Comparative Study of Random Forest and Gradient Boosting Algorithms to Predict Airfoil Self-Noise

Airfoil noise due to pressure fluctuations impacts the efficiency of aircraft and has created significant concern in the aerospace industry. Hence, there is a need to predict airfoil noise. This paper uses the airfoil dataset published by NASA (NACA 0012 airfoils) to predict the scaled sound pressur...

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Veröffentlicht in:Engineering proceedings Jg. 59; H. 1; S. 24
Hauptverfasser: Shantaram B. Nadkarni, G. S. Vijay, Raghavendra C. Kamath
Format: Journal Article
Sprache:Englisch
Veröffentlicht: MDPI AG 01.12.2023
Schlagworte:
airfoil self-noise
extra trees
feature importance
gradient boosting
random forest
XGBoost
ISSN:2673-4591
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Zusammenfassung:Airfoil noise due to pressure fluctuations impacts the efficiency of aircraft and has created significant concern in the aerospace industry. Hence, there is a need to predict airfoil noise. This paper uses the airfoil dataset published by NASA (NACA 0012 airfoils) to predict the scaled sound pressure using five different input features. Diverse Random Forest and Gradient Boost Models are tested with five-fold cross-validation. Their performance is assessed based on mean-squared error, coefficient of determination, training time, and standard deviation. The results show that the Extremely Randomized Trees algorithm exhibits the most superior performance with the highest Coefficient of Determination.
ISSN:2673-4591
DOI:10.3390/engproc2023059024