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

Gregory J Grigoropoulos,George Papadakis,Dimitrios Ntouras,Christos Bakirtzoglou

doi:10.3390/jmse9111234

Gregory J Grigoropoulos, George Papadakis + Show 2 more

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https://doi.org/10.3390/jmse9111234

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Abstract

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

Highlights

Hydrodynamic hull form optimization is a very demanding task in terms of computer and time resources
Even in the single objective case, where only calm water resistance is handled, the use of fine grids for CFD evaluation is too time-consuming to be used in all steps of optimization via a Genetic Algorithm (GA)
This paper proposes a mixed methodology to optimize the hull form for resistance in calm water, using both potential and viscous flow codes, both with grids of suitable density utilizing a limited number of viscous evaluations carried a priori to formal optimization

Summary

Introduction

Hydrodynamic hull form optimization is a very demanding task in terms of computer and time resources. Even in the single objective case, where only calm water resistance is handled, the use of fine grids for CFD evaluation is too time-consuming to be used in all steps of optimization via a Genetic Algorithm (GA). In this respect, metamodels or surrogate models have been widely used in several engineering contexts, such as structural optimization, aeronautics, aerospace and ground or waterborne vehicles, including stochastic applications and uncertainty quantification. Typical surrogate models are polynomial regression, kriging method, artificial neural networks (ANN), support vector machines (SVM), or radial basis functions (RBF) [4,5,6]

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