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Modeling Cycle-to-Cycle Variations of a Spark-Ignited Gas Engine Using Artificial Flow Fields Generated by a Variational Autoencoder

Author

Listed:
  • Stefan Posch

    (LEC GmbH, Inffeldgasse 19, 8010 Graz, Austria)

  • Clemens Gößnitzer

    (LEC GmbH, Inffeldgasse 19, 8010 Graz, Austria)

  • Andreas B. Ofner

    (Know-Center GmbH, Graz University of Technology, Inffeldgasse 13, 8010 Graz, Austria)

  • Gerhard Pirker

    (LEC GmbH, Inffeldgasse 19, 8010 Graz, Austria)

  • Andreas Wimmer

    (LEC GmbH, Inffeldgasse 19, 8010 Graz, Austria
    Institute of Thermodynamics and Sustainable Propulsion Systems, Graz University of Technology, Inffeldgasse 19, 8010 Graz, Austria)

Abstract

A deeper understanding of the physical nature of cycle-to-cycle variations (CCV) in internal combustion engines (ICE) as well as reliable simulation strategies to predict these CCV are indispensable for the development of modern highly efficient combustion engines. Since the combustion process in ICE strongly depends on the turbulent flow field in the cylinder and, for spark-ignited engines, especially around the spark plug, the prediction of CCV using computational fluid dynamics (CFD) is limited to the modeling of turbulent flows. One possible way to determine CCV is by applying large eddy simulation (LES), whose potential in this field has already been shown despite its drawback of requiring considerable computational time and resources. This paper presents a novel strategy based on unsteady Reynolds-averaged Navier–Stokes (uRANS) CFD in combination with variational autoencoders (VAEs). A VAE is trained with flow field data from presimulated cycles at a specific crank angle. Then, the VAE can be used to generate artificial flow fields that serve to initialize new CFD simulations of the combustion process. With this novel approach, a high number of individual cycles can be simulated in a fraction of the time that LES needs for the same amount of cycles. Since the VAE is trained on data from presimulated cycles, the physical information of the cycles is transferred to the generated artificial cycles.

Suggested Citation

  • Stefan Posch & Clemens Gößnitzer & Andreas B. Ofner & Gerhard Pirker & Andreas Wimmer, 2022. "Modeling Cycle-to-Cycle Variations of a Spark-Ignited Gas Engine Using Artificial Flow Fields Generated by a Variational Autoencoder," Energies, MDPI, vol. 15(7), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2325-:d:777442
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    References listed on IDEAS

    as
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