Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-22T10:49:51.906Z Has data issue: false hasContentIssue false
  • English
  • Français

Enhancement of light aircraft 6 DOF simulation using flight test data in longitudinal motion

Published online by Cambridge University Press:  29 April 2021

L.V.T. Nguyen Open the ORCID record for L.V.T. Nguyen [Opens in a new window] ,
M. Tyan Open the ORCID record for M. Tyan [Opens in a new window] ,
J.-W. Lee Open the ORCID record for J.-W. Lee [Opens in a new window]  and
S. Kim
L.V.T. Nguyen
Affiliation:
Department of Aerospace Information Engineering, Konkuk University, Seoul, Korea
M. Tyan
Affiliation:
Department of Aerospace Information Engineering, Konkuk University, Seoul, Korea
J.-W. Lee*
Affiliation:
Department of Aerospace Information Engineering, Konkuk University, Seoul, Korea
S. Kim
Affiliation:
Department of Smart Vehicle Engineering, Konkuk University, Seoul, Korea
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper proposes a procedure to improve the accuracy of the light aircraft 6 DOF simulation model by implementing model tuning and aerodynamic database correction using flight test data. In this study, the full-scale flight testing of a 2-seater aircraft has been performed in specific longitudinal manoeuver for model enhancement and simulation validation purposes. The baseline simulation model database is constructed using multi-fidelity analysis methods such as wind tunnel (W/T) test, computational fluid dynamic (CFD) and empirical calculation. The enhancement process starts with identifying longitudinal equations of motion for sensitivity analysis, where the effect of crucial parameters is analysed and then adjusted using the model tuning technique. Next, the classical Maximum Likelihood (ML) estimation method is applied to calculate aerodynamic derivatives from flight test data, these parameters are utilised to correct the initial aerodynamic table. A simulation validation process is introduced to evaluate the accuracy of the enhanced 6 DOF simulation model. The presented results demonstrate that the applied enhancement procedure has improved the simulation accuracy in longitudinal motion. The discrepancy between the simulation and flight test response showed significant improvement, which satisfies the regulation tolerance.

Keywords

6 DOF simulationmodel enhancementparameter estimationMaximum Likelihoodflight test
Type
Research Article
Information
The Aeronautical Journal , Volume 125 , Issue 1290 , August 2021 , pp. 1358 - 1379
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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

Allerton, D. Principles of Flight Simulation. Wiley, 2009.Google Scholar
Napolitano, M.R. Aircraft Dynamics: From Modeling to Simulation. J. Wiley, 2012.Google Scholar
Liu, F., Wang, L. and Tan, X. Digital virtual flight testing and evaluation method for flight characteristics airworthiness compliance of civil aircraft based on HQRM, Chin J Aeronaut, 2015, 28, pp 112–120.CrossRefGoogle Scholar
Baltes, E. and Spitz, W. Virtual flight test as advanced step in aircraft development, AIAA Paper 2002-5823, 2002.10.2514/6.2002-5823CrossRefGoogle Scholar
Nicolosi, F., De Marco, A., Sabetta, V. and Della Vecchia, P. Roll performance assessment of a light aircraft: flight simulations and flight tests, Aerosp Sci Technol, 2018, 76, pp 471–483.CrossRefGoogle Scholar
Flight Design Gmbh. KLA-100 Aircraft Specification, 2014.Google Scholar
Royal Aeronautical Society. Aeroplane Flight Simlulator Evaluation Handbook: International Standards for the Qualification of Aeroplane Flight Simulators, Royal Aeronautical Society, 2005.Google Scholar
Mifsud, M., Vendl, A., Hansen, L.-U. and GÖrtz, S. Fusing wind-tunnel measurements and CFD data using constrained gappy proper orthogonal decomposition, Aerosp Sci Technol, 2019, 86, pp 312–326.10.1016/j.ast.2018.12.036CrossRefGoogle Scholar
Tyan, M. and Lee, J.-W. Efficient multi-response adaptive sampling algorithm for construction of variable-fidelity aerodynamic tables, Chin J Aeronaut, 2019, 32, pp 547–558.10.1016/j.cja.2018.12.012CrossRefGoogle Scholar
Jategaonkar, R.V. Flight Vehicle System Identification - A Time Domain Methodology, 2005.CrossRefGoogle Scholar
Morelli, E.A. and Klein, V. Accuracy of aerodynamic model parameters estimated from flight test data, J Guid Cont Dyn, 1997, 20, pp 74–80.10.2514/2.3997CrossRefGoogle Scholar
Saderla, S., Kim, Y. and Ghosh, A.K. Online system identification of mini cropped delta UAVs using flight test methods, Aerosp Sci Technol, 2018, 80, pp 337–353.10.1016/j.ast.2018.07.008CrossRefGoogle Scholar
Morelli, E.A. and Klein, V. Application of system identification to aircraft at NASA langley research center, J Aircr, 2005, 42, pp 12–25.CrossRefGoogle Scholar
Kumar, R. and Ghosh, A.K. Parameter estimation using maximum likelihood method from flight data at high angles of attack, World Acad Sci Eng Technol Int J Mech Aerosp Ind Mechatron Manufact Eng, 2011, 5, pp 2350–2355.Google Scholar
Saderla, S., Dhayalan, R. and Ghosh, A.K. Non-linear aerodynamic modelling of unmanned cropped delta configuration from experimental data, Aeronaut J, 2017, 121, pp 320–340.10.1017/aer.2016.124CrossRefGoogle Scholar
Saderla, S., Rajaram, D. and Ghosh, A.K. Lateral directional parameter estimation of a miniature unmanned aerial vehicle using maximum likelihood and Neural Gauss Newton methods, Aeronaut J, 2018, 122, pp 889–912.10.1017/aer.2018.36CrossRefGoogle Scholar
Saderla, S., Dhayalan, R., Singh, K., Kumar, N. and Ghosh, A.K. Longitudinal and lateral aerodynamic characterisation of reflex wing Unmanned Aerial Vehicle from flight tests using maximum likelihood, least square and Neural Gauss Newton methods. Aeronaut J, 2019, 123, pp 1807–1839.10.1017/aer.2019.70CrossRefGoogle Scholar
Verma, H.O. and Peyada, N.K. Parameter estimation of aircraft using extreme learning machine and Gauss-Newton algorithm, Aeronaut J, 2020, 124, pp 271–295.CrossRefGoogle Scholar
Yili, S., Ziyang, Z., Chaojie, O. and Huangzhong, P. 3D scene simulation of UAVs formation flight based on FlightGear simulator, 2014 IEEE Chinese 1978–1982 Guidance, Navigation and Control Conference (CGNCC), IEEE, 2014.Google Scholar
Moness, M., Mostafa, A.M., Abdel-Fadeel, M.A., Aly, A.I. and Al-Shamandy, A. Automatic control education using FlightGear and MATLAB based virtual lab, 8th International Conference on Electrical Engineering, pp 1157–1160, 2012.CrossRefGoogle Scholar
Nusyirwan, I.F. Engineering flight simulator using Matlab, Python and Flightgear. SimTecT, Melbourne, Australia, 2011.Google Scholar
Leong, H.I., Jager, R., Keshmiri, S. and Colgren, R. Development of a pilot training platform for UAVs using a 6DOF nonlinear model with flight test validation, AIAA Modeling and Simulation Technologies Conference and Exhibit, American Institute of Aeronautics and Astronautics. doi: 10.2514/6.2008-6368CrossRefGoogle Scholar
Cantarelo, O.C., Rolland, L. and O’Young, S. Validation discussion of an Unmanned Aerial Vehicle (UAV) using JSBSim flight dynamics model compared to MATLAB/Simulink AeroSim blockset, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 003989–003994, IEEE, 2016.Google Scholar
FAA Advisory Circular. Airplane Simulator Qualification. vols 120–40B, 1991.Google Scholar
ICAO. Manual of Criteria for the Qualification of Flight Simulation Training Devices. Vol. 1: Aeroplanes, Internat. Civil Aviation Organization, 2009.Google Scholar
Sargent, R.G. Use of the interval statistical procedure for simulation model validation, J Simul, 2015, 9, pp 232–237.10.1057/jos.2014.30CrossRefGoogle Scholar
Nelson, R.C. Flight Stability and Automatic Control, 2nd ed., 1997.Google Scholar
Iliff, K.W. Parameter estimation for flight vehicles, J Guid Cont Dyn, 1989, 12, pp 609–622.CrossRefGoogle Scholar
Maine, R.E. and Iliff, K.W. Application of Parameter Estimation to Aircraft Stability and Control - The Output-Error Approach, 1986.10.2514/6.1986-2020CrossRefGoogle Scholar
Tyan, M., Nguyen, N.V., Kim, S. and Lee, J.-W. Database adaptive fuzzy membership function generation for possibility-based aircraft design optimization. J Aircr, 2017, 54, pp 114–124.CrossRefGoogle Scholar
Van Nguyen, N., Tyan, M., Lee, J.-W. and Kim, S. Investigations on stability and control characteristics of a CS-VLA certified aircraft using wind tunnel test data, Proc Inst Mech Eng G: J Aerosp Eng 2016, 230, pp 2728–2743.10.1177/0954410016632016CrossRefGoogle Scholar
Van Nguyen, N., Lee, D., Tyan, M., Lee, J.-W. and Kim, S. Efficient stall compliance prediction method for trimmed very light aircraft with high-lift devices, Proc Inst Mech Eng G: J Aerosp Eng, 2017, 231, pp 1124–1137.10.1177/0954410016648981CrossRefGoogle Scholar
Vessel Company and Konkuk University. KLA-100 Full Scale Flight Test Report, 2017.Google Scholar
LV Thang, N., Maxim, T., Seung-Hyeog, N. and Jae-Woo, L. Flight test maneuver classification using pattern recognition for aerodynamic parameter estimation of light aircraft, 2018.Google Scholar
Li, H. Time works well: Dynamic time warping based on time weighting for time series data mining. Inform Sci, 2021, 547, pp. 592–608.10.1016/j.ins.2020.08.089CrossRefGoogle Scholar
Ward, D.T., Strganac, T.W. and Niewoehner, R. Introduction to Flight Test Engineering, Kendall Hunt Publishing Company, 2008.Google Scholar