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The possibility of simulating the interaction of hull, propeller, and engine is gathering the interest of several researches, in view of a more realistic design and control of the whole propulsion chain. The engine performance in rough seas is affected by the dynamics of the hull and consequently by the complex flow regime at the propeller, which induces fluctuating engine torques and revolutions. In a previous research, the case study was limited to straight run simulations in irregular waves for a Ro-pax ferry, including the power delivery contribution by a diesel engine. Herein, the numerical simulation model is further developed for allowing the simulation of the main engine behavior in waves during maneuvering tasks. In order to assess the accuracy and the limits of applicability of the implemented maneuvering model, a benchmark ship is introduced, i.e. the KVLCC2, for which several experimental data are available in the technical literature. The focus on the numerical modeling for the KVLCC2 stays in the implementation of a comprehensive maneuvering model (i.e. including rudder and propeller interactions), capable of simulating the complex dynamics of a ship maneuvering in waves with a fair level of accuracy. The comparisons between simulation and experimental data will disclose the range of applicability and the validity of the numerical models under investigation.

The loadability curves of three different types of single-circuit overhead transmission lines are computed and compared in this paper, for line lengths up to 500 km. The examined solutions include traditional 400 kV ac three-phase lines, innovative 325 kV ac four-phase lines, and monopolar 500 kV HVDC lines with metallic return. The loadability curves are computed accounting for thermal limits, voltage drop, power losses, steady-state and transient stability.

An efficient use of many power systems, from smart grids to mobile devices, requires a careful assessment of the lifetime of the employed batteries. In this paper, a stochastic method for estimating the probability distributions of the battery lifetime is illustrated, which is in particular focused on smart grid applications. The method is based upon a Poisson random process for describing the current load, as motivated by the intermittent supply capability of renewable resources, and uses an advanced battery model, with a proper stochastic input, for deducing the battery lifetime. Extensive numerical experiments performed by means of such battery model and laboratory tests have shown that the best fitting reliability model of a generic battery is the Inverse Gaussian model. This model is thoroughly discussed in the paper and, in order to address the problem of the parameter estimation of such reliability model, a novel practical Bayes approach is proposed, based upon an Inverted Generalized Gamma Distribution. Then, the feasibility and efficiency of such estimation method is illustrated by extensive stochastic simulations. An insight on some key aspects on reliability model identification closes the paper.