A MULTI-OBJECTIVE APPROACH FOR DETERMINING THE NUMBER OF BLADES ON A NACA MARINE PROPELLER

This paper numerically discusses how the performance of a NACA marine propeller is affected by the number of blades, which is one of the most crucial geometrical parameters determining the performance of a propeller. Results are presented in terms of the hydrodynamic and structural parameters. The results show that changing the number of blades changes the hydrodynamic efficiency, torque, thrust, cavitation behaviour and structural stiffness of the propeller nonlinearly. Furthermore, it is shown that the propellers structural lifetime is shortened by increasing the number of blades. Hence, the propeller’s number of blades is a multi-objective function and will be discussed in this research. The applied tool which is used to study the hydrodynamic performance of the propellers is a RANS-based CFD one and the FEM is considered to study the structural behaviour of the propellers

Developing general acoustic model for noise sources and parameters estimation

Noise measured at various points around the environment can be evaluated by a series of acoustic sources. Acoustic sources with wide surface can be broken down in fluid environment using some smaller acoustic sources. The aim of this study is to make a model to indicate the type, number, direction, position and strength of these sources in a way that the main sound and the sound of equivalent sources match together in an acceptable way. When position and direction of the source is given, the strength of the source can be found using inverse method. On the other hand, considering the non-uniqueness of solution in inverse method, a different acoustic strength is obtained for the sources if different positions are selected. Selecting an arrangement of general source and using the optimization algorithm, the least possible mismatch between the main sound and the sound of equivalent sources can be achieved

Position parameters optimization of surface piercing propeller by artificial neural network

Abstract Improving the performance of surface-piercing propellers is achieved by investigating the influential factors. In this study, Artificial Neural Network is used to identify nonlinear models for estimating various phenomena. Non-Dominated Sorting Genetic Algorithm II is considered as an optimization tool. In this study, in order to optimize the position parameters, including the immersion ratio, angle of attack, and yaw angle, data from experimental tests at the HYDROTECH center of IUST were collected as the initial data field for the generation of training data by the artificial neural network, then experimental tests were implemented in the position of the Non-Dominated Sorting Genetic Algorithm II proposed as the output, and the results were compared. The Artificial Neural Network results showed that the mean error of the trained verified and test data is 7.5e−5, 1e−4, and 1e−4, respectively. Comparing the experimental and optimization results, the thrust coefficient showed a relative error of 9.7%, while the torque coefficient showed a relative error of 7.5%, this algorithm can be used as a cost-effective, time-saving method for a similar problem