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Carlo Simulation Method (carlo + simulation_method)

Distribution by Scientific Domains

Polymers and Materials Science	66%

Kinds of Carlo Simulation Method

monte carlo simulation method

Selected Abstracts

Comprehensive Study of Free Radical Copolymerization Using a Monte Carlo Simulation Method, 1

MACROMOLECULAR THEORY AND SIMULATIONS, Issue 5 2005
Yousef Mohammadi
Abstract Summary: In order to investigate the influence of reactivity ratios and initial feed composition on the microstructure of macromolecules in free radical copolymerization, a comprehensive study was carried out using a Monte Carlo simulation method. As a result, a new procedure was introduced to modify the works of others on the initiation step. The variation of the copolymer composition and the fashion of the arrangement of monomers in simulated chains were evaluated as a function of copolymerization parameters. The model was capable of monitoring any change in azeotropy as well as the magnitude and direction of composition drift from the azeotrope point. The maximum reachable conversion (MRC) was predicted for different combinations of initial feed compositions and reactivity ratios. According to the simulation results, a critical conversion where the macromolecules produced inherited the maximum allowed alternation was obtained for the reactivity ratios given. Change of sequence distribution of simulated copolymer chains with conversion for various initial feed compositions on a triangular graph (rA,=,0.5, rB,=,0.9). [source]

Reliability analysis of universal joint of a compliant platform

FATIGUE & FRACTURE OF ENGINEERING MATERIALS AND STRUCTURES, Issue 7 2010
M. M. ZAHEER
ABSTRACT The paper describes a methodology for computation of fatigue reliability of universal joint in an articulated offshore tower. Failure criteria were formulated using the conventional Palmgren-Miner rule (S-N curve approach) and the fracture mechanics (F-M) principle. The dynamic analysis of double hinged articulated tower under wind and waves is carried out in time domain. The response histories of hinge shear stresses are employed for the reliability analysis. Advanced first-order reliability method and Monte Carlo simulation method were used to estimate the reliability. Various parametric studies were carried out, which yield important information for the reliability based design. The S-N curve approach yields a significantly conservative estimate of probability of failure when compared to the F-M approach. [source]

Probabilistic high cycle fatigue behaviour of nodular cast iron containing casting defects

FATIGUE & FRACTURE OF ENGINEERING MATERIALS AND STRUCTURES, Issue 4 2009
A. NASR
ABSTRACT Theoretical and experimental investigations were combined to characterize the influence of surface casting defects (shrinkages) on the high cycle fatigue (HCF) reliability. On fracture surfaces of fatigue samples, the defect is located at the surface. The shape used for the calculation is a spherical void with variable radius. Finite-element simulations were then performed to determine stress distribution around defects for different sizes and different loadings. Correlated expressions of the maximum hydrostatic stress and the amplitude of the shear stress were obtained by using the response surface technique. The loading representative point in the HCF criterion was then transformed into a scattering surface, which has been obtained by a random sampling of the defect sizes. The HCF reliability has been computed by using the Monte Carlo simulation method. Tension and torsion fatigue tests were conducted on nodular cast iron with quantification of defect size on the fracture surface. The S,N curves show a large fatigue life scattering; shrinkages are at the origin of the fatal crack leading to the final failure. The comparison of the computed HCF reliability to the experimental results shows a good agreement. The capability of the proposed model to take into account the influence of the range of the defect sizes and the type of its statistical distribution has been demonstrated. It is shown that the stress distribution at the fatigue limit is log-normal, which can be explained by the log-normal defect distribution in the nodular cast iron tested. [source]

Frequency analysis for predicting 1% annual maximum water levels along Florida coast, US

HYDROLOGICAL PROCESSES, Issue 23 2008
Sudong Xu
Abstract In the Coastal Flood Insurance Study by the Federal Emergency Management Agency (FEMA, 2005), 1% annual maximum coastal water levels are used in coastal flood hazard mitigation and engineering design in coastal areas of USA. In this study, a frequency analysis method has been developed to provide more accurate predictions of 1% annual maximum water levels for the Florida coast waters. Using 82 and 94 years of annual maximum water level data at Pensacola and Fernandina, performances of traditional frequency analysis methods, including advanced method of Generalized Extreme Value distribution method, have been evaluated. Comparison with observations of annual maximum water levels with 83 and 95 years of return periods indicate that traditional methods are unable to provide satisfactory predictions of 1% annual maximum water levels to account for hurricane-induced extreme water levels. Based on the characteristics of annual maximum water level distribution of Pensacola and Fernandina stations, a new probability distribution method has been developed in this study. Comparison with observations indicates that the method presented in this study significantly improves the accuracy of predictions of 1% annual maximum water levels. For Fernandina station, predictions of extreme water level match well with the general trend of observations. With a correlation coefficient of 0·98, the error for the maximum observed extreme water level of 3·11 m (National Geodetic Vertical Datum) with 95 years of return period is 0·92%. For Pensacola station, the prediction error for the maximum observed extreme water level with a return period of 83 years is 5·5%, with a correlation value of 0·98. The frequency analysis has also been reasonably compared to the more costly Monte Carlo simulation method. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Optimal fuzzy reasoning and its robustness analysis

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 11 2004
Lei Zhang
Fuzzy reasoning methods are extensively used in intelligent systems and fuzzy control. Most existing fuzzy reasoning methods follow rules of logical inference. In this article, fuzzy reasoning is treated as an optimization problem. The idea of optimal fuzzy reasoning is reviewed and three new optimal fuzzy reasoning methods are given by using new optimization objective functions. The robustness of fuzzy reasoning, that is, how errors in premises affect conclusions in fuzzy reasoning, is evaluated in a probabilistic or statistical context by using the Monte Carlo simulation method. Six optimal fuzzy reasoning methods are evaluated in comparison with the CRI method in terms of probabilistic robustness. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1033,1049, 2004. [source]