The Use of Data Mining Techniques in Predicting the Noise Emitted By the Trailing Edge of Aerodynamic Objects

Abdusalam Shaltooki - University of Human Development, Sulaymaniyah, Iraq
Mojtaba Jamshidi - Islamic Azad University, Qazvin, Iran

Citation Format:



Aerodynamic is a branch of fluid dynamics that evaluates the behavior of airflow and its interaction with moving objects. The most important application of aerodynamic is in aerospace engineering, designing and construction of flying objects. Reduction of noise emitted by aerodynamic objects is one of the most important challenges in this area and many efforts have been to reduce its negative effects. The prediction of noise emitted from these aerodynamic objects is a low-cost and fast approach that can partially replace the "fabrication and testing" phase. One of the most common and successful tools in prediction procedures is data mining technology. In this paper, the performance of different data mining algorithms such as Random Forest, J48, RBF Network, SVM, MLP, Logistic, and Bagging is evaluated in predicting the amount of noise emitted from aerodynamic objects. The experiments are conducted on a dataset collected by NASA, which is called "Airfoil Self-Noise". The obtained results illustrate that the proposed hybrid model derived from the combination of Random Forest and Bagging algorithms has better performance compared to other methods with an accuracy of 77.6% and mean absolute error of 0.2279.


Data mining; Classification; Hybrid model; Noise prediction; Aerodynamic objects.

Full Text:



Amiet, R.K., 1975. Acoustic radiation from an airfoil in a turbulent stream. Journal of Sound and vibration, 41(4), pp.407-420.

Brooks, T.F., Pope, D.S. and Marcolini, M.A., 1989. Airfoil Self-Noise and Prediction. NASA Reference Publication, NASA-RP-1219.

Schlinker, R. and Amiet, R., 1981. Helicopter Rotor Trailing Edge Noise. NASA CR-3470.

Barnes, J. and Gomez. R., 2007. A variety of wind turbine noise regulations in the United States. Second International Meeting on Wind Turbines Noise, Lyon, France.

Jiawei, H. and Micheline, K., 2006. Data Mining: Concepts and Techniques, Second Edition, Morgan Kaufmann Publishers, Publisher’s name:Diane Cerra, Elsevier.

Brooks, T.F. and Hodgson, T.H., 1981. Trailing edge noise prediction from measured surface pressures. Journal of sound and vibration, 78(1), pp.69-117.

Lutz, T., Herrig, A., Würz, W., Kamruzzaman, M. and Krämer, E., 2007. Design and wind-tunnel verification of low-noise airfoils for wind turbines. AIAA journal, 45(4), pp.779-785.

Roger, M., Moreau, S. and Wang, M., 2002. An analytical model for predicting airfoil self-noise using wall-pressure statistics. Annual Research Brief, Center for Turbulence Research, Stanford University, 2002, pp.405-414.

Errasquin, L., 2009. Airfoil self-noise prediction using neural networks for wind turbines (Doctoral dissertation, Virginia Tech).

Moriarty, P., Guidati, G. and Migliore, P., 2005. Prediction of turbulent inflow and trailing-edge noise for wind turbines. In 11th AIAA/CEAS Aeroacoustics Conference (p. 2881)

Jones, L. and Sandberg, R., 2009. Direct numerical simulations of noise generated by the flow over an airfoil with trailing edge serrations. In 15th AIAA/CEAS Aeroacoustics Conference (30th AIAA Aeroacoustics Conference) (p. 3195)

Gerhard, T. and Carolus, T., 2014. INVESTIGATION OF AIRFOIL TRAILING EDGE NOISE WITH ADVANCED EXPERIMENTAL AND NUMERICAL METHODS. In The 21st International Congress on Sound and Vibration

Wang, M., Moreau, S., Iaccarino, G. and Roger, M., 2009. LES prediction of wall-pressure fluctuations and noise of a low-speed airfoil. International journal of aeroacoustics, 8(3), pp.177-197.

Lee, S., Lee, S. and Lee, S., 2013. Numerical modeling of wind turbine aerodynamic noise in the time domain. The Journal of the Acoustical Society of America, 133(2), pp. EL94-EL100.

Croaker, P., Kessissoglou, N., Karimi, M., Doolan, C. and Chen, L., 2014. Self-noise prediction of a flat plate using a hybrid RANS-BEM technique. Inter-noise, Melbourne, Australia.

Tan, P.N., 2018. Introduction to data mining. Pearson Education India.

Poor, S.S.A. and Shiri, M.E., 2017. A Genetic Programming based Algorithm for Predicting Exchanges in Electronic Trade using Social Networks’ Data. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 8(5), pp.189-196.