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Quantification of Aerodynamic Variables Using Analytical Technique and Computational Fluid Dynamics

Authors: Adil Loya, Kamran Maqsood, Muhammad Duraid


Aerodynamic stability coefficients are necessary to be known before any unmanned aircraft flight is performed. This requires expertise on aerodynamics and stability control of the aircraft. To enable efficacious performance of aircraft requires that a well-defined flight path and aerodynamics should be defined beforehand. This paper presents a study on the aerodynamics of an unmanned aero vehicle (UAV) during flight conditions. Current research holds comparative studies of different parameters for flight aerodynamic, measured using two different open source analytical software programs. These software packages are DATCOM and XLRF5, which help in depicting the flight aerodynamic variables. Computational fluid dynamics (CFD) was also used to perform aerodynamic analysis for which Star CCM+ was used. Output trends of the study demonstrate high accuracies between the two software programs with that of CFD. It can be seen that the Coefficient of Lift (CL) obtained from DATCOM and XFLR is similar to CL of CFD simulation. In the similar manner, other potential aerodynamic stability parameters obtained from analytical software are in good agreement with CFD.

Keywords: Computational Fluid Dynamic, Datcom, XFLR5, unmanned aero vehicle

Digital Object Identifier (DOI):

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[1] Mi, B.-g., H. Zhan, and B.-b. Chen, New Systematic CFD Methods to Calculate Static and Single Dynamic Stability Derivatives of Aircraft. Mathematical Problems in Engineering, 2017. 2017.
[2] Sheibani, A. and M. A. Pourmina, Simulation and Analysis of the Stability of a PID Controller for Operation of Unmanned Aerial Vehicles, in Mechanical Engineering and Technology. 2012, Springer. p. 757-765.
[3] Lichota, P. and P. Ohme, Design and Analysis of new Multi Axis Input Manoeuvres for Aircraft Sys-ID. 2014.
[4] Haque, A. U., et al. Comparison of data correction methods for blockage effects in semispan wing model testing. in EPJ Web of Conferences. 2016. EDP Sciences.
[5] Macha, J. and R. Buffington, Wall-interference corrections for parachutes in a closed wind tunnel. Journal of Aircraft, 1990. 27(3): p. 320-325.
[6] Saha, N., Gap size effect on low Reynolds number wind tunnel experiments. 1999.
[7] Loya, A., et al., Dependency of Torque on Aerofoilcamber Variation in Vertical Axis Wind Turbine. World Journal of Mechanics, 2016. 6(11): p. 472.
[8] Huber, K. C., A. Schütte, and M. Rein, Numerical investigation of the aerodynamic properties of a flying wing configuration. 2010, Dt. Zentrum für Luft-und Raumfahrt.
[9] Vallespin, D., et al., Vortical flow prediction validation for an unmanned combat air vehicle model. Journal of Aircraft, 2011. 48(6): p. 1948-1959.
[10] Jirasek, A. and R. M. Cummings. SACCON static and dynamic motion flow physics simulations using COBALT. in Proceedings of the 29th AIAA Applied Aerodynamics Conference. 2011.
[11] Deperrois, A., XFLR5 Analysis of foils and wings operating at low Reynolds numbers. Guidelines for XFLR5, 2009.