Generative AI and image based numerical mechanics in wind blade adhesive composites

Abstract

Numerical modelling of adhesive composites in wind energy is complicated in part due to material heterogeneity. Microstructural CT scan fibre composite patterns or representative elements, which play a major role in defining the mechanical behaviour of these adhesive structures, are both difficult to characterize as well as hard to numerically simulate. With advances in deep learning based generative AI, new ways of predicting the mechanical behaviour of heterogeneous materials is now possible. Here we put forward a data driven method to relate input composite adhesive microstructures with field data using deep learning and generative AI based methods. The prediction of mechanical stress or strain fields or other similar patterns and combining them as a function of boundary conditions, fibre composite microstructure and material models is achieved and the models are trained such that they closely approximate computationally expensive simulations based on numerical FE techniques and would have the ability to generalize. We also create a dataset of wind energy adhesives with their numerical mechanics based FE simulations subject to different boundary conditions and material models for further deep learning based composite studies.