Automated Marine Propeller Design Combining Hydrodynamics Models and Neural Networks

An automated computational methodology for the preliminary design of marine propellers is presented. A neural network architecture is developed to describe the performance of propeller models from a virtually-generated systematic series. Propeller performance predictions are based on a propeller hydrodynamics model based on a inviscid-flow model. Next, propeller geometry optimization is investigated through a Genetic Algorithm formulation. The structure of the resulting propeller design environment is illustrated and Neural Network, optimization models are validated through comparisons with existing experimental data and results from alternative approaches. Numerical applicatuions to a notional dessign exercise addressing a propeller retrofit study for an aged fishing vessel are also presented.