Advertisement

Turbomachinery Research and Design: The Role of DNS and LES in Industry

  • Vittorio MichelassiEmail author
Conference paper
Part of the Notes on Numerical Fluid Mechanics and Multidisciplinary Design book series (NNFM, volume 143)

Abstract

The role of high-fidelity CFD in industry is rapidly evolving due to the growth of computational power. Direct and large eddy simulations of realistic turbomachinery flows are now possible to analyze not only fundamental problems, but also to investigate compressor and turbine design spaces. Nevertheless, it is practically impossible to replace conventional Reynolds averaged models with scale resolving simulations in the framework of industrial design iterations. Along these lines, this paper describes how scale resolving simulations can have a direct impact on both the design and the design tools of turbomachinery components. The presented results prove how high-fidelity simulations can explain unsteady loss generation, and how advanced post-processing indicates performance top offenders. When coupled with machine-learning, scale-resolved simulations are also capable of improving the accuracy Reynolds averaged models routinely used in design work.

Notes

Acknowledgements

The author gratefully acknowledges Baker Hughes, a GE Company for allowing the publication of this paper.

References

  1. 1.
    EIA, U.S. Energy Information Administration. https://www.eia.gov/todayinenergy/detail.php?id=26912
  2. 2.
    Laskowski, G.M., Kopriva, J., Michelassi, V., Shankaran, S., Paliath, U., Bhaskaran, R., Wang, Q., Talnikar, C., Wang, Z.J., Jia, F.: Future directions of high-fidelity CFD for aero-thermal turbomachinery research, analysis and design. In: 46th AIAA Fluid Dynamics Conference, AIAA AVIATION Forum, (AIAA 2016-3322)Google Scholar
  3. 3.
    Fischberg, C.J., Rhie, C.M., Zacharias, R.M., Bradley, P.C., Des Sureault, T.M.: Using hundreds of workstations for production running of parallel CFD applications. In: Ecer, A., Periaux, J., Satofuka, N., Taylor, S. (eds.) Parallel Computational Fluid Dynamics: Implementations and Results Using Parallel Computers (1995)Google Scholar
  4. 4.
    Sandberg, R., Michelassi, V.: The current state of high-fidelity simulations for main gas path turbomachinery components and their industrial impact. Flow Turbul. Combust. 102, 797 (2019). https://doi.org/10.1007/s10494-019-00013-3
  5. 5.
    Wheeler, A.P., Sandberg, R.D., Sandham, N.D., Pichler, R., Michelassi, V., Laskowski, G.: Direct numerical simulations of a high-pressure turbine vane. ASME J. Turbomach. 138 (2016)Google Scholar
  6. 6.
    Leggett, J., Priebe, S., Shabbir, A., Michelassi, V., Sandberg, R., Richardson, E.: LES loss prediction in an axial compressor cascade at off-design incidences with free stream disturbances. ASME J. Turbomach. 140(7), (2018)Google Scholar
  7. 7.
    Michelassi, V., Sandberg, R.D., Pichler, R., Chen, L., Johnstone, R.: Compressible direct numerical simulation of low-pressure turbines-Part II: effect of inflow disturbances. ASME J. Turbomach. 137 (2015)Google Scholar
  8. 8.
    Michelassi, V., Chen, L., Pichler, R., Sandberg R.D., Bhaskaran, R.: High-fidelity simulations of low-pressure turbines: effect of flow coefficient and reduced frequency on losses. J. Turbomach. 138(11) (2016)Google Scholar
  9. 9.
    Pichler, R., Michelassi, V., Sandberg, R., Ong, J.: Highly resolved large eddy simulation study of gap size effect on low-pressure turbine stage. ASME J. Turbomach. 140 (2018)Google Scholar
  10. 10.
    Akolekar, H.D., Weatheritt, J., Hutchins, N., Laskowski, G., Michelassi, V.: Development and use of machine-learnt algebraic reynolds stress models for enhanced prediction of wake mixing in LPTS. ASME J. Turbomach. ASME GT2018-75447, Recommended for PublicationGoogle Scholar
  11. 11.
    Tan, R., Weatheritt, J., Ooi, A., Sandberg, R.D., Michelassi, V., Laskowski, G.: Applying machine learnt explicit algebraic stress and scalar flux models to a fundamental trailing edge slot. ASME J. Turbomach. ASME GT2018-75444. Recommended for PublicationGoogle Scholar
  12. 12.
    Michelassi, V.: Modeling and resolving turbulence (and unsteadiness) in turbomachinery flows. Tutorial at ASME Turbo Expo. Montreal, Canada (2015)Google Scholar
  13. 13.
    Pichler, R., Sandberg, R.D., Michelassi, V., Bhaskaran, R.: Investigation of the accuracy of RANS models to predict the flow through a low-pressure turbine. ASME J. Turbomach. 138 (2016)Google Scholar
  14. 14.
    Lengani, D., Simoni, D., Pichler, R., Sandberg, R.D., Michelassi, V., Bertini, F.: Identification and quantification of losses in a LPT cascade by POD applied to LES data. Int. J. Heat Fluid Flow 70 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Baker-Hughes a GE CompanyFlorenceItaly

Personalised recommendations