• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.1095-1099
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• 2017

A fission source can act as a stabilization element in coupled Monte Carlo simulations. We have observed this while studying numerical instabilities in nonlinear steady-state simulations performed by a Monte Carlo criticality solver that is coupled to a xenon feedback solver via fixed-point iteration. While fixed-point iteration is known to be numerically unstable for some problems, resulting in large spatial oscillations of the neutron flux distribution, we show that it is possible to stabilize it by reducing the number of Monte Carlo criticality cycles simulated within each iteration step. While global convergence is ensured, development of any possible numerical instability is prevented by not allowing the fission source to converge fully within a single iteration step, which is achieved by setting a small number of criticality cycles per iteration step. Moreover, under these conditions, the fission source may converge even faster than in criticality calculations with no feedback, as we demonstrate in our numerical test simulations.

• Korean Journal of Food Science and Technology
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• v.38 no.4
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• pp.589-593
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• 2006

The objective of this study was to evaluate the use of fuzzy math strategy to calculate variability and uncertainty in quantitative risk assessment. We compared the propagation of uncertainty using fuzzy math simulation with Monte Carlo simulation. The risk far Listeria monocytogenes contamination was estimated for carcass and processed pork by fuzzy math and Monte Carlo simulations, respectively. The data used in these simulations were taken from a recent report on pork production. In carcass, the mean values for the risk from fuzzy math and Monte Carlo simulations were -4.393 log $CFU/cm^2$ and -4.589 log $CFU/cm^2$, respectively; in processed pork, they were -4.185 log $CFU/cm^2$ and -4.466 log $CFU/cm^2$ respectively. The distribution of values obtained using the fuzzy math simulation included all of the results obtained using the Monte Carlo simulation. Consequently, fuzzy math simulation was found to be a good alternative to Monte Carlo simulation in quantitative risk assessment of pork production.

• Structural Engineering and Mechanics
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• v.34 no.4
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• pp.417-447
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• 2010

The uncertainty often observed in experimental strengths of masonry constituents makes critical the selection of the appropriate inputs in finite element analysis of complex masonry buildings, as well as requires modelling the building ultimate load as a random variable. On the other hand, the utilization of expensive Monte Carlo simulations to estimate collapse load probability distributions may become computationally impractical when a single analysis of a complex building requires hours of computer calculations. To reduce the computational cost of Monte Carlo simulations, direct computer calculations can be replaced with inexpensive Response Surface (RS) models. This work investigates the use of RS models in Monte Carlo analysis of complex masonry buildings with random input parameters. The accuracy of the estimated RS models, as well as the good estimations of the collapse load cumulative distributions obtained via polynomial RS models, show how the proposed approach could be a useful tool in problems of technical interest.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.113-115
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• 2002

The Monte Carlo simulation method is a numerical solution to a problem that models objects interacting with other objects or their environment based upon simple object-object or object-environment relationships. In spite of its great accuracy, It was turned away because of long calculation time to simulate a model. But, it is used to simulate a linear accelerator frequently with the advance of computer technology. To simulate linear accelerator in Monte Carlo simulations, there are many parameters needed to input to Monte Carlo code. These data can be supported by a linear accelerator manufacturer. Although the model of a linear accelerator is the same, a different characteristic property can be found. Thus, we performed a commissioning process of 6MV photon beam in Varian 2300C/D model with BEAM/EGS4 Monte Carlo code. The head geometry data were put into BEAM/EGS4 data. The mean energy and energy spread of the electron beam incident on the target were varied to match Monte Carlo simulations to measurements. TLDs (thermoluminescent dosimeter) and radiochromic films were employed to measure the absorbed dose in a water phantom. Beam profile was obtained in 40cm${\times}$40cm field size and Depth dose was in 10cm${\times}$10cm. At first, we compared the depth dose between measurements and Monte Carlo simulations varying the mean energy of an incident electron beam. Then, we compared the beam profile with adjusting the beam radius of the incident electron beam in Monte Carlo simulation. The results were found that the optimal mean energy was 6MV and beam radius of 0.1mm was well matched to measurements.

• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.225-232
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• 1998

Results of Monte Carlo simulations on amorphous silicon based x-ray imaging arrays are described. In order to investigate the characteristics of amorphous silicon x-ray imaging devices and to provide the optimum design parameter, Monte Carlo simulations were performed. Monte Carlo simulation codes for our purpose were developed and various combinations of x-ray peak voltages, aluminum filter thicknesses, CsI(TI) thicknesses, and amorphous silicon photodiode pixel sizes were tested in connection with detection efficiency and spatial resolution of the amorphous silicon based x-ray imager. With usual Csl(TI) thickness of 300${\mu}{\textrm}{m}$-500${\mu}{\textrm}{m}$, detection efficiency was in the range of 70%-95% and energy absorption efficiency was in the range of 40%-70% for 60kVp-120kVp x-ray. From the simulations it was found that amorphous silicon pixel size and Csl(TI) thickness were the most important parameters which determine the resolution of the imager. By use of our simulation results we could provide proper combinations of Csl(TI) thicknesses and pixels sizes for optimum sensitivity and resolution.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.4
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• pp.479-486
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• 2012

Two of fundamental approaches that can be used to understand ion-solid interaction are Monte Carlo (MC) and Molecular Dynamic (MD) simulations. For the simplicity of simulation Monte Carlo simulation method is widely preferred. In this paper, basic consideration and algorithm of Monte Carlo simulation will be presented as well as simulation results. Sputtering caused by incident ion beam will be discussed with distribution of sputtered particles and their energy distributions. Redeposition of sputtered particles that are experienced refraction at the substrate-vacuum interface additionally presented. In addition, reflection of incident ions with reflection coefficient will be presented together with spatial and energy distributions. This Monte Carlo simulation will be useful in simulating and describing ion beam related processes such as Ion beam induced deposition/etching process, local nano-scale distribution of focused ion beam implanted ions, and ion microscope imaging process etc.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.2
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• pp.127-136
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• 2008

Monte Carlo simulation methods are especially useful in studying a variety of problems difficult to calculate by experimental or analytical approaches. Nowadays, they are extensively applied to simulate nuclear medicine instrumentations such as single photon emission computed tomography (SPECT) and positron emission tomography (PET) for assisting system design and optimizing imaging and processing protocols. The goal of this paper is to address the practical issues, a potential user of Monte Carlo simulations for nuclear medicine can encounter, to help them to choose a code. This review introduces the different types of Monte Carlo codes currently available for nuclear medicine, comments main features and properties for a code to be proper for a given purpose, and discusses current research trends in Monte Carlo codes.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.3044-3050
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• 2021

This study offers a Monte Carlo alternative for computing mass energy absorption coefficients of any material through calculation of photon energy deposited per mass of the sample and the energy flux obtained inside a sample volume. This approach is applied in this study to evaluate mass energy absorption coefficients of some amino acids found in human body at twenty-eight different photon energies between 10 keV and 20 MeV. The simulations involved a pencil beam source modeled to emit a parallel beam of mono-energetic photons toward a 1 mean free path thick sample of rectangular parallelepiped geometry. All the components in the problem geometry were surrounded by a 100 cm vacuum sphere to avoid any interactions in materials other than the absorber itself. The results computed using the Monte Carlo radiation transport packages MCNP6.2 and GAMOS5.1 were checked against the theoretical values available from the tables of XMUDAT database. These comparisons indicate very good agreement and support the conclusion that Monte Carlo technique utilized in this fashion may be used as a computational tool for determining the mass energy absorption coefficients of any material whose data are not available in the literature.

• Bulletin of the Korean Chemical Society
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• v.26 no.5
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• pp.769-775
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• 2005

An efficiency of Monte Carlo (MC) docking simulations was examined for the prediction of chiral discrimination by cyclodextrins. Docking simulations were performed with various computational parameters for the chiral discrimination of a series of 17 enantiomers by $\beta$-cyclodextrin ($\beta$-CD) or by 6-amino-6-deoxy-$\beta$-cyclodextrin (am-$\beta$-CD). A total of 30 sets of enantiomeric complexes were tested to find the optimal simulation parameters for accurate predictions. Rigid-body MC docking simulations gave more accurate predictions than flexible docking simulations. The accuracy was also affected by both the simulation temperature and the kind of force field. The prediction rate of chiral preference was improved by as much as 76.7% when rigid-body MC docking simulations were performed at low-temperatures (100 K) with a sugar22 parameter set in the CHARMM force field. Our approach for MC docking simulations suggested that the conformational rigidity of both the host and guest molecule, due to either the low-temperature or rigid-body docking condition, contributed greatly to the prediction of chiral discrimination.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.122-128
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• 2013

Computer simulations designed to predict technical and financial returns of wind turbine installations are used to make informed investment decisions. These simulations used fixed values to represent real-world variables, while the actual projects can be highly uncertain, resulting in predictions that are less accurate and less useful. In this article, by modifying a popular wind power simulation sourced from the American Wind Energy Association to use Monte Carlo techniques in its calculations, the authors have proposed a way to improve simulation usability by producing probability distributions of likely outcomes, which can be used to draw broader, more useful conclusions about the simulated project.