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http://dx.doi.org/10.5139/JKSAS.2021.49.11.901

Application of Artificial Neural Network to Predict Aerodynamic Coefficients of the Nose Section of the Missiles  

Lee, Jeongyong (Interdisciplinary Program in Space Systems, Seoul National University)
Lee, Bok Jik (Department of Aerospace Engineering, Seoul National University)
Publication Information
Journal of the Korean Society for Aeronautical & Space Sciences / v.49, no.11, 2021 , pp. 901-907 More about this Journal
Abstract
The present study introduces an artificial neural network (ANN) that can predict the missile aerodynamic coefficients for various missile nose shapes and flow conditions such as Mach number and angle of attack. A semi-empirical missile aerodynamics code is utilized to generate a dataset comprised of the geometric description of the nose section of the missiles, flow conditions, and aerodynamic coefficients. Data normalization is performed during the data preprocessing step to improve the performance of the ANN. Dropout is used during the training phase to prevent overfitting. For the missile nose shape and flow conditions not included in the training dataset, the aerodynamic coefficients are predicted through ANN to verify the performance of the ANN. The result shows that not only the ANN predictions are very similar to the aerodynamic coefficients produced by the semi-empirical missile aerodynamics code, but also ANN can predict missile aerodynamic coefficients for the untrained nose section of the missile and flow conditions.
Keywords
Machine Learning; Artificial Neural Network; Missile DATCOM; Aerodynamic Coefficient;
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