This work presents a simulation-based design environment for the solution of optimum ship design problems based on a global optimization (GO) algorithm that prevents the optimizer from being trapped into local minima. The procedure, illustrated in the framework of multiobjective optimization problems, makes use of high-fidelity, CPU-time-expensive computational models, including a free surface-capturing Reynolds-averaged Navier Stokes equation (RANSE) solver. The optimization process is composed of a global and a local phase. In the global stage of the search, a few computationally expensive simulations are needed for creating analytical approximations(i.e., surrogate models) of the objective functions. Tentative designs, created to explore the design space, are then evaluated with these inexpensive approximations. The more promising designs are then clustered and locally minimized and eventually verified with high-fidelity simulations. New exact values are used to improve the surrogate models, and repeated cycles of the algorithm are performed. A decision maker strategy is finally adopted to select the more interesting solution, and a final local refinement stage is performed by a gradient-based local optimization technique. A key point in the algorithm is the introduction of the surrogate models for the reduction of the overall time needed for the objective functions evaluation and their dynamic evolution and refinement along the optimization process. Moreover, an attractive alternative to adjoint formulations, the approximation management framework (AMF), based on a combined strategy that joins variable fidelity models and trust region techniques, is tested. Numerical examples are given demonstrating both the validity and usefulness of the proposed approach.
Visualization Performance Through Simulation Based Learning
