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

Jae-Hyeon Bae, Kyoungsik Chang, Gong-Hee Lee and Byeong-Cheon Kim
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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

Energies, 2022, vol. 15, issue 12, 1-18

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.

Keywords: uncertainty quantification (UQ); Bayesian inference; point-collocation nonintrusive polynomial chaos (PC-NIPC); cavitation; Zwart–Gerber–Belamri (ZGB) cavitation model; in-service testing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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