On Surrogate Methods in Propeller Optimisation
Journal article, 2014

In marine propeller design, tools for propeller performance evaluation are often time consuming and automated optimisation of the blade geometry is thus not conducted. This paper discusses several response surface methods to replace the main part of the needed computations: the Kriging predictor, standard and with input improvement; the feed forward neural network; the cascade correlation neural network; and a mixed version. Optimisation assignments are performed by applying each of the surrogates to find the best solution in a multi-objective propeller design task including advanced constraints on cavitation. The final performance regarding geometry trends and degree of improvement are evaluated. Further, an approach is presented on a practical application of minimum computational effort by combining a response surface method to fill the design space and calculations in a local search method.

Constraint optimisation

Kriging predictor

Multi-objective propeller design

Response surface method

Neural network

Cavitation

Author

Florian Vesting

Chalmers, Shipping and Marine Technology, Division of Marine Design

Rickard Bensow

Chalmers, Shipping and Marine Technology, Division of Marine Design

Ocean Engineering

0029-8018 (ISSN)

Vol. 88 0 214-227

Driving Forces

Sustainable development

Areas of Advance

Transport

Subject Categories

Other Civil Engineering

DOI

10.1016/j.oceaneng.2014.06.024