Uncertainty Quantification of a Flapping Airfoil with a Stochastic Velocity Deviation Based on a Surrogate Model

Liang Yu Zhao; Xia Qing Zhang

doi:10.4028/www.scientific.net/AMR.201-203.1209

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 201-203Uncertainty Quantification of a Flapping Airfoil...

Uncertainty Quantification of a Flapping Airfoil with a Stochastic Velocity Deviation Based on a Surrogate Model

Abstract:

A practical flapping wing micro aerial vehicle should have ability to withstand stochastic deviations of flight velocities. The responses of the time-averaged thrust coefficient and the propulsive efficiency with respect to a stochastic flight velocity deviation under Gauss distribution were numerically investigated using a classic Monte Carlo method. The response surface method was employed to surrogate the high fidelity model to save computational cost. It is observed that both of the time-averaged thrust coefficient and the propulsive efficiency obey a Gauss-like but not the exact Gauss distribution. The effect caused by the velocity deviation on the time-averaged thrust coefficient is larger than the one on the propulsive efficiency.

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Periodical:

Advanced Materials Research (Volumes 201-203)

Pages:

1209-1212

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.201-203.1209

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Online since:

February 2011

Authors:

Liang Yu Zhao, Xia Qing Zhang

Keywords:

Flapping Airfoil, Surrogate Model, Uncertainty Quantification

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References

[1] J.M. McMichael, and M.S. Francis. DARPA, USA, (1997).

Google Scholar

[2] M.F. Platzer, K.D. Jones, J . Young, and J.C. S Lai. AIAA J., Vol. 46, No. 9 (2008), pp.2136-2148.

Google Scholar

[3] K.D. Jones, and M.F. Platzer. In: Proceeding of 44th AIAA Aerospace Sciences Meeting and Exhibit, AIAA-2006-0037 (2006).

DOI: 10.2514/6.2006-500

Google Scholar

[4] Y. Lian, and W. Shyy. In: Proceeding of 45th AIAA Aerospace Sciences Meeting and Exhibit, AIAA 2007-0794 (2007).

DOI: 10.2514/6.2007-71

Google Scholar

[5] H. Gopalan, and A. Povitsky. In: Proceeding of 26th AIAA Applied Aerodynamics Conference, AIAA 2008-6394 (2008).

DOI: 10.2514/6.2008-6394

Google Scholar

[6] Y. Lian. In: Proceeding of 27th AIAA Applied Aerodynamics Conference, AIAA 2009-3952 (2009).

Google Scholar

[7] K. Viswanath, and D. Tafti. In: Proceeding of 40th Fluid Dynamics Conference and Exhibit, AIAA-2010-4869 (2010).

Google Scholar

[8] L. Zhao, S. Yang. In: Proceeding of the 6th International Conference on Intelligent Unmanned systems (2010).

Google Scholar

[9] N.V. Queipo, R.T. Haftka, W. Shyy, T. Goel, R. Vaidyanathan, P.K. Tucker, Progress in Aerospace Sciences, Vol. 41 (2005), pp.1-28.

DOI: 10.1016/j.paerosci.2005.02.001

Google Scholar