Hydrodynamic ship design optimization considering uncertainty

The development of advanced and efficient tools for uncertainty quantification and design optimization under uncertainty of ships operating in a real scenario are described. Under the assumption that objective functions and constraints are evaluated via high-fidelity, computationally expensive, unsteady Reynolds averaged Navier-Stokes equations solvers (URANSE), the complexity of the task - compared to deterministic approaches - requires a significant mathematical reformulation of the optimization problem and of the solution methods. To afford the cost of the stochastic optimization process, a number of advancements have been developed by the authors and their co-workers: (i) dynamic metamodels for the high-fidelity solvers and associated uncertainty quantification of stochastic simulation outputs; (ii) progress in evolutionary type derivative-free algorithms for global optimization; (iii) a new application of the Karhunen-Loève Expansion (KLE) method to - a priori - identify reduced dimensionality representations of large-scale design spaces, truncating basis functions (i.e. design variables) with small significance to the solution. An example of a ship hydrodynamic design optimization in real seas is finally presented.