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

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

doi:10.3390/en15124204

Cited by 4 publications

(1 citation statement)

References 27 publications

(68 reference statements)

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“…Furthermore, Xie et al [10] combined IUQ and quantitative validation via Bayesian hypothesis testing to improve the predictive capability of computer simulations. Besides the applications in nuclear energy, the Bayesian approach for IUQ has also been applied to many other fields such as biotechnology [11], geophysics [12], additive manufacturing [13], computational fluid dynamics [14,15], etc.…”

Section: Introductionmentioning

confidence: 99%

Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference

Wang,

Wu,

Xie

et al. 2023

Energies

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Inverse Uncertainty Quantification (IUQ) has gained increasing attention in the field of nuclear engineering, especially nuclear thermal-hydraulics (TH), where it serves as an important tool for quantifying the uncertainties in the physical model parameters (PMPs) while making the model predictions consistent with the experimental data. In this paper, we present an extension to an existing Bayesian inference-based IUQ methodology by employing a hierarchical Bayesian model and variational inference (VI), and apply this novel framework to a real-world nuclear TH scenario. The proposed approach leverages a hierarchical model to encapsulate group-level behaviors inherent to the PMPs, thereby mitigating existing challenges posed by the high variability of PMPs under diverse experimental conditions and the potential overfitting issues due to unknown model discrepancies or outliers. To accommodate computational scalability and efficiency, we utilize VI to enable the framework to be used in applications with a large number of variables or datasets. The efficacy of the proposed method is evaluated against a previous study where a No-U-Turn-Sampler was used in a Bayesian hierarchical model. We illustrate the performance comparisons of the proposed framework through a synthetic data example and an applied case in nuclear TH. Our findings reveal that the presented approach not only delivers accurate and efficient IUQ without the need for manual tuning, but also offers a promising way for scaling to larger, more complex nuclear TH experimental datasets.

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Section: Introductionmentioning

confidence: 99%

Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference

Wang,

Wu,

Xie

et al. 2023

Energies

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Modeling the mass transfer at acoustically generated bubble interface using Rayleigh–Plesset equation second-order derivatives

2022

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One of the many ways of cavitation utilized for process intensification is through acoustically inducing it. As acoustic cavitation gained traction in recent industrial works, numerical modeling became an important study tool to scrutinize and optimize acoustic cavitation applications. However, available hydrodynamic cavitation models are found incapable of accurately predicting acoustic cavitation structures and flow features. This could source from the oversimplification of the Rayleigh–Plesset equation or from obscure effects of empirical model constants. To address this issue, new mass transfer source terms for Zwart–Gerber–Belamri model were derived based on the consideration of Rayleigh–Plesset's second-order derivatives. In addition, a design of experiments statistical approach, coupled with Monte Carlo simulations, was implemented to assess the influence of empirical model constants on the model's performance by examining variations in amplitude and frequency responses. Moreover, a set of optimized model constants was obtained: evaporation constant = 17.359 88, condensation constant = 0.1, Bubble Radius = 25 × 10−6 m, and Nucleation Site Volume Fraction = 5 × 10−4, to obtain a maximum pressure and frequency of 3.62 bar and 4128.73 Hz, respectively. The new model, with the new constants, was configured into ANSYS Fluent 22.1 and validated against experimental values. The new model resulted with maximum pressure and frequency of 3.48 bar and 4894.56 Hz, respectively, validating the statistical model and showing drastic improvement in qualitatively and quantitatively capturing acoustic cavitation.

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Uncertainty quantification of the standard k-ε turbulence model closure coefficients in predicting aerodynamics of high-speed train

Liu,

Kong,

et al. 2024

Engineering Applications of Computational Fluid Mechanics

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

Cited by 4 publications

References 27 publications

Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference

Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference

Modeling the mass transfer at acoustically generated bubble interface using Rayleigh–Plesset equation second-order derivatives

Uncertainty quantification of the standard k-ε turbulence model closure coefficients in predicting aerodynamics of high-speed train

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