Dynamic Modeling of Two-Phase Gas/Liquid Flow in Pipelines Academic Article uri icon

abstract

  • Summary Presented is a reduced–order thermal fluid dynamic model for gas/liquid two–phase flow in pipelines. Specifically, a two–phase–flow thermal model is coupled with a two–phase–flow hydraulics model to estimate the gas and liquid properties at each pressure and temperature condition. The proposed thermal model estimates the heat–transfer coefficient for different flow patterns observed in two–phase flow. For distributed flows, where the two phases are well–mixed, a weight–based averaging is used to estimate the equivalent fluid thermal properties and the overall heat–transfer coefficient. Conversely, for segregated flows, where the two phases are separated by a distinct interface, the overall heat–transfer coefficient is dependent on the liquid holdup and pressure drop estimated by the fluid model. Intermittent flows are considered as a combination of distributed and segregated flow. The integrated model is developed by dividing the pipeline into segments. Equivalent fluid properties are identified for each segment to schedule the coefficients of a modal approximation of the transient single–phase–flow pipeline–distributed–parameter model to obtain dynamic pressure and flow rate, which are used to estimate the transient temperature response. The resulting model enables a computationally efficient estimation of the pipeline–mixture pressure, temperature, two–phase–flow pattern, and liquid holdup. Such a model has utility for flow–assurance studies and real–time flow–condition monitoring. A sensitivity analysis is presented to estimate the effect of model parameters on the pipeline–mixture dynamic response. The model predictions of mixture pressure and temperature are compared with an experimental data set and OLGA (2014) simulations to assess model accuracy.

author list (cited authors)

  • Meziou, A., Khan, Z., Wassar, T., Franchek, M. A., Tafreshi, R., & Grigoriadis, K.

citation count

  • 3

publication date

  • October 2019