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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| Vydané v: | Engineering proceedings Ročník 59; číslo 1; s. 24 |
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| Hlavní autori: | , , |
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| Jazyk: | English |
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MDPI AG
01.12.2023
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| ISSN: | 2673-4591 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Raghavendra C. Kamath G. S. Vijay Shantaram B. Nadkarni |
| Author_xml | – sequence: 1 fullname: Shantaram B. Nadkarni organization: Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India – sequence: 2 fullname: G. S. Vijay organization: Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India – sequence: 3 fullname: Raghavendra C. Kamath organization: Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India |
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| Snippet | Airfoil noise due to pressure fluctuations impacts the efficiency of aircraft and has created significant concern in the aerospace industry. Hence, there is a... |
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| SubjectTerms | airfoil self-noise extra trees feature importance gradient boosting random forest XGBoost |
| Title | Comparative Study of Random Forest and Gradient Boosting Algorithms to Predict Airfoil Self-Noise |
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