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  • Open Access

    ARTICLE

    Phonon Transport and Thermal Conductivity Percolation in Random Nanoparticle Composites

    Weixue Tian1, Ronggui Yang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.24, No.2&3, pp. 123-142, 2008, DOI:10.3970/cmes.2008.024.123

    Abstract In this paper, we investigated the effective thermal conductivity of three dimensional nanocomposites composed of randomly distributed binary nanoparticles with large differences (contrast ratio) in their intrinsic (bulk) thermal conductivity. When random composites are made from particles with very different thermal conductivity (large contrast ratio), a continuous phase of high thermal conductivity constituent is formed when its volumetric concentration reaches beyond the percolation threshold. Such a continuous phase of material can provide a potentially low resistance pathway for thermal transport in random composites. The percolation theory predicts the thermal conductivity of the random composites to increase according to a scaling… More >

  • Open Access

    ARTICLE

    Prediction of Crack Growth in Steam Generator Tubes Using Monte Carlo Simulation

    Jae Bong Lee1, Jai Hak Park1, Sung Ho Lee2, Hong-Deok Kim2, Han-Sub Chung2

    CMES-Computer Modeling in Engineering & Sciences, Vol.11, No.1, pp. 9-16, 2006, DOI:10.3970/cmes.2006.011.009

    Abstract The growth of stress corrosion cracks in steam generator tubes is predicted using the Monte Carlo simulation and statistical approaches. The statistical parameters that represent the characteristics of crack growth and crack initiation are derived from in-service inspection (ISI) non-destructive evaluation (NDE) data. Based on the statistical approaches, crack growth models are proposed and applied to predict crack distribution at the end of cycle (EOC). Because in-service inspection (ISI) crack data is different from physical crack data, a simple method for predicting the physical number of cracks from periodic in-service inspection data is proposed in this study. Actual number of… More >

  • Open Access

    ARTICLE

    Excluded Volumes of Anisotropic Convex Particles in Heterogeneous Media: Theoretical and Numerical Studies

    Wenxiang Xu1,2,3,4, Ganquan Yang5, Peng Lan2, Huaifa Ma1

    CMC-Computers, Materials & Continua, Vol.52, No.1, pp. 25-40, 2016, DOI:10.3970/cmc.2016.052.025

    Abstract Understanding the excluded volume of anisotropic particle is of great importance in the evaluation of continuum percolation and random packing behaviors of soft/hard particle systems in heterogeneous disordered media. In this work, we obtain the excluded volumes of several anisotropic convex particles including prolate spheroids, oblate spheroids, spherocylinders, and Platonic particles, using theoretical and numerical approaches. According to the second virial coefficient, we first present a theoretical scheme for determining the excluded volumes of anisotropic particles. Also, the mean tangent diameters of anisotropic convex particles are formulated by the quantitative stereology. Subsequently, Monte Carlo simulations are demonstrated to numerically evaluate… More >

  • Open Access

    ARTICLE

    Monte Carlo Simulation of Ti-6Al-4V Grain Growth during Fast Heat Treatment

    Amir Reza Ansari Dezfoli1, Weng-Sing Hwang1,2

    CMC-Computers, Materials & Continua, Vol.49-50, No.1, pp. 1-11, 2015, DOI:10.3970/cmc.2015.049.001

    Abstract Investigations of the microstructural evolution of Titanium (Ti) alloys during high temperature processes and heat treatment are attracting more attention due to wide variety of applications for such alloys. In most of these processes the Titanium alloys are subjected to fast heating or cooling rates. In this paper, Monte Carlo simulation is used to simulate the grain growth kinetics of Ti-6Al-4V alloy during fast heat treatment. Here, Monte Carlo simulation of grain growth is based on the Q-state Potts model. Our model is calibrated using the parabolic grain growth law, dn-d0n = kt, where the empirical constants are taken… More >

  • Open Access

    ARTICLE

    Stochastic Macro Material Properties, Through Direct Stochastic Modeling of Heterogeneous Microstructures with Randomness of Constituent Properties and Topologies, by Using Trefftz Computational Grains (TCG)

    Leiting Dong1,2, Salah H. Gamal3, Satya N. Atluri2,4

    CMC-Computers, Materials & Continua, Vol.37, No.1, pp. 1-21, 2013, DOI:10.3970/cmc.2013.037.001

    Abstract In this paper, a simple and reliable procedure of stochastic computation is combined with the highly accurate and efficient Trefftz Computational Grains (TCG), for a direct numerical simulation (DNS) of heterogeneous materials with microscopic randomness. Material properties of each material phase, and geometrical properties such as particles sizes and distribution, are considered to be stochastic with either a uniform or normal probabilistic distributions. The objective here is to determine how this microscopic randomness propagates to the macroscopic scale, and affects the stochastic characteristics of macroscopic material properties. Four steps are included in this procedure: (1) using the Latin hypercube sampling,… More >

  • Open Access

    ARTICLE

    Modeling Intergranular Crack Propagation in Polycrystalline Materials

    M.A.Arafin1, J.A.Szpunar2

    CMC-Computers, Materials & Continua, Vol.14, No.2, pp. 125-140, 2009, DOI:10.3970/cmc.2009.014.125

    Abstract A novel microstructure, texture and grain boundary character based model has been proposed to simulate the intergranular crack propagation behavior in textured polycrystalline materials. The model utilizes the Voronoi algorithm and Monte Carlo simulations to construct the microstructure with desired grain shape factor, takes the texture description of the materials to assign the orientations of the grains, evaluates the grain boundary character based on the misorientation angle - axis calculated from the orientations of the neighboring grains, and takes into account the inclination of grain boundaries with respect to the external stress direction. Markov Chain theory has been applied to… More >

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