A neural network prediction method has been developed to compute self-noise of airfoils typically used in wind turbines. The neural networks were trained using experimental data corresponding to tests of several different airfoils over a range of flow conditions. The experimental data corresponds to the NACA 0012, Delft DU96, Sandia S831, S822 and S834, Fx63-137, SG6043 and SD-2030 airfoils. The chord of these airfoils range from 0.025 to 0.91 m and they were tested at Reynolds numbers of up to 3.8 million and angle of attack up to 15° depending on the airfoil. Using experimental data corresponding to different airfoils provides to the neural network the capacity to take into account the geometry of the airfoils in the predictions.geometry of the airfoils in the predictions. The input parameters to the network are the flow speed, chord length, effective angle of attack and parameters describing the geometrical shape of the airfoil. In addition, boundary layer displacement thickness was used for some models. The parameters used for taking into account the airfoil's geometry are based on a conformal mapping method or a polynomial approximation. The output of the neural network is given by sound pressure level in 1/3rd octave bands for nine frequencies ranging from 630 to 4000 Hz.

The present work constitutes an application of neural networks to aeroacoustics. The main objective was to assess the potential of using neural networks to model airfoil noise. Therefore, this work is focused in the modeling of the problem, and no mathematical analyses about neural networks are intended. To this end, several models were investigated both in terms of the configuration and training approach. The performance of the networks was evaluated for a range of flow conditions. The neural network technique was first investigated for the NACA 0012 airfoil only. For this case, the geometry of the airfoil was not incorporated as input into the model. The neural network approach was then extended to account for airfoils of any geometry by including data from all airfoils in the training.

The results show that the neural networks are capable of predicting the airfoils self-noise reasonably well for most of the flow conditions. The broadband noise due to the turbulent boundary layer interacting with the trailing edge is estimated very well. The tonal vortex shedding noise due to laminar boundary layer-trailing edge interaction is not predicted as well, most likely due to the limited data available for this noise source. In summary, the research here demonstrated the potential of the neural network as a tool to predict noise from typical wind turbine airfoils.