Data-driven multiscale simulation of FRP based on material twins

Abstract In this paper, we propose a multiscale data-driven framework for Fiber Reinforced Polymer (FRP) composites. At the mesoscopic scale, the 3D stress–strain material database is collected by the multilevel computational homogenization (FE2), in which the Representative Volume Elements (RVEs) are generated through the X-ray microtomography (Micro-CT) aided technique. Such so-called “material twin” technique is able to reproduce the high quality yet operational geometric mesoscopic details of FRP composites. At the macroscopic scale, the distance-minimizing data-driven approach is adopted to simulate the structural behavior by directly searching the material database without employing the constitutive model. This data-driven approach can significantly save the computational cost because the needed mesoscopic behaviors are previously prepared by the offline mode. The numerical results demonstrated that the proposed data-driven framework is a promising scheme. From material database collection to data-driven analysis, this framework opens a new channel for FRP simulation in the data science paradigm.

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