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Data-Driven Prediction of Unsteady Vortex Phenomena in a Conical Diffuser

Author

Listed:
  • Sergey Skripkin

    (Laboratory of Advanced Energy Efficient Technologies, Physics Department, Novosibirsk State University, Novosibirsk 630090, Russia)

  • Daniil Suslov

    (Laboratory of Advanced Energy Efficient Technologies, Physics Department, Novosibirsk State University, Novosibirsk 630090, Russia)

  • Ivan Plokhikh

    (Laboratory of Advanced Energy Efficient Technologies, Physics Department, Novosibirsk State University, Novosibirsk 630090, Russia)

  • Mikhail Tsoy

    (Kutateladze Institute of Thermophysics SB RAS, Novosibirsk 630090, Russia)

  • Evgeny Gorelikov

    (Laboratory of Advanced Energy Efficient Technologies, Physics Department, Novosibirsk State University, Novosibirsk 630090, Russia)

  • Ivan Litvinov

    (Laboratory of Advanced Energy Efficient Technologies, Physics Department, Novosibirsk State University, Novosibirsk 630090, Russia)

Abstract

The application of machine learning to solve engineering problems is in extremely high demand. This article proposes a tool that employs machine learning algorithms for predicting the frequency response of an unsteady vortex phenomenon, the precessing vortex core (PVC), occurring in a conical diffuser behind a radial swirler. The model input parameters are the two components of the time-averaged velocity profile at the cone diffuser inlet. An empirical database was obtained using a fully automated experiment. The database associates multiple inlet velocity profiles with pressure pulsations measured in the cone diffuser, which are caused by the PVC in the swirling flow. In total, over 10 3 different flow regimes were measured by varying the swirl number and the cone angle of the diffuser. Pressure pulsations induced by the PVC were detected using two pressure fluctuations sensors residing on opposite sides of the conical diffuser. A classifier was constructed using the Linear Support Vector Classification (Linear SVC) model and the experimental data. The classifier based on the average velocity profiles at the cone diffuser inlet allows one to predict the emergence of the PVC with high accuracy (99%). By training a regression artificial neural network, the frequency response of the flow was predicted with an error of no more than 1.01 and 5.4% for the frequency and power of pressure pulsations, respectively.

Suggested Citation

  • Sergey Skripkin & Daniil Suslov & Ivan Plokhikh & Mikhail Tsoy & Evgeny Gorelikov & Ivan Litvinov, 2023. "Data-Driven Prediction of Unsteady Vortex Phenomena in a Conical Diffuser," Energies, MDPI, vol. 16(5), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2108-:d:1076148
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    References listed on IDEAS

    as
    1. Kumar, Sandeep & Cervantes, Michel J. & Gandhi, Bhupendra K., 2021. "Rotating vortex rope formation and mitigation in draft tube of hydro turbines – A review from experimental perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    2. Sergey Skripkin & Zhigang Zuo & Mikhail Tsoy & Pavel Kuibin & Shuhong Liu, 2022. "Oscillation of Cavitating Vortices in Draft Tubes of a Simplified Model Turbine and a Model Pump–Turbine," Energies, MDPI, vol. 15(8), pages 1-18, April.
    Full references (including those not matched with items on IDEAS)

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