Skip to main content

Robust Design Measures for Airfoil Shape Optimization

  • Chapter
  • First Online:
Uncertainty Management for Robust Industrial Design in Aeronautics

Abstract

Two kinds of robustness measures are introduced and applied to design optimization of the UMRIDA BC-02 transonic airfoil test case under uncertainty. Robust design optimization (RDO) aims at minimizing the mean and standard deviation of the drag coefficient. Reliability-based design optimization (RBDO) targets minimizing the maximum drag coefficient. Both robustness measures are efficiently evaluated by using efficient sampling techniques assisted by a gradient-enhanced Kriging model. The airfoil is parameterized with 10 deterministic design variables, which are optimized by a gradient-free Subplex algorithm. The nominal airfoil geometry is assumed to be perturbed by a Gaussian random field which is parameterized by 10 independent variables through a truncated Karhunen–Loève expansion. Two operational parameters are also considered uncertain. The airfoil obtained by optimizing the two robustness measures has similar geometrical features and shows better performance in terms of the robustness measures than the initial and the deterministically designed airfoils. The strength and location of the shock wave of the robustly designed airfoils are shown to be less sensitive to random geometrical perturbations than the initial and deterministically designed airfoils.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Liu, D., Görtz, S.: Efficient quantification of aerodynamic uncertainty due to random geometry perturbations. In: New Results in Numerical and Experimental Fluid Mechanics IX, volume 124 of the Series Notes on Numerical Fluid Mechanics and Multidisciplinary Design. Springer (2014), pp. 65–73. ISBN 978-3-319-03157-6. ISSN 1612-2909

    Google Scholar 

  2. Liu, D., Litvinenko, A., Schillings, C., Schulz, V.: Quantification of airfoil geometry-induced aerodynamic uncertainties—comparison of approaches. SIAM/ASA J. Uncertain. Quant. 5(1) (2016)

    Google Scholar 

  3. Galle, M., Gerhold, T., Evans, J.: Parallel computation of turbulent flows around complex geometries on hybrid grids with the DLR-TAU code. In: Ecer, A., Emerson, D.R. (Eds.) Proceedings of 11th Parallel CFD Conference Williamsburg, VA, North-Holland, 23–26 May 1999

    Google Scholar 

  4. Gerhold, T., Hannemann, V., Schwamborn, D.: On the validation of the DLR-TAU code. In: Nitsche, W., Heinemann, H.-J., Hilbig, R. (Eds.) New Results in Numerical and Experimental Fluid Mechanics. Notes on Numerical Fluid Mechanics, vol. 72, Vieweg (1999) ISBN 3–528-03122-0, pp. 426–433

    Google Scholar 

  5. Schwamborn, D., Gerhold, T., Heinrich, R.: The DLR TAU-code: recent applications in research and industry, invited lecture. In: Wesseling, P., Oate, E., Priaux, J. (Eds.) Proceedings of the European Conference on Computational Fluid Dynamics (ECCOMAS CFD 2006), The Netherlands (2006)

    Google Scholar 

  6. Allmaras, S.R., Johnson, F.T., Spalart, P.R.: Modifications and clarifications for the implementation of the Spalart-Allmaras turbulence model. In: Seventh International Conference on Computational Fluid Dynamics (ICCFD7), ICCFD7-1902, Hawaii, July 2012

    Google Scholar 

  7. Heinrich, R., Reimer, K., Michler, A.: Multidisciplinary simulation of maneuvering aircraft interacting with atmospheric effects using the DLR TAU code. In: RTO AVT-189 Specialists’ Meeting on Assessment of Stability and Control Prediction Methods for Air and Sea Vehicles, Portsdown West, Oct 12–14, 2011

    Google Scholar 

  8. Brezillon, J., Abu-Zurayk, M.: Aerodynamic inverse design framework using discrete adjoint method. In: Notes on Numerical Fluid Mechanics and Multidisciplinary Design, vol. 121. Springer (2013) pp. 489–496

    Google Scholar 

  9. Rowan, T.: Functional Stability analysis of numerical algorithms, Ph.D. thesis, Department of Computer Sciences, University of Texas at Austin (1990)

    Google Scholar 

  10. Han, Z.H., Görtz, S., Zimmermann, R.: Improving variable-fidelity surrogate modeling via gradient-enhanced Kriging and a generalized hybrid bridge function. J. Aerosp. Sci. Technol. 25(1) (2013)

    Google Scholar 

  11. Liu, D.: Efficient quantification of aerodynamic uncertainies using gradient-employing surrogate methods. In: Management and Minimisation of Uncertainties and Errors in Numerical Aerodynamics, volume 122 of the Series Notes on Numerical Fluid Mechanics and Multidisciplinary Design. Springer (2013) pp. 283–296. ISBN 978-3-642-36184-5. ISSN 1612-2909

    Google Scholar 

  12. Han, Z.H., Görtz, S., Zimmermann, R.: Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function. J. Aerosp. Sci. Technol. 25(1), 177–189 (2013)

    Google Scholar 

  13. Maruyama, D., Liu, D., Görtz, S.: Comparing surrogates for estimating aerodynamic uncertainties of airfoils. In: Hirsch, C. et al. (eds.), Uncertainty Management for Robust Industrial Design in Aeronautics Chapter 13 (2017)

    Google Scholar 

  14. Maruyama, D., Liu, D., Görtz, S.: Surrogate model based approaches to UQ and their range of applicability. In: Hirsch, C. et al. (eds.), Uncertainty Management for Robust Industrial Design in Aeronautics Chapter 43 (2017)

    Google Scholar 

  15. Maruyama, D., Liu, D., Görtz, S.: An efficient aerodynamic shape optimization framework for robust design of airfoils using surrogate models. In: Proceedings of the European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2016), Crete, Greece (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Daigo Maruyama .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Maruyama, D., Görtz, S., Liu, D. (2019). Robust Design Measures for Airfoil Shape Optimization. In: Hirsch, C., Wunsch, D., Szumbarski, J., Łaniewski-Wołłk, Ł., Pons-Prats, J. (eds) Uncertainty Management for Robust Industrial Design in Aeronautics . Notes on Numerical Fluid Mechanics and Multidisciplinary Design, vol 140. Springer, Cham. https://doi.org/10.1007/978-3-319-77767-2_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-77767-2_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77766-5

  • Online ISBN: 978-3-319-77767-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics