Propeller optimization by interactive genetic algorithms and machine learning
Journal article, 2021

Marine propeller design can be carried out with the aid of automated optimization, but experience shows that a such an approach has still been inferior to manual design in industrial scenarios. In this study, the automated propeller design optimization is evolved by integrating human–computer interaction as an intermediate step. An interactive optimization methodology, based on interactive genetic algorithms (IGAs), has been developed, where the blade designers systematically guide a genetic algorithm towards the objectives. The designers visualize and assess the shape of the blade cavitation and this evaluation is integrated in the optimization method. The IGA is further integrated with a support-vector machine model, in order to avoid user fatigue, IGA's main disadvantage. The results of the present study show that the IGA optimization searches solutions in a more targeted manner and eventually finds more non-dominated feasible designs that also show a good cavitation behaviour in agreement with designer preference.

cavitation constraints

NSGA-II

interactive genetic algorithms

machine learning

support-vector machines

optimization

marine propeller design

Author

Ioli Gypa

Chalmers, Mechanics and Maritime Sciences, Marine Technology

Marcus Jansson

Kongsberg Maritime

Krister Wolff

Chalmers, Mechanics and Maritime Sciences, Vehicle Engineering and Autonomous Systems

Rickard Bensow

Chalmers, Mechanics and Maritime Sciences, Marine Technology

Ship Technology Research

0937-7255 (ISSN)

Vol. In press

SAILPROP – also sailing mechant vessels need an efficient propeller

Swedish Transport Administration, 2020-01-01 -- 2021-12-31.

Driving Forces

Sustainable development

Areas of Advance

Transport

Energy

Subject Categories

Interaction Technologies

Human Computer Interaction

Fluid Mechanics and Acoustics

Marine Engineering

DOI

10.1080/09377255.2021.1973264

More information

Latest update

9/16/2021