Stochastic identification of the structural damage condition of a ship bow section under model uncertainty

• Geometric imperfection parameters are estimated in a fluid–structure interaction system.
• Inverse problem solved using Reversible Jump Markov Chain Monte Carlo sampling.
• Non-contact approach to damage identification of a representative ship bow structure is explored.
• Uncertainty surrounding the parameters is quantified.

A accurate, quantifiable means of assessing structural damage condition are paramount for maintaining the structural integrity of ship hull forms. Toward this end, precise knowledge of the location and magnitude of any imperfections (i.e. geometric imperfections in the form of denting and corrosion patches) must be determined, along with concomitant uncertainties accompanying such predictions. The current paper describes a non-contact approach to identifying and characterizing such imperfections within the submerged bow section of a representative ship hull. By monitoring the pressure field local to the acoustically excited hull section, it is shown how the resulting data can be used to identify the parameters describing the structural damage field. In order to perform the identification, a fluid–structure model that predicts the spatio-temporal pressure field is required. A Bayesian, reversible jump Markov chain Monte Carlo approach is then used to generate the imperfection parameter estimates and quantify the uncertainty in those estimates. This approach is particularly appealing as it does not allow for the damage model to be explicitly known a priori. Convergence of the Markov chains is assessed, and estimates of the Monte Carlo standard error (MCSE) are provided.