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首页> 外文期刊>Journal of Multiscale Modelling >Probabilistic Prediction of Homogenized Property and Update of Prediction for Spherical Porous Material Considering Microstructural Uncertainties
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Probabilistic Prediction of Homogenized Property and Update of Prediction for Spherical Porous Material Considering Microstructural Uncertainties

机译:考虑微观结构不确定性的球形多孔材料均质性质及预测预测的概率预测

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摘要

The purpose of this work is to simulate uncertainties existing in microscopic field of spherical porous material so that the homogenized property of interest can be predicted with high reliability. Moreover, the final goal is to build a bridge of feedback between microstructure design and fabrication to predict microstructure morphology by limited measurement data of macroscopic property. The uncertainties are identified as parametric variables in constituent material property and nonparametric variables in morphological fluctuation such as disordering and clustering in microstructure. First-order perturbation, based stochastic homogenization (FPSH) method together with mixture distribution technique is employed for probabilistic prediction. Furthermore, the update of prediction is accomplished in the case of an assumed virtual experimental trial. Two numerical examples show that the probabilistic prediction has given a better decision in microstructure design than deterministic prediction. The main conclusion coming from the new method derived by gap between measured data and prediction showed that, when the update is used for morphology prediction of microstructure, it is almost perfect agreement with parameters' setup of virtual experiment. After it is applied for update of probabilistic homogenized property, it could make the updated homogenized property closer to measurement data so that it becomes more realistic.
机译:这项工作的目的是模拟存在于球形多孔材料的微观场中存在的不确定性,以便通过高可靠性预测感兴趣的均质性质。此外,最终目标是在微观结构设计和制造之间建立反馈的桥梁,以通过宏观性的有限测量数据来预测微观结构形态。在形态波动中的组成材料特性和非参数变量中被鉴定为参数变量,例如在微观结构中的失调和聚类。基于一阶扰动,基于随机均质化(FPSH)方法与混合分配技术一起用于概率预测。此外,在假定的虚拟实验试验的情况下实现预测的更新。两个数值示例表明,概率预测在微观结构设计中具有比确定性预测更好的决定。来自测量数据和预测之间的间隙的新方法的主要结论显示,当更新用于微观结构的形态预测时,它几乎完美地与参数的虚拟实验设置。在应用概率更新之后,它可以使更新的均质性质更接近测量数据,以便变得更加逼真。

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