scholarly journals APPLICATION OF MONTE CARLO METHOD TO PHASE SEPARATION DYNAMICS OF COMPLEX SYSTEMS

1999 ◽  
Vol 10 (08) ◽  
pp. 1513-1520 ◽  
Author(s):  
YUTAKA OKABE ◽  
TSUKASA MIYAJIMA ◽  
TOSHIRO ITO ◽  
TOSHIHIRO KAWAKATSU
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Phase Separation ◽  
Monte Carlo Method ◽  
Shear Flow ◽  
Late Stage ◽  
Microphase Separation ◽  
Anisotropic Growth ◽  
Phase Separations ◽  
Macrophase Separation

We report the application of the Monte Carlo simulation to phase separation dynamics. First, we deal with the phase separation under shear flow. The thermal effect on the phase separation is discussed, and the anisotropic growth exponents in the late stage are estimated. Next, we study the effect of surfactants on the three-component solvents. We obtain the mixture of macrophase separation and microphase separation, and investigate the dynamics of both phase separations.

scholarly journals Technical-Economic Analysis of Grapple Saw

2020 ◽  
Vol 41 (2) ◽  
pp. 219-229 ◽  
Author(s):  
Ricardo Hideaki Miyajima ◽  
Paulo Torres Fenner ◽  
Gislaine Cristina Batistela ◽  
Danilo Simões
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Distributions ◽  
Simulation Method ◽  
Forest Harvesting ◽  
Production Costs ◽  
Wood Harvesting ◽  
Food And Agriculture ◽  
The Monte Carlo Method

The processing of Eucalyptus logs is a stage that follows the full tree system in mechanized forest harvesting, commonly performed by grapple saw. Therefore, this activity presents some associated uncertainties, especially regarding technical and silvicultural factors that can affect productivity and production costs. To get around this problem, Monte Carlo simulation can be applied, or rather a technique that allows to measure the probabilities of values from factors that are under conditions of uncertainties, to which probability distributions are attributed. The objective of this study was to apply the Monte Carlo method for determining the probabilistic technical-economical coefficients of log processing using two different grapple saw models. Field data were obtained from an area of forest planted with Eucalyptus, located in the State of São Paulo, Brazil. For the technical analysis, the time study protocol was applied by the method of continuous reading of the operational cycle elements, which resulted in production. As for the estimated cost of programmed hour, the applied methods were recommended by the Food and Agriculture Organization of the United Nations. The incorporation of the uncertainties was carried out by applying the Monte Carlo simulation method, by which 100,000 random values were generated. The results showed that the crane empty movement is the operational element that most impacts the total time for processing the logs; the variables that most influence the productivity are specific to each grapple saw model; the difference of USD 0.04 m3 in production costs was observed between processors with gripping area of 0.58 m2 and 0.85 m2. The Monte Carlo method proved to be an applicable tool for mechanized wood harvesting for presenting a range of probability of occurrences for the operational elements and for the production cost.

scholarly journals Monte Carlo simulation of halos in the crystal clouds

2021 ◽  
Vol 2099 (1) ◽  
pp. 012067
Author(s):  
Q Mu ◽  
E G Kablukova ◽  
B A Kargin ◽  
S M Prigarin
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Ray Tracing ◽  
Multiple Scattering ◽  
Radiation Transfer ◽  
Cirrus Clouds ◽  
Ice Crystals ◽  
The Sun ◽  
Different Shapes

Abstract In this paper, we try to answer the question: how the multiple scattering, the sun elevation, shape and orientation of ice crystals in the cirrus clouds affect a halo pattern. To study the radiation transfer in optically anisotropic clouds, we have developed the software based on Monte Carlo method and ray tracing. In addition to halos, this software enables one to simulate “anti-halos”, which above the cloud layer can be seen by observers. We present the visualization of halos and anti-halos generated by the cirrus clouds for different shapes and orientations of ice crystals.

scholarly journals MONTE CARLO SIMULATION IN INTERNAL RADIOTHERAPY OF THYROID CANCER

2020 ◽  
Vol 3 (9) ◽  
pp. 16-24 ◽  
Author(s):  
Hammam Oktajianto ◽  
Evi Setiawati
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Thyroid Cancer ◽  
Monte Carlo Method ◽  
Effective Dose ◽  
Radiation Effect ◽  
Cervical Vertebrae ◽  
Absorbed Dose ◽  
Particle Track ◽  
Internal Radiotherapy

Thyroid radiotherapy is a cancer therapy that is treated by giving radioactive I-131 in Thyroid gland. This cancer is at the ninth from ten of common malignant cancer. A man has higher risk to get Thyroid cancer than a woman has. This organ is lain near human neck. This research aim was to simulate particle track of radiation I-131 and determine an absorbed dose and effective dose in Thyroid and other organs around Thyroid such as Brain, Lung and Cervical vertebrae. The simulation and calculation used Monte Carlo method operated by MCNPX software. Geometry of Thyroid and other organs used ORNL MIRD phantom geometry. From the results, it shown that particle track of radiation was distributed at Thyroid while several particles radiated other organs. The absorbed dose in Thyroid and other organs increased every rising activity of I-131 used, but the absorbed dose in other organs was less than in Thyroid. Radiation effect for damage cancer in Thyroid was shown by an effective dose which it increased every rising activity of I-131 used and the maximum effective dose was at 200 mCi activity of I-131. Although the effective dose in Thyroid increased, the effective dose in other organs like Brain, Lung and Cervical vertebrae was still less than in Thyroid so that the use of I-131 each activity did not really influence other organs around Thyroid.  

Simulation of degradation of the organic contaminants ethylene glycol and phenol by iron nanoparticles using the kinetic Monte Carlo method

RSC Advances ◽  
2014 ◽  
Vol 4 (62) ◽  
pp. 32928-32933 ◽  
Author(s):  
Hamed Moradmand Jalali
Keyword(s):  
Hydrogen Peroxide ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Ethylene Glycol ◽  
Organic Contaminants ◽  
Iron Nanoparticles ◽  
Kinetic Monte Carlo ◽  
Kinetic Monte Carlo Method ◽  
System P

We applied kinetic Monte Carlo simulation to study the kinetics and mechanisms of the degradation of the organic pollutants ethylene glycol and phenol by iron(iii) nanoparticles and hydrogen peroxide as the catalytic system.

scholarly journals Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport

2020 ◽  
Vol 10 (12) ◽  
pp. 4229 ◽  
Author(s):  
Alexander Heilmeier ◽  
Michael Graf ◽  
Johannes Betz ◽  
Markus Lienkamp
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
State Of The Art ◽  
Probability Distributions ◽  
Strategic Decisions ◽  
The Monte Carlo Method ◽  
Random Events ◽  
Example Variability

Applying an optimal race strategy is a decisive factor in achieving the best possible result in a motorsport race. This mainly implies timing the pit stops perfectly and choosing the optimal tire compounds. Strategy engineers use race simulations to assess the effects of different strategic decisions (e.g., early vs. late pit stop) on the race result before and during a race. However, in reality, races rarely run as planned and are often decided by random events, for example, accidents that cause safety car phases. Besides, the course of a race is affected by many smaller probabilistic influences, for example, variability in the lap times. Consequently, these events and influences should be modeled within the race simulation if real races are to be simulated, and a robust race strategy is to be determined. Therefore, this paper presents how state of the art and new approaches can be combined to modeling the most important probabilistic influences on motorsport races—accidents and failures, full course yellow and safety car phases, the drivers’ starting performance, and variability in lap times and pit stop durations. The modeling is done using customized probability distributions as well as a novel “ghost” car approach, which allows the realistic consideration of the effect of safety cars within the race simulation. The interaction of all influences is evaluated based on the Monte Carlo method. The results demonstrate the validity of the models and show how Monte Carlo simulation enables assessing the robustness of race strategies. Knowing the robustness improves the basis for a reasonable determination of race strategies by strategy engineers.

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