Version 1
: Received: 15 July 2024 / Approved: 16 July 2024 / Online: 16 July 2024 (14:51:47 CEST)
How to cite:
Sanchez-Muñoz, J. A.; Lagarza-Cortés, C.; Ramírez-Cruz, J. Aerodynamic Drag Coefficient Prediction of a Spike Blunt Body Based on K-Nearest Neighbours.. Preprints2024, 2024071346. https://doi.org/10.20944/preprints202407.1346.v1
Sanchez-Muñoz, J. A.; Lagarza-Cortés, C.; Ramírez-Cruz, J. Aerodynamic Drag Coefficient Prediction of a Spike Blunt Body Based on K-Nearest Neighbours.. Preprints 2024, 2024071346. https://doi.org/10.20944/preprints202407.1346.v1
Sanchez-Muñoz, J. A.; Lagarza-Cortés, C.; Ramírez-Cruz, J. Aerodynamic Drag Coefficient Prediction of a Spike Blunt Body Based on K-Nearest Neighbours.. Preprints2024, 2024071346. https://doi.org/10.20944/preprints202407.1346.v1
APA Style
Sanchez-Muñoz, J. A., Lagarza-Cortés, C., & Ramírez-Cruz, J. (2024). Aerodynamic Drag Coefficient Prediction of a Spike Blunt Body Based on K-Nearest Neighbours.. Preprints. https://doi.org/10.20944/preprints202407.1346.v1
Chicago/Turabian Style
Sanchez-Muñoz, J. A., Christian Lagarza-Cortés and Jorge Ramírez-Cruz. 2024 "Aerodynamic Drag Coefficient Prediction of a Spike Blunt Body Based on K-Nearest Neighbours." Preprints. https://doi.org/10.20944/preprints202407.1346.v1
Abstract
One of the most used techniques for reducing drag on blunt bodies when they move at speeds above the speed of sound are spikes. An important number of numerical works based on CFD have highlighted the fluid dynamics behind its use, as well as its advantages. However, most of docu-mentation focuses mainly on modified spike physical properties, while keeping constant supersonic or hypersonic flow conditions. In this sense, it is necessary to resort to current numerical techniques as machine learning, which can be obtain the most appropriate technical solution in compared to the costly iterative simulations that can delay the rapid development and testing of new aerody-namic configurations. This investigation postulates a specific hypothesis: that the K-Nearest Neighbours algorithm, renowned for its simplicity and effectiveness in classification problems, can be extrapolated as a robust tool for predicting drag reduction on spike blunt bodies. A wide range of Mach number and aspect ratios between blunt body diameter, spike length, and spike diameter are used for the algorithm dataset. The algorithm presents good predictions of the coefficient with errors of less than 5% percent, applying a multivariable regression method. Even, in the vicinity of the hypersonic speed zone, with a minimum of 3 neighbour nodes. The above validates the flexibility of the method and shows a new area of opportunity for the calculation of aerodynamic properties in a body moving near or above the speed of sound.
Keywords
K nearest neighbours; Aerodynamic Drag Coefficient; Computational Fluid Dynamics; spike blunt body
Subject
Engineering, Aerospace Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
