Abstract
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.
Highlights
The Problem in Propeller DesignSuppliers in the competitive market of ship propellers and propulsion have experienced a change in customers’ awareness for the selection criteria of an order
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 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
Summary
Suppliers in the competitive market of ship propellers and propulsion have experienced a change in customers’ awareness for the selection criteria of an order. For merchant vessels it has predominantly been a question of investment cost to choose which supplier may deliver the propeller. The verification is commonly done by an impartial test facility, at model scale with self-propulsion tests and converted to full-scale according to standard procedures, e.g., the ITTC procedure This competition and evaluation practice initiated a trend of sub-optimising the propeller designs towards efficiency performance and disregarding other performance characteristics [1]. Such practice yield designs that are unsatisfying in operation and may end in failure of the equipment, e.g., due to cavitation erosion
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