  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 engineering purposes. The MCFD solver requires
closure relations to make the conservation equations solvable. The wall boiling
closure relations, for example, provide predictions on wall superheat and heat
partitioning. The accuracy of these closure relations significantly influences
the predictive capability of the solver. In this paper, a study of validation
and uncertainty quantification (VUQ) for the wall boiling closure relations in
MCFD solver is performed. The work has three purposes: i). identify influential
parameters to the quantities of interest of the boiling system through
sensitivity analysis; ii). evaluate the parameter uncertainty through Bayesian
inference with the support of multiple datasets; iii). quantitatively measure
the agreement between solver predictions and datasets. The widely used
Kurul-Podowski wall boiling closure relation is studied in this paper. Several
statistical methods are used, including Morris screening method for global
sensitivity analysis, Markov Chain Monte Carlo (MCMC) for inverse Bayesian
inference, and confidence interval as the validation metric. The VUQ results
indicated that the current empirical correlations-based wall boiling closure
relations achieved satisfactory agreement on wall superheat predictions.
However, the closure relations also demonstrate intrinsic inconsistency and
fail to give consistently accurate predictions for all quantities of interest
over the well-developed nucleate boiling regime.
