Aerodynamic Drag Coefficient Prediction of a Spike Blunt Body Based on K-Nearest Neighbors
Spike blunt bodies are a method to reduce drag when a body moves at speeds above sound. Several numerical works based on computational fluid dynamics (CFD) have deeply studied fluid performance and highlighted its advantages. However, most documentation focuses on modifying spike physical properties...
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| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/11/9/757 |
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| author | Jonathan Arturo Sánchez Muñoz Christian Lagarza-Cortés Jorge Ramírez-Cruz |
| author_facet | Jonathan Arturo Sánchez Muñoz Christian Lagarza-Cortés Jorge Ramírez-Cruz |
| author_sort | Jonathan Arturo Sánchez Muñoz |
| collection | DOAJ |
| description | Spike blunt bodies are a method to reduce drag when a body moves at speeds above sound. Several numerical works based on computational fluid dynamics (CFD) have deeply studied fluid performance and highlighted its advantages. However, most documentation focuses on modifying spike physical properties while keeping constant supersonic or hypersonic flow conditions. In recent years, machine learning models have emerged as viable tools to predict values in almost any field, including aerodynamics. In the case of CFD, many models have been explored, such as support vector regression, ensemble methods, and artificial neural networks. However, a simple and easy-to-implement method such as k-Nearest Neighbors has not been extensively explored. This work extrapoled k-Nearest Neighbors to predict the drag coefficient of a spike blunt body for a range of supersonic and hypersonic speeds based on drag data obtained from CFD analysis. The parametric study of the spike blunt body was performed considering body diameter, spike length, and freestream Mach number as input variables. The algorithm presents proper predictions, with errors less than 5% for the drag coefficient and considering a minimum of three neighbor nodes. The k-NN was compared again Kriging model and k-NN presents a better accuracy. The above validates the flexibility of the method and shows a new area of opportunity for the calculation of aerodynamic properties. |
| format | Article |
| id | doaj-art-01f1a6b517fe4b66a3eb80cc5512aec8 |
| institution | OA Journals |
| issn | 2226-4310 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-01f1a6b517fe4b66a3eb80cc5512aec82025-08-20T01:56:06ZengMDPI AGAerospace2226-43102024-09-0111975710.3390/aerospace11090757Aerodynamic Drag Coefficient Prediction of a Spike Blunt Body Based on K-Nearest NeighborsJonathan Arturo Sánchez Muñoz0Christian Lagarza-Cortés1Jorge Ramírez-Cruz2School of Engineering and Sciences, Tecnologico de Monterrey, Av. Eugenio Garza Sada 300, San Luis Potosí S.L.P 78211, MexicoDepartment of Industrial and Mechanical Engineering, Universidad de las Americas Puebla, Ex Hacienda Sta. Catarina Mártir S/N, San Andrés Cholula Puebla 72810, MexicoDivisión de Ciencias Básicas, Facultad de Ingeniería, Universidad Nacional Autonoma de Mexico, Av. Universidad 3000, Ciudad Universitaria, Coyoacán, Ciudad de México 04510, MexicoSpike blunt bodies are a method to reduce drag when a body moves at speeds above sound. Several numerical works based on computational fluid dynamics (CFD) have deeply studied fluid performance and highlighted its advantages. However, most documentation focuses on modifying spike physical properties while keeping constant supersonic or hypersonic flow conditions. In recent years, machine learning models have emerged as viable tools to predict values in almost any field, including aerodynamics. In the case of CFD, many models have been explored, such as support vector regression, ensemble methods, and artificial neural networks. However, a simple and easy-to-implement method such as k-Nearest Neighbors has not been extensively explored. This work extrapoled k-Nearest Neighbors to predict the drag coefficient of a spike blunt body for a range of supersonic and hypersonic speeds based on drag data obtained from CFD analysis. The parametric study of the spike blunt body was performed considering body diameter, spike length, and freestream Mach number as input variables. The algorithm presents proper predictions, with errors less than 5% for the drag coefficient and considering a minimum of three neighbor nodes. The k-NN was compared again Kriging model and k-NN presents a better accuracy. The above validates the flexibility of the method and shows a new area of opportunity for the calculation of aerodynamic properties.https://www.mdpi.com/2226-4310/11/9/757k-nearest neighborsaerodynamic drag coefficientcomputational fluid dynamicsspike blunt body |
| spellingShingle | Jonathan Arturo Sánchez Muñoz Christian Lagarza-Cortés Jorge Ramírez-Cruz Aerodynamic Drag Coefficient Prediction of a Spike Blunt Body Based on K-Nearest Neighbors Aerospace k-nearest neighbors aerodynamic drag coefficient computational fluid dynamics spike blunt body |
| title | Aerodynamic Drag Coefficient Prediction of a Spike Blunt Body Based on K-Nearest Neighbors |
| title_full | Aerodynamic Drag Coefficient Prediction of a Spike Blunt Body Based on K-Nearest Neighbors |
| title_fullStr | Aerodynamic Drag Coefficient Prediction of a Spike Blunt Body Based on K-Nearest Neighbors |
| title_full_unstemmed | Aerodynamic Drag Coefficient Prediction of a Spike Blunt Body Based on K-Nearest Neighbors |
| title_short | Aerodynamic Drag Coefficient Prediction of a Spike Blunt Body Based on K-Nearest Neighbors |
| title_sort | aerodynamic drag coefficient prediction of a spike blunt body based on k nearest neighbors |
| topic | k-nearest neighbors aerodynamic drag coefficient computational fluid dynamics spike blunt body |
| url | https://www.mdpi.com/2226-4310/11/9/757 |
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