Abstract:

High cycle fatigue (HCF) is the dominant failure mechanism of many engineering applications. For fatigue life predictions safe-life and damage-tolerance approaches have been used extensively, however, they are limited due to the empirical nature. These limitations can be addressed by performing direct numerical simulation of the fatigue loading history using computational methods such as the finite element method (FEM). The time scale associated with the fatigue problem especially in high cycle fatigue (HCF) applications is the main challenge for such a simulation. Semi-discrete methods, currently used for simulating the structural response under dynamic conditions have time-step limitations making HCF simulation an elusive task. In this work, I will present a multiscale HCF simulation approach called the extended space-time finite element method (XTFEM) that was developed in my group. 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 the 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 behavior associated with material microstructures under the HCF loading condition, we established a microstructure-based HCF damage model based on machine learning [2-3] and the Continuum Damage Mechanics (CDM). This implementation enables the 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.

Bio:

Dr. Dong Qian is professor and associate department head of mechanical engineering at the University of Texas at Dallas. He received his B.S. degree in Bridge Engineering from Tongji University in China in 1994, his M.S. degree in Civil Engineering from the University of Missouri in 1998 and Ph.D. degree in Mechanical Engineering from Northwestern University in 2002.  Shortly after his graduation, He was hired as an assistant professor of mechanical engineering at the University of Cincinnati and promoted to associate professor with tenure in 2008.   In the Fall of 2012, he joined the newly established Mechanical Engineering Department as a tenured associate professor at the University of Texas at Dallas and was promoted to Full Professor in 2015. Dr. Qian has conducted research and published extensively in the general areas of computational mechanics of materials.  He served as an assistant editor for the Journal of Computational Mechanics and is an associate editor for the journal of Computer Modeling in Engineering and Sciences. 

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