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Friday, July 24, 2020 | History

2 edition of Monte Carlo-based validation of the ENDF/MC-II/SDX cell homogenization path found in the catalog.

Monte Carlo-based validation of the ENDF/MC-II/SDX cell homogenization path

D. C. Wade

Monte Carlo-based validation of the ENDF/MC-II/SDX cell homogenization path

by D. C. Wade

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Published by Dept. of Energy, [Office of Energy Research], Argonne National Laboratory, for sale by the National Technical Information Service in Argonne, Ill, Springfield, Va .
Written in English

    Subjects:
  • Breeder reactors.,
  • Monte Carlo method.,
  • Nuclear fuels.

  • Edition Notes

    Statementby D. C. Wade, Applied Physics Division, Argonne National Laboratory.
    SeriesANL ; 79-5, ANL -- 79-5.
    ContributionsArgonne National Laboratory., Argonne National Laboratory. Applied Physics Division.
    The Physical Object
    Paginationvii, 56 p. :
    Number of Pages56
    ID Numbers
    Open LibraryOL18015110M

    versatile Monte Carlo algorithms that are currently enjoying a widespread success in many production settings. The goal of this course is to provide the audience with a deep, up-to-date understanding of key techniques for free-path sampling, transmittance estimation, and light-path construction in participat-. Monte Carlo Standard Errors for Markov Chain Monte Carlo a dissertation submitted to the faculty of the graduate school of the university of minnesota by James Marshall Flegal in partial fulfillment of the requirements for the degree of doctor of philosophy Galin L. Jones and Glen D. Meeden, Advisers July

    In Monte Carlo Cross-Validation approach all steps were repeated for 50 bootstraps. For each bootstrap in our approach we obtained (1) DEA, (2) PEA, (3) Mean, (4) DScore, and (5) Classification. After 50 bootstraps we focused on top 10 pairs of .   Monte Carlo Integration suggests that to approximate this ratio, we should generate a set of random points on our inscribed diagram and use the proportion of points that fall inside. You can think of this as if it were a dart board and the probability that a dart is in the circle would give us the ratio of the areas.

    Monte Carlo simulation is often used in business for risk and decision analysis, to help make decisions given uncertainties in market trends, fluctuations, and other uncertain the science and engineering communities, MC simulation is often used for uncertainty analysis, optimization, and reliability-based manufacturing, MC methods are used to help . Tutorial on Monte Carlo Techniques Gabriel A. Terejanu Department of Computer Science and Engineering University at Buffalo, Buffalo, NY [email protected] 1 Introduction Monte Carlo (MC) technique is a numerical method that makes use of random numbers to solve mathematical problems for which an analytical solution is not known.


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Monte Carlo-based validation of the ENDF/MC-II/SDX cell homogenization path by D. C. Wade Download PDF EPUB FB2

MONTE CARLO - BASED VALIDATION OF THE ENDF/MC~-II/SDX CXLL HOMOGENIZATION PATH D. Wade Applied Physics Division Argonne National Laboratory USA Submitted to: IAEA/NEACRP Specialist Meeting on Homogenization Methods in Reactor Physics Wllrenlingen, Switzerland November Title: Monte Carlo-based validation of the ENDF/MC/sup 2/-II/SDX cell homogenization path The results are presented of a program of validation of the unit cell homogenization prescriptions and codes used for the analysis of Zero Power Reactor (ZPR) fast breeder reactor critical experiments.

Get this from a library. Monte Carlo-based validation of the ENDF/MC²-II/SDX cell homogenization path. [D C Wade; Argonne National Laboratory.; Argonne National Laboratory. Applied Physics Division.]. Monte Carlo-based Validation of the ENDF/MC2-II/SDX Cell Homogenization Path by D.

Wade ABSTRACT This report summarizes the results of a program of valida- tion of the unit cell homogenization prescriptions and codes used for the analysis of Zero Power Reactor (ZPR) fast breeder re- actor critical by: 2.

The relative statistical errors on the Monte Carlo results were generally less than %. Table 4 shows the Monte Carlo calculated and measured doses for 6 and MV photon beams of four points inside the phantom.

As shown, good agreement between Monte Carlo calculated and measured doses (within %) was obtained for both by: 4.

Recently a commercial Monte Carlo based IMRT planning system (Monaco version ) was released. In this study the dosimetric accuracy of this new planning system was validated.

Methods: Absolute dose profiles, depth dose curves, and output factors calculated by Monaco were compared with measurements in a water phantom. Dosimetric Validation Chetty et.

“Report of the AAPM Task Group No. Issues associated with clinical implementation of Monte Carlo-based photon and electron external beam treatment planning”, Med.

Phys. 34, (). “Experimental verification of a MC algorithm should include testing to assess. Monte-Carlo integration is the most common application of Monte-Carlo methods Basic idea: Do not use a fixed grid, but random points, because: of dimensionality: a fixed grid in D dimensions requires ND points step size must be chosen first.

Monte Carlo Methods Stéphane Paltani What are Monte-Carlo. Many Monte Carlo techniques for optimization and estimation require billions or more random numbers. Current physical generation methods are no match for simple algorithmic generators in terms of speed.

Large period: The period of a random number generator should be ex-tremely large — on the order of — in order to avoid problems with. Monte Carlo Simulations: Number of Iterations and Accuracy.

by William Oberle. Approved for public release; distribution is unlimited. NOTICES. Disclaimers. The findings in this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.

Cross validation and Monte Carlo cross validation. The fundamental step after the data are available is to determine the number of components (dimension) for the derived model (9).

There are total q possible different models taking the pattern of Eq. (9) corresponding to k=1, 2,q. How to determine k is the problem.

Validation and clinical implementation of an accurate Monte Carlo code for pencil beam scanning proton therapy Sheng Huang, University of Pennsylvania Minglei Kang, Georgetown University Hospital Kevin Souris, Universite Catholique de Louvain Christopher Ainsley, University of Pennsylvania Timothy D.

Solberg, University of California, San Francisco. Journals & Books; Help In order to choose correctly the dimension of calibration model in chemistry, a new simple and effective method named Monte Carlo cross validation (MCCV) is introduced in the present work. Unlike leave-one-out procedure commonly used in chemometrics for cross validation (CV), the Monte Carlo cross validation developed.

The Monte Carlo simulation is an important technique in risk management that many PMP and PMI-RMP exam study books do not describe in detail. Most of the guides say it is a complex technique that requires a computer’s assistance, and so aspirants don’t dig further. The Monte Carlo simulation method is a very valuable tool for planning project schedules and developing budget estimates.

Yet, it is not widely used by the Project Managers. This is due to a misconception that the methodology is too complicated to use and objective of this presentation is to encourage the use of Monte Carlo Simulation in risk identification.

Monte Carlo -- a bit of history •Credit for inventing the Monte Carlo method often goes to Stanislaw Ulam, a Polish born mathematician who worked for John von Neumann on the United States Manhattan Project during World War II. •Ulam is primarily known for designing the hydrogen bomb with Edward Teller in Monte Carlo-based validation of the ENDF/MC/sup 2/-II/SDX cell homogenization path Technical Report Wade, D C The results are presented of a program of validation of the unit cell homogenization prescriptions and codes used for the analysis of Zero Power Reactor (ZPR) fast breeder reactor critical experiments.

Interpretation of Monte-Carlo Simulation Results We provide two result sheets such as ‘Result Sheet’ and ‘Summary Sheet’.Figure 1 shows ‘Result Sheet’.In this sheet, you can see the sampling points and the probability distribution of performance index.

Abstract. This paper presents validation of a Monte-Carlo simulation built for supporting electricity grid capacity planning. The base simulation model is developed to represent the power usages of electricity at residential low voltage grid. Keep in mind that in Monte Carlo, you need 4 times as many samples to reduce the noise (or variance) by 2.

As suggested already a couple of times throughout this lesson, the art of Monte Carlo rendering is mostly about finding ways of reducing this noise. We will talk about variance reduction techniques later in this lesson.

Monte Carlo methods basically refer to class of algorithms which use Randomness to give an estimate. Let’s take an example to show this To give a numerical estimate of this integral of a function using Monte Carlo methods, one can model this integral as E[f(U)] where U is uniform random number in [0,1].

The best way to validate the Monte Carlo based code sequence is to compare the results to reference Serpent full-core calculations. CAD models are read in the stereolitography STL format, in which the surfaces of geometry bodies are represented by a mesh of flat triangles.The quality of the OPLS-DA model was evaluated on the basis of cross-validation by a Monte Carlo leave-n-out proced 50 and CV-ANOVA.

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