CFD data based neural network functions for predicting hydrodynamic performance of a low-pitch marine cycloidal propeller. (January 2020) - Bakhtiari, Mohammad; Ghassemi, Hassan
Highlights: A CFD model is presented for analyzing the flow around a low-pitch marine cycloidal propeller (MCP), and it is validated against experimental data. The open water hydrodynamic coefficients of low-pitch MCP are calculated for different geometries and operating conditions of MCP by the CFD model. Neural network models are trained by CFD data of hydrodynamic coefficients to extract predictive functions of hydrodynamic performance. Two mathematical functions are presented for predicting open water hydrodynamic performance of a low-pitch MCP. Abstract: Today, various types of propulsion systems are used in different purpose ship types. Marine cycloidal propeller (MCP) is one of these propulsion systems, which has been designed for ships that require high maneuverability. MCP can be considered as an especial type of marine propulsion systems, since it produces the thrust force which is perpendicular to propeller axis of rotation. The magnitude and direction of the thrust force can be adjusted by controlling the pitching angle of the blades, so no separate rudder is needed to manoeuvre the ship. In this study, mathematical functions for predicting the open water hydrodynamic performance of a low-pitch MCP are presented by training a neural network based on computational fluid dynamics (CFD) data. For this purpose, the four nondimensional parameters of blade number (Z), ratio of blade thickness to MCP diameter ( t / D ), pitch ( e ) and advance coefficient ( λ ) are considered as input variables, whereas the hydrodynamic coefficients of thrust ( Ks ) and torque ( Kd ) are considered as targets. CFD simulations are performed for different cases of MCP with different combinations of Z, t / D, e and λ . The results showed that a two-layer feedforward network with one hidden layer of sigmoid neurons and at least 4 neurons in the hidden layer can be well trained by CFD data in order to obtain functions with good accuracy in predicting Ks and Kd coefficients of a low-pitch MCP.