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Resistance and Seakeeping Optimization of a Naval Destroyer by Multi-Fidelity Methods

AuthorsSerani, A., Ficini, S., Broglia, R., Diez, M., Grigoropoulos, G., Bakirtzoglou, C., Papdakis, G., Goren, O., Danisman, D. B., Solak, H. P., Scholcz, T., Klinkenberg, J.
Conference/JournalVarna, Bulgaria/Research Workshop AVT-354 on Multi-fidelity methods for military design
DateSep 26, 2022
The paper presents and discusses the so-called L3 sea benchmark problems used to develop and assess multi-fidelity optimization methods for military vehicle design within the AVT-331 Research Task Group on “Goal-driven,
multi-fidelity approaches for military vehicle system-level design.” The hull-form design (bare hull) of the DTMB 5415 model (an open-to-public concept used in the development of the DDG-51, the lead vessel of the Arleigh Burke-class guided missile destroyers) is addressed for optimal resistance and seakeeping performance in calm water, regular/irregular waves, and formulated as a global optimization problem. The design performance is assessed using a variety of physical models and solvers (from Reynolds-Averaged Navier Stokes equations to potential flow models), and spatial discretizations, which are combined together in dedicated multi-fidelity frameworks developed by the AVT-331 Sea Team. Benchmark problems description, design parameterization methods, physical models and solvers are presented and discussed, as well as multi-fidelity approaches and example results. The present effort highlights how the dimensionality of the optimization problem may be a critical issue for the surrogate model training. Nevertheless, it is shown how the proposed multi-fidelity approaches are able to achieve significant design performance improvements, even if only a few high-fidelity computations are used, with a ratio between the number of high- and low-fidelity evaluations required to solve the global optimization problem as low as nearly 1/50. Finally, the challenges arisen during the process are discussed and future research directions outlined.

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data sciencecfdnavydefenceresistance and propulsion