Monte-Carlo Simulation Artefacts in Physical Modeling due to Pseudo-Random Generators

1991 ◽  
Vol 02 (01) ◽  
pp. 246-249 ◽  
Author(s):  
A.S. BERDNICOV ◽  
S.B. TURTIA
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Vector ◽  
Physical Modeling ◽  
Random Vectors ◽  
Special System ◽  
Physical Problems ◽  
Random Generators ◽  
Vector Algorithms

A set of vector algorithms for random vectors generation is tested by the authors. Only the most reliable algorithms are selected for testing. The special system of original tests is developed with the main criteria of the independence of random vector components and the equability of N-dimensional hypercube filling. Some algorithms are discovered to be bad for physical modeling, and for others recommendations for their use for different physical problems are given.

HUMAN FACTOR ANALYSIS OF CONTAINER VESSEL’S GROUNDING ACCIDENTS

2021 ◽  
Vol 159 (A1) ◽  
Author(s):  
U Yildirim ◽  
O Ugurlu ◽  
E Basar ◽  
E Yuksekyildiz
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Human Error ◽  
Human Performance ◽  
Human Factor ◽  
Study Data ◽  
Team Management ◽  
Contribution Rate ◽  
Physical Problems ◽  
Mental Problems

Investigation on maritime accidents is a very important tool in identifying human factor-related problems. This study examines the causes of accidents, in particular the reasons for the grounding of container ships. These are analysed and evaluation according to the contribution rate using the Monte Carlo simulation. The OpenFTA program is used to run the simulation. The study data are obtained from 46 accident reports from 1993 to 2011. The data were prepared by the International Maritime Organization (IMO) Global Integrated Shipping Information System (GISIS). The GISIS is one of the organizations that investigate reported accidents in an international framework and in national shipping companies. The Monte Carlo simulation determined a total of 23.96% human error mental problems, 26.04% physical problems, 38.58% voyage management errors, and 11.42% team management error causes. Consequently, 50% of the human error is attributable to human performance disorders, while 50% team failure has been found.

Analysis on High-Energy Physical Problems in Monte Carlo Simulation

2014 ◽  
Vol 577 ◽  
pp. 762-766
Author(s):  
Bao Guang Sun ◽  
Xiao Feng Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Energy Spectrum ◽  
High Energy ◽  
Extensive Air Shower ◽  
Air Shower ◽  
Secondary Particles ◽  
Physical Problems ◽  
Strip Pattern ◽  
Detector Simulation

This paper analyzes the data got in two Monte Carlo simulations, namely, extensive air shower simulation and detector simulation. Then, based on the data from experimental arrays, some physical problems have been analyzed and illustrated. Those problems include the distribution of energy spectrum of secondary particles, the distribution of zenith angle, of azimuths, of background noises, and that of strip pattern, as well as the atmospheric absorption.

Human Factor Analysis of Container Vessel's Grounding Accidents

2017 ◽  
Vol Vol 159 (A1) ◽  
Author(s):  
U Yildirim ◽  
O Ugurlu ◽  
E Basar ◽  
E Yuksekyildiz
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Human Error ◽  
Human Performance ◽  
Human Factor ◽  
Study Data ◽  
Team Management ◽  
Contribution Rate ◽  
Physical Problems ◽  
Mental Problems

Investigation on maritime accidents is a very important tool in identifying human factor-related problems. This study examines the causes of accidents, in particular the reasons for the grounding of container ships. These are analysed and evaluation according to the contribution rate using the Monte Carlo simulation. The OpenFTA program is used to run the simulation. The study data are obtained from 46 accident reports from 1993 to 2011. The data were prepared by the International Maritime Organization (IMO) Global Integrated Shipping Information System (GISIS). The GISIS is one of the organizations that investigate reported accidents in an international framework and in national shipping companies. The Monte Carlo simulation determined a total of 23.96% human error mental problems, 26.04% physical problems, 38.58% voyage management errors, and 11.42% team management error causes. Consequently, 50% of the human error is attributable to human performance disorders, while 50% team failure has been found.

Combined Physical Modeling and Monte Carlo Simulation of Recrystallization of Hot Deformed AA7020 Aluminum Alloy

2012 ◽  
Vol 715-716 ◽  
pp. 480-485 ◽  
Author(s):  
Ali Reza Eivani ◽  
Jie Zhou ◽  
Jurek Duczczyk
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Aluminum Alloy ◽  
Physical Modeling ◽  
Experimental Results ◽  
Grain Structure ◽  
Stored Energy ◽  
Recrystallization Kinetics ◽  
Model Predictions ◽  
Aa7020 Aluminum Alloy

In this research, recrystallization of AA7020 aluminum alloy after hot compression testing was predicted using a framework being a combination of physical modeling and Monte Carlo simulation. Stored energy was calculated as a function of subgrain size related to the Zener Hollomon parameter. The as-deformed grain structure was mapped into the Monte Carlo simulation from experimental results. Calculated stored energy was assigned to the mapped structure, considering the length scale of the simulation. Results were validated by comparing the microstructures obtained from the model predictions with those from experimental results and a reasonable agreement was reached. The predicted grain size was found to be 15 % smaller than the experimental values. Predicted fractions recrystallized showed a similar trend to the experimental results. However, a discrepancy between the model predictions and experimental results in terms of recrystallization kinetics was found, which was attributed to neglecting the effect of subgrain growth and resulting reduction of the stored energy during recovery on the recrystallization kinetics in the present simulation.

scholarly journals Tests Based on Kurtosis for Multivariate Normality

2019 ◽  
Vol 48 (5) ◽  
pp. 1-10
Author(s):  
Danush Kanchana Wijekularathna ◽  
Huijun Yi ◽  
Adhiraj Roka
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Comparative Study ◽  
Sample Size ◽  
Random Vector ◽  
Multivariate Normality ◽  
Multivariate Normal ◽  
Significance Level ◽  
Normality Test ◽  
Standard Normal

In this paper, we first transform a multivariate normal random vector into a random vector with elements that are approximately independent standard normal random variables. Then we propose the multivariate version generalized from the univariate normality test based on kurtosis from the literature. Power is investigated through the Monte Carlo Simulation with different significance level, dimension, and sample size. To assess the validity and accuracy of the new tests, we carry a comparative study with several other existing tests by selecting certain types of symmetric and asymmetric alternative distributions.

Electron Scattering in Solids

1990 ◽  
Vol 48 (2) ◽  
pp. 4-5
Author(s):  
Ryuichi Shimizu ◽  
Ze-Jun Ding
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Angular Distribution ◽  
Elastic Scattering ◽  
Electron Scattering ◽  
Cross Sections ◽  
Expansion Method ◽  
Electron Excitation ◽  
Partial Wave Expansion ◽  
Backscattered Electrons

Monte Carlo simulation has been becoming most powerful tool to describe the electron scattering in solids, leading to more comprehensive understanding of the complicated mechanism of generation of various types of signals for microbeam analysis.The present paper proposes a practical model for the Monte Carlo simulation of scattering processes of a penetrating electron and the generation of the slow secondaries in solids. The model is based on the combined use of Gryzinski’s inner-shell electron excitation function and the dielectric function for taking into account the valence electron contribution in inelastic scattering processes, while the cross-sections derived by partial wave expansion method are used for describing elastic scattering processes. An improvement of the use of this elastic scattering cross-section can be seen in the success to describe the anisotropy of angular distribution of elastically backscattered electrons from Au in low energy region, shown in Fig.l. Fig.l(a) shows the elastic cross-sections of 600 eV electron for single Au-atom, clearly indicating that the angular distribution is no more smooth as expected from Rutherford scattering formula, but has the socalled lobes appearing at the large scattering angle.

Determination of Stoichiometry of GaAs Component in (Gaas)Ge Epitaxially Grown Thin Films by Electron Microprobe X-Ray Analysis

1990 ◽  
Vol 48 (2) ◽  
pp. 264-265
Author(s):  
D. R. Liu ◽  
S. S. Shinozaki ◽  
R. J. Baird
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Compound Semiconductors ◽  
Energy Dispersive Analysis ◽  
X Ray ◽  
Metastable Alloys ◽  
Lower Voltage

The epitaxially grown (GaAs)Ge thin film has been arousing much interest because it is one of metastable alloys of III-V compound semiconductors with germanium and a possible candidate in optoelectronic applications. It is important to be able to accurately determine the composition of the film, particularly whether or not the GaAs component is in stoichiometry, but x-ray energy dispersive analysis (EDS) cannot meet this need. The thickness of the film is usually about 0.5-1.5 μm. If Kα peaks are used for quantification, the accelerating voltage must be more than 10 kV in order for these peaks to be excited. Under this voltage, the generation depth of x-ray photons approaches 1 μm, as evidenced by a Monte Carlo simulation and actual x-ray intensity measurement as discussed below. If a lower voltage is used to reduce the generation depth, their L peaks have to be used. But these L peaks actually are merged as one big hump simply because the atomic numbers of these three elements are relatively small and close together, and the EDS energy resolution is limited.

Sign in / Sign up

Export Citation Format

Share Document