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In-flight remote sensing and identification of gusts, turbulence, and wake vortices using a Doppler LIDAR

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Abstract

In this paper, in-flight remote sensing technologies are considered for two applications: active load alleviation of gust and turbulence and wake impact alleviation. The paper outlines the strong commonalities in terms of sensors and measurement post-processing algorithms and presents also the few differences and their consequences in terms of post-processing. The way the post-processing is being made is detailed before showing results for both applications based on a complete and coupled simulation (aircraft reaction due to disturbances and control inputs during the simulation is influencing the sensor measurements). The performances in terms of wind reconstruction quality for the gust/turbulence case and in terms of wake impact alleviation performance for the wake vortex case are shown based on simulations and are very promising.

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References

  1. Ehlers, J., Fezans, N.: Airborne Doppler LiDAR sensor parameter analysis for wake vortex impact alleviation purposes. In: Bordeneuve-Guibé J, Drouin A, Roos C (eds.) Advances in aerospace guidance, navigation and control, pp 433–453. Springer, Toulouse (2015) (ISBN 978-3-319-17517-1)

    Google Scholar 

  2. Fezans, N.: An Unusual Structure for a Feedforward Gust Load Alleviation Controller, Submitted to the 2017 CEAS EuroGNC conference, 25–27 April 2017, Warsaw, Poland

  3. Fezans, N., Joos, H.-D., Combined Feedback and LIDAR-based Feedforward Active Load Alleviation, Submitted to the 2017 AIAA Aviation/Atmospheric Flight Mechanics conference, 5 - 9 June 2017, Denver, Colorado, USA

  4. Cézard, N., Besson, C., Dolfi-Bouteyre, A., Lombard, L.: Airflow characterization by Rayleigh-Mie Lidars. AerospaceLab 1, 1–4 (2009)

    Google Scholar 

  5. Hill, C., Harris, M.: Lidar measurement report, Technical report: Remote Sensing (UpWind WP6) – QinetiQ Lidar Measurement Report, Sept. 2010 (QINETIQ/TS/FPPS/TR0900813)

  6. NATO: Optical Air Flow Measurement in Flight, RTO AGARDograph 160, Flight Test Instrumentation Serie –Vol. 20, Dec. 2003

  7. Wolkensinger, C.: Vergleich messtechnischer Konzepte zur bordgestützten Ermittlung atmosphärischer Störphänomene (English title: Comparison of measurement concepts for the detection of atmospheric disturbances), DLR Technical Report IB-111/2010-35, 2010

  8. Hirschberger, M. C.: Beiträge zur Erfassung von Wirbelschleppen mit Lidar – Simulation und Analyse rückgestreuter Signale zur Windfeldbestimmung vor Flugzeugen (English title: Contribution to the recognition of wake vortices with Lidar – Simulation and analysis of backscattered signals for the wind field determination ahead of aircraft), PhD thesis, Ludwig-Maximilian University, Munich, 2013

  9. Sivia, D.S.: Data analysis–a bayesian tutorial, 2nd edn. Oxford University Press, Oxford (2006)

    MATH  Google Scholar 

  10. Gregory, P.: Bayesian logical data analysis for the physical sciences. Cambridge University Press, UK (2005). (ISBN-13: 978-0-521-84150-4)

    Book  MATH  Google Scholar 

  11. Burnham, D.C., Hallock, J.N.: Chicago monostatic acoustic vortex sensing system, Wake Vortex Decay, vol. 4. National Information Service, Springfield (1982)

    Google Scholar 

  12. Lamb, H.: Hydrodynamics. Dover Publications, New York (1932). (ISBN-13 of 6th edition: 978-0486602561)

    MATH  Google Scholar 

  13. Proctor, F.H., Hamilton, D.W.: Evaluation of Fast-Time Wake Vortex Prediction Models, AIAA-2009-0344. AIAA Aerospace Sciences Meeting, Orlando (2009)

    Google Scholar 

  14. Winckelmans, G., Thirifay, F., Ploumhans, P.: Effect of non-uniform wind shear onto vortex wakes: parametric models for operational systems and comparison with CFD studies, 4th Wakenet Workshop on “Wake Vortex Encounter”, NLR, Amsterdam, The Netherlands, October 16–17th, 2000

  15. Fabre, D., Jacquin, L.: Short-wave cooperative instabilities in representative aircraft vortices. AIP Phys. Fluids 16(5), 1366–1378 (2004)

    Article  MATH  Google Scholar 

  16. Luckner, R.: Modeling and Simulation of Wake Vortex Encounters: State-of-the-Art and Challenges, AIAA-2012-4633, AIAA Modeling and Simulation Technologies Conference, Minneapolis, MN, USA, August 2012

  17. Fischenberg, D.: Determination of Wake Vortex Characteristics from Flight Test Data (in German: Bestimmung der Wirbelschleppencharakteristik aus Flugmessdaten), DLRK/Deutscher Luft- und Raumfahrtkongress, Stuttgart, 23rd–26th Sept, 2002

  18. Fischenberg, D.: A method to validate wake vortex encounter models from flight test data, ICAS 2010, 27th International Congress of the Aeronautical Sciences, Nice, France, 2010

  19. Fraley, C.: Algorithms for nonlinear least-squares problems, Technical Report DTIC: ADA196071, May 1988

  20. Cramér, H.: Mathematical methods of statistics. Princeton Univ. Press, Princeton (1946). (ISBN 0-691-08004-6)

    MATH  Google Scholar 

  21. Rao, C.R.: Information and the accuracy attainable in the estimation of statistical parameters. Bull Calcutta Math Soc. 37, 81–89 (1945). (MR 0015748)

    MathSciNet  MATH  Google Scholar 

  22. Rao, C.R.: Minimum variance and the estimation of several parameters. Math. Proc. Cambridge 43(2), 280–283 (1947)

    Article  MathSciNet  MATH  Google Scholar 

  23. Rao, C.R.: Linear Statistical Inference and its Applications, 2nd edn. Wiley, New York (1973)

    Book  MATH  Google Scholar 

  24. Crow, S.C.: Stability theory for a pair of trailing vortices. AIAA J. 8(12), 2172–2179 (1970)

    Article  Google Scholar 

  25. Crow, S.C., Bate, E.R.: Lifespan of trailing vortices in a turbulent atmosphere. J. Aircr. 13(7), 476–482 (1976)

    Article  Google Scholar 

  26. Hennemann, I.: Deformation und Zerfall von Flugzeugwirbelschleppen in turbulenter und stabil geschichteter Atmosphäre (English title: Deformation and decay of aircraft wake vortices in turbulent and stable atmosphere), PhD thesis, Technische Universität München, Germany, 2009

  27. Loucel, R.E., Crouch, J.D.: Flight-simulation study of airplane encounters with perturbed trailing vortices. J. Aircr. 42(4), 924–931 (2005)

    Article  Google Scholar 

  28. Bieniek, D., Luckner, R.: Simulation of aircraft encounters with perturbed vortices considering unsteady aerodynamic effects. J. Aircr. 51(3), 705–718 (2014)

    Article  Google Scholar 

  29. Vechtel, D.: Entwicklung eines analytischen Modells zur Berechnung gekrümmter Wirbelschleppen, DLR Technical Report IB 111-2010/14, Braunschweig, March 2010

  30. Münster, C.: Modellierung, Identifizierung und Bewertung eines analytischen Modells für gekrümmte Wirbelschleppen (English title: Modeling, Identification, and Evaluation of an Analytical Model of Curved Vortices), DLR Technical Report IB-111-2011/09, Braunschweig, March 2011

  31. Tikhonov, A.N., Arsenin, V.Y.: Solutions of Ill-posed problems. Winston & Sons, Washington (1977)

    MATH  Google Scholar 

  32. Marr, D., Hildreth, E.: Theory of edge detection. Proc. R. Soc. Lond. Ser. B Biol. Sci. 207(1167), 187–217 (1980)

    Article  Google Scholar 

  33. König, R., Hahn, K.-U.: Load Alleviation and Ride Smoothing Investigations Using ATTAS. In: Proceedings of the 17th congress of the international council of the aeronautical sciences, Stockholm, Sweden, 1990

  34. Hahn, K.-U., König, R.: ATTAS Flight Test and Simulation Results of the Advanced Gust Management System LARS. Proc. of the AIAA Atmospheric Flight Mechanics Conference, Hilton Head, SC, USA, 1992

  35. König, R., Hahn, K.-U., Winter, J.: Advanced Gust Management Systems—Lessons Learned and Perspectives. In AGARD Flight Mechanics Panel, Symposium on Active Control Technology: Applications and Lessons Learned, Torino, Italy, 1994

  36. Schmitt, N. P., et al.: The AWIATOR Airborne LIDAR turbulence sensor, DLRK/Deutscher Luft- und Raumfahrtkongress 2005, Friedrichshafen, DGLR-2005-067, September 2005

  37. Hecker, S., Hahn, K.-U.: Gust load alleviation system using turbulence sensor and adaptive elements. AWIATOR, Technical Report, DLR-TR-3.1.1-14, Germany, July 2006

  38. Wildscheck, A.: An adaptive feed-forward controller for active wing bending vibration alleviation on large transport aircraft. PhD thesis, Technical University of Munich, 2009

  39. Zeng, J., Moulin, B., de Callafon, R., Brenner, M.J.: Adaptive feedforward control for gust load alleviation. J. Guid. Control Dyn. 33(3), 862–872 (2010)

    Article  Google Scholar 

  40. Rabadan, G.J., Schmitt, N.P., Pistner, T., Rehm, W.: Airborne lidar for automatic feedforward control of turbulent in-flight phenomena. J. Aircr. 47(2), 392–403 (2010)

    Article  Google Scholar 

  41. European Aviation Safety Agency, Certification Specifications for Large Aeroplanes, CS 25, Amendment 3, Sept. 19th 2007, Annex to ED Decision 2007/010/R

  42. Anderson, B.D.O., Dehghani, A.: Challenges of adaptive control–past, permanent and future. Annu. Rev. Control 32, 123–135 (2008)

    Article  Google Scholar 

  43. Fischenberg, D.: Online wake identification algorithms using forward looking LIDAR sensor measurements, DLR Technical Report IB111-2013/11 Braunschweig, Germany, February 2013

  44. Fischenberg, D.: Strömungsermittlungsverfahren/Flow Determination Method/Procédé de détermination d’écoulement, Patent No. EP 2 340 438 B1, European Patent Office 2013; Patent No. US 9075074 B2, United States Patent and Trademark Office 2015

  45. Ehlers, J., Fischenberg, D., Niedermeier, D.: Wake identification based wake impact alleviation control, AIAA-2014-2591. AIAA AVIATION, Atlanta (2014)

    Google Scholar 

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Acknowledgements

The gust and turbulence part of this work has been funded within the framework of the European CleanSky Joint Technology Initiative—Smart Fixed Wing Aircraft (Grant Agreement Number CSJU-GAM-SFWA-2008-01) and is currently being pursued within the framework of the European CleanSky2 Joint Technology Initiative—Airframe (Grant Agreement Number CS2JU-AIR-GAM-2014-2015-01).

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Authors and Affiliations

  1. DLR (German Aerospace Center), Lilienthalplatz 7, 38108, Brunswick, Germany

    N. Fezans, J. Schwithal & D. Fischenberg

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  1. N. Fezans
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  2. J. Schwithal
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  3. D. Fischenberg
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Corresponding author

Correspondence to N. Fezans.

Additional information

This paper is based on a presentation at the German Aerospace Congress, September 22–24, 2015, Rostock, Germany.

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Fezans, N., Schwithal, J. & Fischenberg, D. In-flight remote sensing and identification of gusts, turbulence, and wake vortices using a Doppler LIDAR. CEAS Aeronaut J 8, 313–333 (2017). https://doi.org/10.1007/s13272-017-0240-9

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  • DOI: https://doi.org/10.1007/s13272-017-0240-9

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