The underwater electric potential (UEP) signature is an electric signal, which can be exploited by naval mines to be utilized as a possible trigger indicator and may cause severe damage to the vessel and the onboard crew. Hence, knowing the UEP signature as exactly as possible can help to evaluate a possible risk of the vessel being detected by naval mines or if the UEP signature is within a noncritical region. As the UEP signature differs for changes of the corrosion protection system, the UEP signature is usually unknown for new conditions. In this work, we present a simple mathematical formulation to predict the UEP signature based on the mere use of a single reference UEP signature, and the corresponding currents, which are excited by the impressed current cathodic protection (ICCP) system. With this methodology, deviations below 10% between the maximum of the simulated UEP signature and the predicted UEP signature can be achieved, even in the presence of the nonlinear corrosion process. Furthermore, a corrosion protective coating of the propellers can significantly reduce the influence of the nonlinear corrosion process on the total UEP signature to improve the prediction accuracy of the superposition formulation as presented in this work.

No abstract

The evaluation of the hull condition of naval vessels is a crucial part for corrosion protection systems due to the direct linkage between the electrochemical process at the hull/water interface leading to corrosion and the overall coating of the hull to prevent the corrosion process. In the case of the latter, the condition is unknown while the vessel is on a mission and either has to be evaluated by divers (in open water) or on dry docks which is a time consuming process, respectively. In our work, we present a methodology to localize coating damages without the need of divers or dry docks using an artificial neural network (ANN) combined with the information provided by the onboard impressed current cathodic protection (ICCP) system to predict said damages in a specific sector of a generic ship model. Using only the ICCP currents as highly aggregated input variables for the ANN, approximately 86 % of randomly sized and positioned coating damages are correctly predicted.