(untitled) (Communications in Computational Physics, 2022)
In this paper, a novel mixed wavelet-learning method is developed for predicting macroscopic effective heat transfer conductivities of braided composite materials with heterogeneous thermal conductivity. This innovative methodology integrates respective superiorities of multi-scale modeling, wavelet transform and neural networks together. By the aid of asymptotic homogenization method (AHM), off-line multi-scale modeling is accomplished for establishing the material database with high-dimensional and highly-complex mappings. The multi-scale material database and the wavelet-learning strategy ease the on-line training of neural networks, and enable us to efficiently build relatively simple networks that have an essentially increasing capacity and resisting noise for approximating the high-complexity mappings. Moreover, it should be emphasized that the wavelet-learning strategy can not only extract essential data characteristics from the material database, but also achieve a tremendous reduction in input data of neural networks. The numerical experiments performed using multiple 3D braided composite models verify the excellent performance of the presented mixed approach. The numerical results demonstrate that the mixed wavelet-learning methodology is a robust method for computing the macroscopic effective heat transfer conductivities with distinct heterogeneity patterns. The presented method can enormously decrease the computational time, and can be further expanded into estimating macroscopic effective mechanical properties of braided composites.