A Modification to the Monte Carlo Method—The Exodus Method

1968 ◽  
Vol 90 (3) ◽  
pp. 328-332 ◽  
Author(s):  
A. F. Emery ◽  
W. W. Carson
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Calculation Time ◽  
The Monte Carlo Method

A modification to the Monte Carlo method is described which reduces calculation time and improves the accuracy. This method—termed “Exodus”—is not dependent upon a random number generator and may be applied to any problem which admits of a nodal network.

Line-by-Line Random-Number Database for Monte Carlo Simulations of Radiation in Combustion System

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Tao Ren ◽  
Michael F. Modest
Keyword(s):  
Heat Transfer ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Radiative Heat ◽  
Random Number ◽  
Test Cases ◽  
Combustion System ◽  
Absorption Coefficients ◽  
One Dimensional ◽  
The Monte Carlo Method

With today's computational capabilities, it has become possible to conduct line-by-line (LBL) accurate radiative heat transfer calculations in spectrally highly nongray combustion systems using the Monte Carlo method. In these calculations, wavenumbers carried by photon bundles must be determined in a statistically meaningful way. The wavenumbers for the emitting photons are found from a database, which tabulates wavenumber–random number relations for each species. In order to cover most conditions found in industrial practices, a database tabulating these relations for CO2, H2O, CO, CH4, C2H4, and soot is constructed to determine emission wavenumbers and absorption coefficients for mixtures at temperatures up to 3000 K and total pressures up to 80 bar. The accuracy of the database is tested by reconstructing absorption coefficient spectra from the tabulated database. One-dimensional test cases are used to validate the database against analytical LBL solutions. Sample calculations are also conducted for a luminous flame and a gas turbine combustion burner. The database is available from the author's website upon request.

MONTE CARLO METHODS IN FUZZY NON-LINEAR REGRESSION

2008 ◽  
Vol 04 (02) ◽  
pp. 123-141 ◽  
Author(s):  
AREEG ABDALLA ◽  
JAMES BUCKLEY
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Linear Regression ◽  
Fuzzy Numbers ◽  
Random Number ◽  
Random Number Generator ◽  
Search Space ◽  
Triangular Fuzzy Numbers ◽  
Non Linear ◽  
Regression Problems

We apply our new fuzzy Monte Carlo method to certain fuzzy non-linear regression problems to estimate the best solution. The best solution is a vector of triangular fuzzy numbers, for the fuzzy coefficients in the model, which minimizes an error measure. We use a quasi-random number generator to produce random sequences of these fuzzy vectors which uniformly fill the search space. We consider example problems to show that this Monte Carlo method obtains solutions comparable to those obtained by an evolutionary algorithm.

scholarly journals Simulation Modeling in Assessing the Effectiveness and Risk of Investment Projects

2020 ◽  
Vol 22 (1) ◽  
pp. 119-124
Author(s):  
Volodymyr Kharchenko ◽  
◽  
Hanna Kharchenko ◽  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Agricultural Production ◽  
Random Number ◽  
Investment Project ◽  
Average Risk ◽  
Universal Method ◽  
Investment Projects ◽  
The Monte Carlo Method ◽  
The Impact

Introduction. The article deals with the modeling features in the implementation of investment projects using the Monte Carlo method. The purpose of the article is to substantiate the feasibility of using economic and mathematical models to identify the risks of investment projects in agricultural production, taking into account the randomness of factors. Results. The expediency of using this method during the analysis of projects in agriculture is determined. This type of modeling is a universal method of research and evaluation of the effectiveness of open systems, the behavior of which depends on the influence of random factors. Particular attention is paid in such cases to decisions on the implementation of investment projects. The expediency of using this method in the analysis of projects in agriculture is determined. The main characteristics of the investment project are considered: investments involve significant financial costs; investment return can be obtained in a few years; there are elements of risk and uncertainty in forecasting the results of the investment project. The algorithm of the analysis of investment projects consisting of various stages is offered. The importance of investigating the risks of investment projects in agricultural production is substantiated. It is investigated that the basis of the Monte Carlo method is a random number generator, which consists of two stages: generation of a normalized random number (uniformly distributed from 0 to 1) and conversion of a random number into an arbitrary distribution law. The task of choosing an investment project for a pig farm is proposed. The calculations revealed that the amount of the expected NPV is UAH 63,158.80 with a standard deviation of UAH 43,777.90. The coefficient of variation was 0.69, so the risk of this project is generally lower than the average risk of the investment portfolio of the farm. Conclusions. The results of the analysis obtained using the method of Monte Carlo simulation are quite simple to interpret and reflect the change of factors over a significant interval, taking into account the probabilistic nature of economic factors. Thus, this method allows the implementation of the investment project to assess the impact of uncertainty on the final result of the project.

A new random-number generator for multispin Monte Carlo algorithms

1987 ◽  
Vol 48 (1-2) ◽  
pp. 135-149 ◽  
Author(s):  
L. Pierre ◽  
T. Giamarchi ◽  
H. J. Schulz
Keyword(s):  
Monte Carlo ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Monte Carlo Algorithms

scholarly journals FULL QCD ON APE100 MACHINES

1995 ◽  
Vol 06 (01) ◽  
pp. 25-45
Author(s):  
STEFANO ANTONELLI ◽  
MARCO BELLACCI ◽  
ANDREA DONINI ◽  
RENATA SARNO
Keyword(s):  
Monte Carlo ◽  
Random Number ◽  
Random Number Generator ◽  
Memory Capacity ◽  
Monte Carlo Algorithm ◽  
Number Generator ◽  
Inversion Algorithm ◽  
Hybrid Monte Carlo ◽  
Pure Gauge ◽  
Wilson Fermions

We present the first tests and results from a study of QCD with two flavours of dynamical Wilson fermions using the Hybrid Monte Carlo Algorithm (HMCA) on APE100 machines. The simulations have been performed on 64 lattice for the pure gauge HMCA and on 84, 123×32 lattices for full QCD configurations. We discuss the inversion algorithm for the fermionic operator, the methods used to overcome the problems arising using a 32 bit machine and the implementation of a new random number generator for APE100 machines. We propose different scenarios for the simulation of physical observables, with respect to the memory capacity and speed of different APE100 configurations.

Monte Carlo, Probability, Algebra, and Pi

1981 ◽  
Vol 74 (5) ◽  
pp. 335-339
Author(s):  
Duane C. Hinders
Keyword(s):  
Monte Carlo ◽  
Random Number ◽  
Random Number Generator ◽  
Random Numbers ◽  
Number Generator ◽  
Monte Carlo Techniques

Monte Carlo techniques are those that use a random number generator to solve a problem. Often, but certainly not always, these are problems that might be difficult or even impossible to solve analytically. A random number generator can be a coin, a die or dice, a computer, a table of random numbers, or some other device.

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