Statistical error and optimal parameters of the test particle Monte Carlo method

2012 ◽  
Author(s):  
M. Yu. Plotnikov ◽  
E. V. Shkarupa
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Test Particle ◽  
Statistical Error ◽  
Optimal Parameters

scholarly journals C-beta energy converter efficiency modeling

2019 ◽  
Vol 222 ◽  
pp. 02012
Author(s):  
Oleg Kuznetsov ◽  
Viktor Chepurnov ◽  
Albina Gurskaya ◽  
Mikhail Dolgopolov ◽  
Sali Radzhapov
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Theoretical Models ◽  
Maximum Efficiency ◽  
Equivalent Circuits ◽  
Optimal Parameters ◽  
Energy Converter ◽  
Electron Hole ◽  
The Monte Carlo Method

To construct beta converters with maximum efficiency it is necessary to carry out the theoretical calculation in order to determine their optimal parameters - the geometry of the structure, the thickness of the deposition of the radioisotope layer, the depth and the width of the p-n junction, and others. To date, many different theoretical models and calculations methods had been proposed. There are fairly simple theoretical models based on the Bethe-Bloch formula and the calculation of the rate of generation of electron-hole pairs, and on calculations by equivalent circuits. Also, the Monte-Carlo method is used for theoretical modeling of beta converters. This paper explores beta converter optimization using the Monte-Carlo method. The purpose of the study is to conduct Monte-Carlo simulation of the beta converter to determine its optimal parameters.

scholarly journals The Precision of Positron Emission Tomography: Theory and Measurement

1991 ◽  
Vol 11 (1_suppl) ◽  
pp. A26-A30 ◽  
Author(s):  
Nathaniel M. Alpert ◽  
W. Craig Barker ◽  
Andrew Gelman ◽  
Stephen Weise ◽  
Michio Senda ◽  
...  
Keyword(s):  
Experimental Data ◽  
Positron Emission Tomography ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Random Error ◽  
Human Subjects ◽  
Statistical Error ◽  
Emission Tomography ◽  
Flow Measurements ◽  
Positron Emission

The limits of quantitation with positron emission tomography (PET) are examined with respect to the noise propagation resulting from radioactive decay and other sources of random error. Theoretical methods for evaluating the statistical error have been devised but seldom applied to experimental data obtained on human subjects. This paper extends the analysis in several ways: (1) A Monte Carlo method is described for tracking the propagation of statistical error through the analysis of in vivo measurements; (2) Experimental data, obtained in phantoms, validating the Monte Carlo method and other methods are presented; (3) A difference in activation paradigm, performed on regional CBF (rCBF) data from five human subjects, was analyzed on 1.6-cm diameter regions of interest to determine the mean fractional statistical error in PET tissue concentration and in rCBF before and after stereotactic transformation; and (4) A linear statistical model and calculations of the various statistical errors were used to estimate the magnitude of the subject-specific fluctuations under various conditions. In this specific example, the root mean squared (RMS) noise in flow measurements was about three times higher than the RMS noise in the concentration measurements. In addition, the total random error was almost equally partitioned between statistical error and random fluctuations due to all other sources.

Statistical error of reactor calculations by the Monte Carlo method

2011 ◽  
Vol 74 (14) ◽  
pp. 1871-1877 ◽  
Author(s):  
M. A. Kalugin ◽  
D. S. Oleynik ◽  
E. A. Sukhino-Khomenko
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Statistical Error ◽  
The Monte Carlo Method

scholarly journals Comparison of Monte Carlo Methods Efficiency to Solve Radiative Energy Transfer in High Fidelity Unsteady 3D Simulations

2017 ◽  
Author(s):  
Lorella Palluotto ◽  
Nicolas Dumont ◽  
Pedro Rodrigues ◽  
Chai Koren ◽  
Ronan Vicquelin ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Monte Carlo Methods ◽  
Computational Cost ◽  
Statistical Error ◽  
Radiative Energy ◽  
Maximum Temperature ◽  
Quasi Monte Carlo ◽  
High Scalability ◽  
Transfer Problems

The present work assesses different Monte Carlo methods in radiative heat transfer problems, in terms of accuracy and computational cost. Achieving a high scalability on numerous CPUs with the conventional forward Monte Carlo method is not straightforward. The Emission-based Reciprocity Monte Carlo Method (ERM) allows to treat each mesh point independently from the others with a local monitoring of the statistical error, becoming a perfect candidate for high-scalability. ERM is however penalized by a slow statistical convergence in cold absorbing regions. This limitation has been overcome by an Optimized ERM (OERM) using a frequency distribution function based on the emission distribution at the maximum temperature of the system. Another approach to enhance the convergence is the use of low-discrepancy sampling. The obtained Quasi-Monte Carlo method is combined with OERM. The efficiency of the considered Monte-Carlo methods are compared.

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