Relevant bibliographies by topics / Overtime. Monte Carlo method. Simulation methods

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  2. Dissertations / Theses
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Academic literature on the topic 'Overtime. Monte Carlo method. Simulation methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Overtime. Monte Carlo method. Simulation methods"

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Delgado, H. E., L. M. Sarro, G. Clementini, T. Muraveva, and A. Garofalo. "Hierarchical Bayesian model to inferPL(Z)relations usingGaiaparallaxes." Astronomy & Astrophysics 623 (March 2019): A156. http://dx.doi.org/10.1051/0004-6361/201832945.

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In a recent study we analysed period–luminosity–metallicity (PLZ) relations for RR Lyrae stars using theGaiaData Release 2 (DR2) parallaxes. It built on a previous work that was based on the firstGaiaData Release (DR1), and also included period–luminosity (PL) relations for Cepheids and RR Lyrae stars. The method used to infer the relations fromGaiaDR2 data and one of the methods used forGaiaDR1 data was based on a Bayesian model, the full description of which was deferred to a subsequent publication. This paper presents the Bayesian method for the inference of the parameters ofPL(Z) relations used in those studies, the main feature of which is to manage the uncertainties on observables in a rigorous and well-founded way. The method encodes the probability relationships between the variables of the problem in a hierarchical Bayesian model and infers the posterior probability distributions of thePL(Z) relationship coefficients using Markov chain Monte Carlo simulation techniques. We evaluate the method with several semi-synthetic data sets and apply it to a sample of 200 fundamental and first-overtone RR Lyrae stars for whichGaiaDR1 parallaxes and literatureKs-band mean magnitudes are available. We define and test several hyperprior probabilities to verify their adequacy and check the sensitivity of the solution with respect to the prior choice. The main conclusion of this work, based on the test with semi-syntheticGaiaDR1 parallaxes, is the absolute necessity of incorporating the existing correlations between the period, metallicity, and parallax measurements in the form of model priors in order to avoid systematically biased results, especially in the case of non-negligible uncertainties in the parallaxes. The relation coefficients obtained here have been superseded by those presented in our recent paper that incorporates the findings of this work and the more recentGaiaDR2 measurements.
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Caflisch, Russel E. "Monte Carlo and quasi-Monte Carlo methods." Acta Numerica 7 (January 1998): 1–49. http://dx.doi.org/10.1017/s0962492900002804.

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Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N−1/2), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including convergence theory, sampling methods and variance reduction techniques. Accelerated convergence for Monte Carlo quadrature is attained using quasi-random (also called low-discrepancy) sequences, which are a deterministic alternative to random or pseudo-random sequences. The points in a quasi-random sequence are correlated to provide greater uniformity. The resulting quadrature method, called quasi-Monte Carlo, has a convergence rate of approximately O((logN)kN−1). For quasi-Monte Carlo, both theoretical error estimates and practical limitations are presented. Although the emphasis in this article is on integration, Monte Carlo simulation of rarefied gas dynamics is also discussed. In the limit of small mean free path (that is, the fluid dynamic limit), Monte Carlo loses its effectiveness because the collisional distance is much less than the fluid dynamic length scale. Computational examples are presented throughout the text to illustrate the theory. A number of open problems are described.
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Giles, Michael B. "Multilevel Monte Carlo methods." Acta Numerica 24 (April 27, 2015): 259–328. http://dx.doi.org/10.1017/s096249291500001x.

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Monte Carlo methods are a very general and useful approach for the estimation of expectations arising from stochastic simulation. However, they can be computationally expensive, particularly when the cost of generating individual stochastic samples is very high, as in the case of stochastic PDEs. Multilevel Monte Carlo is a recently developed approach which greatly reduces the computational cost by performing most simulations with low accuracy at a correspondingly low cost, with relatively few simulations being performed at high accuracy and a high cost.In this article, we review the ideas behind the multilevel Monte Carlo method, and various recent generalizations and extensions, and discuss a number of applications which illustrate the flexibility and generality of the approach and the challenges in developing more efficient implementations with a faster rate of convergence of the multilevel correction variance.
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Erfanian, Hamid Reza, Seyed Jaliledin Ghaznavi Bidgoli, and Parvin Shakibaei. "The pricing of spread option using simulation." International Journal of Applied Mathematical Research 6, no. 4 (October 19, 2017): 121. http://dx.doi.org/10.14419/ijamr.v6i4.7914.

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Monte Carlo simulation is one of the most common and popular method of options pricing. The advantages of this method are being easy to use, suitable for all kinds of standard and exotic options and also are suitable for higher dimensional problems. But on the other hand Monte Carlo variance convergence rate is which due to that it will have relatively slow convergence rate to answer the problems, as to achieve accuracy when it has been d-dimensions, complexity is . For this purpose, several methods are provided in quasi Monte Carlo simulation to increase variance convergence rate as variance reduction techniques, so far. One of the latest presented methods is multilevel Monte Carlo that is introduced by Giles in 2008. This method not only reduces the complexity of computing amount in use of Euler discretization scheme and the amount in use of Milstein discretization scheme, but also has the ability to combine with other variance reduction techniques. In this paper, using Multilevel Monte Carlo method by taking Milstein discretization scheme, pricing spread option and compared complexity of computing with standard Monte Carlo method. The results of Multilevel Monte Carlo method in pricing spread options are better than standard Monte Carlo simulation.
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Belova, Irina V., M. J. Brown, and Graeme E. Murch. "Calculation of Phenomenological Coefficients by Monte Carlo Computer Simulation Methods." Defect and Diffusion Forum 249 (January 2006): 27–34. http://dx.doi.org/10.4028/www.scientific.net/ddf.249.27.

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In this paper we first review the principal indirect and direct Monte Carlo methods for calculating the Onsager phenomenological transport coefficients in solid state diffusion. We propose a new Monte Carlo method that makes use of a steady state calculation of a flux of atoms that is driven by a difference in chemical potential of the atoms between a source and a sink plane. The method is demonstrated for the simple cubic one component lattice gas with nearest neighbour interactions. The new method gives results in good agreement with a Monte Carlo method based on Einsteinian expressions for the phenomenological coefficients.
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Suhobokov, Alexander. "APPLICATION OF MONTE CARLO SIMULATION METHODS IN RISK MANAGEMENT." Journal of Business Economics and Management 8, no. 3 (September 30, 2007): 165–68. http://dx.doi.org/10.3846/16111699.2007.9636165.

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The paper deals with Monte Carlo simulation method and its application in Risk Management. The author with the help of MATLAB 7.0 introduces new modification of Monte Carlo algorithm aimed at fast and effective calculation of financial organization's Value at Risk (VaR) by the example of Parex Bank's FOREX exposure.
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Pažický, Martin. "Stock Price Simulation Using Bootstrap and Monte Carlo." Scientific Annals of Economics and Business 64, no. 2 (June 27, 2017): 155–70. http://dx.doi.org/10.1515/saeb-2017-0010.

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Abstract In this paper, an attempt is made to assessment and comparison of bootstrap experiment and Monte Carlo experiment for stock price simulation. Since the stock price evolution in the future is extremely important for the investors, there is the attempt to find the best method how to determine the future stock price of BNP Paribas′ bank. The aim of the paper is define the value of the European and Asian option on BNP Paribas′ stock at the maturity date. There are employed four different methods for the simulation. First method is bootstrap experiment with homoscedastic error term, second method is blocked bootstrap experiment with heteroscedastic error term, third method is Monte Carlo simulation with heteroscedastic error term and the last method is Monte Carlo simulation with homoscedastic error term. In the last method there is necessary to model the volatility using econometric GARCH model. The main purpose of the paper is to compare the mentioned methods and select the most reliable. The difference between classical European option and exotic Asian option based on the experiment results is the next aim of tis paper.
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Zhao, Zhiqiang, and Feiyue Zhou. "Optimal Control Methods of Experiment Times in System-of-Systems Combat Computer Simulation." ITM Web of Conferences 26 (2019): 03004. http://dx.doi.org/10.1051/itmconf/20192603004.

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In the process of scheme optimization, in order to eliminate the influence of random factor, it needs to conduct computer simulation of Monte Carlo. Therefore, it is proposed to introduce confidence interval into systemof-systems combat simulation, and confirm whether the Monte Carlo simulation finishes according to data sample generated in simulation process. According to characteristic of data sample, extend correspondingly confidence interval method, and under the condition of obtaining the solution meeting accuracy requirements, reduce simulation experiment times as far as possible. The simulation experiment results show that confidence interval extension method is able to possess self-adaptation control to Monte Carlo simulation.
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Wang, Liangliang, Shijia Wang, and Alexandre Bouchard-Côté. "An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics." Systematic Biology 69, no. 1 (June 6, 2019): 155–83. http://dx.doi.org/10.1093/sysbio/syz028.

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Abstract We describe an “embarrassingly parallel” method for Bayesian phylogenetic inference, annealed Sequential Monte Carlo (SMC), based on recent advances in the SMC literature such as adaptive determination of annealing parameters. The algorithm provides an approximate posterior distribution over trees and evolutionary parameters as well as an unbiased estimator for the marginal likelihood. This unbiasedness property can be used for the purpose of testing the correctness of posterior simulation software. We evaluate the performance of phylogenetic annealed SMC by reviewing and comparing with other computational Bayesian phylogenetic methods, in particular, different marginal likelihood estimation methods. Unlike previous SMC methods in phylogenetics, our annealed method can utilize standard Markov chain Monte Carlo (MCMC) tree moves and hence benefit from the large inventory of such moves available in the literature. Consequently, the annealed SMC method should be relatively easy to incorporate into existing phylogenetic software packages based on MCMC algorithms. We illustrate our method using simulation studies and real data analysis.
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Spanos, P. D., and B. A. Zeldin. "Monte Carlo Treatment of Random Fields: A Broad Perspective." Applied Mechanics Reviews 51, no. 3 (March 1, 1998): 219–37. http://dx.doi.org/10.1115/1.3098999.

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A review of a number of methods for random fields simulation in conjunction with Monte Carlo studies of probabilistic mechanics problems is presented from a broad perspective. This article complements some of the previous review articles in that it compares various simulation algorithms, assesses their relative computational efficiency and versatility, discusses the properties of generated field samples, and incorporates some of the recent developments. Collectively, a comprehensive discussion of the covariance decomposition method, the spectral method, the ARMA method, the noise shower method, the scale refinement methods, and the turning band method is attempted. For tutorial effectiveness univariate, uni-dimensional, Gaussian, and homogeneous fields are discussed, primarily in connection with various simulation methods. Nevertheless, appropriate references are included addressing the simulation of more general fields. This review article contains 110 references.
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Dissertations / Theses on the topic "Overtime. Monte Carlo method. Simulation methods"

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Woo, Sungkwon. "Monte Carlo simulation of labor performance during overtime and its impact on project duration /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Homem, de Mello Tito. "Simulation-based methods for stochastic optimization." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/24846.

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Obradovic, Borna Josip. "Multi-dimensional Monte Carlo simulation of ion implantation into complex structures /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Mansour, Nabil S. "Inclusion of electron-plasmon interactions in ensemble Monte Carlo simulations of degerate GaAs." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/13862.

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Melson, Joshua Hiatt. "Fatigue Crack Growth Analysis with Finite Element Methods and a Monte Carlo Simulation." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/48432.

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Fatigue crack growth in engineered structures reduces the structures load carrying capacity and will eventually lead to failure. Cycles required to grow a crack from an initial length to the critical length is called the fatigue fracture life. In this thesis, five different methods for analyzing the fatigue fracture life of a center cracked plate were compared to experimental data previously collected by C.M. Hudson in a 1969 NASA report studying the R-ratio effects on crack growth in 7075-T6 aluminum alloy. The Paris, Walker, and Forman fatigue crack growth models were fit the experimental data. The Walker equation best fit the data since it incorporated R-ratio effects and had a similar Root Mean Square Error (RMSE) compared to the other models. There was insufficient data in the unstable region of crack growth to adequately fit the Forman equation. Analytical models were used as a baseline for all fatigue fracture life comparisons. Life estimates from AFGROW and finite elements with mid-side nodes moved to their quarter point location compared very with the analytical model with errors less than 3%. The Virtual Crack Closure Technique (VCCT) was selected as a method for crack propagation along a predefined path. Stress intensity factors (SIFs) for shorter crack lengths were found to be low, resulting in an overestimated life of about 8%. The eXtended Finite Element Method with Phantom Nodes (XFEM-PN) was used, allowing crack propagation along a solution dependent path, independent of the mesh. Low SIFs throughout growth resulted in life estimates 20% too large. All finite element analyses were performed in Abaqus 6-13.3. An integrated polynomial method was developed for calculating life based on Abaqus' results, leading to coarser meshes with answers closer to the analytical estimate. None of the five methods for estimating life compared well with the experimental data, with analytical errors on life ranging from 10-20%. These errors were attributed to the limited number of crack growth experiments run at each R-ratio, and the large variability typically seen in growth rates. Monte Carlo simulations were run to estimate the distribution on life. It was shown that material constants in the Walker model must be selected based on their interrelation with a multivariate normal probability density function. Both analytical and XFEM-PN simulations had similar coefficients of variation on life of approximately 3% with similar normal distributions. It was concluded that Abaqus' XFEM-PN is a reasonable means of estimating fatigue fracture life and its variation, and this method could be extended to other geometries and three-dimensional analyses.
Master of Science
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Shrestha, Surendra Prakash. "An effective medium approximation and Monte Carlo simulation in subsurface flow modeling." Diss., Virginia Tech, 1993. http://hdl.handle.net/10919/38642.

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Samant, Asawari. "Multiscale Monte Carlo methods to cope with separation of scales in stochastic simulation of biological networks." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 146 p, 2007. http://proquest.umi.com/pqdweb?did=1407500711&sid=13&Fmt=2&clientId=8331&RQT=309&VName=PQD.

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De, Ponte Candice Natasha. "Pricing barrier options with numerical methods / Candice Natasha de Ponte." Thesis, North-West University, 2013. http://hdl.handle.net/10394/8672.

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Barrier options are becoming more popular, mainly due to the reduced cost to hold a barrier option when compared to holding a standard call/put options, but exotic options are difficult to price since the payoff functions depend on the whole path of the underlying process, rather than on its value at a specific time instant. It is a path dependent option, which implies that the payoff depends on the path followed by the price of the underlying asset, meaning that barrier options prices are especially sensitive to volatility. For basic exchange traded options, analytical prices, based on the Black-Scholes formula, can be computed. These prices are influenced by supply and demand. There is not always an analytical solution for an exotic option. Hence it is advantageous to have methods that efficiently provide accurate numerical solutions. This study gives a literature overview and compares implementation of some available numerical methods applied to barrier options. The three numerical methods that will be adapted and compared for the pricing of barrier options are: • Binomial Tree Methods • Monte-Carlo Methods • Finite Difference Methods
Thesis (MSc (Applied Mathematics))--North-West University, Potchefstroom Campus, 2013
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Saleh, Ali, and Ahmad Al-Kadri. "Option pricing under Black-Scholes model using stochastic Runge-Kutta method." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-53783.

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The purpose of this paper is solving the European option pricing problem under the Black–Scholes model. Our approach is to use the so-called stochastic Runge–Kutta (SRK) numericalscheme to find the corresponding expectation of the functional to the stochastic differentialequation under the Black–Scholes model. Several numerical solutions were made to study howquickly the result converges to the theoretical value. Then, we study the order of convergenceof the SRK method with the help of MATLAB.
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PIUVEZAM, FILHO HELIO. "Estudo de um sistema de coincidência 4-pi-beta-gama para a medida absoluta de atividade de radionuclídeos empregando cintiladores plásticos." reponame:Repositório Institucional do IPEN, 2007. http://repositorio.ipen.br:8080/xmlui/handle/123456789/11507.

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Dissertação (Mestrado)
IPEN/D
Instituto de Pesquisas Energéticas e Nucleares - IPEN-CNEN/SP
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Books on the topic "Overtime. Monte Carlo method. Simulation methods"

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Pierre, L' Ecuyer, and Owen Art B, eds. Monte Carlo and quasi-Monte Carlo methods 2008. Heidelberg: Springer, 2009.

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Franklin, Mendivil, ed. Explorations in Monte Carlo methods. Dordrecht: Springer, 2009.

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George, Casella, and SpringerLink (Online service), eds. Introducing Monte Carlo Methods with R. New York, NY: Springer Science+Business Media, LLC, 2010.

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Monte Carlo simulation with applications to finance. Boca Raton: CRC Press, 2012.

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Wang, Hui. Monte Carlo simulation with applications to finance. Boca Raton: CRC Press, 2012.

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Biswas, A. Application of monte-carlo method in simulation of sputtered thin film surfaces. Mumbai: Bhabha Atomic Research Centre, 2005.

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Bufler, Fabian M. Full-band Monte Carlo simulation of nanoscale strained silicon MOSFETs. Konstanz: Hartung-Gorre, 2003.

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Koura, Katsuhisa. Comparison between the null-collision and time-counter direct-simulation Monte Carlo methods: leading-edge flow. Tokyo: National Aerospace Laboratory, 1989.

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Geostatistical simulation: Models and algorithms. New York: Springer, 2002.

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Monte Carlo simulation for the pharmaceutical industry: Concepts, algorithms, and case studies. Boca Raton: CRC Press, 2011.

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Book chapters on the topic "Overtime. Monte Carlo method. Simulation methods"

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Ermakov, S., and I. Kaloshin. "Solving the Nonlinear Algebraic Equations with Monte Carlo Method." In Advances in Stochastic Simulation Methods, 3–15. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1318-5_1.

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Macedo, Antonini Puppin, and Antonio C. P. Brasil. "A Coupled Monte Carlo/Explicit Euler Method for the Numerical Simulation of a Forest Fire Spreading Model." In Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, 333–45. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-2552-2_21.

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"The Monte Carlo Method." In Electron Beam-Specimen Interactions and Simulation Methods in Microscopy, 9–51. Chichester, UK: John Wiley & Sons Ltd, 2018. http://dx.doi.org/10.1002/9781118696545.ch2.

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"Monte Carlo Method and Its Application in Urban Traffic Simulation." In Quantitative Methods and Socio-Economic Applications in GIS, 292–311. CRC Press, 2014. http://dx.doi.org/10.1201/b17967-20.

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T. Lima Jr, Ivan, and Sherif S. Sherif. "Monte Carlo Methods for Simulation of Optical Coherence Tomography of Turbid Media." In Theory, Application, and Implementation of Monte Carlo Method in Science and Technology. IntechOpen, 2019. http://dx.doi.org/10.5772/intechopen.89555.

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Durmaz, V., K. Fackeldey, and M. Weber. "A rapidly Mixing Monte Carlo Method for the Simulation of Slow Molecular Processes." In Applications of Monte Carlo Methods in Biology, Medicine and Other Fields of Science. InTech, 2011. http://dx.doi.org/10.5772/15018.

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Hulett, David Todd. "Monte Carlo Simulation for Integrated Cost-Schedule Risk Analysis." In Advances in IT Personnel and Project Management, 29–60. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1790-0.ch002.

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Evidence shows that project costs and schedules often overrun their initial plans. The purpose of this chapter is to illustrate the most recent tools and methodologies available today in the application of Monte Carlo simulation techniques to quantify possible overruns in cost and schedule and to understand the sources of those overruns to facilitate risk mitigation actions. Notable methods described include the Risk Driver method, collecting risk data using individual confidential interviews, and use of iterative risk prioritization that facilitates risk focused risk mitigation. Emphasis is placed on the quality of the project schedule and on the quality of the risk data used. The use of prioritization of pre-mitigated for risk mitigation strategies shows how the methodology can be used as a dynamic tool of successful project management.
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KAMARUDIN, A. "A Censoring Technique in the Monte Carlo Simulation Method Applied to Probability Based Distribution Line Wood Pole Design." In Probabilistic Methods Applied to Electric Power Systems, 101–8. Elsevier, 1987. http://dx.doi.org/10.1016/b978-0-08-031874-5.50016-4.

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Eti, Serkan. "The Use of Quantitative Methods in Investment Decisions." In Handbook of Research on Global Issues in Financial Communication and Investment Decision Making, 256–75. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-9265-5.ch013.

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Quantitative methods are mainly preferred in the literature. The main purpose of this chapter is to evaluate the usage of quantitative methods in the subject of the investment decision. Within this framework, the studies related to the investment decision in which quantitative methods are taken into consideration. As for the quantitative methods, probit, logit, decision tree algorithms, artificial neural networks methods, Monte Carlo simulation, and MARS approaches are taken into consideration. The findings show that MARS methodology provides a more accurate results in comparison with other techniques. In addition to this situation, it is also concluded that probit and logit methodologies were less preferred in comparison with decision tree algorithms, artificial neural networks methods, and Monte Carlo simulation analysis, especially in the last studies. Therefore, it is recommended that a new evaluation for investment analysis can be performed with MARS method because it is understood that this approach provides better results.
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Eti, Serkan. "The Use of Quantitative Methods in Investment Decisions." In Research Anthology on Personal Finance and Improving Financial Literacy, 1–20. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8049-3.ch001.

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Quantitative methods are mainly preferred in the literature. The main purpose of this chapter is to evaluate the usage of quantitative methods in the subject of the investment decision. Within this framework, the studies related to the investment decision in which quantitative methods are taken into consideration. As for the quantitative methods, probit, logit, decision tree algorithms, artificial neural networks methods, Monte Carlo simulation, and MARS approaches are taken into consideration. The findings show that MARS methodology provides a more accurate results in comparison with other techniques. In addition to this situation, it is also concluded that probit and logit methodologies were less preferred in comparison with decision tree algorithms, artificial neural networks methods, and Monte Carlo simulation analysis, especially in the last studies. Therefore, it is recommended that a new evaluation for investment analysis can be performed with MARS method because it is understood that this approach provides better results.
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Conference papers on the topic "Overtime. Monte Carlo method. Simulation methods"

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Castro, Jose F. Costa, Armando M. Leite da Silva, Mauro A. Rosa, and Pedro Cesar C. Vieira. "Spinning Reserve Assessment in Multiarea Systems via Monte Carlo Simulation and CE Method." In 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2018. http://dx.doi.org/10.1109/pmaps.2018.8440533.

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Kindt, James T. "Simulation Methods for Self-Assembled Polymers and Rings." In THE MONTE CARLO METHOD IN THE PHYSICAL SCIENCES: Celebrating the 50th Anniversary of the Metropolis Algorithm. AIP, 2003. http://dx.doi.org/10.1063/1.1632158.

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Banzarov, Bair V., and Alexander A. Vinokurov. "Fast Monte Carlo method for simulation of gamma density well logging measurements." In Недропользование. Горное дело. Направления и технологии поиска, разведки и разработки месторождений полезных ископаемых. Экономика. Геоэкология. Федеральное государственное бюджетное учреждение науки Институт нефтегазовой геологии и геофизики им. А.А. Трофимука Сибирского отделения Российской академии наук, 2020. http://dx.doi.org/10.18303/b978-5-4262-0102-6-2020-032.

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The paper introduces a fast Monte Carlo method developed for simulation of gamma density measurements. A main advantage of this method is its high performance. For simulation of tool responses, the proposed method is faster than conventional Monte Carlo methods by several orders of magnitude. The demonstrated performance of the proposed method allows its using in analysis and interpretation of measurements obtained in beds with complex geometry.
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Naess, A., B. J. Leira, and O. Batsevych. "Estimation of System Reliability by Monte Carlo Simulation." In ASME 2009 28th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/omae2009-79623.

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A new method for estimating the reliability of structural systems is proposed. The method is based on the use of Monte Carlo simulation. Monte Carlo based methods for system reliability analysis has several attractive features, the most important being that the system failure criterion is usually relatively easy to check almost irrespective of the complexity of the system. The disadvantage of such methods is the amount of computational efforts that may be involved. However, by reformulating the reliability problem to depend on a parameter and exploiting the regularity of the failure probability as a function of this parameter, it is shown that a substantial reduction of the computational efforts involved can be obtained.
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Wei, Zhigang, Limin Luo, Burt Lin, Dmitri Konson, and Kamran Nikbin. "Design Curve Construction Based on Monte Carlo Simulation." In ASME 2013 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/pvp2013-97631.

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Good durability/reliability performance of products can be achieved by properly constructing and implementing design curves, which are usually obtained by analyzing test data, such as fatigue S-N data. A good design curve construction approach should consider sample size, failure probability and confidence level, and these features are especially critical when test sample size is small. The authors have developed a design S-N curve construction method based on the tolerance limit concept. However, recent studies have shown that the analytical solutions based on the tolerance limit approach may not be accurate for very small sample size because of the assumptions and approximations introduced to the analytical approach. In this paper a Monte Carlo simulation approach is used to construct design curves for test data with an assumed underlining normal (or lognormal) distribution. The difference of factor K, which measures the confidence level of the test data, between the analytical solution and the Monte Carlo simulation solutions is compared. Finally, the design curves constructed based on these methods are demonstrated and compared using fatigue S-N data with small sample size.
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6

Kaymaz, Irfan, and Chris A. McMahon. "An Approach to Reliability Analysis Using the Response Surface Method and Monte Carlo Simulation." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/rsafp-8846.

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Abstract It is important in reliability evaluation to take an approach in which the required calculations can be performed efficiently in terms of time and cost. In this study, an approach is proposed whereby reliability analysis is carried out by means of Monte Carlo simulation in which the actual performance function is replaced by a function obtained using the response surface method (RSM). The common approach in the conventional RSM is to use a second-degree polynomial for the response surface function, but in many reliability problems this may not be the best choice. This paper first reviews the approaches and limitations of reliability methods, and then goes on to discuss a method of modelling error when using the response surface method for reliability analysis. It shows the errors obtained for different response functions under different circumstances, and then describes the application of a network-based analysis system to reliability problems.
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7

Koreshi, Zafar Ullah. "Stationarity Issues in Monte Carlo Simulation for Neutron Transport." In 2013 21st International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icone21-15016.

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Monte Carlo (MC) simulation, especially suitable for large and complex nuclear systems, can become computationally expensive due to the large number of neutrons which must be simulated for statistically accurate and precise estimates. It is generally understood that a sample estimate will converge to the population mean when a ‘large’ sample size is taken. The term ‘large’ is usually based on a guess and hence MC simulation is understood to be both an art and a science. Considerable work has been done to analyze convergence of MC results and develop posterior diagnostic tools. This paper addresses the convergence of MC simulation for two problems viz (i) a fixed-source non-multiplying system, and (ii) a critical system represented by Godiva. A traditional approach is used in the first part of the work while a ‘new’ approach essentially following Signals and Systems techniques from Digital Signal Processing gives ‘orginality’ to the analysis as it provides insight into the convergence of didactic problems in neutron transport simulation. The methods used are (i) comparison of MC flux with exact transport and diffusion solutions and relative entropy, with the Kullback-Leibler (KL) divergence, to quantify the convergence of estimates for flux as a function of sample size in Monte Carlo simulations, (ii) the effect of ‘skip cycles’ on the keff estimate, and (iii) a system identification approach based on the ARX (Auto Regressive Exogenous Source) method to determine the correlation between generations. The latter can be incorporated in Monte Carlo codes leading to a priori rather than to a posteriori diagnostic tools for establishment of convergence. The main findings of this work for simple one-group problems are that a Kullback Leibler ε∼10−3 can be specified a priori for the convergence criteria of a fixed source problem while a system-identification approach for a simple Godiva simulation would need a large number of data points to build an accurate ARX model and hence would be more difficult to include as an a priori tool; so it would essentially serve a purpose similar to the FOM which gives a quality metric only after the simulation is completed.
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Gao, Jinsong, Kenneth W. Chase, and Spencer P. Magleby. "Comparison of Assembly Tolerance Analysis by the Direct Linearization and Modified Monte Carlo Simulation Methods." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0047.

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Abstract Two methods for performing statistical tolerance analysis of mechanical assemblies are compared: the Direct Linearization Method (DLM), and Monte Carlo simulation. A selection of 2-D and 3-D vector models of assemblies were analyzed, including problems with closed loop assembly constraints. Closed vector loops describe the small kinematic adjustments that occur at assembly time. Open loops describe critical clearances or other assembly features. The DLM uses linearized assembly constraints and matrix algebra to estimate the variations of the assembly or kinematic variables, and to predict assembly rejects. A modified Monte Carlo simulation, employing an iterative technique for closed loop assemblies, was applied to the same problem set. The results of the comparison show that the DLM is accurate if the tolerances are relatively small compared to the nominal dimensions of the components, and the assembly functions are not highly nonlinear. Sample size is shown to have great influence on the accuracy of Monte Carlo simulation.
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Valenti, Mario, Z. J. Delalic, and S. Jahanian. "Estimation of Power Density in VLSI Circuits Using Monte Carlo Simulation." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/rsafp-8847.

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Abstract CMOS technology in the past twenty years has followed the path of device scaling for achieving density, speed, and power improvements. The advancement of lithographic techniques propelled the scaling also of the fine lines to 1 urn width and below. Since the integration density on chip level is constantly increasing there is need to study power dissipation and resulting heat propagation between circuit components. This research studies different methods to analyze the power dissipations and temperature distributions of a full CMOS adder. Through examining many methods, STEPS method was selected for determination of the power dissipation in a VLSI CMOS chip. An experiment was developed for the dynamic power dissipation since static power dissipation is negligible in case of CMOS devices. Using this method one can look at each circuit node individually as signals are propagated through the chip and determine power distribution in the form of heat. Therefore, it is possible to construct a 3-dimensional diagram of the actual distribution of the heat across the chip.
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10

Li, Duo, Zhaojun Hao, Shuqiao Zhou, and Chao Guo. "Application of Monte Carlo Methods in Reactor Protection System Reliability Research." In 2018 26th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/icone26-81300.

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Digital Reactor Protection System (RPS) is one of the most important systems in instrumentation and control systems of Nuclear Power Plants (NPP). The reliability analysis of RPS plays an important role both in theory and engineering application. Traditional reliability methods, such as fault tree analysis and Markov chain theory, have many limitations in the research of RPS reliability, since the number of system states increases exponentially with the growth of system complexity. Aiming at the reliability analysis of complex system like RPS, the Monte Carlo method simulates the system behaviors and obtains the reliability calculations through a large number of simulations. This paper takes a preliminary research of RPS reliability based on Monte Carlo Methods, including static reliability analysis based on Monte Carlo simulation of the behavior of every equipment in the RPS, and dynamic characters of the RPS based on the simulation of RPS period tests.
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