1. Automated Marine Propeller Optimal Design Combining Hydrodynamics Models and Neural Networks
- Author
-
Calcagni Danilo, Bernardini Giovanni, Salvatore Francesco, Calcagni, D, Bernardini, Giovanni, and Salvatore, F.
- Subjects
Naval hydrodynamics - boundary element method ,ducted propellers ,numerical optimization - genetic algorithms ,parametric mode ,regression model - neural networks - Abstract
In the present paper, a computationally efficient methodology to develop fast and reliable propeller selection procedures based on a fully automated optimization technique is described. To this aim, a comprehensive propeller hydrodynamics model is combined with performance prediction acceleration techniques based on Neural Networks. Under given operating conditions, screw characteristics and blade shape details are optimized around a baseline configuration via general-purpose numerical optimization software based on genetic algorithms and via a parametric model. Numerical applications concern the propulsion retrofitting of marine vessels. A off-design performance verification study is presented to evaluate the robustness of the identified optimal configurations.
- Published
- 2012