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A concurrent multiscale scheme FFT-SCA for meso-micro damage evolution of 3D woven composites independent of mesoscopic material parameters

  • Siyang Wu
  • , Licheng Guo*
  • , Zhixing Li
  • , Tao Zheng
  • , Junfeng Ding
  • , Fenghao Jia
  • *Corresponding author for this work
  • Harbin Institute of Technology

Research output: Contribution to journalArticlepeer-review

Abstract

Developing an efficient and accurate scheme for meso-micro concurrent damage evolution analysis of 3D woven composites (3DWC) is challenging. In our work, a concurrent multiscale scheme FFT-SCA is proposed to capture the meso-micro damage evolution of 3DWC. In the scheme, the non-linear behavior of the mesoscale 3DWC representative volume element (RVE) is solved by Fast Fourier Transform (FFT) method, and the non-linear behavior of the microscale yarn RVE is solved concurrently by Self-consistent clustering analysis (SCA) method. The two-scale computations are dynamically coupled based on the homogenization theory. Benefiting from this, there is no need to define mesoscopic constitutive models and calibrate the difficult-to-obtain mesoscale parameters, such as the fracture toughness and strengths of yarns. The FFT-SCA scheme is utilized to predict the failure strength and meso-micro damage evolution of 3DWC. The comparison of the predictions with experiments indicates that the FFT-SCA method has high prediction accuracy for the tensile strength of 3DWC. The high-fidelity mesoscale stress field and cluster-based microscale damage field can be simultaneously captured, which is not available for one-scale Finite Element method (FEM) or experiments. The FFT-SCA scheme enables controllable computational dimensions while guaranteeing the accuracy of meso-micro damage evolution.

Original languageEnglish
Article number111021
JournalComposites Science and Technology
Volume261
DOIs
StatePublished - 1 Mar 2025

Keywords

  • 3D woven composites
  • Computational method
  • Concurrent multiscale scheme
  • Self-consistent clustering analysis

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