Life Cycle Fatigue Management for High-Speed Vessel Using Bayesian Updating Approaches

Structural fatigue cracking in lightweight high-speed vessel structures is a central maintenance and lifecycle costing concern. While traditional pass-fail approaches provide a simple design oriented metric to limit the amount of fatigue cracking observed in service, these approaches struggle to make accurate mid-life predictions of future fatigue performance and the associated uncertainties and risks. A stochastic method of modeling crack growth and fatigue life prediction is proposed based on dynamic Bayesian networks. This is a graphical model represented by sequences of random variables with defined conditional independences between these variables. The aim is not only developing a computationally efficient and robust fatigue life prediction model, but also to incorporate the life cycle monitoring results to determine as-built fatigue properties of vessels via a Bayesian updating approach. A robust discretization technique is also studied to facilitate determining specific reliability levels. The model is then extended to consider variable amplitude loading with a Markov chain Monte Carlo load updating strategy. By sampling from the posterior load distribution data at available sea states, the uncertainties of engineering model used at the initial design stage can be identified and corrected. Thus, a more accurate load prediction integrated with through life information updating is obtained. The proposed framework is then further extended by utilizing simulation data for a specific structural detail generated by using extended finite element method (XFEM). The core idea behind XFEM is to generate the mesh independent of discontinuity domains which makes cycle-by-cycle fatigue cracking simulation possible. The example case study addresses a stiffened panel from joint high-speed sealift (JHSS) with load information simulated by the Large Amplitude Motion Program (LAMP). The results show that both load updating and crack inspection updating are necessary for accurate reliability estimation.