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Discrete Ordinates Cost Optimization Of Weight Dependent Variance Reduction Techniques For Monte Carlo Neutral Particle Transport
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Book Synopsis Discrete-ordinates Cost Optimization of Weight-dependent Variance Reduction Techniques for Monte Carlo Neutral Particle Transport by : Clell J. Jr Solomon
Download or read book Discrete-ordinates Cost Optimization of Weight-dependent Variance Reduction Techniques for Monte Carlo Neutral Particle Transport written by Clell J. Jr Solomon and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A method for deterministically calculating the population variances of Monte Carlo particle transport calculations involving weight-dependent variance reduction has been developed. This method solves a set of equations developed by Booth and Cashwell [1979], but extends them to consider the weight-window variance reduction technique. Furthermore, equations that calculate the duration of a single history in an MCNP5 (RSICC version 1.51) calculation have been developed as well. The calculation cost, defined as the inverse figure of merit, of a Monte Carlo calculation can be deterministically minimized from calculations of the expected variance and expected calculation time per history. The method has been applied to one- and two-dimensional multi-group and mixed material problems for optimization of weight-window lower bounds. With the adjoint (importance) function as a basis for optimization, an optimization mesh is superimposed on the geometry. Regions of weight-window lower bounds contained within the same optimization mesh element are optimized together with a scaling parameter. Using this additional optimization mesh restricts the size of the optimization problem, thereby eliminating the need to optimize each individual weight-window lower bound. Application of the optimization method to a one-dimensional problem, designed to replicate the variance reduction iron-window effect, obtains a gain in efficiency by a factor of 2 over standard deterministically generated weight windows. The gain in two dimensional problems varies. For a 2-D block problem and a 2-D two-legged duct problem, the efficiency gain is a factor of about 1.2. The top-hat problem sees an efficiency gain of 1.3, while a 2-D 3-legged duct problem sees an efficiency gain of only 1.05. This work represents the first attempt at deterministic optimization of Monte Carlo calculations with weight-dependent variance reduction. However, the current work is limited in the size of problems that can be run by the amount of computer memory available in computational systems. This limitation results primarily from the added discretization of the Monte Carlo particle weight required to perform the weight-dependent analyses. Alternate discretization methods for the Monte Carlo weight should be a topic of future investigation. Furthermore, the accuracy with which the MCNP5 calculation times can be calculated deterministically merits further study.
Book Synopsis A Variationally-based Variance Reduction Method for Monte Carlo Particle Transport Problems by : Carla Lynn Barrett
Download or read book A Variationally-based Variance Reduction Method for Monte Carlo Particle Transport Problems written by Carla Lynn Barrett and published by . This book was released on 1999 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis An Automated Variance Reduction Method for Global Monte Carlo Neutral Particle Transport Problems by : Marc. A. Cooper
Download or read book An Automated Variance Reduction Method for Global Monte Carlo Neutral Particle Transport Problems written by Marc. A. Cooper and published by . This book was released on 1999 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Monte Carlo Methods for Particle Transport by : Alireza Haghighat
Download or read book Monte Carlo Methods for Particle Transport written by Alireza Haghighat and published by CRC Press. This book was released on 2020-08-09 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fully updated with the latest developments in the eigenvalue Monte Carlo calculations and automatic variance reduction techniques and containing an entirely new chapter on fission matrix and alternative hybrid techniques. This second edition explores the uses of the Monte Carlo method for real-world applications, explaining its concepts and limitations. Featuring illustrative examples, mathematical derivations, computer algorithms, and homework problems, it is an ideal textbook and practical guide for nuclear engineers and scientists looking into the applications of the Monte Carlo method, in addition to students in physics and engineering, and those engaged in the advancement of the Monte Carlo methods. Describes general and particle-transport-specific automated variance reduction techniques Presents Monte Carlo particle transport eigenvalue issues and methodologies to address these issues Presents detailed derivation of existing and advanced formulations and algorithms with real-world examples from the author’s research activities
Book Synopsis Monte Carlo Particle Transport Methods by : I. Lux
Download or read book Monte Carlo Particle Transport Methods written by I. Lux and published by CRC Press. This book was released on 2018-05-04 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt: With this book we try to reach several more-or-less unattainable goals namely: To compromise in a single book all the most important achievements of Monte Carlo calculations for solving neutron and photon transport problems. To present a book which discusses the same topics in the three levels known from the literature and gives us useful information for both beginners and experienced readers. It lists both well-established old techniques and also newest findings.
Book Synopsis Advanced Quadrature Selection for Monte Carlo Variance Reduction by : Kelly Rowland
Download or read book Advanced Quadrature Selection for Monte Carlo Variance Reduction written by Kelly Rowland and published by . This book was released on 2018 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neutral particle radiation transport simulations are critical for radiation shielding and deep penetration applications. Arriving at a solution for a given response of interest can be computationally difficult because of the magnitude of particle attenuation often seen in these shielding problems. Hybrid methods, which aim to synergize the individual favorable aspects of deterministic and stochastic solution methods for solving the steady-state neutron transport equation, are commonly used in radiation shielding applications to achieve statistically meaningful results in a reduced amount of computational time and effort. The current state of the art in hybrid calculations is the Consistent Adjoint-Driven Importance Sampling (CADIS) and Forward-Weighted CADIS (FW-CADIS) methods, which generate Monte Carlo variance reduction parameters based on deterministically-calculated scalar flux solutions. For certain types of radiation shielding problems, however, results produced using these methods suffer from unphysical oscillations in scalar flux solutions that are a product of angular discretization. These aberrations are termed “ray effects”. The Lagrange Discrete Ordinates (LDO) equations retain the formal structure of the traditional discrete ordinates formulation of the neutron transport equation and mitigate ray effects at high angular resolution. In this work, the LDO equations have been implemented in the Exnihilo parallel neutral particle radiation transport framework, with the deterministic scalar flux solutions passed to the Automated Variance Reduction Generator (ADVANTG) software and the resultant Monte Carlo variance reduction parameters’ efficacy assessed based on results from MCNP5. Studies were conducted in both the CADIS and FW-CADIS contexts, with the LDO equations’ variance reduction parameters seeing their best performance in the FW-CADIS method, especially for photon transport.
Book Synopsis Automatic Variance Reduction for Monte Carlo Simulations Via the Local Importance Function Transform by : Scott Allen Turner
Download or read book Automatic Variance Reduction for Monte Carlo Simulations Via the Local Importance Function Transform written by Scott Allen Turner and published by . This book was released on 1996 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Geometrically-compatible 3-D Monte Carlo and Discrete-ordinates Methods by :
Download or read book Geometrically-compatible 3-D Monte Carlo and Discrete-ordinates Methods written by and published by . This book was released on 1998 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the final report of a three-year, Laboratory Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The purpose of this project was two-fold. The first purpose was to develop a deterministic discrete-ordinates neutral-particle transport scheme for unstructured tetrahedral spatial meshes, and implement it in a computer code. The second purpose was to modify the MCNP Monte Carlo radiation transport code to use adjoint solutions from the tetrahedral-mesh discrete-ordinates code to reduce the statistical variance of Monte Carlo solutions via a weight-window approach. The first task has resulted in a deterministic transport code that is much more efficient for modeling complex 3-D geometries than any previously existing deterministic code. The second task has resulted in a powerful new capability for dramatically reducing the cost of difficult 3-D Monte Carlo calculations.
Book Synopsis A Hybrid Monte Carlo-Deterministic Method for Global Binary Stochastic Medium Transport Problems by :
Download or read book A Hybrid Monte Carlo-Deterministic Method for Global Binary Stochastic Medium Transport Problems written by and published by . This book was released on 2010 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: Global deep-penetration transport problems are difficult to solve using traditional Monte Carlo techniques. In these problems, the scalar flux distribution is desired at all points in the spatial domain (global nature), and the scalar flux typically drops by several orders of magnitude across the problem (deep-penetration nature). As a result, few particle histories may reach certain regions of the domain, producing a relatively large variance in tallies in those regions. Implicit capture (also known as survival biasing or absorption suppression) can be used to increase the efficiency of the Monte Carlo transport algorithm to some degree. A hybrid Monte Carlo-deterministic technique has previously been developed by Cooper and Larsen to reduce variance in global problems by distributing particles more evenly throughout the spatial domain. This hybrid method uses an approximate deterministic estimate of the forward scalar flux distribution to automatically generate weight windows for the Monte Carlo transport simulation, avoiding the necessity for the code user to specify the weight window parameters. In a binary stochastic medium, the material properties at a given spatial location are known only statistically. The most common approach to solving particle transport problems involving binary stochastic media is to use the atomic mix (AM) approximation in which the transport problem is solved using ensemble-averaged material properties. The most ubiquitous deterministic model developed specifically for solving binary stochastic media transport problems is the Levermore-Pomraning (L-P) model. Zimmerman and Adams proposed a Monte Carlo algorithm (Algorithm A) that solves the Levermore-Pomraning equations and another Monte Carlo algorithm (Algorithm B) that is more accurate as a result of improved local material realization modeling. Recent benchmark studies have shown that Algorithm B is often significantly more accurate than Algorithm A (and therefore the L-P model) for deep penetration problems such as examined in this paper. In this research, we investigate the application of a variant of the hybrid Monte Carlo-deterministic method proposed by Cooper and Larsen to global deep penetration problems involving binary stochastic media. To our knowledge, hybrid Monte Carlo-deterministic methods have not previously been applied to problems involving a stochastic medium. We investigate two approaches for computing the approximate deterministic estimate of the forward scalar flux distribution used to automatically generate the weight windows. The first approach uses the atomic mix approximation to the binary stochastic medium transport problem and a low-order discrete ordinates angular approximation. The second approach uses the Levermore-Pomraning model for the binary stochastic medium transport problem and a low-order discrete ordinates angular approximation. In both cases, we use Monte Carlo Algorithm B with weight windows automatically generated from the approximate forward scalar flux distribution to obtain the solution of the transport problem.
Book Synopsis Deterministic Cost Optimization of Monte Carlo Weight-window Variance-reduction Parameters by :
Download or read book Deterministic Cost Optimization of Monte Carlo Weight-window Variance-reduction Parameters written by and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Particle Transport Simulation with the Monte Carlo Method by : Leland Lavele Carter
Download or read book Particle Transport Simulation with the Monte Carlo Method written by Leland Lavele Carter and published by . This book was released on 1975 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Variance Reduction Techniques for the Monte Carlo Calculations of Electron Dose Distributions by : Mark Albert Holmes
Download or read book Variance Reduction Techniques for the Monte Carlo Calculations of Electron Dose Distributions written by Mark Albert Holmes and published by . This book was released on 1995 with total page 578 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Minimization Of Computational Costs Of Non-analogue Monte Carlo Methods by : G A Mikhailov
Download or read book Minimization Of Computational Costs Of Non-analogue Monte Carlo Methods written by G A Mikhailov and published by World Scientific. This book was released on 1992-01-10 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: Non-analogue Monte Carlo methods are useful when the direct simulation techniques are insufficient. To use the additional discretization, Monte Carlo estimates are biased and it is desirable to optimize the connection between discretization parameters and the sample size. In this connection, the book investigates variances of non-analogue Monte Carlo estimates, uniform minimization of variances by choosing a computational model and the minimization of computational cost of non-analogue Monte Carlo methods.This book is essentially new with respect to previous monographs on the Monte Carlo methods.
Book Synopsis First Collision Source Method for Coupling Monte Carlo and Discrete Ordinates for Localized Source Problems by :
Download or read book First Collision Source Method for Coupling Monte Carlo and Discrete Ordinates for Localized Source Problems written by and published by . This book was released on 1985 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A difficult class of problems for the discrete-ordinates neutral particle transport method is to accurately compute the flux due to a spatially localized source. Because the transport equation is solved for discrete directions, the so-called ray effect causes the flux at space points far from the source to be inaccurate. Thus, in general, discrete ordinates would not be the method of choice to solve such problems. It is better suited for calculating problems with significant scattering. The Monte Carlo method is suited to localized source problems, particularly if the amount of collisional interactions in minimal. However, if there are many scattering collisions and the flux at all space points is desired, then the Monte Carlo method becomes expensive. To take advantage of the attributes of both approaches, we have devised a first collision source method to combine the Monte Carlo and discrete-ordinates solutions. That is, particles are tracked from the source to their first scattering collision and tallied to produce a source for the discrete-ordinates calculation. A scattered flux is then computed by discrete ordinates, and the total flux is the sum of the Monte Carlo and discrete ordinates calculated fluxes. In this paper, we present calculational results using the MCNP and TWODANT codes for selected two-dimensional problems that show the effectiveness of this method.
Book Synopsis Optimization of Weighted Monte Carlo Methods by : Gennadii A. Mikhailov
Download or read book Optimization of Weighted Monte Carlo Methods written by Gennadii A. Mikhailov and published by Springer. This book was released on 1992-02-13 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Monte Carlo method is based on the munerical realization of natural or artificial models of the phenomena under considerations. In contrast to classical computing methods the Monte Carlo efficiency depends weakly on the dimen sion and geometric details of the problem. The method is used for solving complex problems of the radiation transfer theory, turbulent diffusion, chemi cal kinetics, theory of rarefied gases, diffraction of waves on random surfaces, etc. The Monte Carlo method is especially effective when using multi-processor computing systems which allow many independent statistical experiments to be simulated simultaneously. The weighted Monte Carlo estimates are constructed in order to diminish errors and to obtain dependent estimates for the calculated functionals for different values of parameters of the problem, i.e., to improve the functional dependence. In addition, the weighted estimates make it possible to evaluate special functionals, for example, the derivatives with respect to the parameters. There are many works concerned with the development of the weighted estimates. In Chap. 1 we give the necessary information about these works and present a set of illustrations. The rest of the book is devoted to the solution of a series of mathematical problems related to the optimization of the weighted Monte Carlo estimates.
Book Synopsis Introduction to Monte Carlo Methods for Transport and Diffusion Equations by : Bernard Lapeyre
Download or read book Introduction to Monte Carlo Methods for Transport and Diffusion Equations written by Bernard Lapeyre and published by OUP Oxford. This book was released on 2003 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text is used by for the resolution of partial differential equations, trasnport equations, the Boltzmann equation and the parabolic equations of diffusion.
Book Synopsis Hybrid S[sub N]/Monte Carlo Research and Results by :
Download or read book Hybrid S[sub N]/Monte Carlo Research and Results written by and published by . This book was released on 1993 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt: The neutral particle transport equation is solved by a hybrid method that iteratively couples regions where deterministic (S[sub N]) and stochastic (Monte Carlo) methods are applied. The Monte Carlo and S[sub N] regions are fully coupled in the sense that no assumption is made about geometrical separation or decoupling. The hybrid Monte Carlo/S[sub N] method provides a new means of solving problems involving both optically thick and optically thin regions that neither Monte Carlo nor S[sub N] is well suited for by themselves. The hybrid method has been successfully applied to realistic shielding problems. The vectorized Monte Carlo algorithm in the hybrid method has been ported to the massively parallel architecture of the Connection Machine. Comparisons of performance on a vector machine (Cray Y-MP) and the Connection Machine (CM-2) show that significant speedups are obtainable for vectorized Monte Carlo algorithms on massively parallel machines, even when realistic problems requiring variance reduction are considered. However, the architecture of the Connection Machine does place some limitations on the regime in which the Monte Carlo algorithm may be expected to perform well.
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