Data driven methodology for model selection in flow pattern prediction.

ABSTRACT: The determination of multiphase flow parameters such as flow pattern, pressure drop and liquid holdup, is a very challenging and valuable problem in chemical, oil and gas industries, especially during transportation. There are two main approaches to solve this problem in literature: data based algorithms and mechanistic models. Although data based methods may achieve better prediction accuracy, they fail to explain the two-phase characteristics (i.e. pressure gradient, holdup, gas and liquid local velocities, etc.). Recently, many approaches have been made for establishing a unified mechanistic model for steady-state two-phase flow to predict accurately the mentioned properties. This paper proposes a novel data-driven methodology for selecting closure relationships from the models included

SUBMITTER: Hernandez JS 

PROVIDER: S-EPMC6872860 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature