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Two-scale elastic parameter identification from noisy macroscopic data

机译:从嘈杂的宏观数据中识别两尺度弹性参数

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摘要

A two-scale parameter identification procedure to identify microscopic elastic parameters from macroscopic data is introduced and thoroughly analyzed. The macroscopic material behavior of microscopically linear elastic heterogeneous materials is described by means of numerical homogenization. The microscopic material parameters are assumed to be unknown and are identified from noisy macroscopic displacement data. Various examples of microscopically heterogeneous materials-with regularly distributed pores, particles, or layers-are considered, and their parameters are identified from different macroscopic experiments by means of a gradient-based optimization procedure. The reliability of the identified parameters is analyzed by their standard deviations and correlation matrices. It was found that the two-scale parameter identification works well for cellular materials, but has to be designed carefully for layered materials. If the homogenized macroscopic material behavior can be described by less material parameters than the microscopic material behavior, as, e.g., for regularly distributed particles, the identification of all microscopic parameters from macroscopic experiments is not possible.
机译:介绍并彻底分析了从宏观数据中识别微观弹性参数的两尺度参数识别程序。微观线性弹性异质材料的宏观材料行为通过数值均化来描述。假定微观材料参数是未知的,并从嘈杂的宏观位移数据中识别出来。考虑了具有均匀分布的孔,颗粒或层的微观异质材料的各种示例,并通过基于梯度的优化程序从不同的宏观实验中识别了它们的参数。所识别参数的可靠性通过其标准偏差和相关矩阵进行分析。已经发现,两尺度参数识别对于蜂窝材料非常有效,但是对于分层材料则必须仔细设计。如果均质的宏观材料行为可以用比微观材料行为更少的材料参数来描述,例如对于规则分布的粒子,则无法从宏观实验中识别所有微观参数。

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