Abstract
A genetic algorithm (GA) is shown to be a feasible approach to determining worst-case gust loads in aircraft structures. The mathematical modeling of extreme turbulence is discussed, as well as methods of analysis and prediction of aircraft response to atmospheric turbulence in the context of aircraft design. A representation of this problem for a GA approach is proposed and results of the technique are presented.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Etkin, B. (1981), “Turbulent wind and its effect on flight,”Journal of Aircraftvol. 18, no. 5, pp. 327–345.
Goldberg, D.E. (1989) Genetic Algorithms in Search Optimization and Machine Learning Reading, MA: Addison Wesley.
Jones, J.G. (1973), “Statistical Discrete Gust Theory for Aircraft Loads,” Royal Aircraft Establishment, Farnborough, England, U.K., RAE TR-73167.
Jones, J.G. (1984), “A Relationship between the Power-SpectralDensity and Statistical-Discrete-Gust Methods of Aircraft Response Analysis,” Royal Aircraft Establishment, Farnborough, England, U.K., RAE TM Space 347.
Jones, J.G. (1989), “Statistical discrete gust method for predicting aircraft loads and dynamic response,”Journal of Aircraftvol. 26, no. 4, pp 382–392.
Jones, J.G. (1992), “Aircraft response to turbulence: recent research using a wavelet model,” presented atGust Specialists MeetingDallas, Texas, April 15.
Jones, J.G., Foster, G.W., and Earwicker, P.G. (1993), “Wavelet analysis of gust structure in measured atmospheric turbulence data,”AIAA Journal of Aircraftvol. 30, pp. 94–99.
Jones, J.G. (1994), “The case for a future airworthiness requirement for aircraft limit loads based on the statistical discrete gust (sdg) method,” presented atSpecial Gust Specialists MeetingHampton, Virginia, September.
Michalewicz, Z. (1994)Genetic Algorithms + Data Structures = Evolution ProgramsAI Series, Springer-Verlag, New York.
Northy, D.O. (1943), “Analysis of the Factors which Determine Signal/Noise Discrimination in Radar,” RCA Lab., Princeton, NJ, Rept. PTR-6C, June.
Papoulis, A. (1970), “Maximum response with input energy constraints and the matched filter principle,”IEEE Transactions on Circuit TheoryIEEE, New York, vol. CT-17, no. 2, May, pp. 175–182.
Pfeil, A.L. (1996), “An Investigation of a Proposed Airworthiness Requirement Based on the SDG Method: Aircraft Mathematical Model,” Stirling Dynamics Ltd. Report No. SDL-231-TR-1, Issue 1, June.
Rosenberg, G., Cowling, D.A., and Hockenhull, M. (1993), “The deterministic spectral procedure for gust response analysis of nonlinear aircraft modes,” (Stirling Dynamics Ltd. Report No. 233)International Forum on Aeroelasticity and DynamicsStrasbourg, France, May.
Wingrove, R. and Bach, R. (1994), “Severe turbulence and maneuvering from airline flight records,”Journal of Aircraftvol. 31, no. 4, pp. 753–760
Wright, A. (1991), “Genetic algorithms for real parameter optimization,” in Rawlins, G.J.E. (Ed.)op. cit.pp. 205–220.
Watson, G.H. and Jones, J.G. (1993), “Positive wavelet representation of fractations of fractals and chaos,” in Crilly, A.J. et al. (Eds.), Springer Verlag.
Zeiler, T.A. (1997), “ Matched filter concept and maximum gust loads,”AIAA Journal of Aircraftvol. 34, no. 1, January-February.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer Science+Business Media New York
About this chapter
Cite this chapter
Mehrotra, R., Karr, C.L., Zeiler, T.A. (2001). Genetic Algorithm for Optimizing the Gust Loads for Predicting Aircraft Loads and Dynamic Response. In: Jain, L., De Wilde, P. (eds) Practical Applications of Computational Intelligence Techniques. International Series in Intelligent Technologies, vol 16. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0678-1_8
Download citation
DOI: https://doi.org/10.1007/978-94-010-0678-1_8
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-3868-3
Online ISBN: 978-94-010-0678-1
eBook Packages: Springer Book Archive