Aerodynamic Drag Coefficient Prediction of a Spike Blunt Body Based on K-Nearest Neighbors

Abstract

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.

Keywords

• k-nearest neighbors
• aerodynamic drag coefficient
• computational fluid dynamics
• spike blunt body