Marine Propeller Optimisation - Strategy and Algorithm Development
Doctoral thesis, 2015
Recent trends in the shipping industry, e.g., expanded routing in ecologically sensitive areas and emission regulations, have sharpened the perception of efficient propeller designs. Currently, propeller efficiency, estimated fuel consumption and, more often, propeller-radiated noise are parameters that steer the business Zeitgeist. However, a practical propeller design that performs reliably and sufficiently throughout the lifetime of a ship requires numerous limitations, which are typically in conflict with the objectives. This requires judgement by experienced propeller designers to make decisions during the design process. To be ahead of competitors, a propeller designer needs to present a better design for a specific purpose, in a shorter time and at lower costs than the adversary. The current challenge for propeller designers is to develop a propeller that fulfils all the requirements and expectations within a short time frame.
The increasing interest in designing the optimal propeller shape is the motivation for this thesis, whose purpose is to further improve the state-of-the-art of the propeller design procedure by means of supplementing the propeller designer with automated optimisation. The art of designing a propeller, with the multi-disciplinary evaluation and consideration of numerous limitations, yields a systematic investigation of the design space, which is due to the generally limited time. Automated optimisation can fill the design space with numerous designs that gravitate, guided by the optimisation algorithm, towards an optimal design. This thesis therefore examines two tracks: i) the development of strategies and concepts for propeller optimisation, with the objective of developing optimisation algorithms that enhance the convergence and consideration of constraints, and ii) the extension and exploration of constraints that are adapted to the principles and design considerations of a typical manual design procedure.
Throughout this thesis, automated propeller design is improved. Population-based optimisation algorithms, design strategies and constraints, which automatically judge the cavitation on the propeller, are further developed. Additional constraints and limitations are added to the optimisation procedure, which are often neglected in study cases but which commonly have to be considered by designers. The algorithms and constraints are implemented in the designer's toolbox for computer-aided propeller design and can be used on an everyday design task with access to all the analysis tools available to the designer.
artificial neural network