Eventos Anais de eventos
DINAME 2023
XIX International Symposium on Dynamic Problems of Mechanics
A stochastic model for intermittent two-phase flow in horizontal pipes
Submission Author:
Adriano Todorovic Fabro , DF , Brazil
Co-Authors:
Saon Vieira, Adriano Todorovic Fabro, Rômulo Rodrigues, Marco Jose Da Silva, Rigoberto Morales, Marcelo Souza de Castro
Presenter: Adriano Todorovic Fabro
doi://10.26678/ABCM.DINAME2023.DIN2023-0174
Abstract
Intermittent flows are common flow patterns in gas-liquid horizontal flow and attract attention and great research effort due to its importance for industrial and engineering applications. The slug flow is typically modelled based on a unit cell varying from an elongated air bubble with a liquid film in segregated flow pattern and an aerated liquid plug, the slug region, with remarkable stochastic characteristics of its alternating regions. In this paper, a two-state Markov chain model is proposed to represent the stochastic dynamics of developed slug flow in horizontal pipes. Each state represents either the liquid slug or the elongated bubble regions and the transition probabilities dictate the change of the given discrete time measurement to stay at a given state or change. This simple but insightful description of the phenomenon allows an analytical treatment of the statistics of Markov chain stochastic process. Measurement stations with two double wire resistive sensors are used to obtain the void fraction time series and a corresponding two-state representation. It is shown that the Markov chain model can successfully represent second-order statistics of the measurement, such as the autocorrelation and power spectral density, given an appropriate choice of the chain order. Subsequently, statistics of some slug flow features are estimated using the proposed approach and their interpretation as random variables derived from the void fraction stochastic process is discussed.
Keywords
Two-phase Flow, Slug flow-pattern, Markov chain, Experimental Characterisation, stochastic process
