Uncertainty propagation through a point model for steady-state two-phase pipe flow

Uncertainty propagation is used to quantify the uncertainty in model predictions in the
presence of uncertain input variables. In this study, we analyze a steady-state point-model for
two-phase gas-liquid flow. We present prediction intervals for holdup and pressure drop that
are obtained from knowledge of the measurement error in the variables provided to the model.
The analysis also uncovers which variables the predictions are most sensitive to. Sensitivity indices
and prediction intervals are calculated by two different methods, Monte Carlo and polynomial
chaos. The methods give similar prediction intervals, and they agree that the predictions are most
sensitive to the pipe diameter and the liquid viscosity. However, the Monte Carlo simulations require
fewer model evaluations and less computational time. The model predictions are also compared to
experiments while accounting for uncertainty, and the holdup predictions are accurate, but there is
bias in the pressure drop estimates