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.

近来，人们对可再生能源的兴趣增加了，因此对于噪声较小的风力涡轮机的设计，需要噪声预测模型。特别地，后沿噪声是主要的噪声源。已经报道了使用带有机器学习技术和人工神经网络的复杂预测模型得到的许多结果，根据应用的不同，结果通常很好。但是，对产生这样的预测结果的模型本身进行严格的验证是非常不足的，并且缺乏对产生结果的过程的理解，并且很难解释实验中观察到的各种物理现象。 。在这项研究中，Brooks，Pope和Marcolini（BPM）模型是机翼噪声预测中最流行的半经验模型，使用多层次建模进行了分析。多层次模型是既包含固定效应又包含随机效应的统计模型。我们能够对BPM模型中无法解释的两个问题进行解释，并提高了我们对风力涡轮机噪声现象的理解。随着进一步的研究，多层建模有望成为有效设计和分析风力涡轮机噪声的复杂预测模型的有效工具。