Shock Waves

, Volume 28, Issue 2, pp 285–297 | Cite as

Optimization of bump and blowing to control the flow through a transonic compressor blade cascade

Original article


Shock control bump (SCB) and blowing are two flow control methods, used here to improve the aerodynamic performance of transonic compressors. Both methods are applied to a NASA rotor 67 blade section and are optimized to minimize the total pressure loss. A continuous adjoint algorithm is used for multi-point optimization of a SCB to improve the aerodynamic performance of the rotor blade section, for a range of operational conditions around its design point. A multi-point and two single-point optimizations are performed in the design and off-design conditions. It is shown that the single-point optimized shapes have the best performance for their respective operating conditions, but the multi-point one has an overall better performance over the whole operating range. An analysis is given regarding how similarly both single- and multi-point optimized SCBs change the wave structure between blade sections resulting in a more favorable flow pattern. Interactions of the SCB with the boundary layer and the wave structure, and its effects on the separation regions are also studied. We have also introduced the concept of blowing for control of shock wave and boundary-layer interaction. A geometrical model is introduced, and the geometrical and physical parameters of blowing are optimized at the design point. The performance improvements of blowing are compared with the SCB. The physical interactions of SCB with the boundary layer and the shock wave are analyzed. The effects of SCB on the wave structure in the flow domain outside the boundary-layer region are investigated. It is shown that the effects of the blowing mechanism are very similar to the SCB.


Flow control Blowing Shock control bump Multi-point optimization Axial compressor 


  1. 1.
    Biollo, R., Benini, E.: Recent advances In transonic axial compressor aerodynamics. Prog. Aerosp. Sci. 56, 1–18 (2013). doi: 10.1016/j.paerosci.2012.05.002 CrossRefGoogle Scholar
  2. 2.
    Wang, D.X., He, L.: Adjoint aerodynamic design optimization for blades in multistage turbomachines–Part II: Validation and application. J. Turbomach. 132, 021012 (2010). doi: 10.1115/1.3072498 CrossRefGoogle Scholar
  3. 3.
    Lian, Y., Liou, M.S.: Multi-objective optimization of transonic compressor blade using evolutionary algorithm. J. Propuls. Power 21, 979–987 (2005). doi: 10.2514/1.14667 CrossRefGoogle Scholar
  4. 4.
    Lee, S.Y., Kim, K.Y.: Design optimization of axial flow compressor blades with three-dimensional Navier-Stokes solver. KSME Int. J. 14(9), 1005–1012 (2000). doi: 10.1007/BF03185803 CrossRefGoogle Scholar
  5. 5.
    Wang, D.X., He, L.: Adjoint aerodynamic design optimization for blades in multistage turbomachines—Part I: Methodology and verification. J. Turbomach. 132(2), 021011 (2010). doi: 10.1115/1.3072498
  6. 6.
    Walther, B., Nadarajah, S.: Constrained adjoint-based aerodynamic shape optimization of a single-stage transonic compressor. J. Turbomach. 135(2), 021017 (2012). doi: 10.1115/1.4007502 CrossRefGoogle Scholar
  7. 7.
    Lian, Y., Liou, M.S.: Aerostructural optimization of a transonic compressor rotor. J. Propuls. Power 22(4), 880–888 (2006). doi: 10.2514/1.15397 CrossRefGoogle Scholar
  8. 8.
    Thiede, P., Dargel, G.: Assessment of Shock and Boundary Layer Control Concepts for Hybrid Laminar Flow (HFL) Wing Design, EUROSHOCK II Final Technical Report, TR BRPR-95-76/1, 1999 and DASA-Airbus DA-Report No. EF-069/99, (1999)Google Scholar
  9. 9.
    Ashill, P.R., Fulker, J.L., Shires, A.: A novel technique for controlling shock strength of laminar-flow aerofoil sections. First European Forum on Laminar Flow Technology, Hamburg (1992)Google Scholar
  10. 10.
    Tian, Y., Liu, P., Feng, P.: Shock control bump parametric research on supercritical airfoil. Sci. China Technol. Sci 54(11), 2935–2944 (2011). doi: 10.1007/s11431-011-4582-y CrossRefGoogle Scholar
  11. 11.
    Qin, N., Zhu, Y., Ashill, P.: CFD study of shock control at Cranfield, \(22^{{\rm nd}}\) International Congress of Aeronautical Sciences (ICAS CONGRESS), Harrogate (2000)Google Scholar
  12. 12.
    Mazaheri, K., Kiani, K.C., Nejati, A., Zeinalpour, M., Taheri, R.: Optimization and analysis of shock wave/boundary layer interaction for drag reduction by Shock Control Bump. Aerosp. Sci. Technol. 42, 196–208 (2015). doi: 10.1016/j.ast.2015.01.007 CrossRefGoogle Scholar
  13. 13.
    Mazaheri, K., Nejati, A., Kiani, K.C., Taheri, R.: The application of the gradient-based adjoint multi-point optimization of single and double shock control bumps for transonic airfoils. Shock Waves 26(4), 491–511 (2016). doi: 10.1007/s00193-015-0591-2 CrossRefGoogle Scholar
  14. 14.
    Nejati, A., Mazaheri, K.: Drag reduction by a multi-point optimised hybrid flow control method for two supercritical airfoils. Eur. J. Comput. Mech. 25(5), 359–387 (2016). doi: 10.1080/17797179.2016.1240535 Google Scholar
  15. 15.
    Jameson, A., Leoviriyakit, K., Shankaran, S.: Multi-point aero-structural optimization of wings including planform variations. In: 45th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada, AIAA Paper 2007-764 (2007). doi: 10.2514/6.2007-764
  16. 16.
    Buckley, H., Zhou, B., Zingg, D.W.: Airfoil optimization using practical aerodynamic design requirements. J. Aircr. 47(5), 1707–1719 (2010). doi: 10.2514/1.C000256 CrossRefGoogle Scholar
  17. 17.
    Mazaheri, K., Nejati, A.: The multi-point optimization of shock control bump with constant-lift constraint enhanced with suction and blowing for a supercritical airfoil. Flow Turbul. Combust. 96(3), 639–666 (2016). doi: 10.1007/s10494-015-9671-8 CrossRefGoogle Scholar
  18. 18.
    Mazaheri, K., Khatibirad, S.: Using a shock control bump to improve the performance of an axial compressor blade section. Shock Waves 27(2), 299–312 (2017). doi: 10.1007/s00193-016-0672-x CrossRefGoogle Scholar
  19. 19.
    Babinsky, H., Harvey, J.K.: Shock Wave–Boundary-Layer Interactions. Cambridge University Press, Cambridge (2011)CrossRefMATHGoogle Scholar
  20. 20.
    Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009). doi: 10.1109/TEVC.2008.927706 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Center of Excellence in Aerospace SystemsSharif University of TechnologyTehranIran
  2. 2.Department of Aerospace EngineeringSharif University of TechnologyTehranIran

Personalised recommendations