Procedure for Application-Oriented Optimisation of Marine Propellers

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.

Design Optimization of Marine Propeller using Elitist Particle Swarm Intelligence

“…Vesting et al conducted a detailed study of two propellers to improve their performance using PSO. The main constraint chosen in the design is cavitation nuisance [36]. Three PSO algorithms have been developed and applied on four commercial propellers.…”

“…Updated value is attained by the particle based on the local best, it has achieved in the previous iterations and global best value is the best value attained by the whole swarm. Velocity and position in design space are given in [36] as;…”

Maritime Energy Efficiency in a Sociotechnical System: A Collaborative Learning Synergy via Mediating Technologies

“…Kataria, Holder [10] suggested that technology mediated collaboration is very important for interdepartmental interactions, such as using shared displays to create better situational awareness. In today's context of EE, technologies have been mainly exploited in the manner of sensing complex environmental factors contributing to EE and automatically manipulating operational configurations for optimized performance [49][50][51]. They are not necessarily designed in a way to support the practitioners' awareness and assessment of EE performance or collaborative learning activities, which are considered crucial in energy saving activities [42,43,52].…”