Hostname: page-component-8448b6f56d-tj2md Total loading time: 0 Render date: 2024-04-25T01:55:55.147Z Has data issue: false hasContentIssue false
  • English
  • Français

Semi-empirical estimation and experimental method for determining inertial properties of the Unmanned Aerial System – UAS-S4 of Hydra Technologies

Published online by Cambridge University Press:  11 October 2017

Y. Tondji  and
R. M. Botez
Y. Tondji
Affiliation:
ETS, Laboratory of Applied Research in Active Controls, University of Quebec, Avionics and AeroServoElasticity LARCASE, Montreal, Quebec, Canada
R. M. Botez*
Affiliation:
ETS, Laboratory of Applied Research in Active Controls, University of Quebec, Avionics and AeroServoElasticity LARCASE, Montreal, Quebec, Canada
*
ruxandra.botez@etsmtl.ca
Get access Rights & Permissions [Opens in a new window]

Abstract

This article presents a structural analysis of the Unmanned Aerial System UAS-S4 ETHECATL. Mass, centre of gravity position and principal mass moment of inertia are numerically determined and further experimentally verified using the ‘pendulum method’. The numerical estimations are computed through Raymer and DATCOM statistical-empirical methods coupled with mechanical calculations. The mass of the UAS-S4 parts are estimated according to their sizes and the UAS-S4 class, by the means of Raymer statistical equations. The UAS-S4 is also decomposed in several simple geometrical figures which centres of gravity are individually computed, weighted and then arithmetically averaged to find the whole UAS-S4 centre of gravity. In the same way, DATCOM equations allows us to estimate the mass moments of inertia of each UAS-S4 parts that are finally sum up according to the Huygens-Steiner theorem for computing the principal moment of inertia of the whole UAS-S4. The mass of de UAS-S4 is experimentally determined with two scales. Its centre of gravity coordinates and its mass moment of inertia are found using the pendulum method. A bifilar torsion-type pendulum methodology is used for the vertical axis(14) and a simple pendulum methodology is used for the longitudinal and transversal axes(12). The test object is installed on a pendulum (simple or bifilar torsion pendulum) which is led to oscillate freely while recording the oscillation's angles and speed, by the means of three sensors (an accelerometer, a gyroscope and a magnetometer) that the calibration is also discussed. Simultaneously, nonlinear dynamic models are developed for the rotational motion of pendulums, including the effects of large-angle oscillations, aerodynamic drag, viscous damping and additional momentum of air. ‘Algorithms of minimization’ are then used to simulate and actualise the dynamic models and finally chose the model that simulated data best fit the experimentally recorded one. Pendulum parameters, such as mass moment of inertia, are lastly extracted from the chosen model. To determine the accuracy of the nonlinear dynamics approach of the pendulum method, the experimental results for an object of uniform density for which the mass moments of inertia are computed numerically from geometrical data are presented along with the experimental results obtained for the UAS-S4 ETHECATL. For the uniform density object, the experimental method gives, with respect to the numerical results, an error of 4.4% for the mass moment of inertia around the Z axis and 9.5% for the moment of inertia around the X and Y axes. In addition, the experimental results for the UAS-S4 inertial values validate the numerical calculation through DATCOM method with a relative error of 6.52% on average.

Keywords

UAVpendulum methodRaymer equationsDATCOMmoments of Inertiacentre of gravityexperimental methodssemi-empirical methodsHuygens-Steiner theorem
Type
Research Article
Information
The Aeronautical Journal , Volume 121 , Issue 1245 , November 2017 , pp. 1648 - 1682
Copyright
Copyright © Royal Aeronautical Society 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Raymer, D. Aircraft Design: A Conceptual Approach, Edition 5, 2012, AIAA Education Series, Washington, DC, US.Google Scholar
2. Williams, J.E. and Vukelich, S.R. The USAF Stability and Control Digital DATCOM. Vol. I: User Manual, 1979, AFFDL-TR-79-3032, St-Louis, MI, US.Google Scholar
3. Williams, J.E. and Vukelich, S.R. The USAF Stability and Control Digital DATCOM. Vol. II: Implementation of Datcom Methods, 1979, AFFDL-TR-79-3032, St-Louis, MI, US.Google Scholar
4. Williams, J.E. and Vukelich, S.R. The USAF Stability and Control Digital DATCOM. Vol. III: Plot Module, 1979, AFFDL-TR-79-3032, St-Louis, MI, US.Google Scholar
5. Anton, N., Botez, R.M. and Popescu, D. Stability derivatives for X-31 delta-wing aircraft validated using wind tunnel test data, Proceedings of the Institution of Mechanical Engineers, Part G, J Aerospace Engineering, 2011, 225, pp 403–416.Google Scholar
6. Anton, N., Popescu, D. and Botez, R.M. New methods and code for stability derivatives calculations from hawker 800 XP aircraft geometrical data knowledge, Aeronautical J, 2010, 114, (1156), pp 367–376.Google Scholar
7. Anton, N. and Botez, R.M. A new type of the stability derivatives for X-31 model aircraft validated using wind tunnel test data, Applied Vehicle Technology Panel Specialists Meeting AVT-189, Assessment of Stability and Control Prediction Methods for NATO Air and Sea Vehicles, 12–14 October 2011, Dstl Portsdown West, Fareham, Hampshire, Great Britain.Google Scholar
8. Anton, N., Botez, R.M. and Popescu, D. New methods and code for aircraft stability derivatives calculations from its geometrical data, CASI Aircraft Design and Development Symposium, 5–7 May 2009, Kanata, Ontario, Canada.Google Scholar
9. Şugar Gabor, O., Koreanschi, A. and Botez, R. Optimization of an unmanned aerial system' wing using a flexible skin morphing wing, SAE Int J Aerospace, 2013, 6, (1), pp 115–121, doi: 10.4271/2013-01-2095.Google Scholar
10. Şugar Gabor, O., Koreanschi, A. and Botez, R.M. An efficient numerical lifting line method for practical wing optimization through morphing on the hydra technologies UAS-S4, CASI AÉRO 2013 Conference, 30 April–2 May 2013, Toronto, Canada.Google Scholar
11. Şugar Gabor, O., Koreanschi, A. and Botez, R.M. Unmanned aerial system hydra technologies éhecatl wing optimization using a morphing approach, AIAA Guidance, Navigation, and Control Conference, 19–22 August 2013, Boston, Massachusetts, US.Google Scholar
12. Soulé, H.A. and Miller, M.P. The experimental determination of the moments of inertia of airplanes, NACA Report 467, 1933.Google Scholar
13. Dubois, F. Modélisation structurelle et optimisation numérique d'un drone à voilure fixe, Internship Report, 2012, ETS, Montreal, Canada.Google Scholar
14. Matt, R.J. and Muller, E.R. Optimized measurements of unmanned-aircraft-vehicle mass moment of inertia with a bifilar pendulum, J Aircraft, 2009, 46, (3), pp 763–775.Google Scholar
15. Ozyagcilar, T. Calibrating and eCompass in the presence of Hard and Soft-Iron Interference, Freescale Semiconductor, Application Note, Document Number AN4246, Rev. 4.0. 2015, Available at: https://www.nxp.com/docs/en/application-note/AN4246.pdf.Google Scholar
16. Jardin, M. Improving mass moment of inertia measurements, 2010, Available at: http://www.mathworks.com.Google Scholar
17. Hydra technologies S4 Ehecatl specification, 2013, Available at: https://en.wikipedia.org/wiki/Hydra_Technologies_Eh%C3%A9catl.Google Scholar