Mixed-Fidelity Design Optimization of Hull Form Using CFD and Potential Flow Solvers

نویسندگان

چکیده

The present paper proposes a new mixed-fidelity method to optimize the shape of ships using genetic algorithms (GA) and potential flow codes evaluate hydrodynamics variant hull forms, enhanced by surrogate model based on an Artificial Neural Network (ANN) account for viscous effects. performance forms generated GA is evaluated calm water resistance methods which are quite fast when they run modern computers. However, these do not take into effects dominant in stern region ship. Solvers Reynolds-Averaged Navier-Stokes Equations (RANS) should be used this respect, which, however, too time-consuming evaluation some hundreds variants within search. In study, RANS solver prior execution train ANN modeling effect design geometrical parameters only. Potential results, accounting rest hull, combined with aforementioned trained meta-model final form evaluation. This work concentrates provision more reliable framework without significant increase computing time.

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ژورنال

عنوان ژورنال: Journal of Marine Science and Engineering

سال: 2021

ISSN: ['2077-1312']

DOI: https://doi.org/10.3390/jmse9111234