CFD-Derived Parasite-Drag Law of an Octocopter Airframe with Endurance Implications

Kazi Tauhid Mokbul Hussain; Mobin Kabir; Irfan Talukder; Farhan Rahman Atul

doi:10.38032/scse.2026.4.223

Authors

Kazi Tauhid Mokbul Hussain Department of Aeronautical Engineering, Military Institute of Science and Technology, Dhaka-1216, Bangladesh
Mobin Kabir Department of Aeronautical Engineering, Military Institute of Science and Technology, Dhaka-1216, Bangladesh
Irfan Talukder Department of Aeronautical Engineering, Military Institute of Science and Technology, Dhaka-1216, Bangladesh
Farhan Rahman Atul Department of Aeronautical Engineering, Military Institute of Science and Technology, Dhaka-1216, Bangladesh

DOI:

https://doi.org/10.38032/scse.2026.4.223

Keywords:

Drone, Aerodynamics, Endurance, Parasite Drag, Computational Fluid Dynamics (CFD)

Abstract

Octocopters are increasingly utilized, but designers lack a straightforward and reliable method for calculating airframe drag, critical for accurate power and endurance estimation. This study isolates airframe-induced drag, excluding propellers, using computational simulations of a 7.2-kg octocopter operating at speeds from 1 to 15 m/s. Standard simulation techniques resolve near-wall flow phenomena, and the computational setup is verified for accuracy. Comparative analysis of airflow at 4 and 15 m/s shows that increased drag at higher speeds is mainly due to the expansion of disturbed flow regions, especially at arm–hub and landing-gear junctions, where flow separation and wake formation intensify. The measured drag values closely follow a single airspeed-based formula, allowing straightforward drag prediction within this velocity range. Expressed in terms of electrical power, this law enables the determination of optimal cruising speeds for maximum range without further simulations. The results also show that geometric modifications, such as rounding sharp corners at critical junctions and refining landing-gear profiles, can significantly reduce drag.

Downloads

PDF

Downloads

Download data is not yet available.

References

[1] Hammer, T., Quitter, J., Mayntz, J. and Bauschat, J.-M., 2024. Free-Fall Drag Estimation of Small-Scale Multirotor Unmanned Aircraft Systems Using Computational Fluid Dynamics and Wind Tunnel Experiments. CEAS Aeronautical Journal, 15(2), pp. 269–282. DOI: https://doi.org/10.1007/s13272-023-00702-w

[2] Ventura Diaz, P. and Yoon, S., 2018, January. High-Fidelity Computational Aerodynamics of Multi-Rotor Unmanned Aerial Vehicles. AIAA SciTech 2018 Forum (AIAA Aerospace Sciences Meeting), Kissimmee, FL, USA, Paper 2018-1266. DOI: https://doi.org/10.2514/6.2018-1266

[3] Wolf, C.C., Schanz, D., Schwarz, C., 2024. Volumetric Wake Investigation of a Free-Flying Quadcopter Using Shake-The-Box Lagrangian Particle Tracking. Experiments in Fluids, 65, Art. no. 152. DOI: https://doi.org/10.1007/s00348-024-03880-3

[4] Bauersfeld, L. and Scaramuzza, D., 2024. Range, Endurance, and Optimal Speed Estimates for Multicopters. arXiv preprint, arXiv:2109.04741, v3.

[5] Götten, F., Havermann, M., Braun, C., Marino, M. and Bil, C., 2020. Wind-Tunnel and CFD Investigations of UAV Landing Gears and Turrets—Improvements in Empirical Drag Estimation. Aerospace Science and Technology, 107, Art. no. 106306. DOI: https://doi.org/10.1016/j.ast.2020.106306

[6] Throneberry, G., Takeshita, A., Hocut, C.M., Shu, F. and Abdelkefi, A., 2022. Wake Propagation and Characteristics of a Multi-Rotor Unmanned Vehicle in Forward Flight. Drones, 6(5), p. 130. DOI: https://doi.org/10.3390/drones6050130

[7] NASA Langley Research Center, 2025. Turbulence Modeling Resource. Available at: http://turbmodels.larc.nasa.gov (Date of access: 29 September 2025).

[8] Ghirardelli, M., Kral, S.T., Müller, N.C., Hann, R., Cheynet, E. and Reuder, J., 2023. Flow Structure Around a Multicopter Drone: A Computational Fluid Dynamics Analysis for Sensor Placement Considerations. Drones, 7(7), Art. no. 467. DOI: https://doi.org/10.3390/drones7070467

[9] Paz, C., Suárez, E., Gil, C. and Baker, C., 2020. CFD Analysis of the Aerodynamic Effects on the Stability of the Flight of a Quadcopter UAV in the Proximity of Walls and Ground. Journal of Wind Engineering and Industrial Aerodynamics, 206, Art. no. 104378. DOI: https://doi.org/10.1016/j.jweia.2020.104378

[10] Lei, Y., Wang, J., and Li, Y., 2023. The Aerodynamic Performance of a Novel Overlapping Octocopter Considering Horizontal Wind. Aerospace, vol. 10, Art. no. 902. DOI: https://doi.org/10.3390/aerospace10100902

[11] Pätzold, F., Bauknecht, A., Schlerf, A., Sotomayor Zakharov, D., Bretschneider, L. and Lampert, A., 2023. Flight Experiments and Numerical Simulations for Investigating Multicopter Flow Field and Structure Deformation. Atmosphere, 14(9), Art. no. 1336. DOI: https://doi.org/10.3390/atmos14091336

[12] Kwon, M. and Eun, Y., 2024. Quadrotor Dynamics in a Wind Field: Equilibria Analysis and Energy Dissipation. International Journal of Control, Automation and Systems, 22(11), pp. 3275–3284. DOI: https://doi.org/10.1007/s12555-024-0048-4