Multilevel-Modeling Interpretation of Trailing-Edge Noise Models for Wind Turbines with NACA 0012 Airfoil
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Multilevel‑Modeling Interpretation of Trailing‑Edge Noise Models for Wind Turbines with NACA 0012 Airfoil Deok‑Kee Choi1 Received: 30 January 2020 / Revised: 7 August 2020 / Accepted: 12 August 2020 © Korean Society for Precision Engineering 2020
Abstract 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. Keywords Multilevel modeling · Random effects · Trailing edge noise · Predictive model · Wind turbine
1 Introduction In recent years, for more efficient energy generation, wind turbines have grown in size yearly, which raises the flow velocity of the blade, the main source of the noise. Noise is taken very seriously because wind turbines are located close to residential areas for convenience. In the vicinity of residential areas, many complaints about the noise have been raised, which often has a negative effect on national energy policy [1]. Therefore, research on the design of low noise wind turbines is drawing attention. The trailing edge noise is caused from the interaction of turbulence with the trailing edge. Particular interest has been placed on modeling and prediction of wind turbine noise for design purposes, such as noise reduction, and to optimize the trade-off between noise and power production [2–5]. * Deok‑Kee Choi [email protected] 1
Department of Mechanical Engineering, Dankook University, 152 Jukjeon‑ro, Suji‑gu, Yongin‑si, Gyeonggi‑do, Republic of Korea
The way to develop noise prediction models can be divided into three categories: empirical or semi-empirical [6–8], equation-based numerical analysis and machine learning or artificial neural networks (ANN). The current industry practice is to use simple semi-empirical aeroacoustic prediction tools dedicated to traili
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