Marine propeller optimisation tools for scenario-based design
Doctoral thesis, 2022

The marine propulsion system is one of the most important components of a ship in order to cover the demanding operating needs of propulsion nowadays and to increase performance in a wide range of operating conditions. Marine propellers are designed with the purpose of matching the hull and machinery system, create the required thrust for the entire operational profile, and fulfil the techno-economical requirements that depend on the decision-making of several stakeholders. The final product must represent a unique propeller, designed for a specific vessel, and is a trade-off between all requirements. In an industrial framework, the marine propeller design process should therefore be straightforward and well-developed. The limited time under which the design process must be performed, plays a decisive role in the methods utilised to carry it out, as for example in the selection of the analysis tools, which must be fast and they usually involve semi-empirical evaluations. Since blade design is a multi-objective and multidisciplinary problem, automated optimisation has been used with the aim to search good solutions in the design space efficiently. However, automated optimisation has failed to be used in industrial applications due to obtaining solutions with high performance but with infeasible geometries, and as a method it proved to be inferior to the manual design process, something that shows the importance of the designer's expertise. The main research question of this thesis is therefore related to incorporating optimisation in a systematic way in order to improve the propeller design process and assist the blade designers to obtain feasible and high-performing propellers in strict time constraints. A methodology is proposed that combines interactive optimisation with machine learning and in parallel new objectives are implemented for more complex scenarios. The designer is enabled to manually evaluate cavitation nuisance during the optimisation and guide the algorithm towards areas of the design space with satisfactory cavitation characteristics. Several scenario-based situations have been investigated by using the proposed methodology, that involve different propeller types, design and off-design conditions, several objectives and constraints, cavitation nuisance on the suction and the pressure side of the blade, and applications within conventional and wind propulsion. The results have shown that by involving the blade designer's expertise in the design and optimisation process systematically, competitive propeller designs with feasible geometries can be obtained efficiently.

marine propeller design

machine learning

scenario-based design

wind propulsion

interactive optimisation

user-code interaction

cavitation nuisance

SB3-L111 Sven Hultins Gata 8
Opponent: Tom van Terwisga, TU Delft & MARIN, Netherlands

Author

Ioli Gypa

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Interactive evolutionary computation for propeller design optimization of wind-assisted vessels

AIAA AVIATION 2020 FORUM,;Vol. 1 PartF(2020)p. 1-10

Paper in proceeding

Propeller optimization by interactive genetic algorithms and machine learning

Ship Technology Research,;Vol. 70(2023)p. 56-71

Journal article

Propeller design procedure for a wind-assisted KVLCC2

Book of Abstracts of PRADS 2022!,;(2022)

Other conference contribution

Cavitation nuisance identification through machine learning during propeller optimisation

Proceedings of the seventh International Symposium on Marine Propulsors - smp'22,;(2022)p. 384-391

Paper in proceeding

Subject Categories

Mechanical Engineering

Energy Engineering

Computational Mathematics

Driving Forces

Sustainable development

Areas of Advance

Transport

ISBN

978-91-7905-760-2

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5226

Publisher

Chalmers

SB3-L111 Sven Hultins Gata 8

Online

Opponent: Tom van Terwisga, TU Delft & MARIN, Netherlands

More information

Latest update

11/13/2023