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In the present work, an impressed current cathodic protection (ICCP) system for the protection against corrosion of a 399-m-length container ship throughout its service life is designed. The study is carried out with the aid of a boundary element method code, accelerated by an adaptive cross approximation scheme, utilizing a detailed large-scale model. The exact geometry of the ship, the progressive damage of the coating system, and the dynamic state during the cruise of the ship are the main parameters taken into consideration in modelling. The main objective of the design process is to minimize the electric power, delivered by the ICCP system, determining the optimal number and location of the installed inert anodes to accomplish the absolute minimum protection potential on the immersed steel surfaces of the ship and, simultaneously, avoid overprotection. Performing an extensive parametric study, a six-zone ICCP system is proposed, consisting of 10 anodes at the hull and four identical anodes at each of four thrusters.

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In the current work, the mechanical response of multiscale cellular materials with hollow variable-section inner elements is analyzed, combining experimental, numerical and machine learning techniques. At first, the effect of multiscale designs on the macroscale material attributes is quantified as a function of their inner structure. To that scope, analytical, closed-form expressions for the axial and bending inner element-scale stiffness are elaborated. The multiscale metamaterial performance is numerically probed for variable-section, multiscale honeycomb, square and re-entrant star-shaped lattice architectures. It is observed that a substantial normal, bulk and shear specific stiffness increase can be achieved, which differs depending on the upper-scale lattice pattern. Subsequently, extended mechanical datasets are created for the training of machine learning models of the metamaterial performance. Thereupon, neural network (NN) architectures and modeling parameters that can robustly capture the multiscale material response are identified. It is demonstrated that rather low-numerical-cost NN models can assess the complete set of elastic properties with substantial accuracy, providing a direct link between the underlying design parameters and the macroscale metamaterial performance. Moreover, inverse, multi-objective engineering tasks become feasible. It is shown that unified machine-learning-based representation allows for the inverse identification of the inner multiscale structural topology and base material parameters that optimally meet multiple macroscale performance objectives, coupling the NN metamaterial models with genetic algorithm-based optimization schemes.