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

- 56 Want to read
- 2 Currently reading

Published
**1979**
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

- Breeder reactors.,
- Monte Carlo method.,
- Nuclear fuels.

**Edition Notes**

Statement | by D. C. Wade, Applied Physics Division, Argonne National Laboratory. |

Series | ANL ; 79-5, ANL -- 79-5. |

Contributions | Argonne National Laboratory., Argonne National Laboratory. Applied Physics Division. |

The Physical Object | |
---|---|

Pagination | vii, 56 p. : |

Number of Pages | 56 |

ID Numbers | |

Open Library | OL18015110M |

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

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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.

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“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.

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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.

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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.

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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.

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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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