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An inverse micro-mechanical analysis toward the stochastic homogenization of nonlinear random composites

机译：非线性随机复合材料随机均质化的反相微力分析

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An inverse Mean-Field Homogenization (MFH) process is developed to improve the computational efficiency of non-linear stochastic multiscale analyzes by relying on a micro-mechanics model. First full-field simulations of composite Stochastic Volume Element (SVE) realizations are performed to characterize the homogenized stochastic behavior. The uncertainties observed in the non-linear homogenized response, which result from the uncertainties of their micro-structures, are then translated to an incremental-secant MFH formulation by defining the MFH input parameters as random effective properties. These effective input parameters, which correspond to the micro-structure geometrical information and to the material phases model parameters, are identified by conducting an inverse analysis from the full-field homogenized responses. Compared to the direct finite element analyzes on SVEs, the resulting stochastic MFH process reduces not only the computational cost, but also the order of uncertain parameters in the composite micro-structures, leading to a stochastic Mean-Field Reduced Order Model (MF-ROM). A data-driven stochastic model is then built in order to generate the random effective properties under the form of a random field used as entry for the stochastic MF-ROM embedded in a Stochastic Finite Element Method (SEEM). The two cases of elastic Unidirectional (UD) fibers embedded in an elasto-plastic matrix and of elastic UD fibers embedded in a damage-enhanced elasto-plastic matrix are successively considered. In order to illustrate the capabilities of the method, the stochastic response of a ply is studied under transverse loading condition. (C) 2019 Elsevier B.V. All rights reserved.
机译：开发了逆平均场均匀化（MFH）过程，以通过依靠微型力学模型来提高非线性随机多尺度分析的计算效率。进行复合随机体积元素（SVE）实现的第一全场模拟，以表征均质随机行为。在非线性均质响应中观察到的不确定性，然后由其微结构的不确定性导致的，然后通过将MFH输入参数定义为随机有效属性来平移到增量分割MFH制剂。这些有效输入参数对应于微结构几何信息和材料阶段模型参数，通过从全场均化反应进行逆分析来识别。与SVERS上的直接有限元分析相比，所产生的随机MFH过程不仅减少了计算成本，而且还减少了复合微结构中不确定参数的顺序，导致随机平均场缩小阶模型（MF-ROM ）。然后建立数据驱动的随机模型，以便在随着随机有限元方法（似乎）中嵌入的随机MF-ROM的流程的随机场的形式下的随机有效性。嵌入在弹性塑料基质和嵌入在损坏的弹塑性基质中的弹性塑料基质和弹性UD纤维中的弹性单向（UD）纤维的两种情况被认为是考虑的。为了说明方法的能力，在横向负载条件下研究了帘布层的随机响应。 （c）2019 Elsevier B.v.保留所有权利。

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