Fast Monte Carlo method for simulation of gamma density well logging measurements

2020 ◽  
Author(s):  
Bair V. Banzarov ◽  
◽  
Alexander A. Vinokurov ◽  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Monte Carlo Methods ◽  
High Performance ◽  
Complex Geometry ◽  
Well Logging ◽  
Density Measurements ◽  
Gamma Density

The paper introduces a fast Monte Carlo method developed for simulation of gamma density measurements. A main advantage of this method is its high performance. For simulation of tool responses, the proposed method is faster than conventional Monte Carlo methods by several orders of magnitude. The demonstrated performance of the proposed method allows its using in analysis and interpretation of measurements obtained in beds with complex geometry.

MONTE CARLO METHODS IN FUZZY GAME THEORY

2007 ◽  
Vol 03 (02) ◽  
pp. 259-269 ◽  
Author(s):  
AREEG ABDALLA ◽  
JAMES BUCKLEY
Keyword(s):  
Game Theory ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Monte Carlo Methods ◽  
Mixed Strategy ◽  
Minimax Theorem ◽  
Approximate Solutions ◽  
Mixed Strategies ◽  
Fuzzy Game ◽  
Zero Sum

In this paper, we consider a two-person zero-sum game with fuzzy payoffs and fuzzy mixed strategies for both players. We define the fuzzy value of the game for both players [Formula: see text] and also define an optimal fuzzy mixed strategy for both players. We then employ our fuzzy Monte Carlo method to produce approximate solutions, to an example fuzzy game, for the fuzzy values [Formula: see text] for Player I and [Formula: see text] for Player II; and also approximate solutions for the optimal fuzzy mixed strategies for both players. We then look at [Formula: see text] and [Formula: see text] to see if there is a Minimax theorem [Formula: see text] for this fuzzy game.

scholarly journals Monte Carlo method for determination and analysis damage to the power system

Doklady BGUIR ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 21-29
Author(s):  
D. E. Marmysh ◽  
U. I. Babaed
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Boundary Element Method ◽  
Power System ◽  
Boundary Element ◽  
Half Space ◽  
Half Plane ◽  
Monte Carlo Methods ◽  
Mechanics Of Solids ◽  
Element Method

The purpose of the work, the results of which are presented within the framework of the article, was to develop algorithms for calculating the damage to a solid or a system of solids based on the Monte Carlo method and the analytical boundary element method. The analytical boundary element method was used to calculate and analyze the stress-strain state of a solid under the distributed surface load. Based on indicators of the stress state, the algorithms for numerically assessing the dangerous volume and integral damage using the Monte Carlo methods, have been developed. Based on the pattern of distribution of stress fields, the technique of determining the area for randomly generating integration nodes is described. General recommendations have been developed for determining the boundaries of a subdomain containing a dangerous volume. Based on the features of the Monte Carlo methods, a numerical assessment of the indicators of damage of continuous media for a different number of integration nodes was carried out. Methods and algorithms were used to calculate the dangerous volume and integral damage in the plane and spatial cases for the two most common laws of the distribution of surface forces in the contact mechanics of solids: in case of contact interaction of two non-conformal bodies (Hertz problem) and when a non deformable rigid stamp is pressed into elastic half-plane or half-space. The scientific novelty of the work is to combine analytical and numerical approaches for the quantitative assessment of damage indicators of the power system. As a result the quantitative indicators of the dangerous volume (in the flat case - the dangerous area) and the integral damage of the half-plane and half-space related to the value of the applied load are obtained.

scholarly journals Monte Carlo method and High Performance Computing for solving Fokker–Planck equation of minority plasma particles

2015 ◽  
Vol 81 (3) ◽  
Author(s):  
E. Hirvijoki ◽  
T. Kurki-Suonio ◽  
S. Äkäslompolo ◽  
J. Varje ◽  
T. Koskela ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
High Performance Computing ◽  
High Performance ◽  
Collision Operator ◽  
Planck Equation ◽  
Energetic Ions ◽  
The Monte Carlo Method ◽  
Particle Simulations ◽  
Performance Computing

This paper explains how to obtain the distribution function of minority ions in tokamak plasmas using the Monte Carlo method. Since the emphasis is on energetic ions, the guiding-center transformation is outlined, including also the transformation of the collision operator. Even within the guiding-center formalism, the fast particle simulations can still be very CPU intensive and, therefore, we introduce the reader also to the world of high-performance computing. The paper is concluded with a few examples where the presented method has been applied.

Hybrid Monte Carlo methods for Geant4-based nuclear well logging implementation

2022 ◽  
Vol 169 ◽  
pp. 108824
Author(s):  
Xinyang Wang ◽  
Jingang Liang ◽  
Yulian Li ◽  
Qiong Zhang
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Well Logging ◽  
Hybrid Monte Carlo

Calculation of the Effective Thermal Conductivity in Composites Using Finite Element and Monte Carlo Methods

2007 ◽  
Vol 553 ◽  
pp. 51-56 ◽  
Author(s):  
Thomas Fiedler ◽  
Andreas Öchsner ◽  
Nilindu Muthubandara ◽  
Irina V. Belova ◽  
Graeme E. Murch
Keyword(s):  
Thermal Conductivity ◽  
Finite Element ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Effective Thermal Conductivity ◽  
Maxwell Equation ◽  
Excellent Agreement ◽  
Monte Carlo Methods ◽  
Lattice Monte Carlo ◽  
Square Planar

In this paper, the Finite Element and lattice Monte Carlo methods are used to calculate the effective thermal conductivity of two models of a composite: circular and square inclusions arranged in a square planar arrangement. A new lattice Monte Carlo method based around Fick’s First Law is also presented. Excellent agreement is found between these quite different methods. It is also shown that the results are in excellent agreement with the century-old Maxwell Equation.

QUASI MONTE–CARLO EVALUATION OF SENSITIVITIES OF OPTIONS IN COMMODITY AND ENERGY MARKETS

2003 ◽  
Vol 06 (08) ◽  
pp. 865-884 ◽  
Author(s):  
FRED E. BENTH ◽  
LARS O. DAHL ◽  
KENNETH H. KARLSEN
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Monte Carlo Methods ◽  
Random Variable ◽  
Numerical Differentiation ◽  
Energy Markets ◽  
Forward Contracts ◽  
Quasi Monte Carlo ◽  
Adaptive Techniques ◽  
Monte Carlo Evaluation

In this paper we consider the evaluation of sensitivities of options on spots and forward contracts in commodity and energy markets. We derive different expressions for these sensitivities, based on techniques from the recently introduced Malliavin approach [8, 9]. The Malliavin approach provides representations of the sensitivities in terms of expectations of the payoff and a random variable only depending on the underlying dynamics. We apply Monte–Carlo methods to evaluate such expectations, and to compare with numerical differentiation. We propose to use a refined quasi Monte–Carlo method based on adaptive techniques to reduce variance. Our approach gives a significant improvement of convergence.

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