Abstract
This paper proposes a strategy to achieve robust optimization of structures against high-cycle fatigue when a potentially large number of uncertain load cases are considered. The strategy is heavily based on a convexity property of some of the most commonly used high-cycle design criteria. The convexity property is rigorously proven for the Crossland fatigue criterion. The proof uses a perturbation technique and involves the principal stress components and analytical expressions for the applicable fatigue criteria. The multiplicity of load cases is treated using load ratios which are bounded but are otherwise free to vary within certain limits. The strategy is applied to a notched plate subject to traditional normal and shear loadings that possess uncertain or unspecified components.
Highlights
Structural design does not take into account fatigue life issues
Structures break down even when admissible loads, determined by the design criteria above, are applied. It occurs after a long time the structure becomes operational and the cause of failure is the end of the component fatigue life
The normalized maximum critical fatigue stress σ∗ assumes values of up to 15.7 which is way beyond the fatigue threshold b. 6 CONCLUSIONS Robust optimal designs may be heavier than designs optimized against one individual load case but they are definitely safer
Summary
Structural design does not take into account fatigue life issues. Engineers conceive projects based on parameters such as stress limits, maximum displacements, fundamental frequency and buckling. Steenackers et al [13] integrated an optimization method with a robustness strategy applied to the slat track (airplane component) finite element model taking into account the uncertainty of design parameters. They concluded that in order to obtain accurate results, an extensive computation time is necessary to run a large number of Monte Carlo simulations in combination with FE models. For the purpose of saving computational time, removing sensitivity analysis of design variables and performing a minimum number of finite element analyses as a possible, the optimization strategy is based on extreme modeling which adds robustness to the optimal design. A numerical example was solved applying the proposed robust optimization technique and the results were discussed
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