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CFD data based neural network functions for predicting hydrodynamic performance of a low-pitch marine cycloidal propeller
توابع شبکه عصبی مبتنی بر داده های CFD برای پیش بینی عملکرد هیدرودینامیکی یک پروانه سیکلوئید دریایی کم فشار-2020 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. Keywords: Marine cycloidal propeller | Hydrodynamic performance | CFD simulation | Neural network | Predictive function |
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