A Numerical Study on the Influence of Variations in Poisson’s Ratio, Bulk Modulus, and Shear Modulus on the Fatigue Life in Structural Components

Predicting the fatigue lives of high-performance alloys, specifically aluminum 7075-T6 and Inconel 718, is essential for ensuring structural integrity in applications such as aerospace and energy. While Poisson’s ratio is typically treated as a constant within fracture mechanics and finite element analysis, it has been found to vary significantly with increased temperatures and substantial amounts of plastic deformation. Variations in Poisson’s ratio can, therefore, have a significant impact on local stress fields around cracks and the behavior at crack tips. This study introduces a novel approach by systematically isolating the effects of varying Poisson’s ratios on fatigue life cycles, stress distributions, and fatigue crack growth using finite element analysis with the robust ANSYS SMART crack growth feature. The results indicate a stark difference in the effects of Poisson’s ratio on the fatigue life of aluminum 7075-T6 compared to Inconel 718. A strong negative correlation exists between Poisson’s ratio and fatigue life cycle numbers for aluminum 7075-T6, whereas a more linear trend is observed for all fatigue life cycle numbers of Inconel 718. The underlying reasons for these trends lie in the differing sensitivities of elastic, shear, and bulk moduli between the two alloys. Overall, a higher Poisson’s ratio intensifies the maximum principal stress for both alloys. Additionally, an increase in Poisson’s ratio leads to a decrease in von Mises stress for both metals. Furthermore, these numerical results demonstrate that an increase in Poisson’s ratio corresponds to a decrease in the cyclic plastic zone size at the crack tip for both alloys, indicating enhanced hydrostatic constraint and reduced shear deformation. The findings presented herein underscore the necessity of eliminating the use of static values for Poisson’s ratio when evaluating the structural performance of high-performance alloys under extreme operational environments. Additionally, this research highlights several key areas where existing modeling approaches are lacking and establishes a framework for developing improved constitutive models for fatigue life prediction.