Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments

Abstract

In this paper, we develop a machine learning-based Bayesian approach to inversely quantify and reduce the uncertainties of the two-fluid model-based multiphase computational fluid dynamics (MCFD) for bubbly flow simulations. The proposed approach is supported by high-resolution two-phase flow measurement techniques, including double-sensor conductivity probes, high-speed imaging, and particle image velocimetry. Local distribution of key physical quantities of interest (QoIs), including void fraction and phasic velocities, are obtained to support the modular Bayesian inference. In the process, the epistemic uncertainties of the closure relations are inversely quantified, the aleatory uncertainties from stochastic fluctuation of the system are evaluated based on experimental uncertainty analysis. The combined uncertainties are then propagated through the MCFD solver to obtain uncertainties of QoIs, based on which probability-boxes are constructed for validation. The proposed approach relies on three machine learning methods: feedforward neural networks and principal component analysis for surrogate modeling, and Gaussian processes for model form uncertainty modeling. The whole process is implemented within the framework of open-source deep learning library PyTorch with graphics processing unit (GPU) acceleration, thus ensuring the efficiency of the computation. The results demonstrate that with the support of high-resolution data, the uncertainty of MCFD simulations can be significantly reduced.