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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MDPI AG
2023-12-01
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| Series: | Engineering Proceedings |
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| author | Shantaram B. Nadkarni G. S. Vijay Raghavendra C. Kamath |
| author_facet | Shantaram B. Nadkarni G. S. Vijay Raghavendra C. Kamath |
| author_sort | Shantaram B. Nadkarni |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dfd8f2315bc64361991245a341227784 |
| institution | Kabale University |
| issn | 2673-4591 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-dfd8f2315bc64361991245a3412277842025-08-20T03:43:02ZengMDPI AGEngineering Proceedings2673-45912023-12-015912410.3390/engproc2023059024Comparative Study of Random Forest and Gradient Boosting Algorithms to Predict Airfoil Self-NoiseShantaram B. Nadkarni0G. S. Vijay1Raghavendra C. Kamath2Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaDepartment of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaDepartment of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, IndiaAirfoil 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.https://www.mdpi.com/2673-4591/59/1/24airfoil self-noiserandom forestextra treesgradient boostingXGBoostfeature importance |
| spellingShingle | Shantaram B. Nadkarni G. S. Vijay Raghavendra C. Kamath Comparative Study of Random Forest and Gradient Boosting Algorithms to Predict Airfoil Self-Noise Engineering Proceedings airfoil self-noise random forest extra trees gradient boosting XGBoost feature importance |
| title | Comparative Study of Random Forest and Gradient Boosting Algorithms to Predict Airfoil Self-Noise |
| title_full | Comparative Study of Random Forest and Gradient Boosting Algorithms to Predict Airfoil Self-Noise |
| title_fullStr | Comparative Study of Random Forest and Gradient Boosting Algorithms to Predict Airfoil Self-Noise |
| title_full_unstemmed | Comparative Study of Random Forest and Gradient Boosting Algorithms to Predict Airfoil Self-Noise |
| title_short | Comparative Study of Random Forest and Gradient Boosting Algorithms to Predict Airfoil Self-Noise |
| title_sort | comparative study of random forest and gradient boosting algorithms to predict airfoil self noise |
| topic | airfoil self-noise random forest extra trees gradient boosting XGBoost feature importance |
| url | https://www.mdpi.com/2673-4591/59/1/24 |
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