5 papers
Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments
Yang Liu, Dewei Wang, Xiaodong Sun +2
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…
Validation and Uncertainty Quantification for Wall Boiling Closure Relations in Multiphase-CFD Solver
Yang Liu, Nam Dinh
The two-fluid model based Multiphase Computational Fluid Dynamics (MCFD) has been considered as one of the most promising tools to investigate two-phase flow and boiling system for…
Data-driven modeling for boiling heat transfer: using deep neural networks and high-fidelity simulation results
Yang Liu, Nam Dinh, Yohei Sato +1
Boiling heat transfer occurs in many situations and can be used for thermal management in various engineered systems with high energy density, from power electronics to heat exchan…
A Validation and Uncertainty Quantification Framework for Eulerian-Eulerian Two-Fluid-Model based Multiphase-CFD Solver. Part II: Applications
Yang Liu, Nam Dinh, Ralph Smith
This paper is the second part of a two-part series, which introduces and demonstrates a Validation and Uncertainty Quantification (VUQ) framework that serves two major purposes: i)…
A Validation and Uncertainty Quantification Framework for Eulerian-Eulerian Two-Fluid Model based Multiphase-CFD Solver. Part I: Methodology
Yang Liu, Nam Dinh, Ralph Smith
In this paper, a validation and uncertainty quantification (VUQ) framework for the Eulerian-Eulerian two-fluid-model based multiphase-computational fluid dynamics solver (MCFD) is…