Szczegóły

Tytuł artykułu

Identification of longitudinal aerodynamic characteristics of a strake-wing micro aerial vehicle by using artificial neural networks

Tytuł czasopisma

Bulletin of the Polish Academy of Sciences: Technical Sciences

Rocznik

2021

Wolumin

69

Numer

4

Autorzy

Słowa kluczowe

water tunnel measurements ; neural networks ; unsteady aerodynamic characteristics ; low Reynolds number aerodynamics

Wydział PAN

Nauki Techniczne

Zakres

e137508

Bibliografia

  1.  C. Galiński and R. Żbikowski, “Some problems of micro air vehicles development,” Bull. Polish Acad. Sci. Tech. Sci., vol. 55, no. 1, pp. 91–98, 2007.
  2.  K. Sibilski, M. Nowakowski, D. Rykaczewski, P. Szczepaniak, A. Żyluk, A. Sibilska-Mroziewicz, M. Garbowski, and W. Wróblewski, “Identification of fixed-wing micro aerial vehicle aerodynamic derivatives from dynamic water tunnel tests,” Aerospace, vol. 7, no. 8, p. 116, 2020, doi: 10.3390/aerospace7080116.
  3.  K. Sibilski, M. Lasek, A. Sibilska-Mroziewicz, and M. Garbowski, Dynamcs of Flight of Fixed Wings Micro Aerial Vehicles, Publishing House of the Warsaw University of Technology, Warsaw, 2020.
  4.  M. Abdulrahim, S. Watkins, R. Segal, M. Marino, and J. Sheridan, “Dynamic sensitivity to atmospheric turbulence of fixed-wing UAV with varying configuration,” J. Aircaft, vol. 47, no. 6, pp. 1873–1883, 2010, doi: 10.2514/1.46860.
  5.  A.N. Kolmogorov, “On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition,” Dokl. Akad. Nauk SSSR, vol. 114, no. 5, pp. 953–956, 1957, [in Russian].
  6.  W.E. Faller, S.J. Schreck, and H.E. Helin, “Real-time model of three dimensional dynamic reattachment using neural networks,” J. Aircraft, vol. 32, no. 6, pp. 1177–1182, 1995, doi: 10.2514/3.46861.
  7.  W.F. Faller and S.J. Schreck, “Unsteady fluid mechanics applications of neural networks,” J. Aircraft, vol. 34, no. 1, pp. 48–55, 1997, doi: 10.2514/2.2134.
  8.  M. Kerho and B. Kramer, Research water tunnels – specification, Rolling Hills Research Corporation (RHRC), El Segundo, CA, USA, 2003.
  9.  M. Kerho and B. Kramer, Five-component balance and computer-controlled model support system for water tunnel applications, Rolling Hills Research Corporation (RHRC), El Segundo, CA, USA, 2009.
  10.  M. Kerho and B. Kramer, Ultrasonic flowmeter and temperature probe, Rolling Hills Research Corporation (RHRC), El Segundo, CA, USA, 2010.
  11.  P.H. Reisenthel, “Development of nonlinear indicial model using response functions generated by a neural network,” in Proceedings of the 35th Aerospace Sciences Meeting and Exhibit, Reno, NV, USA, 6–9 January 1997, p. AIAA 97‒0337, doi: 10.2514/6.1997-337.
  12.  S. Hitzel and D. Zimper, “Wind tunnel simulation and ‘Real’ flight of advanced combat aircraft: industrial perspective,” J. Aircraft, vol. 55, no. 2, pp. 587–602, 2018, doi: 10.2514/1.C033696.
  13.  D. Rohlf, S. Schmidt, and J. Irving, “Stability and control analysis for an unmanned aircraft configuration using system-identification techniques,” J. Aircraft, vol. 49, no. 6, pp. 1597–1609, 2012, doi: 10.2514/1.C031392.
  14.  D.J. Ignatyev and A.N. Khrabrov, “Neural network modelling of unsteady aerodynamic characteristics at high angles of attack,” Aerospace Sci. Technol., vol. 41, pp. 106–115, 2015, doi: 10.1016/j.ast.2014.12.017.
  15.  D. Ignatyev and A. Khrabrov, “Experimental study and neural network modeling of aerodynamic characteristics of canard aircraft at high angles of attack,” Aerospace, vol. 5, no. 1, p. 26, 2018, doi: 10.3390/aerospace5010026.
  16.  P.C. Murphy, V. Klein, and N.T. Frink, “Nonlinear unsteady aerodynamic modeling using wind-tunnel and computational data,” J. Aircraft, vol. 54, no. 2, pp. 659–683, 2017, doi: 10.2514/1.C033881.
  17.  P. Murphy, V. Klein, and N. Szyba, “Progressive aerodynamic model identification from dynamic water tunnel test of the F-16XL aircraft,” in Proceedings of the AIAA Atmospheric Flight Mechanics Conference and Exhibit, Guidance, Navigation, and Control and Co-located Conferences, 2004, Providence, RI, USA, p. AIAA 2004–5727, doi: 10.2514/6.2004-5277.
  18.  B. Paprocki, A. Pregowska, and J. Szczepański, “Optimizing information processing in brain-inspired neural networks,” Bull. Polish Acad. Sci. Tech. Sci., vol. 8, no. 2, pp. 225–233, 2020, doi: 10.24425/bpasts.2020.131844.
  19.  W.S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bull. Math. Biophys., vol. 5, no. 4, pp. 115– 133, 1943, doi: 10.1007/BF02478259.
  20.  J. Hertz, A. Krogh, and R. Palmer, Introduction to the theory of neural computation, CRC Press, Taylor & Francis Inc., London – N-York, 1991.
  21.  R.A. Kosiński, Artificial neural networks. non-linear dynamics and chaos, PWN, Warszawa, 2017.
  22.  S. Osowski, Neural networks for information processing, 4th Edition, Publishing House of the Warsaw University of Technology, Warsaw, 2020.
  23.  R. Tadeusiewicz, Neural networks, Akademic Publisching House, Warsaw, 1993.
  24.  K. Diamantaras and S. Kung, Principal component neural networks, theory and application, J. Wiley, New York, 1996.
  25.  R. Lippmann, “An introduction to computing with neural nets,” IEEE ASSP Mag., vol. 4, no. 2, pp. 4–22, 1987, doi: 10.1109/ MASSP.1987.1165576.
  26.  K.S. Narendra and K. Parthasarathy, “Identification and control of dynamical systems using neural network”, IEEE Trans. Neural Networks, vol. 1, no. 1, pp. 4–27, 1990, doi: 10.1109/72.80202.
  27.  A. Cichocki and R. Unbehauen, “Neural networks for solving systems of linear equations and related problems,” IEEE Trans. Circuits Syst. I: Fundam. Theory Appl., vol. 39, no. 2, pp. 124–138, 1992, doi: 10.1109/81.167018.
  28.  J. Denoeux and R. Lengalle, “Initialising back propagation networks with prototypes,” Neural Networks, vol. 6, no. 3, pp. 351–363, 1993, doi: 10.1016/0893-6080(93)90003-F.
  29.  E. Karnin, “A simple procedure for pruning backpropagation trained neural networks,” IEEE Trans. Neural Networks, vol. 1, no. 2, pp. 239–242, 1990, doi: 10.1109/72.80236.
  30.  J. Manerowski and D. Rykaczewski, “Modelling of UAV flight dynamics using perceptron artificial neural networks,” J. Theor. App. Mech., vol. 43, no. 2, pp. 297–307, 2005.
  31.  R. Barron, “Approximation and estimation bounds for artificial neural networks,” Machine Learning, vol. 14, pp. 115–133, 1994, doi: 10.1007/BF00993164.
  32.  J.F. Horn, A.J. Calise, and J.V.R. Prasad, “Flight Envelope Cueing on a Tilt-Rotor Aircraft Using Neural Network Limit Prediction,” J. Amer. Helic. Soc., vol. 46, no. 1, pp. 23–31, 2001, doi: 10.4050/JAHS.46.23.
  33.  T. Cepowski and T. Szelangiewicz, “Application of Artificial Neural Networks to investigations of ship seakeeping ability,” Pol. Marit. Res., vol. 8, no. 3, pp. 11–15, 2001.
  34.  T. Mueller, “Aerodynamic Measurements at Low Reynolds Number for Fixed Wing Micro-Air Vehicles,” in AVT/VKI Special Course on Development and Operation of UAVs for Military and Civil Applications, NATO/VKI, Brussel, Belgium, 1999.
  35.  Dong Sun, Huaiyu Wu, Rong Zhu, and Ling Che Hung, “Development of Micro Air Vehicle Based on Aerodynamic Modeling Analysis in Tunnel Tests,” in Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain, 2005, pp. 2235–2240, doi: 10.1109/ROBOT.2005.1570445.
  36.  R. Randall, S. Shkarayev, G. Abate, and J. Babcock, “Longitudinal aerodynamics of rapidly pitching fixed-wing Micro Air Vehicles,” J. Aircraft, vol. 49, no. 2, pp. 453–471, 2012, doi: 10.2514/1.C031378.
  37.  C. Tongchitpakdee, W. Hlusriyakul, C. Pattanathummasid, and C. Thipyopas, “Aerodynamic investigation and analysis of wingtip thickness’s effect on low aspect ratio wing,” in Proc. International Micro Air Vehicle Conference and Flight Competition (IMAV2013), Toulouse, France, 2013.
  38.  J-M. Moschetta, “The aerodynamics of micro air vehicles: technical challenges and scientific issues,” Int. J. Eng. Sys. Model. Sim., vol. 6, no. 3/4, pp. 134–148, 2014, doi: 10.1504/IJESMS.2014.063122.
  39.  D. Viieru, J. Tang, Y. Lian, H. Liu, and W. Shy, “Flapping and flexible wing aerodynamics of low Reynolds number flight vehicles,” in Proc. 44th AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, USA, 2006, p. AIAA 2006–503, doi: 10.2514/6.2006-503.
  40.  D. Gyllhem, K. Mohseni, and D. Lawrence, “Numerical simulation of flow around the Colorado Micro Aerial Vehicle,” in Proceedings 35th AIAA Fluid Dynamics Conference and Exhibit, Toronto, Canada, 2005, p. AIAA 2005–4757, doi: 10.2514/6.2005-4757.
  41.  V.V. Golubev and M,R. Visbal, “Modeling MAV response in gusty urban environment,” Int. J. Micro Air Veh., vol. 4, no. 1, pp. 79–92, 2012, doi: 10.1260/1756-8293.4.1.79.
  42.  R. Cory and R. Tedrake, “Experiments in fixed-wing UAV perching,” in Proceedings AIAA Guidance, Navigation and Control Conference and Exhibit, Honolulu, Hi, USA, 2008, p. AIAA 2008–7256, doi: 10.2514/6.2008-7256.
  43.  D.V. Uhlig and M.S. Selig, “Stability characteristics of Micro Air Vehicles from experimental measurements,” in Proc. 29th AIAA Applied Aerodynamics Conference, Honolulu, HI, USA, 2011, p. AIAA 2011–3659. doi: 10.2514/6.2011-3659.

Data

01.06.2021

Typ

Article

Identyfikator

DOI: 10.24425/bpasts.2021.137508 ; ISSN 2300-1917

Źródło

Bulletin of the Polish Academy of Sciences: Technical Sciences; 2021; e137508
×