Limitations in Turbomachinery CFD

  • Francesco MontomoliEmail author
  • Mauro Carnevale
  • Antonio D’Ammaro
  • Michela Massini
  • Simone Salvadori
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


Nowadays, Computational Fluid Dynamics (CFD) is a widely used method for the analysis and the design of gas turbines. The accuracy of CFD is rapidly increasing thanks to the available computational resources that allow simulating high-speed flows using hi-fidelity methodologies. However CFD uses models, and several approximations and errors derive from the process, for example from the truncation errors due to the discretization of the Navier-Stokes equations and from the turbulence models. Typical examples of such kind of limitations may be the steady flow assumption, the turbulence closure or the mesh resolution. The impact of approximations could be minimum to evaluate the trends of variation of global parameters, but it will have a strong impact on the prediction of local values of important parameters such as flow temperature and heat transfer. It is worth highlighting that the available computational resources are pushing towards the so called high fidelity CFD and it is important to highlight what is needed to achieve this goal and to reduce the impact of approximations.


CFD limits Mesh dependence Component interactions 


  1. 1.
    Salvadori, S., Montomoli, F., Martelli, F., Chana, K. S., Qureshi, I., & Povey, T. (2012). Analysis on the effect of a nonuniform inlet profile on heat transfer and fluid flow in turbine stages. Journal of Turbomachinery, 134(1), 011012-1-14. doi: 10.1115/1.4003233.
  2. 2.
    Insinna, M., Griffini, D., Salvadori, S., & Martelli, F. (2014). Conjugate heat transfer analysis of a film cooled high-pressure turbine vane under realistic combustor exit flow conditions. In Proceedings of the ASME Turbo Expo 2014, Volume 5A: Heat Transfer, Dusseldorf, Germany, June 16–20, pp. V05AT11A007 (14 pages). doi: 10.1115/GT2014-25280.
  3. 3.
    Pau, M., Paniagua, G., Delhaye, D., de la Loma, A., & Ginibre, P. (2010). Aerothermal impact of stator-rim purge flow and rotor-platform film cooling on a transonic turbine stage. Journal of Turbomachinery, 132(2), 021006-1-12. doi: 10.1115/1.3142859.
  4. 4.
    Bernardini, C., Carnevale, M., Manna, M., Martelli, F., Simoni, D., Zunino, P. (2012). Turbine blade boundary layer separation suppression via synthetic jet: An experimental and numerical study. Journal of Thermal Science 21(5):404–412. doi: 10.1007/s11630-012-0561-2
  5. 5.
    Medic, G., Kalitzin, G., You, D., van der Weide, E., Alonso, J. J., & Pitschk, H. (2007). Integrated RANS/LES computations of an entire gas turbine jet engine. In 45th AIAA Aerospace Sciences Meeting and Exhibit, January 8–11, 2007/Reno, NV, AIAA 2007-1117.Google Scholar
  6. 6.
    Adami, P., Martelli, F., & Cecchi, S. (2007). Analysis of the shroud leakage flow and mainflow interactions in high-pressure turbines using an unsteady computational fluid dynamics approach. Proceedings of the IMechE Part A: Journal of Power and Energy, 21. doi: 10.1243/09576509JPE466.
  7. 7.
    Montomoli, F., Massini, M., & Salvadori, S. (2011). Geometrical uncertainty in turbomachinery: Tip gap and fillet radius. Elsevier Computers and Fluids, 46(1), 362–368. doi: 10.1016/j.compfluid.2010.11.031.CrossRefzbMATHGoogle Scholar
  8. 8.
    Roache, P. J. (1994). Perspective: A method for uniform reporting of grid refinement studies. Journal of Fluids Engineering, 116(3), 405–413. doi: 10.1115/1.2910291.CrossRefGoogle Scholar
  9. 9.
    Roache, P. J. (1997). Quantification of uncertainty in computational fluid dynamics. Annual Review of Fluid Mechanics, 29, 123–160. doi: 10.1146/annurev.fluid.29.1.123.CrossRefMathSciNetGoogle Scholar
  10. 10.
    Roache, P. J. (1998). Verification of codes and calculations. AIAA Journal, 36, 696–702. doi: 10.2514/2.457.CrossRefGoogle Scholar
  11. 11.
    Celik, I. (1993). Numerical uncertainty in fluid flow calculations: Needs for future research. Journal of Fluids Engineering, 115, 194–195. doi: 10.1115/1.2910123.CrossRefGoogle Scholar
  12. 12.
    Roache, P. J., Kirti, N. G., & White, F. M. (1986). Editorial policy statement on the control of numerical accuracy. Journal of Fluids Engineering, 108, 2. doi: 10.1115/1.3242537.CrossRefGoogle Scholar
  13. 13.
    Richardson, L. F. (1910). The approximate arithmetical solution by finite differences of physical problems involving differential equations with an application to the stresses in the masonry dam. Transactions of the Royal Society of London, Series A, 210, 307–357. doi: 10.1098/rsta.1911.0009.CrossRefGoogle Scholar
  14. 14.
    Richardson, L. F., & Gaunt, J. A. (1927). The deferred approach to the limit. Part I. Single lattice. Part II. Interpenetrating lattices. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 226, 299–361. doi: 10.1098/rsta.1927.0008.CrossRefzbMATHGoogle Scholar
  15. 15.
    Saracoglu, B. H., Paniagua, G., & Salvadori, S. (2014). Energy analysis of pulsating coolant ejection. In ASME Turbo Expo 2014, Volume 2D: Turbomachinery, Dusseldorf, Germany, June 16–20, pp. V02DT44A016 (10 pages). doi: 10.1115/GT2014-25868.
  16. 16.
    He, L. (1996). VKI Lecture Series Part I: Modelling issues for computations of unsteady turbomachinery flows. VKI Lecture Series on “Unsteady Flows in Turbomachines”, Von Karman Institute for Fluid Dynamics.Google Scholar
  17. 17.
    He, L. (1996). VKI Lecture Series Part II: Time marching calculations for blade row interaction and flutter. VKI Lecture Series on “Unsteady Flows in Turbomachines”, Von Karman Institute for Fluid Dynamics.Google Scholar
  18. 18.
    Payne, S. J., Ainsworth, R. W., Miller, R. J., Moss, R. W., & Harvey, N. W. (2005). Unsteady loss in a high pressure turbine stage: Interaction effects. International Journal of Heat and Fluid Flow, 26, 695–708.CrossRefGoogle Scholar
  19. 19.
    Pullan, G. (2006). Secondary flows and loss caused by blade row interaction in a turbine stage. ASME Journal of Turbomachinery, 128(3), 484–491.CrossRefMathSciNetGoogle Scholar
  20. 20.
    Butler, T. L., Sharma, O. P., Joslyn, H. D., & Dring, R. P. (1989). Redistribution of an inlet temperature distortion in an axial flow turbine stage. AIAA Journal of Propulsion and Power, 5, 64–71.CrossRefGoogle Scholar
  21. 21.
    Munk, M., & Prim, R. (1947). On the multiplicity of steady gas flows having the same streamline patterns. Proceedings of the National Academy of Science, 33, 137–141.CrossRefGoogle Scholar
  22. 22.
    Kerrebrock, J. L., & Mikolajczak, A. A. (1970). Intra-stator transport of rotor wakes and its effect on compressor performance. ASME Journal of Engineering for Power, 92(4), 359–368.CrossRefGoogle Scholar
  23. 23.
    Dorney, D. J., Davis, R. L., Edwards, D. E., & Madavan, N. K. (1992). Unsteady analysis of hot streak migration in a turbine stage. AIAA Journal of Propulsion and Power, 8(2), 520–529.CrossRefGoogle Scholar
  24. 24.
    Adamczyk, J. J., Mulac, R. A., & Celestina, M. L. (1986). A model for closing the inviscid form of the average-passage equation system. Transactions of the ASME, 108, 180–186.Google Scholar
  25. 25.
    Dorney, D. J., Davis, R. L., & Sharma, O. P. (1996). Unsteady multistage analysis using a loosely coupled blade row approach. AIAA Journal of Propulsion and Power, 12(2), 274–282.CrossRefGoogle Scholar
  26. 26.
    Rai, M. M., & Madavan, N. K. (1988). Multi-airfoil Navier-Stokes simulations of turbine rotor-stator interaction. Reno: NASA Ames Research Centre.Google Scholar
  27. 27.
    Giles, M. B. (1988). Calculation of unsteady wake-rotor interaction. AIAA Journal of Propulsion and Power, 4(4), 356–362.CrossRefGoogle Scholar
  28. 28.
    Giles, M. B. (1990). Stator/rotor interaction in a transonic turbine. AIAA Journal of Propulsion and Power, 6(5), 621–627.CrossRefMathSciNetGoogle Scholar
  29. 29.
    He, L. (1990). An Euler solution for unsteady flows around oscillating blades. ASME Journal of Turbomachinery, 112(4), 714–722.CrossRefGoogle Scholar
  30. 30.
    He, L. (1992). Method of simulating unsteady turbomachinery flows with multiple perturbations. AIAA Journal, 30(11), 2730–2735.CrossRefzbMATHGoogle Scholar
  31. 31.
    Klapdor, E. V., di Mare, F., Kollmann, W., & Janicka, J. (2013). A compressible pressure-based solution algorithm for gas turbine combustion chambers using the PDF/FGM model. Flow, Turbulence and Combustion, 91(2), 209–247.CrossRefGoogle Scholar
  32. 32.
    Insinna, M., Salvadori, S., & Martelli, F. (2014). Simulation of combustor/NGV interaction using coupled RANS solvers: Validation and application to a realistic test case. In Proceedings of the ASME Turbo Expo 2014, Volume 2D: Turbomachinery, Dusseldorf, Germany, June 16–20, pp. V02CT38A010 (12 pages). doi: 10.1115/GT2014-25433.
  33. 33.
    Kim, S., Schluter, J. U., Wu, X., Alonso, J. J, & Pitsch, H. (2004). Integrated simulations for multi-component analysis of gas turbines: RANS boundary conditions. In Proceedings of the 40th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, AIAA-2004-3415.Google Scholar
  34. 34.
    Medic, G., Kalitzin, G., You, D., Herrmann, M., Ham, F., van der Weide, E., et al. (2006). Integrated RANS/LES computations of turbulent flow through a turbofan jet engine. Annual Research Brief, Center for Turbulence Research, University of Stanford.Google Scholar
  35. 35.
    Collado Morata, E. (2012). Impact of the unsteady aerothermal environment on the turbine blades temperature. Ph.D. thesis, Université de Toulouse.Google Scholar
  36. 36.
    Bunker, R. S. (2009). The effects of manufacturing tolerances on gas turbine cooling. ASME Journal of Turbomachinery, 131, 041018-1-11. doi: 10.1115/1.3072494.CrossRefGoogle Scholar
  37. 37.
    Montomoli, F., Massini, M., Salvadori, S., & Martelli, F. (2012). Geometrical uncertainty and film cooling fillet radii. ASME Journal of Turbomachinery, 134(1), 011019-1-8. doi: 10.1115/1.4003287.
  38. 38.
    Carnevale, M., Salvadori, S., Manna, M., & Martelli, F. (2013). A comparative study of RANS, URANS and NLES approaches for flow prediction in pin fin array. In Proceedings of the ETC Conference, 10th European Turbomachinery Conference, 15–19 April 2013, Lappeenranta, Finland, pp. 928–937, Paper No. ETC2013-111.Google Scholar
  39. 39.
    Carnevale, M., Montomoli, F., D’Ammaro, A., Salvadori, S., & Martelli, F. (2013). Uncertainty quantification: a stochastic method for heat transfer prediction using les. ASME Journal of Turbomachinery, 135(5), 051021-1-8. doi: 10.1115/1.4007836.
  40. 40.
    Adami, P., Martelli, F., Chana, K. S., & Montomoli, F. (2003). Numerical predictions of film cooled NGV blades. In Proceedings of IGTI, ASME Turbo Expo 2003, June 16–19, Atlanta, Georgia, USA, Paper No. GT-2003-38861.Google Scholar
  41. 41.
    Montomoli, F., Adami, P., Della Gatta, S., & Martelli, F. (2004). Conjugate heat transfer modelling in film cooled blades. In Proceedings of IGTI, ASME Turbo Expo 2004, June 14–17, Vienna, Austria, Paper No. GT-2004-53177.Google Scholar
  42. 42.
    Takahashi, T., Funazaki, K., Salleh, H. B., Sakai, E., & Watanabe, K. (2012). Assessment of URANS and DES for prediction of leading edge film cooling. Journal of Turbomachinery, 134, 031008-1-10.CrossRefGoogle Scholar
  43. 43.
    Luo, J., & Razinsky, E. H. (2007). Conjugate heat transfer analysis of a cooled turbine vane using the V2F turbulence model. Journal of Turbomachinery, 129(4), 773–781.CrossRefGoogle Scholar
  44. 44.
    Lien, F. S., & Kalitzin, G. (2001). Computations of transonic flow with the υ2-f turbulence model. International Journal of Heat and Fluid Flow, 22(1), 53–61.CrossRefGoogle Scholar
  45. 45.
    Walters, D. K., & Cokljat, D. (2008) A three-equation eddy-viscosity model for Reynolds-averaged Navier-Stokes simulations of transitional flow. Journal of Fluids Engineering, 130(4).Google Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  • Francesco Montomoli
    • 1
    Email author
  • Mauro Carnevale
    • 1
  • Antonio D’Ammaro
    • 2
  • Michela Massini
    • 1
  • Simone Salvadori
    • 3
  1. 1.Imperial College of LondonLondonUK
  2. 2.University of CambridgeCambridgeUK
  3. 3.University of FlorenceFlorenceItaly

Personalised recommendations