Marine propeller design using artificial neural networks

Abstract

The proper selection of marine propellers is in general an involved and time-consuming design task. It is mostly based on searching large mounts of experimental data in order to find a proper matching of performance requirements and dimensional constraints. The present work has been done in order to improve the task of propeller design using techniques from the field of computational intelligence. An important requirement of the task was to help a designer to reach an acceptably good design in a fast and simple manner in which the most readily available propeller data are used as raw inputs. A neural network system has been developed that can help a naval architectural designer to select a suitable marine propeller that satisfies desired propulsion requirements. Different neural network architectures and learning parameters were tested, aiming at establishing a near optimum setup. To achieve this, a large number of experimental data was used. The end result in the network output is a set of suitable dimensional characteristics and a desired performance.