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Bayesian Inference of Cavitation Model Coefficients and Uncertainty Quantification of a Venturi Flow Simulation

Author

Listed:
  • Jae-Hyeon Bae

    (School of Mechanical Engineering, University of Ulsan, Ulsan 44610, Korea)

  • Kyoungsik Chang

    (School of Mechanical Engineering, University of Ulsan, Ulsan 44610, Korea)

  • Gong-Hee Lee

    (Regulatory Assessment Department, Korea Institute of Nuclear Safety, Daejeon 34142, Korea)

  • Byeong-Cheon Kim

    (School of Mechanical Engineering, University of Ulsan, Ulsan 44610, Korea)

Abstract

In the present work, uncertainty quantification of a venturi tube simulation with the cavitating flow is conducted based on Bayesian inference and point-collocation nonintrusive polynomial chaos (PC-NIPC). A Zwart–Gerber–Belamri (ZGB) cavitation model and RNG k-ε turbulence model are adopted to simulate the cavitating flow in the venturi tube using ANSYS Fluent, and the simulation results, with void fractions and velocity profiles, are validated with experimental data. A grid convergence index (GCI) based on the SLS-GCI method is investigated for the cavitation area, and the uncertainty error ( U G ) is estimated as 1.12 × 10 −5 . First, for uncertainty quantification of the venturi flow simulation, the ZGB cavitation model coefficients are calibrated with an experimental void fraction as observation data, and posterior distributions of the four model coefficients are obtained using MCMC. Second, based on the calibrated model coefficients, the forward problem with two random inputs, an inlet velocity, and wall roughness, is conducted using PC-NIPC for the surrogate model. The quantities of interest are set to the cavitation area and the profile of the velocity and void fraction. It is confirmed that the wall roughness with a Sobol index of 0.72 has a more significant effect on the uncertainty of the cavitating flow simulation than the inlet velocity of 0.52.

Suggested Citation

  • Jae-Hyeon Bae & Kyoungsik Chang & Gong-Hee Lee & Byeong-Cheon Kim, 2022. "Bayesian Inference of Cavitation Model Coefficients and Uncertainty Quantification of a Venturi Flow Simulation," Energies, MDPI, vol. 15(12), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4204-:d:833556
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    References listed on IDEAS

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    1. Gonghee Lee & Myungjo Jhung & Juneho Bae & Soonho Kang, 2021. "Numerical Study on the Cavitation Flow and Its Effect on the Structural Integrity of Multi-Stage Orifice," Energies, MDPI, vol. 14(6), pages 1-24, March.
    2. Cheung, Sai Hung & Oliver, Todd A. & Prudencio, Ernesto E. & Prudhomme, Serge & Moser, Robert D., 2011. "Bayesian uncertainty analysis with applications to turbulence modeling," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1137-1149.
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    Cited by:

    1. Chen Wang & Xu Wu & Ziyu Xie & Tomasz Kozlowski, 2023. "Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference," Energies, MDPI, vol. 16(22), pages 1-23, November.

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