Application of Neural Network in Predicting Optimization of Axisymmetric Boattail Angle for Drag Reduction

The study tries to classify the axisymmetric boattail models with minimum drag using numerical simulation and neural networks. Numerical simulation was conducted for the boattail model in a range of angles from 0 to 22°and length from 0.5 to 1.5 diameter of the model. The Mach number was changed from 0.1 to 3.0. The results revealed that, the angle with minimum drag is around 14° at subsonic but it dramatically shifts to 7-9° at supersonic conditions. The maximum error of the neural network in predicting aerodynamic drag is less than 2%. At subsonic flow, the angle with minimum drag is around 14° and boattail length was 1.5 times the model diameter. At supersonic conditions, the angle and length are around 7° and 1.5 diameter of the model, respectively. Increasing boattail length results in reducing drag. This study provides a good reference for further design of flying objects and proposes control method for drag reduction.