Prediction on nonlinear mechanical performance of random particulate composites by a statistical second-order reduced multiscale approach

Abstract

A novel statistical second-order reduced multiscale (SSRM) approach is established for nonlinear composite materials with random distribution of grains. For these composites considered in this work, the complex microstructure of grains, including their shape, orientation, size, spatial distribution, volume fraction and so on, results in changing of the macroscopic mechanical properties. The first- and second-order unit cell functions based on two-scale asymptotic expressions are constructed at first. Then, the expected homogenized parameters are defined, and the nonlinear homogenization equation on global structure is established, successively. Further, an effective reduced model format for analyzing second-order nonlinear unit cell problem with less computation cost is introduced in detail. Finally, some numerical examples for the materials with varying distribution models are evaluated and compared with the data by theoretical models and experimental results. These examples illustrate that the proposed SSRM approaches are effective for predicting the macroscopic properties of the random composite materials and supply a potential application in actual engineering computation.