Development and application of optimisation algorithms for propeller design

Vesting, Florian; Gustafsson, Robert; Bensow, Rickard

doi:10.1080/09377255.2016.1145916

Cited by 27 publications

(11 citation statements)

References 15 publications

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“…We adapted common optimisation algorithms (NSGA-II and PSO) to three algorithms for propeller design and implemented and applied six optimisation strategies [13] which are in this paper introduced to a broader range of propeller types. The algorithms are implemented in the design toolbox at Rolls-Royce Hydrodynamic Research Centre and thereby have access to all analysis tools that are part of the manual design process.…”

Section: Automated Propeller Design Proceduresmentioning

confidence: 99%

“…Hence, our PSO is capable of multiple objectives and non-dominated sorting and utilises an archive of best solutions which continuously updates the rank and crowding distance, with respect to the latest swarm. A detailed explanation of the algorithms is presented by Vesting et al [13].…”

Section: Automated Propeller Design Proceduresmentioning

confidence: 99%

“…In particular, the constraints target the cavity sheet thickness at the blade tip, the maximal cavitation volume as well as its length in chord-wise direction and the change in cavity volume between two consecutive time steps. We have additionally developed new constraints which rate the shape of the cavity and assign a harm-factor according to the similarity of the cavity closure line to a convex closure line and the location of the maximum sectional cavity centroid (e.g., harmful at the tip, root and downstream in chord-wise direction) [13]. We summarize the cavitation constraints with a brief description in Table 1.…”

Section: Automated Propeller Design Proceduresmentioning

confidence: 99%

“…The assessment is repeated with each constraint and eventually the constraint measure is accumulated in the Constraint Violation Measure (CVM) which determines the feasibility of a solution. A solution can thereby be feasible to the algorithm even if one or more constraints are marginal violated [13]. A designer judges a design differently on the level of violation and rates constraints unequal and is possibly interested in designs that perform excellent although they might be marginally infeasible, to find the next design step.…”

Section: Clengthmentioning

confidence: 99%

“…We have developed and applied new constraints for sheet cavitation, based on potential flow analysis and two novel optimisation algorithms based on the NSGA-II and the standard particle swarm optimisation (PSO) algorithm [13]. We proposed, in addition, a constraint handling method for infeasibility handling, which is adapted to manual propeller design.…”

Section: Introduction-the Problem In Propeller Designmentioning

confidence: 99%

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Procedure for Application-Oriented Optimisation of Marine Propellers

Vesting

Bensow

Johansson

et al. 2016

JMSE

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The use of automated optimisation in engineering applications is emerging. In particular, nature inspired algorithms are frequently used because of their variability and robust application in constraints and multi-objective optimisation problems. The purpose of this paper is the comparison of four different algorithms and several optimisation strategies on a set of seven test propellers in realistic industrial design setting. The propellers are picked from real commercial projects and the manual final designs were delivered to customers. The different approaches are evaluated and final results of the automated optimisation toolbox are compared with designs generated in a manual design process. We identify a two-stage optimisation for marine propellers, where the geometry is first modified by parametrised geometry distribution curves to gather knowledge of the test case. Here we vary the optimisation strategy in terms of applied algorithms, constraints and objectives. A second supporting optimisation aims to improve the design by locally changing the geometry, based on the results of the first optimisation. The optimisation algorithms and strategies yield propeller designs that are comparable to the manually designed propeller blade geometries, thus being suitable as robust and advanced design support tools. The supporting optimisation, with local modification of the blade geometry and the proposed cavity shape constraints, features particular good performance in modifying cavitation on the blade and is, with the AS NSGA-II (adaptive surrogate-assisted NSGA-II), superior in lead time.

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Section: Automated Propeller Design Proceduresmentioning

confidence: 99%

Section: Automated Propeller Design Proceduresmentioning

confidence: 99%

Section: Automated Propeller Design Proceduresmentioning

confidence: 99%

Section: Clengthmentioning

confidence: 99%

Section: Introduction-the Problem In Propeller Designmentioning

confidence: 99%

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