Numerical Approximation of Stochastic Systems for Composite Materials Based on Markov Chains

Hua Yang; Feng Jiang

doi:10.4028/www.scientific.net/AMR.321.88

Paper Titles

Particle Swarm Optimization MPPT Method for PV Materials in Partial Shading
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Improved Output Characteristic of Distributed Hybrid Solar–Wind Generating Materials by Using Fuzzy and Immune MPPT Control Method
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The Combined Fuzzy and PO MPPT Method for PV Materials under Partially Shaded Conditions
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Finite Element Stress Analysis on Structure of Hydraulic Support
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Numerical Approximation of Stochastic Systems for Composite Materials Based on Markov Chains
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Porterage Robot for Crystal Silicon Solar Cell with Photovoltaic Material
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Research on the Effect of Doping Ca Ion and Organic Solvent on the Luminescent Intensity of Tb Complex Material
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Research on the Effect of Mg (II) Ion Concentration on the Luminescent Intensity of Tb Complex
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Force Analysis and Studies on Track Frame of Hydraulic Drill
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Numerical Approximation of Stochastic Systems for Composite Materials Based on Markov Chains

Abstract:

The crack density and crack growth rate are important parameters which are used to describe the fatigue damage and predict fatigue life of a composite material. Even the same good manufacturing practice, the fatigue damage of materials may be different. Also material properties often accompany random fluctuation. Thus stochastic systems are used to present the crack density and crack growth rate. It is surprising that there are not any numerical schemes established for hybrid stochastic systems in composite materials. In this paper, based on Markov chains, the Euler-aruyama method is developed, and the main aim is to show the convergence of the numerical solutions under the non-Lipschitz condition for hybrid stochastic material systems.