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The Current State of High-Fidelity Simulations for Main Gas Path Turbomachinery Components and Their Industrial Impact

Abstract

Over the past two decades high-fidelity simulations have become feasible for most main gas path turbomachinery components. This paper introduces the key challenges of simulating and modelling turbomachinery flows and presents an overview of possible simulation strategies. A comprehensive overview is given of which particular design challenges have to date been addressed by high-fidelity simulations, with a particular focus on high-pressure and centrifugal compressors, and high-pressure and low-pressure turbines. Recommendations are provided for how quality and accuracy can be ensured for high-fidelity simulations, using direct numerical simulations of a low-pressure turbine as a case study. It is argued that industrial impact from high-fidelity simulations can be achieved in two ways, either by conducting design-of-experiment-like studies that can provide designers with insight into certain physical mechanisms and phenomena, or by helping mature and improve lower-order models. The latter is discussed with particular emphasis on recent advances in machine learning for model assessment and improvement, and the potential of one selected data-driven approach is demonstrated on predictions of wake mixing for low-pressure and high-pressure turbines.

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Acknowledgements

The authors acknowledge funding for part of the work by General Electric and their permission to publish. RDS also acknowledges support from veski. Some of the presented data were obtained through INCITE and ALCC projects using resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. Additional computing time was provided through the Partnership for Advanced Computing in Europe (PRACE) and the UK Turbulence Consortium funded by the EPSRC under Grant No. EP/L000261/1, and a grant from the Swiss National Supercomputing Centre (CSCS) under project ID S622. Some work was also supported by resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia.

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This work was partly funded by veski and by General Electric.

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Sandberg, R.D., Michelassi, V. The Current State of High-Fidelity Simulations for Main Gas Path Turbomachinery Components and Their Industrial Impact. Flow Turbulence Combust 102, 797–848 (2019). https://doi.org/10.1007/s10494-019-00013-3

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Keywords

  • High-fidelity simulations
  • Turbomachinery
  • DNS
  • LES
  • Machine learning