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Monte Carlo simulation techniques are discussed, with special emphasis on those technical aspects that are important for the simulation of dense liquids and solids. In these notes, the Metropolis sampling scheme is introduced as a special case of importance sampling. The choice of the optimum trial...
Distribution System Reliability Assessment Based on the Improved Monte Carlo Method Monte Carlo simulationIn order to assess the reliability of complicated distribution networks, the complicated network structure is simplified and stored in ... X Sun,H Liu - 《Lecture Notes in Electrical Engineering》 ...
The Direct Simulation Monte Carlo (DSMC) method is widely considered as the most accurate method for dilute gas flow simulation when they violate continuum hypothesis. However the application of DSMC is a computationally intensive method... J Benzi,M Damodaran - 《Lecture Notes in Computational Scie...
Info Radiation Monte Carlo Model Matteo CFX 1 December 22, 2006 10:01 Knudsen / Molecular Flow - Monte Carlo Simulation Axel Rohde Main CFD Forum 1 July 30, 2004 12:32 Fluent incident radiation problem Michael Schwarz Main CFD Forum 0 October 21, 1999 06:56 Why FVM for...
MonteCarlosimulationrequiresthat theanalystsetupamathematicalmodelof theprocess.Thissetupcanbeverytime consumingandprovidesthesimulationa verylowbenefit-costratio.Philippatos [1973]notesthatwhilesomedynamic propertiescanbeobtainedthroughsimu- lation,thereareothertechniquesthatcan ...
By applying Monte-Carlo simulation methods,a simulation model is established in this paper,which considers factors such as RM characteristics of the aircraft,weather conditions,management,flight dispatching,number of aircraft,route structure,maintenance level,personnel skills,spare parts supply,etc.The analy...
A Guide to Monte Carlo Simulations in Statistical Physics - 2nd Edition This new and updated edition deals with all aspects of Monte Carlo simulation of complex physical systems encountered in condensed-matter physics, statisti... DP Landau,K Binder - A Guide to Monte Carlo Simulations in Statist...
The topics covered in this volume range from theoretical issues in Monte Carlo and simulation methods, low-discrepancy point sets and sequences, lattice rules, and pseudorandom number generation to applications such as numerical integration, numerical linear algebra, integral equations, binary search, ...
The analysis of the results obtained provides insights into the strengths and limitations of each Monte Carlo code and nuclear data library combination. The observed discrepancies were analyzed, and possible sources of error were also identified. The analytical procedure followed in this work will ...