High cycle fatigue (HCF) is the dominant failure mechanism of many engineering components and structures. Traditional fatigue life prediction approaches depend on extensive fatigue tests to establish the characteristic data and lack the critical insight on the microstructural effects. It is also difficult to extend these empirical approaches to engineering parts of complicated geometry and subjected to complex loading conditions. With the rapid advancement in computing technologies in the past few decades, there is a strong interest in developing simulation-based approaches to HCF. The key challenges lie in the fact that the HCF failure mechanism involves multiple scales in both time and space, which need to be properly addressed. In this work, we present a multiscale HCF simulation approach that based on extended space-time finite element method (XTFEM) and machine learning of the microstructural effects. The XTFEM is established based on the time-discontinuous Galerkin (TDG) approach. By augmenting the regular space-time shape functions with enrichment functions that represent problem physics, we further extended its predictive capability in handling multiple temporal scales for simulations of the HCF problem [1]. To address the challenge in capturing nonlinear material behaviour associated with material microstructures under the HCF loading condition, we established a microstructure-based HCF damage model based on the self-consistent clustering analysis (SCA) [2-3] and the Continuum Damage Mechanics (CDM). As a novel approach derived from machine learning, SCA enables direct modeling of complex material microstructures with much reduced computational cost. Finally, Examples of HCF life prediction are presented to demonstrate the robustness of the proposed multiscale approach.

References

[1]    R. Zhang, S. Naboulsi, T. Eason, and D. Qian. A high-performance multiscale space-time approach to high cycle fatigue simulation based on hybrid CPU/GPU computing. Finite Elements in Analysis & Design, 2019, 166: 103320.

[2]    Z. Liu, M.A. Bessa, and W.K. Liu. Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials. Computer Methods in Applied Mechanics and Engineering, 2016, 306: 319-341.

[3]    C. Yu, O.L. Kafka, W.K. Liu. Self-consistent clustering analysis for multiscale modeling at finite strains. Computer Methods in Applied Mechanics and Engineering, 2019, 349: 339-359.