Application of Neural Network in Predicting Optimization of Axisymmetric Boattail Angle for Drag Reduction
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Abstract
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.
Keywords
Axisymmetric blunt body, boattail, drag reduction, ANN, wake structure
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
[2] The Hung Tran et al., Effect of Reynolds number on flow behavior and pressure drag of axisymmetric conical boattails at low speeds, Experiments in Fluids, vol. 60, pp. 1-19, Feb. 2019.
[3] W. A. Mair, Reduction of base drag by boat-tailed afterbodies in low speed flow, Aeronautical Quarterly, vol. 20, iss. 4, pp. 307-320, Jun. 2016.
[4] P. R. Viswanath, Flow management techniques for base and afterbody drag reduction, Progress in Aerospace Sciences, vol. 32, iss. 2-3, pp. 79-129, 1996.
[5] A. Mariotti et al., Separation control and drag reduction for boat-tailed axisymmetric bodies through contoured transverse grooves, Journal of Fluid Mechanics, vol. 832, pp. 514-549, Oct. 2017.
[6] T. H. Tran et al., Effect of boattail angle on near-wake flow and drag of axisymmetric models: a numerical approach, Journal of Mechanical Science and Technology, vol. 35, pp. 563-573, Jan. 2021.
[7] R. Thirumalainambi and J. Bardina, Training data requirement for a neural network to predict aerodynamic coefficients, Independent Component Analyses, Wavelets, and Neural Networks, vol. 5102, pp. 92-103, Apr. 2003.
[8] A. S. Platou, Improved projectile boattail, J. Spacecraft Rockets, vol. 12, no. 12, pp. 727-732, Dec. 1975.
[9] F. R. Menter, Two-equation eddy-viscosity turbulence models for engineering applications, AIAA Journal, vol. 32, no. 8, pp. 1598-1605, Aug. 1994.
[10] R. M. Cummings et al., Supersonic, turbulent flow computation and drag optimization for axisymmetric afterbodies, Computers and Fluids, vol. 24, iss. 4, pp. 487-507, 1995.
[11] J. Feng and S. Lu, Performance analysis of various activation functions in artificial neural networks, Journal of Physics: Conference Series, vol. 1237, iss. 2, pp. 22030, 2019.
[12] D. F. Kurtulus, Ability to forecast unsteady aerodynamic forces of flapping airfoils by artificial neural network, Neural Computing Applications, vol. 18, pp. 359-368, Apr. 2008.
[13] T. Rajkumar et al., Prediction of aerodynamic coefficients for wind tunnel data using a genetic algorithm optimised neural network, WIT Transaction on Information and Communication Technology, vol. 28, 2002.
[14] M. Carpente et al., A comprehensive approach to cataloging missile aerodynamic performance using surrogate modeling techniques and statistical learning, in 29th AIAA Applied Aerodynamics Conference, Honolulu, HI, USA, Jun. 27-30, 2011, pp. 3029.