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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Development of a Single-Pass Amplifier for an Optical Stochastic Cooling Proof-of-Principle Experiment at Fermilab's IOTA Facility

Optical stochastic cooling (OSC) is a method of beam cooling which is expected to provide cooling rates orders of magnitude larger than ordinary stochastic cooling. Light from an undulator (the pickup) is amplified and fed back onto the particle beam via another undulator (the kicker). Fermilab is currently exploring a possible proof-of-principle experiment of the OSC at the integrable-optics test accelerator (IOTA) ring. To implement effective OSC a good correction of phase distortions in the entire band of the optical amplifier is required. In this contribution we present progress in experimental characterization of phase distortions associated to a Titanium Sapphire crystal laser-gain medium (a possible candidate gain medium for the OSC experiment to be performed at IOTA). We also discuss a possible option for a mid-IR amplifier

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

A modular methodology for time-domain stochastic seismic wave propagation

Presented here is a modular methodology for time-domain stochastic seismic wave propagation analysis. Presented methodology is designed to analyse uncertain seismic motions as an input, propagating through uncertain material. Traditional approach for uncertain wave propagation relies on models that include deep bedrock, local soil site, and their random process and random field information. Such models can become quite large and computationally intractable. The modular approach proposed herein features two step approach that allows separate consideration of the deep bedrock and local site along with corresponding random field information. In this work, the first step considers an auxiliary stochastic motions problem in the bedrock. Stochastic local site response can then be simulated in a reduced domain within certain depth from the surface. Application of uncertain seismic motions at depth, for local uncertain site response is done using stochastic effective forces developed through the Domain Reduction Method. By using Hermite polynomial chaos expansion to represent the non-Gaussian random field of material parameters and non-stationary random process of seismic motion, the proposed modular methodology is formulated using intrusive stochastic Galerkin approach, as seen in the Stochastic Elastic–Plastic Finite Element Method (SEPFEM). Developed modular methodology is illustrated using a 1-D stochastic seismic wave propagation analysis with three cases, and simulation results are also verified with results from conventional approach.

58 GEOSCIENCES↗
DOE OSTI2022

Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems

The flow-driven spectral chaos (FSC) is a recently developed method for tracking and quantifying uncertainties in the long-time response of stochastic dynamical systems using the spectral approach. The method uses a novel concept called enriched stochastic flow maps as a means to construct an evolving finite-dimensional random function space that is both accurate and computationally efficient in time. In this paper, we present a multi-element version of the FSC method (the ME-FSC method for short) to tackle (mainly) those dynamical systems that are inherently discontinuous over the probability space. In ME-FSC, the random domain is partitioned into several elements, and then the problem is solved separately on each random element using the FSC method. Subsequently, results are aggregated to compute the probability moments of interest using the law of total probability. To demonstrate the effectiveness of the ME-FSC method in dealing with discontinuities and long-time integration of stochastic dynamical systems, four representative numerical examples are presented in this paper, including the Van-der-Pol oscillator problem and the Kraichnan-Orszag three-mode problem. Results show that the ME-FSC method is capable of solving problems that have strong nonlinear dependencies over the probability space, both reliably and at low computational cost.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

How Hall electric fields intrinsically chaotize and heat ions during collisionless magnetic reconnection

The origin of extreme and anomalous ion heating during magnetic reconnection has been a longstanding mystery. It is shown in this paper that, at the core of the process, the stochastic heating mechanism is responsible for this energization. The in-plane Hall electric fields that intrinsically develop during collisionless reconnection not only accelerate ions, but also thermalize them by inducing chaotic motion. The mechanism explains various observed features of ion heating, e.g., its relation to the guide field strength and the ion mass-to-charge ratio.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Dynamic Network Analysis of Nuclear Science Literature for Research Influence Assessment

Analyzing nuclear science literature via data-driven methods is a critical step for assessing research influence and technology advancements. Indicators of scholarly activities may be buried in large volumes of nuclear research publications and collaboration networks over time. Mining for relevant scholarly influence trends in large volumes of text can be computationally challenging; however, open-source information on research collaborations over time can offer opportunities to extract meaningful insights. While network centrality analysis of scholarly research provides topology-based insights, additional emphasis on dynamics associated with the diffusion of information through these networks is important. Here this paper represents a step in that direction through the development of a novel dynamic network analysis framework and computational engine to identify key entities and capabilities over time within global scholarly nuclear science collaboration networks. Network theoretic, stochastic simulation, and optimization methods are leveraged to address variability in scholarly interactions, influence propagation, and collaboration patterns via network connections. A topic-aware influence maximization algorithm is developed to address the goal of identifying key influential authors in diverse research topics over time. Efficient parallelized implementation of the algorithm is applied to reduce computational costs. A proof-of-concept case study using open-source Scopus data with 33,517 published nuclear research papers from 2000-2019 is presented and representative analytic insights are generated. Broad implications of these insights are discussed and future research directions are also identified.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Competitive formation of intercalated versus supported metal nanoclusters during deposition on layered materials with surface point defects

Intercalated metal nanoclusters (NCs) can be formed under the surface of graphite after sputtering to generate surface “portal” defects that allow deposited atoms to reach the subsurface gallery. Although, there is a competition between formation of supported NCs on top of the surface and intercalated NCs under the surface, the latter only dominating at sufficiently high temperature. A stochastic model incorporating appropriate system thermodynamics and kinetics is developed to capture this complex and competitive nucleation and growth process. Kinetic Monte Carlo simulation shows that the model captures experimental trends observed for Cu and other metals and reveals that higher temperatures are needed to facilitate detachment of atoms from supported NCs enabling them to reach the gallery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Optimization of the determinant of the Vandermonde matrix and related matrices

Various techniques for interpolation of data, moment matching in stochastic applications and various methods in numerical analysis can be described using Vandermonde matrices. For this reason the properties of the determinant of the Vandermonde matrix and related matrices are interesting. Here the extreme points of the Vandermonde determinant, and related determinants, on some simple surfaces such as the unit sphere are analyzed, both numerically and analytically. Some results are also visualized in various dimensions. The extreme points of the Vandermonde determinant are also related to the roots of certain orthogonal polynomials such as the Hermite polynomials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Ensemble Kalman filters for dynamical systems with unresolved turbulence

Ensemble Kalman filters are developed for turbulent dynamical systems where the forecast model does not resolve all the active scales of motion. Coarse-resolution models are intended to predict the large-scale part of the true dynamics, but observations invariably include contributions from both the resolved large scales and the unresolved small scales. The error due to the contribution of unresolved scales to the observations, called ‘representation’ or ‘representativeness’ error, is often included as part of the observation error, in addition to the raw measurement error, when estimating the large-scale part of the system. It is here shown how stochastic superparameterization (a multiscale method for subgridscale parameterization) can be used to provide estimates of the statistics of the unresolved scales. In addition, a new framework is developed wherein small-scale statistics can be used to estimate both the resolved and unresolved components of the solution. The one-dimensional test problem from dispersive wave turbulence used here is computationally tractable yet is particularly difficult for filtering because of the non-Gaussian extreme event statistics and substantial small scale turbulence: a shallow energy spectrum proportional to k{sup −5/6} (where k is the wavenumber) results in two-thirds of the climatological variance being carried by the unresolved small scales. Because the unresolved scales contain so much energy, filters that ignore the representation error fail utterly to provide meaningful estimates of the system state. Inclusion of a time-independent climatological estimate of the representation error in a standard framework leads to inaccurate estimates of the large-scale part of the signal; accurate estimates of the large scales are only achieved by using stochastic superparameterization to provide evolving, large-scale dependent predictions of the small-scale statistics. Again, because the unresolved scales contain so much energy, even an accurate estimate of the large-scale part of the system does not provide an accurate estimate of the true state. By providing simultaneous estimates of both the large- and small-scale parts of the solution, the new framework is able to provide accurate estimates of the true system state.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

A flexible method to calculate the distributions of discretization errors in operator-split codes with stochastic noise in problem data

This report investigates a technique to calculate the distributions of discretization errors for a model of advection-diffusion-reaction with stochastic noise in problem data. The focus is on operator-split discretization methods. The error is decomposed into components due to the splitting and due to the discretization within each component. We present a method to estimate the distributions of the total discretization error for a quantity of interest as well as the aforementioned error components. These computations require information about the propagation of uncertainty within and between code modules, for which purpose we use a flexible code framework for uncertainty quantification. Computational examples illustrate that in certain cases the distribution of numerical error can have a significant impact on the code output. Using information about the components of the numerical error, decisions can be made regarding discretization parameters to efficiently reduce the impact of numerical error on the distribution of model outputs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Simulating diffusion processes in discontinuous media: A numerical scheme with constant time steps

In this article, we propose new Monte Carlo techniques for moving a diffusive particle in a discontinuous media. In this framework, we characterize the stochastic process that governs the positions of the particle. The key tool is the reduction of the process to a Skew Brownian motion (SBM). In a zone where the coefficients are locally constant on each side of the discontinuity, the new position of the particle after a constant time step is sampled from the exact distribution of the SBM process at the considered time. To do so, we propose two different but equivalent algorithms: a two-steps simulation with a stop at the discontinuity and a one-step direct simulation of the SBM dynamic. Some benchmark tests illustrate their effectiveness.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Bayesian filtering for model predictive control of stochastic gene expression in single cells

This study describes a method for controlling the production of protein in individual cells using stochastic models of gene expression. By combining modern microscopy platforms with optogenetic gene expression, experimentalists are able to accurately apply light to individual cells, which can induce protein production. Here we use a finite state projection based stochastic model of gene expression, along with Bayesian state estimation to control protein copy numbers within individual cells. We compare this method to previous methods that use population based approaches. We also demonstrate the ability of this control strategy to ameliorate discrepancies between the predictions of a deterministic model and stochastic switching system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Characterizing phonon dynamics using stochastic sampling

Predicting phonon relaxation time from molecular dynamics (MD) requires a long simulation time to compute the mode energy auto-correlation function. Here, we present an alternative approach to infer the phonon life-time from an approximate form of the energy auto-correlation function. The method requires as an input a set of sampled equilibrium configurations. A stochastic sampling method is used to generate the equilibrium configurations. We consider a truncated Taylor series expansion of the phonon energy auto-correlation function. The different terms in the truncated correlation function are obtained using the stochastic sampling approach. The expansion terms, thus, obtained are in good agreement with the corresponding values obtained using MD. We then use the approximate function to compute the phonon relaxation time. The relaxation time computed using this method is compared with that obtained from the exact correlation function. The two values are in agreement with each other.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

Neutron dosimetry in organs of an adult human phantom using linacs with multileaf collimator in radiotherapy treatments

Purpose: To calculate absorbed doses due to neutrons in 87 organs/tissues for anthropomorphic phantoms, irradiated in position supine (head first into the gantry) with orientations anteroposterior (AP) and right-left (RLAT) with a 18 MV accelerator. Conversion factors from monitor units to {mu}Gy per neutron in organs, equivalent doses in organs/tissues, and effective doses, which permit to quantify stochastic risks, are estimated. Methods: MAX06 and FAX06 phantoms were modeled with MCNPX and irradiated with a 18 MV Varian Clinac 2100C/D accelerator whose geometry included a multileaf collimator. Two actual fields of a pelvic treatment were simulated using electron-photon-neutron coupled transport. Absorbed doses due to neutrons were estimated from kerma. Equivalent doses were estimated using the radiation weighting factor corresponding to an average incident neutron energy 0.47 MeV. Statistical uncertainties associated to absorbed doses, as calculated by MCNPX, were also obtained. Results: Largest doses were absorbed in shallowest (with respect to the neutron pathway) organs. In {mu}GyMU{sup -1}, values of 2.66 (for penis) and 2.33 (for testes) were found in MAX06, and 1.68 (for breasts), 1.05 (for lenses of eyes), and 0.94 (for sublingual salivary glands) in FAX06, in AP orientation. In RLAT, the largest doses were found for bone tissues (leg) just at the entrance of the beam in the body (right side in our case). Values, in {mu}GyMU{sup -1}, of 1.09 in upper leg bone right spongiosa, for MAX06, and 0.63 in mandible spongiosa, for FAX06, were found. Except for gonads, liver, and stomach wall, equivalent doses found for FAX06 were, in both orientations, higher than for MAX06. Equivalent doses in AP are higher than in RLAT for all organs/tissues other than brain and liver. Effective doses of 12.6 and 4.1 {mu}SvMU{sup -1} were found for AP and RLAT, respectively. The organs/tissues with larger relative contributions to the effective dose were testes and breasts, in AP, and breasts and red marrow, in RLAT. Equivalent and effective doses obtained for MAX06/FAX06 were smaller (between 2 and 20 times) than those quoted for the mathematical phantoms ADAM/EVA in ICRP-74. Conclusions: The new calculations of conversion coefficients for neutron irradiation in AP and RLAT irradiation geometries show a reduction in the values of effective dose by factors 7 (AP) and 6 (RLAT) with respect to the old data obtained with mathematical phantoms. The existence of tissues or anatomical regions with maximum absorbed doses, such as penis, lens of eyes, fascia (part of connective tissue), etc., organs/tissues that classic mathematical phantoms did not include because they were not considered for the study of stochastic effects, has been revealed. Absorbed doses due to photons, obtained following the same simulation methodology, are larger than those due to neutrons, reaching values 100 times larger as the primary beam is approached. However, for organs far from the treated volume, absorbed photon doses can be up to three times smaller than neutron ones. Calculations using voxel phantoms permitted to know the organ dose conversion coefficients per MU due to secondary neutrons in the complete anatomy of a patient.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2011

Weighted Flow Algorithms (WFA) for stochastic particle coagulation

Stochastic particle-resolved methods are a useful way to compute the time evolution of the multi-dimensional size distribution of atmospheric aerosol particles. An effective approach to improve the efficiency of such models is the use of weighted computational particles. Here we introduce particle weighting functions that are power laws in particle size to the recently-developed particle-resolved model PartMC-MOSAIC and present the mathematical formalism of these Weighted Flow Algorithms (WFA) for particle coagulation and growth. We apply this to an urban plume scenario that simulates a particle population undergoing emission of different particle types, dilution, coagulation and aerosol chemistry along a Lagrangian trajectory. We quantify the performance of the Weighted Flow Algorithm for number and mass-based quantities of relevance for atmospheric sciences applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Joint Modeling of Quasar Variability and Accretion Disk Reprocessing Using Latent Stochastic Differential Equations

Quasars are bright active galactic nuclei powered by the accretion of matter around supermassive black holes at the center of galaxies. Their stochastic brightness variability depends on the physical properties of the accretion disk and black hole. The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe tens of millions of quasars, so there is a need for efficient techniques like machine learning that can handle the large volume of data. Quasar variability is believed to be driven by an X-ray corona, which is reprocessed by the accretion disk and emitted as UV/optical variability. We are the first to introduce an auto-differentiable simulation of the accretion disk and reprocessing. We use the simulation as a direct component of our neural network to jointly model the driving variability and reprocessing, trained with supervised learning on simulated LSST-like 10 yr quasar light curves. We encode the light curves using a transformer encoder, and the driving variability is reconstructed using latent stochastic differential equations, a physically motivated generative deep learning method that can model continuous-time stochastic dynamics. By embedding the physical processes of the driving signal and reprocessing into our network, we achieve a model that is more robust and interpretable. We demonstrate that our model outperforms a Gaussian process regression baseline and can infer accretion disk parameters and time delays between wave bands, even for out-of-distribution driving signals. Our approach provides a powerful framework that can be adapted to solve other inverse problems in multivariate time series.

Fagin, Joshua [City Univ. of New York (CUNY), NY (↗
DOE OSTI2012

Assessment of the mechanical performance of the Westinghouse BWR control rod CR 99 at high depletion levels

A long-term program assessing the mechanical performance of the Westinghouse BWR control rod CR 99 at high depletion levels has been performed. The scope of the program has mainly been based on the operation of four CR 99 Generation 2 control rods in demanding positions during 6 and 7 cycles in the Leibstadt Nuclear Power Plant (KKL) and on the detailed visual inspections and blade wing thickness measurements that were performed after the rods were discharged. By correlating statistically the blade wing thickness measurements to the appearance of irradiation-assisted stress corrosion cracking (IASCC), the probability of IASCC appearance as function of the blade wing swelling was estimated. In order to correlate the IASCC probability of a CR 99 to its depletion, the {sup 10}B depletion of the studied rods was calculated in detail on a local level with the stochastic Monte Carlo code MCNP in combination with the Westinghouse nodal code system PHOENIX4/POLCA7. Using this information coupled to the blade wing measurement data, a finite element model describing the blade wing swelling of an arbitrary CR 99 design as function of {sup 10}B depletion could then be generated. In the final step, these relationships were used to quantify the probability of IASCC appearance as function of the {sup 10}B depletion of the CR 99 Generations 2 and 3. Applying this detailed mapping of the CR 99 behavior at high depletion levels and using an on-line core monitoring system with explicit {sup 10}B depletion tracking capabilities will enable a reliable prediction of the probability for IASCC appearance, thus enhancing the optimized design and the sound operation of the CR 99 control rod. Another important outcome of the program was that it was clearly shown that no significant amount of boron leakage did occur through any of the detected IASCC cracks, despite the very high depletion levels achieved. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Characterization of the Sub-mm Delay Plates for the IOTA Optical-Stochastic-Cooling Experiment

This note describes the methods used to measure the thickness profile of the glass plates used in the optical-delay line of the Optical Stochastic Cooling (OSC) experiment at the Fermilab’s IOTA ring. The plates have a nominal thickness of 250 μm and the measurement was performed to verify the nominal thickness and determine its variation over the entire plate surface. All the available plates were characterized and the impact of the thickness variation on the radiation wave front was found to be negligible using wave-optics simulation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Adaptive Uncertainty Quantification for Stochastic Hyperbolic Conservation Laws

Here, we propose a predictor-corrector adaptive method for the study of hyperbolic partial differential equations (PDEs) under uncertainty. Constructed around the framework of stochastic finite volume (SFV) methods, our approach circumvents sampling schemes or simulation ensembles while also preserving fundamental properties, in particular hyperbolicity of the resulting systems and conservation of the discrete solutions. Furthermore, we augment the existing SFV theory with a priori convergence results for statistical quantities, in particular push-forward densities, which we demonstrate through numerical experiments. By linking refinement indicators to regions of the physical and stochastic spaces, we drive anisotropic refinements of the discretizations, introducing new degrees of freedom where deemed profitable. To illustrate our proposed method, we consider a series of numerical examples for nonlinear hyperbolic PDEs based on Burgers’ and Euler’s equations.

97 MATHEMATICS AND COMPUTING↗