Safety-critical components, such as aircraft landing gear, are designed using the 'safelife' fatigue analysis process. Variability exists within materials data, loads data and component dimensions and is currently mitigated using safety factors. Probabilistic approaches to safe-life fatigue design have been proposed to better represent this variability. However, challenges currently exist that prevent the wider utilisation of a probabilistic approach. This paper presents a framework that aims to overcome these challenges. The statistical characterisation, probabilistic, surrogate modelling and sensitivity analysis methods required to implement the framework are introduced. Finally, a discussion of how recent advances within aerospace fatigue design, such as 'big-data', can be used to support a probabilistic framework is presented.
The variability present in S-N datasets is typically characterised using probability distributions to enable the construction of Probability-S-N curves for design. 3-Parameter Log-Normal and Weibull distributions have been proposed as alternative distributions to the commonly assumed 2-Parameter Log-Normal distribution. This paper performs statistical characterisation of a 4340 steel S-N dataset from the Engineering Sciences Data Unit using a systematic methodology. The 3-Parameter Weibull distribution provided improved characterisation of the S-N dataset. Using a case study, it was also demonstrated that use of a 3-Parameter Weibull distribution can increase component safe-life values by 20% when compared to the 2-Parameter Log-Normal distribution.
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