The turning circle manoeuvre of a self-propelled tanker like ship model is numerically simulated through the integration of the unsteady Reynolds Averaged Navier-Stokes (URANS) equations coupled with the equations of the motion of a rigid body. The solution is achieved by means of the unsteady RANS solver developed at CNR-INSEAN. The model is considered with two different stern appendages configurations (each one providing a different dynamic behaviour): twin screw with a single rudder and twin screw, twin rudder with a central skeg.

The problem of ship manoeuvrability has reached nowadays a significant consideration, both for merchant ships, with the adoption of IMO standards, and naval ships, with the production of various documents by NATO Specialist Teams. In literature many works regarding manoeuvrability of single screw slow/medium speed ships are present, while a lack of information about twin screw ships (cruise ships, RoRo ferries, megayachts, naval vessels) exists.

We investigate the uniform convergence of Lagrange interpolation at the zeros of the orthogonal polynomials with respect to a Freud-type weight in the presence of constraints. We show that by a simple procedure it is always possible to transform the matrices of these zeros into matrices such that the corresponding Lagrange interpolating polynomial with re- spect to the given constraints well approximates a given function. This procedure was, at ¯rst, successfully introduced for the polynomial inter- polation with constraints on bounded intervals [1].

We investigated a nonlinear advection-diffusion-reaction equation for a passive scalar field. The purpose is to understand how the compressibility can affect the front dynamics and the bulk burning rate. We study two classes of flows: periodic shear flow and cellular flow, analyzing the system by varying the extent of compressibility and the reaction rate. We find that the bulk burning rate vf in a shear flow increases with compressibility intensity ?, following the relation ?vf??2. Furthermore, the faster the reaction is, the more important the difference is with respect to the laminar case.

We investigate the asymptotic optimality of several Bayesian wavelet estimators, namely, posterior mean, posterior median and Bayes Factor, where the prior imposed on wavelet coefficients is a mixture of a mass function at zero and a Gaussian density.

We consider an empirical Bayes approach to standard nonparametric regression estimation using a nonlinear wavelet methodology. Instead of specifying a single prior distribution on the parameter space of wavelet coefficients, that is usually the case in the existing literature, we elicit the $\epsilon$-contamination class of prior distributions that is particularly attractive to work with when one seeks robust priors in Bayesian analysis.

Clustering is one of the most important unsupervised learning problems and it consists of finding a common structure in a collection of unlabeled data. However, due to the ill-posed nature of the problem, different runs of the same clustering algorithm applied to the same data-set usually produce different solutions. In this scenario choosing a single solution is quite arbitrary. On the other hand, in many applications the problem of multiple solutions becomes intractable, hence it is often more desirable to provide a limited group of ''good'' clusterings rather than a single solution.