IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i7p2325-d777442.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/7/2325/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/7/2325/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michael E. Tipping & Christopher M. Bishop, 1999. "Probabilistic Principal Component Analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 611-622.
    2. Achilles Kefalas & Andreas B. Ofner & Gerhard Pirker & Stefan Posch & Bernhard C. Geiger & Andreas Wimmer, 2021. "Detection of Knocking Combustion Using the Continuous Wavelet Transformation and a Convolutional Neural Network," Energies, MDPI, vol. 14(2), pages 1-19, January.
    3. Clemens Gößnitzer & Shawn Givler, 2021. "A New Method to Determine the Impact of Individual Field Quantities on Cycle-to-Cycle Variations in a Spark-Ignited Gas Engine," Energies, MDPI, vol. 14(14), pages 1-14, July.
    4. Mohammed El-Adawy & Morgan R. Heikal & A. Rashid A. Aziz & Ibrahim Khalil Adam & Mhadi A. Ismael & Mohammed E. Babiker & Masri B. Baharom & Firmansyah & Ezrann Zharif Zainal Abidin, 2018. "On the Application of Proper Orthogonal Decomposition (POD) for In-Cylinder Flow Analysis," Energies, MDPI, vol. 11(9), pages 1-22, August.
    5. S.D. Martinez-Boggio & S.S. Merola & P. Teixeira Lacava & A. Irimescu & P.L. Curto-Risso, 2019. "Effect of Fuel and Air Dilution on Syngas Combustion in an Optical SI Engine," Energies, MDPI, vol. 12(8), pages 1-23, April.
    6. George M. Kosmadakis & Constantine D. Rakopoulos, 2019. "A Fast CFD-Based Methodology for Determining the Cyclic Variability and Its Effects on Performance and Emissions of Spark-Ignition Engines," Energies, MDPI, vol. 12(21), pages 1-15, October.
    7. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Clemens Gößnitzer & Shawn Givler, 2021. "A New Method to Determine the Impact of Individual Field Quantities on Cycle-to-Cycle Variations in a Spark-Ignited Gas Engine," Energies, MDPI, vol. 14(14), pages 1-14, July.
    2. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    3. Farrokhifar, Meisam & Nie, Yinghui & Pozo, David, 2020. "Energy systems planning: A survey on models for integrated power and natural gas networks coordination," Applied Energy, Elsevier, vol. 262(C).
    4. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    5. Hosseini, M. & Chitsaz, I., 2023. "Knock probability determination employing convolutional neural network and IGTD algorithm," Energy, Elsevier, vol. 284(C).
    6. Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
    7. Wang, Shao-Hsuan & Huang, Su-Yun, 2022. "Perturbation theory for cross data matrix-based PCA," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    8. Fridgen, Gilbert & Keller, Robert & Körner, Marc-Fabian & Schöpf, Michael, 2020. "A holistic view on sector coupling," Energy Policy, Elsevier, vol. 147(C).
    9. Wentao Qu & Xianchao Xiu & Huangyue Chen & Lingchen Kong, 2023. "A Survey on High-Dimensional Subspace Clustering," Mathematics, MDPI, vol. 11(2), pages 1-39, January.
    10. Zhang, Dongjie & Ma, Ting, 2022. "Study on slagging in a waste-heat recovery boiler associated with a bottom-blown metal smelting furnace," Energy, Elsevier, vol. 241(C).
    11. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble of Models for Optimal Predictive Performance with Applications to Sector Rotation Strategy," Papers 2304.09947, arXiv.org.
    12. Berjawi, A.E.H. & Walker, S.L. & Patsios, C. & Hosseini, S.H.R., 2021. "An evaluation framework for future integrated energy systems: A whole energy systems approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    13. Jingying Yang, 2024. "Element Aggregation for Estimation of High-Dimensional Covariance Matrices," Mathematics, MDPI, vol. 12(7), pages 1-16, March.
    14. Dorota Toczydlowska & Gareth W. Peters, 2018. "Financial Big Data Solutions for State Space Panel Regression in Interest Rate Dynamics," Econometrics, MDPI, vol. 6(3), pages 1-45, July.
    15. Jung, WoongHee & Taflanidis, Alexandros A., 2023. "Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    16. Matteo Barigozzi, 2023. "Asymptotic equivalence of Principal Components and Quasi Maximum Likelihood estimators in Large Approximate Factor Models," Papers 2307.09864, arXiv.org, revised Jun 2024.
    17. Arthur Pewsey & Eduardo García-Portugués, 2021. "Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 1-58, March.
    18. Saletti, Costanza & Morini, Mirko & Gambarotta, Agostino, 2022. "Smart management of integrated energy systems through co-optimization with long and short horizons," Energy, Elsevier, vol. 250(C).
    19. Johannes Burge & Priyank Jaini, 2017. "Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-32, February.
    20. Tilman M. Davies & Sudipto Banerjee & Adam P. Martin & Rose E. Turnbull, 2022. "A nearest‐neighbour Gaussian process spatial factor model for censored, multi‐depth geochemical data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 1014-1043, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2325-:d:777442. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.