Automated Design Process of a Fixed Wing UAV Maximizing Endurance

Sahraoui, M.; Boutemedjet, A.; Mekadem, M.; Scholz, D.

doi:10.47176/jafm.17.11.2647

Automated Design Process of a Fixed Wing UAV Maximizing Endurance

Document Type : Regular Article

Authors

¹ Laboratory of Fluid Mechanics, Ecole Militaire Polytechnique, Bordj El Bahri, 16046, Algiers, Algeria

² Aircraft Design and Systems Group (AERO), Hamburg University of Applied Sciences, Hamburg, 20099, Germany

10.47176/jafm.17.11.2647

Abstract

Unmanned aerial vehicle (UAV) design necessitates significant effort in prototyping, testing, and design iterations. To reduce design time and improve wing performance, an automated design and optimization framework is proposed utilizing open-source software, including OpenVSP: VSPAERO & Parasite Drag Tool, XFOIL, and Python. This study presents a preliminary UAV wing design methodology, emphasizing weight estimation, drag analysis, stall prediction, and endurance optimization. The maximum takeoff weight of the UAV was calculated after estimating the empty weight using a linear regression from data from 20 existing similar UAVs. The wing and engine sizing were determined using the matching plot technique. A solver with low-fidelity models, combining the Vortex Lattice Method (VLM) and analytical expressions, was used to predict the drag coefficient and maximum lift coefficient of the designed wing. An optimization process using a genetic algorithm was applied to maximize endurance while satisfying requirements such as rate of climb, stall, and maximum speeds. The optimized wing was analyzed with computational fluid dynamics (CFD), and its aerodynamic characteristics were compared with those obtained using VLM and the suggested aerodynamic solver. According to the CFD results, the proposed aerodynamic solver estimated the drag coefficient at zero angle of attack with an error of 17.2% compared to 63.1% using the VLM classic method. The error on the maximum lift coefficient estimation was limited to 5.3%. In terms of optimization, the framework showed an increase in the endurance ratio of up to 2% compared to the Artificial Neural Network method coupled with XFLR5. The primary advantage of the suggested framework is the utilization of open-source software, giving a cost-effective and accessible solution for small and medium-sized startups to design and optimize UAVs to achieve mission objectives.

Keywords

Main Subjects

Computational Fluid Dynamics

References

OpenVSP. (2023, July 24). Open Vehicle Sketch Pad (Version 3.25.0). https://openvsp.org

Air Force Technology | Air Defence News & Views Updated Daily. (2024, February 2). Airforce Technology. https://www.airforce-technology.com

Ansys CFX. (2023, October 3). Industry-Leading CFD Software. https://www.ansys.com/products/fluids/ansys-cfx

Ansys Fluent. (2023, October 3). Fluid Simulation Software. https://www.ansys.com/products/fluids/ansys-fluent

Anderson, J. D. (2016). Introduction to flight (Eighth edition). McGraw-Hill Education.

Anılır, B., & Kurtuluş, D. F. (2023). Unsteady aerodynamic performance of SD7062 airfoil at high Reynolds number. Progress in Computational Fluid Dynamics, An International Journal, 23(2), 65. https://doi.org/10.1504/PCFD.2023.129760

Azabi, Y., Savvaris, A., & Kipouros, T. (2019). Artificial intelligence to enhance aerodynamic shape optimisation of the aegis UAV. Machine Learning and Knowledge Extraction, 1(2), 552–574. https://doi.org/10.3390/make1020033

Bahrami, A., Hoseinzadeh, S., Heyns, P. S., & Mirhosseini, S. M. (2019). Experimental investigation of co-flow jet’s airfoil flow control by hot wire anemometer. Review of Scientific Instruments, 90(12), 125107. https://doi.org/10.1063/1.5113592

Benaouali, A., & Kachel, S. (2019). Multidisciplinary design optimization of aircraft wing using commercial software integration. Aerospace Science and Technology, 92, 766–776. https://doi.org/10.1016/j.ast.2019.06.040

Blasius, H. (1950). The boundary layers in fluids with little friction. National Advisory Committee for Aeronautics.

Boutemedjet, A., Samardžić, M., Rebhi, L., Rajić, Z., & Mouada, T. (2019). UAV aerodynamic design involving genetic algorithm and artificial neural network for wing preliminary computation. Aerospace Science and Technology, 84, 464–483. https://doi.org/10.1016/j.ast.2018.09.043

Cheeseman, I. (1976). Fluid-Dynamic Drag: Practical Information on Aerodynamic Drag and Hydrodynamic Resistance. SF Hoerner. Hoerner Fluid Dynamics, Brick Town, New Jersey. 1965. 455 pp. Illustrated. $24.20. The Aeronautical Journal, 80(788), 371–371.

Covert, E. E. (1985). Progress in astronautics and aeronautics: Thrust and drag: Its prediction and verification (Vol. 98). AIAA.

dos Santos, C. R., & Marques, F. D. (2018). Lift prediction including stall, using vortex lattice method with Kirchhoff-based correction. Journal of Aircraft, 55(2), 887–891. https://doi.org/10.2514/1.C034451

Du, X., He, P., & Martins, J. R. R. A. (2021). Rapid airfoil design optimization via neural networks-based parameterization and surrogate modeling. Aerospace Science and Technology, 113, 106701. https://doi.org/10.1016/j.ast.2021.106701

Elham, A., & Van Tooren, M. J. L. (2014). Winglet multi-objective shape optimization. Aerospace Science and Technology, 37, 93–109. https://doi.org/10.1016/j.ast.2014.05.011

Falkner, V. M. (1943). The calculation of aerodynamic loading on surfaces of any shape.

Gudmundsson, S. (2013). General aviation aircraft design: Applied Methods and Procedures. Butterworth-Heinemann.

Haryanto, I., Utomo, T. S., Sinaga, N., Rosalia, C. A., & Putra, A. P. (2014). Optimization of maximum lift to drag ratio on airfoil design based on artificial neural network utilizing genetic algorithm. Applied Mechanics and Materials, 493, 123–128. https://doi.org/10.4028/www.scientific.net/AMM.493.123

Hedman, S. G. (1966). Vortex lattice method for calculation of quasi steady state loadings on thin elastic wings in subsonic flow (p. 0018).

Hoak, D.E., and Carlson, J. (1978). USAF Stability and Control DATCOM, Air Force Flight Dynamics Laboratory.

Hoseinzadeh, S., Bahrami, A., Mirhosseini, S. M., Sohani, A., & Heyns, S. (2020). A detailed experimental airfoil performance investigation using an equipped wind tunnel. Flow Measurement and Instrumentation, 72, 101717. https://doi.org/10.1016/j.flowmeasinst.2020.101717

Hoseinzadeh, S., Sohani, A., & Heyns, S. (2021). Comprehensive analysis of the effect of air injection on the wake development of an airfoil. Ocean Engineering, 220, 108455. https://doi.org/10.1016/j.oceaneng.2020.108455

Hoseinzadeh, S., & Stephan Heyns, P. (2022). Development of a model efficiency improvement for the designing of feedwater heaters network in thermal power plants. Journal of Energy Resources Technology, 144(7), 072102. https://doi.org/10.1115/1.4054196

Hutagalung, M. R. A., Latif, A. A., & Israr, H. A. (2016). Structural design of UAV semi-monoque composite wing. Journal of Transport System Engineering, 26-34.

Kapsalis, S., Panagiotou, P., & Yakinthos, K. (2021). CFD-aided optimization of a tactical blended-wing-body UAV platform using the taguchi method. Aerospace Science and Technology, 108, 106395. https://doi.org/10.1016/j.ast.2020.106395

Katz, J., & Plotkin, A. (2001). Low-speed aerodynamics. (Vol. 13), Cambridge university press.

Masood, K., & Wei, Z. (2018). Robust multidisciplinary optimization for wing of a low subsonic UAV. Journal of Biodiversity & Endangered Species, 09(02). https://doi.org/10.4172/2332-2543.1000226

Ostadhossein, R., & Hoseinzadeh, S. (2024). Developing computational methods of heat flow using bioheat equation enhancing skin thermal modeling efficiency. International Journal of Numerical Methods for Heat & Fluid Flow, 34(3), 1380–1398. https://doi.org/10.1108/HFF-06-2023-0355

Pritchard, P. J., & Mitchell, J. W. (2016). Fox and McDonald’s introduction to fluid mechanics. John Wiley & Sons.

Sadraey, M. H. (2013). Aircraft design: A systems engineering approach. Wiley.

Schlichting, H., & Kestin, J. (1961). Boundary layer theory (Vol. 121). Springer.

Singh, D. K., Jain, A., & Paul, A. R. (2021). Active flow control over a naca23012 airfoil using hybrid jet. Defence Science Journal, 71(6), 721–729. https://doi.org/10.14429/dsj.71.16468

Sun, G., & Wang, S. (2019). A review of the artificial neural network surrogate modeling in aerodynamic design. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 233(16), 5863–5872. https://doi.org/10.1177/0954410019864485

Traub, L. W. (2013). Aerodynamic impact of aspect ratio at low reynolds number. Journal of Aircraft, 50(2), 626–634. https://doi.org/10.2514/1.C031980