As a popular simulation of photon propagation in turbid media, the main problem of Monte Carlo (MC) method is its cumbersome computation. In this work a table-based random sampling simulation (TBRS) is proposed. The key idea of TBRS is to simplify multisteps of scattering to a single-step process, through randomly table querying, thus greatly reducing the computing complexity of the conventiona...

Flood hydrograph simulation is affected by uncertainty in Rainfall – Runoff )RR( parameters. Uncertainty of RR parameters in Gharasoo catchment, part of the great Karkheh river basin, is evaluated by Monte–Carlo (MC) approach. A conceptual-distributed model, called ModClark, was used for basin simulation, in which the basin’s hydrograph was determined using the superposition of runoff generated...

We report on the RTGrid project, which investigates approaches for using high-performance computing infrastructures, such as the grid, in order to reduce the turnaround time of Monte Carlo (MC) simulation-based radiotherapy treatment planning. The main aim of this project is to render accurate dose calculations using MC simulations clinically feasible. To this end, we have successfully implemen...

Introduction Radiotherapy with small fields is used widely in newly developed techniques. Additionally, dose calculation accuracy of treatment planning systems in small fields plays a crucial role in treatment outcome. In the present study, dose calculation accuracy of two commercial treatment planning systems was evaluated against Monte Carlo method. Materials and Methods Siemens Once or linea...

Flood hydrograph simulation is affected by uncertainty in Rainfall – Runoff )RR( parameters. Uncertainty of RR parameters in Gharasoo catchment, part of the great Karkheh river basin, is evaluated by Monte–Carlo (MC) approach. A conceptual-distributed model, called ModClark, was used for basin simulation, in which the basin’s hydrograph was determined using the superposition of runoff generated...

Monte Carlo (MC) technique is a numerical method that makes use of random numbers to solve mathematical problems for which an analytical solution is not known. The first article, “The Monte Carlo Method” by Metropolis and Ulam, has appeared for the first time in 1949 [9], even though well before that certain statistical problems were solved using random numbers. Since the simulation of random n...

This paper is intended to provide a numerical algorithm based on random sampling for solving the linear Volterra integral equations of the second kind. This method is a Monte Carlo (MC) method based on the simulation of a continuous Markov chain. To illustrate the usefulness of this technique we apply it to a test problem. Numerical results are performed in order to show the efficiency and accu...

We calculate analytically the dynamic critical exponent z(MC), measured in Monte Carlo simulations for a vortex loop model of the superconducting transition, and account for the simulation results. In the weak screening limit, where magnetic fluctuations are neglected, the dynamic exponent is found to be z(MC) = 3/2. In the perfect screening limit, z(MC) = 5/2. We relate z(MC) to the actual val...

Background: Monte Carlo (MC) modeling of a linear accelerator is a prerequisite for Monte Carlo dose calculations in external beam radiotherapy. In this study, a simple and efficient model was developed for Elekta SL-25 linear accelerator using MCNP4C Monte Carlo code Materials and methods: The head of Elekta SL-25 linac was simulated for 6 and 18 MV photon beams using MCNP4C MC code. Energ...