Multilevel-Modeling Interpretation of Trailing-Edge Noise Models for Wind Turbines with NACA 0012 Airfoil

Recently, good interest in renewable energy has been increased and thereby the need for noise prediction model is required for the design of wind turbines with less noise. In particular, trailing edge noise is a major noise source. A number of results using complex predictive models with machine learning techniques and artificial neural network have been reported, and the results are generally good, depending on the application. However, the rigorous verification of the model itself that produces such predictive results is very insufficient, and there is a lack of understanding of the process in which the results are produced, and an explanation of the various physical phenomena observed in the experiment is not easy. In this study, Brooks, Pope, and Marcolini (BPM) model, the most popular semi-empirical model for airfoil noise prediction, was subjected to an analysis using multilevel modeling. The multilevel model is a statistical model containing both fixed effects and random effects. We were able to reckon the interpretation of two issues that are left unexplained in BPM model and to improve our understanding of the noise phenomenon of wind turbines. With further research, multilevel modeling is expected to be an effective tool for better design and analysis of complex predictive models for wind turbines noise.