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New methodology combining neural network and extended great deluge algorithms for the ATR-42 wing aerodynamics analysis

Published online by Cambridge University Press:  27 May 2016

A. Ben Mosbah ,
R.M. Botez  and
T.-M. Dao
A. Ben Mosbah
Affiliation:
Département de Génie de la Production Automatisée, École de Technologie Supérieure (ÉTS), LARCASE, Montreal, Quebec, Canada
R.M. Botez*
Affiliation:
École de Technologie Supérieure (ÉTS), LARCASE, Montreal, Quebec, Canada
T.-M. Dao
Affiliation:
Département de Génie Mécanique, École de Technologie Supérieure (ÉTS), Montreal, Canada
*
Ruxandra.Botez@etsmtl.ca
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Abstract

The fast determination of aerodynamic parameters such as pressure distributions, lift, drag and moment coefficients from the known airflow conditions (angles of attack, Mach and Reynolds numbers) in real time is still not easily achievable by numerical analysis methods in aerodynamics and aeroelasticity. A flight parameters control system is proposed to solve this problem. This control system is based on new optimisation methodologies using Neural Networks (NNs) and Extended Great Deluge (EGD) algorithms. Validation of these new methodologies is realised by experimental tests using a wing model installed in a wind tunnel and three different transducer systems (a FlowKinetics transducer, an AEROLAB PTA transducer and multitube manometer tubes) to determine the pressure distribution. For lift, drag and moment coefficients, the results of our approach are compared to the XFoil aerodynamics software and the experimental results for different angles of attack and Mach numbers. The main purpose of this new proposed control system is to improve, in this paper, wing aerodynamic performance, and in future to apply it to improve aircraft aerodynamic performance.

Keywords

aerodynamicsneural networkwind-tunnel testsextended great deluge
Type
Research Article
Information
The Aeronautical Journal , Volume 120 , Issue 1229 , July 2016 , pp. 1049 - 1080
Copyright
Copyright © Royal Aeronautical Society 2016 

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References

REFERENCES

1.Wrong, B.K., Vincent, S.L. and Lam, J.A bibliography of neural network business applications research: 1994-1998, Computers and Operation Research, 2002, 27, pp 1045–1076.Google Scholar
2.Hunt, K.J., Sbarbaro, D., Zbikowski, R. and Gawthrop, P.J.Neural networks for control systems—a survey, Automatica, 1992, 28, pp 1083–1112.Google Scholar
3.Udo, G.J.Neural networks application in manufacturing process. Computers and Industrial Engineering, 1992, 23, (1–4), pp 97–100.Google Scholar
4.Wong, B.K., Bodnovich, T.A. and Selvi, Y.Neural network application in business: A review and analysis of the literature (1988-1995), Decision Support Systems, 1997, 19, pp 301–320.Google Scholar
5.Chen, D. and Burrel, P.On the optimal structure design of multilayer feed-forward neural networks for pattern recognition, International J Pattern Recognition and Artificial Intelligence, 2002, 6, (4), pp 375–398.Google Scholar
6.Nourelfath, M. and Nahas, N.Quantized Hopfield networks for reliability optimization, Reliability Engineering and System Safety 2003, 81, pp 191–196.Google Scholar
7.Nourelfath, M. and Nahas, N.Artificial neural networks for reliability maximization under budget and weight constraints, J Quality in Maintenance Engineering, 2005, 11, (2), pp 139–151.Google Scholar
8.Yuan, R. and Guangchen, B.New neural network response surface methods for reliability analysis, Chinese J Aeronautics, 2011, 24, pp 25–31.Google Scholar
9.Seera, M., Lim, C.P., Ishak, D. and Singh, H.Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model, IEEE Transactions on Neural Networks and Learning Systems, 2012, 23, (1), pp 97–108.Google Scholar
10.Faller, W.E. and Schreck, S.J.Neural networks: Applications and opportunities in aeronautics, Progress in Aerospace Sciences, 1996, 32, pp 433–456.Google Scholar
11.Rauch, H.E., Kline-Schoder, R.J., Adams, J.C. and Youssef, H.M. Fault detection, isolation, and reconfiguration for aircraft using neural networks, AIAA-Guidance, Navigation, and Control and Co-located Conferences, 1993, pp 1527-1537.Google Scholar
12.Linse, D.J. and Stengel, R.F.Identification of aerodynamic coefficients using computational neural networks, J Guidance, Control and Dynamics, 1993, 16, (6), pp 1018–1025.Google Scholar
13.Amin, S.M., Gerhart, V. and Rodin, E.Y. System identification via artificial neural networks: Applications to online aircraft parameter estimation, SAE Technical Paper, 1997, doi:10.4271/975612.Google Scholar
14.Johnson, M.D. and Rokhsak, K. Using artificial neural network and self-organizing maps for detection of airframe icing, J Aircraft, 2001, 38, (2), pp 224–230.Google Scholar
15.Aykan, R. Kalman filter and neural network-based icing identification applied to A340 aircraft dynamics, Aircr Engineering and Aerospace Technology: An International Journal, 2005, 77, (1), pp 23–33.Google Scholar
16.Johnson, M.D. and Rokhsaz, K. Using artificial Neural networks and self organizing maps for detection of airframe icing, Atmospheric Flight Mechanics Conference, AIAA 2000, pp 373-383.Google Scholar
17.Napolitano, M.R. and Kincheloe, M.On-line learning neural network controllers for autopilot systems, J Guidance, Control and Dynamics, 1995, 33, (6), pp 1008–1015.Google Scholar
18.Yavrucuk, I., Prasad, J.V.R. and Calise, A. Adaptive limit detection and avoidance for carefree manoeuvring, AIAA Atmospheric Flight Mechanics Conference and Exhibit, 2001, Montreal, Canada.Google Scholar
19.Wallach, R., De Mattos, B.S. and Da Mota Girardi, R. Aerodynamic coefficient prediction of a general transport aircraft using neural network, 25th International Congress of the Aeronautical Sciences (ICAS), 2006, Hamburg, Germany.Google Scholar
20.Lunia, A., Isaac, K.M., Chandrashekhara, K. and Watkins, S.E.Aerodynamic testing of a smart composite wing using fiber-optic strain sensing and neural networks, Smart Materials and Structure, 2000, 9, pp 767–773.Google Scholar
21.Scott, R.C.Active control of wind-tunnel model aeroelastic response using neural networks, SPIE Smart Structures and Materials 2000: Industrial and Commercial Applications of Smart Structures Technologies, 2000, 3991, pp 232–243.Google Scholar
22.Suresh, S., Omkar, S.N., Mani, V. and Guru Prakash, T.N.Lift coefficient prediction at high angle of attack using recurrent neural network, Aerospace Science and Technology, 2003, 7, pp 595–602.Google Scholar
23.Fei, H., Zhu, R., Zhou, Z. and Wang, J.Aircraft flight parameter detection based on a neural network using multiple hot-film flow speed sensors, Smart Materials and Structure, 2007, 16, pp 1239–1245.Google Scholar
24.Peyada, N.K. and Ghosh, A.K.Aircraft parameter estimation using a new filtering technique based upon a neural network and Gauss-Newton method, Aeronautical J, 2009, 113, (1142), pp 243–252.Google Scholar
25.Samy, I., Postlethwaite, I. and Gu, D.W.Neural-network-based flush air data sensing system demonstrated on a mini air vehicle, J Aircraft, 2010, 47, (1), pp 18–31.Google Scholar
26.Guo, Y., Zhang, Y. and Jiang, B. Multi-model-based flight control system reconfiguration control in the presence of input constraints, Proceedings of the 8th World Congress on Intelligent Control and Automation, 2010, Jinan, China, pp 5819-5824.Google Scholar
27.Xuan, C.Z., Chen, Z., Wu, P., Zhang, Y. and Guo, W. Study of fuzzy neural network on wind velocity control of low-speed wind tunnel, International Conference on Electrical and Control Engineering, 2010, Wuhan, China, pp 2024-2027.Google Scholar
28.Sivanandam, S.N., Sumathi, S. and Deepa, S.N.Introduction to Fuzzy Logic using MATLAB, 2007, Springer, Berlin, Heidelberg.Google Scholar
29.De Jesus Mota, S. and Botez, R. New identification method based on neural network for helicopters from flight test data, AIAA Atmospheric Flight Mechanics Conference, 2009, Chicago, Illinois, US, pp 10-13.Google Scholar
30.Boeły, N. and Botez, R.M.New approach for the identification and validation of a nonlinear F/A-18 model by use of neural networks, IEEE Transactions Neural Networks, 2010, 21, (11), pp 1759–1765.Google Scholar
31.Boeły, N., Botez, R. and Kouba, G.Identification of a non-linear F/A-18 model by the use of fuzzy logic and neural network methods, Proceedings of the Institution of Mechanical Engineers, Part G: J of Aerospace Engineering, 2011, 225, (5), pp 559–574.Google Scholar
32.Zhang, Y. A fast and numerically robust neural network training algorithm, in Rutkowski, L. et al (Eds.), Artificial Intelligence and Soft Computing, LANI 2049, 2006, Springer-Verlag, Berlin, pp 160–169.Google Scholar
33.Ben Mosbah, A., Botez, R. and Dao, T.M. New methodology for calculating flight parameters with neural network–EGD method, AIAA Modeling and Simulation Technologies (MST) Conference, 2013, Boston, Massachusetts, US, pp 19-22.Google Scholar
34.Xiao, B., Hu, Q. and Zhang, Y.Adaptive sliding mode fault tolerant attitude tracking control for flexible spacecraft under actuator saturation, IEEE Transactions on Control Systems Technology, 2012, 20, (6), pp 1605–1612.Google Scholar
35.Roudbari, A. and Saghafi, F.Intelligent modeling and identification of aircraft nonlinear flight, Chinese J Aeronautics, 2014, 27, (4), pp 759–771.Google Scholar
36.Xin-lai, L., Hu, L., Gang-lin, W. and Zhe, W.Helicopter sizing based on genetic algorithm optimized neural network, Chinese J Aeronautics, 2006, 19, (3), pp 212–218.Google Scholar
37.Yuan-ming, X., Shuo, L. and Xiao-min, R.Composite structural optimization by genetic algorithm and neural network response surface modeling, Chinese J Aeronautics, 2005, 18, (4), pp 301–316.Google Scholar
38.Kouba, G., Botez, R. and Boeły, N. Identification of F/A-18 model from flight tests using the fuzzy logic method, 47th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, 2009, Orlando, Florida, US, pp 5–8.Google Scholar
39.Kouba, G., Botez, R. and Boeły, N.Fuzzy logic method use in F/A-18 aircraft model identification, J Aircraft, 2010, 47, (1), pp 10–17.Google Scholar
40.Grigorie, T.L., Botez, R.M. and Popov, A.V.Adaptive neuro-fuzzy controllers for an open-loop morphing wing system, Proceedings of the Institution of Mechanical Engineers, Part G: J Aerospace Engineering, 2009, 223, (7), pp 965–975.Google Scholar
41.Piroozan, P.Pressure measurement and pattern recognition by using neural networks, American Society of Mech Engineers, Dynamic Systems and Control Division (Publication) DSC, 2005, 74, (1), pp 897–905.Google Scholar
42.Panigrahi, P.K, Dwivedi, M., Khandelwal, V. and Sen, M. Prediction of turbulence statistics behind a square cylinder using neural networks and fuzzy logic, J Fluids Engineering, Transactions of the ASME, 2003, 125, (2), pp 385–387.Google Scholar
43.Voitcu, O., Wong, Y.S. An improved neural network model for nonlinear aeroelastic analysis, A Collection of Technical Papers—AIAA/ASME/ASCE/AHS Structures, Structural Dynamics, and Materials Conference, 2003, 2, pp 825-835.Google Scholar
44.Li, S., Fairbank, M. and Johnson, C.Artificial neural networks for control of a grid-connected rectifier/inverter under disturbance, dynamic and power converter switching conditions, IEEE Transactions on Neural Networks and Learning Systems, 2014, 25, (4), pp 738–750.Google Scholar
45.Grigorie, T.L. and Botez, R.Adaptive neuro-fuzzy inference system-based controllers for smart material actuator modeling, Proceedings of the Institution of Mech Engineers, Part G: J Aerospace Engineering, 2009, 223, (6), pp 655–668.Google Scholar
46.Grigorie, T.L. and Botez, R. Applications of fuzzy logic in the design and control of a morphing wing using smart material actuators, Fuzzy Controllers, Theory and Applications, 2011, USA: InTech, pp 253–296.Google Scholar
47.Grigorie, T.L. and Botez, R., Popov, A.V., Mamou, M. and Mébarki, Y. Application of fuzzy logic in the design and control of a morphing wing using smart material actuators, in 58th Aeronautics Conference and AGM (AERO 2011), Montreal, Canada, 2011.Google Scholar
48.Grigorie, T.L., Botez, R., Popov, A.V., Mamou, M. and Mébarki, Y. An intelligent controller based fuzzy logic techniques for a morphing wing actuation system using shape memory alloy, 52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials, Conference 19th, 2011, Denver, Colorado, US.Google Scholar
49.Grigorie, T.L. and Botez, R., Popov, A.V., Mamou, M. and Mébarki, Y.A hybrid fuzzy logic proportional integral-derivative and conventional on-off controller for morphing wing actuation using shape memory alloy, Part 1: Morphing system mechanisms and controller architecture design, Aeronautical J, 2012, 116, (1179), pp 433–449.Google Scholar
50.Grigorie, T.L. and Botez, R., Popov, A.V., Mamou, M. and Mébarki, Y.A hybrid fuzzy logic proportional integral-derivative and conventional on-off controller for morphing wing actuation using shape memory alloy, Part 2: Controller implementation and validation, Aeronautical J, 2012, 116, (1179), pp 451–465.Google Scholar
51.Burke, E., Bykov, Y., Newell, J. and Petrovic, S.A time-predefined local search approach to exam timetabling problems, IIE Transactions, 2004, 36, (6), pp 509–528.Google Scholar
52.Nahas, N., Khatab, A., Ait-Kadi, D. and Nourelfath, M.Extended great deluge algorithm for the imperfect preventive maintenance optimization of multi-state systems, Reliability Engineering and System Safety, 2008, 93, pp 1658–1672.Google Scholar
53.Ben Mosbah, A. and Dao, T.M. Optimization of group scheduling using simulation with the meta-heuristic extended great deluge (EGD) approach, IEEE International Conference, Industrial Engineering and Engineering Management (IEEM), 2010, Macao, China.Google Scholar
54.Ben Mosbah, A. and Dao, T.M. Optimization of manufacturing cell formation with extended great deluge meta-heuristic approach, International Conference on Industrial Engineering and Systems Management, 2011, Metz, France,.Google Scholar
55.Ben Mosbah, A. and Dao, T.M.Optimization of group scheduling problem using the hybrid meta-heuristic extended great deluge (EGD) approach: A case study, J Management and Engineering Integration, 2011, 4, (2), pp 1–13.Google Scholar
56.Dueck, G.New optimization heuristics. The great deluge algorithm and the record-to-record travel, J Computational Physics, 1993, 104, pp 86–92.Google Scholar
57.Ben Mosbah, A. and Dao, T.M.Optimization of manufacturing cell formation with extended great deluge meta-heuristic approach, International J Services Operations and Informatics, 2013, 7, (4), pp 280–293.Google Scholar
58.Drela, M. and Giles, M.B.Viscous-inviscid analysis of transonic and low Reynolds number airfoils, AIAA J, 1987, 25, (10), pp 1347–1355.Google Scholar
59.Madsen, H.A. and Filippone, A. Implementation and test of the XFoil code for airfoil analysis and design, Risø National Laboratory, Roskilde, Denmark, 1995, p 59.Google Scholar
60.Coutu, D., Brailovski, V. and Terriault, P.Optimized design of an active extrados structure for an experimental morphing laminar wing, Aerospace Science and Technology, 2010, 48, (1), pp 451–458.Google Scholar