Strecker, MaikeMaikeStreckerScharf, MartinMartinScharfAbdel-Maksoud, MoustafaMoustafaAbdel-Maksoud2024-04-122024-04-122024-04-048th International Symposium on Marine Propulsors (smp 2024)978-82-691120-5-4https://hdl.handle.net/11420/46450While powerful and reliable CFD methods are available today, designing propellers using them is unfortunately timeconsuming and cost-intensive. On the contrary, existing CFD results for the hydrodynamic behaviour of propellers can be stored and processed with neural networks. This paper deals with the acceleration of the early propeller design stage by using this data for the training of neural networks. For the generation of the dataset for the network training different radial propeller parameters including skew, chord length, camber and pitch for three to six bladed propellers were developed. The outputs of the automatic CFD calculations are the pressure distribution over the blade surface and the thrust and moment coefficients for different advance ratios. Then all parameters are processed in a two-step neural network; the first neural network processes the operating point data and the second network the geometry data and estimated cavitation area. Based on the requirements such as installed power or the required propeller thrust the optimal geometry regarding a maximized efficiency is determined. The grid sensitivity study in combination with processing different amounts of training data show that with sufficient data quality, a higher amount of data reduces the averaged error values in both networks until a certain point.enhttp://rightsstatements.org/vocab/InC/1.0/propeller designneural networkscomputational fluid dynamicsEngineering and Applied OperationsConference Paper10.15480/882.931410.15480/882.931410.15480/882.9294Conference Paper