COMPOSITES 2023

Size Scale Effects in Fiber-Reinforced composites using phase field

  • Asur Vijaya Kumar, Pavan Kumar (TU Wien)
  • Dean, Aamir (Sudan Univerity of technology)
  • Pettermann, Heinz (TU Wien)

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he study of size scale effects on fracture is an important area of research in materials science. Although computer modeling has made significant advances, there is still much to be learned about how the size and shape of a structure affect its toughness, particularly in long fiber-reinforced composites(LFRCs) where research has been limited. Understanding size scale effects is critical for predicting and improv- ing the mechanical properties of materials. Bazant [1] noted that without grasping scaling, the theory itself is not understood. Recent developments in phase field theory for fiber-reinforced composites using PUCK failure theory [2,3] have renewed focus on understanding size effects in composites and how they influence material behavior. To address these challenges, a multi-phase field model for triggering intra-laminar cracking in long fiber- reinforced composites is considered to account for the size scale effects. The numerical model is based on Puck’s failure theory and hence differentiates between fiber and inter-fiber (matrix-dominated) failure phenomena using two independent energy-driving forces with corresponding length scales. The model can also easily incorporate an invariant-based plasticity model for matrix-dominated deformation states. The proposed model is applied to several benchmark examples to investigate the effects of size and scale in LFRCs. The results demonstrate that as the size of the material increases, the nominal strength of the structure decreases, in accordance with the size law. This model can provide valuable insights into the fracture behavior of composite materials at different size scales, aiding in the design and optimization of composite structures for various engineering applications especially when mechanical testing is not available, time-consuming, or expensive