On non-parametric fatigue optimization

The present work presents a novel approach for semi-analytic adjoint sensitivity-based design optimization for nonproportional fatigue damage. In order to apply fatigue damage in sensitivity-based design optimizations, an essential part is to calculate correct sensitivities. However, this is not straight forward since fatigue damage calculation typically include rainflow counting and critical plane search algorithms. Therefore, no derivatives are directly available for the fatigue damage calculation, only functional values given by numerical computation. In existing literature the considered fatigue damage calculation is simplified until a closed-form differentiability is satisfied. However, these simplifications are not applicable for industrial examples where accurate fatigue life estimates are required. In the present work numerical differentiation of the fatigue damage values with respect to the stress tensor is applied to calculate semi-analytical adjoint sensitivities at material points for multiple load cases. The proposed method is verified and demonstrated using different damage parameter types including critical plane analysis. Additionally, different academic and industrial numerical examples are compared to stress and stiffness optimized designs. The fatigue damage optimized designs show improved fatigue damage results for both the specific damage parameter types and when comparing to stress and stiffness optimized designs. Furthermore, it is successfully applied for different design variables (sizing, nonparametric shape and bead) as well as different optimization formulations using fatigue damage either as objective or constraint.

Subjects

critical planes

damage parameters

fatigue damage

nonparametric optimization

rainflow counting

semi-analytical adjoint sensitivities

DDC Class

600: Technik

620: Ingenieurwissenschaften

Funding(s)

Projekt DEAL

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by-nc/4.0/

Name

Numerical Meth Engineering - 2022 - Sartorti - On non‐parametric fatigue optimization.pdf

Size

8.45 MB

Format

Adobe PDF

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On non-parametric fatigue optimization