm that roughly mimics the range collected by the ChemCam instrument on the Mars rover Curiosity. The fine resolution simulations produce spectra over a range of 550nm to 600nm and were produced to mimic the included experimental results. The experimental results cover four mixtures of sodium and copper. All data sets are kept in zip files whose names indicate the elemental composition (NaCu, Na, or Cu), resolution (coarse or fine), and purpose (training 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. See attached abstract for figures.
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.