Simulating Flows Passing a Wind Turbine with a Fully Implicit Domain Decomposition Method

  • Rongliang ChenEmail author
  • Zhengzheng Yan
  • Yubo Zhao
  • Xiao-Chuan Cai
Conference paper
Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 104)


Wind power is an increasingly popular renewable energy. In the design process of the wind turbine blade, the accurate aerodynamic simulation is important. In the past, most of the wind turbine simulations were carried out with some low fidelity methods, such as the blade element momentum method [9]. Recently, with the rapid development of the supercomputers, high fidelity simulations based on 3D unsteady Navier-Stokes (N-S) equations become more popular. For example, Sorensen et al. studied the 3D wind turbine rotor using the Reynolds-Averaged Navier-Stokes (RANS) framework where a finite volume method and a semi-implicit method are used for the spatial and temporal discretization, respectively [17]. Bazilevs et al. investigated the aerodynamic of the NREL 5 MW offshore baseline wind turbine rotor using large eddy simulation built with a deforming-spatial-domain/stabilized space-time formulation [3, 11] and later extended the simulation to the full wind turbine including both the rotor and the tower [10]. Li et al. conducted dynamic overset CFD simulations for the NREL phase VI wind turbine using RANS and detached eddy models [15].


Wind Turbine Large Eddy Simulation Wind Turbine Blade High Fidelity Simulation Inexact Newton Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research was supported in part by the NSF of China under 11401564, the Chinese National 863 Plan Program under 2015AA01A302, the Knowledge Innovation Program of the Chinese Academy of Sciences under KJCX2-EW-L01 and the Shenzhen Peacock Plan (China) under KQCX20130628112914303.


  1. 1.
    S. Balay, S. Abhyankar, M.F. Adams, J. Brown, P. Brune, K. Buschelman, V. Eijkhout, W.D. Gropp, D. Kaushik, M.G. Knepley, L.C. McInnes, K. Rupp, B.F. Smith, H. Zhang, PETSc users manual. Technical Report, Argonne National Laboratory (2014)Google Scholar
  2. 2.
    Y. Bazilevs, V. Calo, T. Hughes, Y. Zhang, Isogeometric fluid-structure interaction: theory, algorithms, and computations. Comput. Mech. 43, 3–37 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Y. Bazilevs, M. Hsu, I. Akkerman, S. Wright, K. Takizawa, B. Henicke, T. Spielman, T. Tezduyar, 3D simulation of wind turbine rotors at full scale. Part I: geometry modeling and aerodynamics. Int. J. Numer. Methods Fluids 65, 207–235 (2011)zbMATHGoogle Scholar
  4. 4.
    X.-C. Cai, M. Sarkis, A restricted additive Schwarz preconditioner for general sparse linear systems. SIAM J. Sci. Comput. 21, 792–797 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    X.-C. Cai, W. Gropp, D. Keyes, R. Melvin, D. Young, Parallel Newton-Krylov-Schwarz algorithms for the transonic full potential equation. SIAM J. Sci. Comput. 19, 246–265 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    R. Chen, Q. Wu, Z. Yan, Y. Zhao, X.-C. Cai, A parallel domain decomposition method for 3D unsteady incompressible flows at high Reynolds number. J. Sci. Comput. 58, 275–289 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    F. Duarte, R. Gormaz, S. Natesan, Arbitrary Lagrangian-Eulerian method for Navier-Stokes equations with moving boundaries. Comput. Methods Appl. Mech. Eng. 45, 4819–4836 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    S. Eisenstat, H. Walker, Choosing the forcing terms in an inexact Newton method. SIAM J. Sci. Comput. 17, 16–32 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    M. Hansen, Aerodynamics of Wind Turbines, 2nd edn. (Erthscan, London, 2008)Google Scholar
  10. 10.
    M. Hsu, Y. Bazilevs, Fluid-structure interaction modeling of wind turbines: simulating the full machine. Comput. Mech. 50, 821–833 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    M. Hsu, I. Akkerman, Y. Bazilevs, High-performance computing of wind turbine aerodynamics using isogeometric analysis. Comput. Fluids 49, 93–100 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    F.-N. Hwang, C.-Y. Wu, X.-C. Cai, Numerical simulation of three-dimensional blood flows using domain decomposition method on parallel computer. J. Chin. Soc. Mech. Eng. 31, 199–208 (2010)Google Scholar
  13. 13.
    G. Karypis, METIS/ParMETIS webpage, University of Minnesota (2013),
  14. 14.
    A. Klawonn, L. Pavarino, Overlapping Schwarz methods for mixed linear elasticity and Stokes problems. Comput. Methods Appl. Mech. Eng. 165, 233–245 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Y. Li, K. Paik, T. Xing, P. Carrica, Dynamic overset CFD simulations of wind turbine aerodynamics. Renew. Energy 37, 285–298 (2012)CrossRefGoogle Scholar
  16. 16.
    Y. Saad, Iterative Methods for Sparse Linear Systems (PWS Publishing Company, Boston, 1996)zbMATHGoogle Scholar
  17. 17.
    N. Sorensen, J. Michelsen, S. Schreck, Navier-Stokes predictions of the NREL phase VI rotor in the NASA Ames 80 ft × 120 ft wind tunnel. Wind Energy 5, 151–169 (2002)Google Scholar
  18. 18.
    Y. Wu, X.-C. Cai, A fully implicit domain decomposition based ALE framework for three-dimensional fluid-structure interaction with application in blood flow computation. J. Comput. Phys. 258, 524–537 (2014)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Rongliang Chen
    • 1
    Email author
  • Zhengzheng Yan
    • 1
  • Yubo Zhao
    • 1
  • Xiao-Chuan Cai
    • 2
  1. 1.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenP.R. China
  2. 2.Department of Computer ScienceUniversity of Colorado BoulderBoulderUSA

Personalised recommendations