论文标题
使用多尺度野外测量的随机异质材料的中尺度上明显的弹性特性的强大多尺度识别
Robust Multiscale Identification of Apparent Elastic Properties at Mesoscale for Random Heterogeneous Materials with Multiscale Field Measurements
论文作者
论文摘要
这项工作的目的是有效，鲁棒地解决与鉴定弹性特性在宏观和介镜尺度上识别异质性各向异性材料的弹性特性有关的复杂微观结构，通常无法用其机械组成在Microscale的机械组成部分进行正确描述。在线性弹性理论的背景下，给定的中尺度上的明显弹性张量场是由先前的非高斯张量值随机场建模的。最近，通过使用随机计算模型的多尺度统计倒数问题解决了这种先前随机模型的超参数的一般方法，用于识别这种先前随机模型的超参数的超参数，并使用随机计算模型和Mocroscale和Mescoscale位移中的某些信息来解决此类随机模型的高参数。本文通过引入（i）与运动域的空间相关长度（S）有关，可以提高这种方法的计算效率，准确性和鲁棒性，从而使时间耗时的全局优化算法（遗传算法）在II中允许使用的时间更高（GengorithM），并允许II替换（遗传算法）。在先前的随机模型中涉及的超参数的随机表示，以增强统计反向识别方法的鲁棒性和精度。最后，首先在2D平面应力和3D线性弹性的框架内在硅材材料中验证了所提出的改进方法（使用数值计算获得的多尺度模拟数据），然后在2D平面压力弹性（通过多个弹性数据）中进行型号在实际异质生物学材料（牛肉皮层骨）上进行实际异质生物学材料（牛肉皮质骨）的例证。
The aim of this work is to efficiently and robustly solve the statistical inverse problem related to the identification of the elastic properties at both macroscopic and mesoscopic scales of heterogeneous anisotropic materials with a complex microstructure that usually cannot be properly described in terms of their mechanical constituents at microscale. Within the context of linear elasticity theory, the apparent elasticity tensor field at a given mesoscale is modeled by a prior non-Gaussian tensor-valued random field. A general methodology using multiscale displacement field measurements simultaneously made at both macroscale and mesoscale has been recently proposed for the identification the hyperparameters of such a prior stochastic model by solving a multiscale statistical inverse problem using a stochastic computational model and some information from displacement fields at both macroscale and mesoscale. This paper contributes to the improvement of the computational efficiency, accuracy and robustness of such a method by introducing (i) a mesoscopic numerical indicator related to the spatial correlation length(s) of kinematic fields, allowing the time-consuming global optimization algorithm (genetic algorithm) used in a previous work to be replaced with a more efficient algorithm and (ii) an ad hoc stochastic representation of the hyperparameters involved in the prior stochastic model in order to enhance both the robustness and the precision of the statistical inverse identification method. Finally, the proposed improved method is first validated on in silico materials within the framework of 2D plane stress and 3D linear elasticity (using multiscale simulated data obtained through numerical computations) and then exemplified on a real heterogeneous biological material (beef cortical bone) within the framework of 2D plane stress linear elasticity (using multiscale experimental data obtained through mechanical testing monitored by digital image correlation).
