Abstract

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.

Highlights

  • Multiphase flow models are used in a range of applications, such as petroleum transport, nuclear energy and biomechanics

  • Monte Carlo methods (MC), polynomial chaos expansions (PC), full-factorial numerical integration (FFNI) and univariate dimension reduction (UDR). They explain the relative strengths of each method, and one conclusion is that PC is most viable in comparison to FFNI and UDR when input distributions are normal but output distributions are not

  • The presented approach is implemented in Python 3.6, and the uncertainty analysis is based on the Python module Chaospy presented in Feinberg and Langtangen [35]

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Summary

Introduction

Multiphase flow models are used in a range of applications, such as petroleum transport, nuclear energy and biomechanics. There is a rapid development in methods for quantifying the uncertainty in these models. Lee and Chen [1] compared several types of uncertainty propagation methods, including. Monte Carlo methods (MC), polynomial chaos expansions (PC), full-factorial numerical integration (FFNI) and univariate dimension reduction (UDR). They explain the relative strengths of each method, and one conclusion is that PC is most viable in comparison to FFNI and UDR when input distributions are normal but output distributions are not. This is the situation in our analysis

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