Data driven methodology for model selection in flow pattern prediction (2017)
The determination of multiphase flow parameters such as flow pattern, pressure drop and liquid holdup, is a very challenging and valuable problem in chemical industry, especially during petroleum transport. Recently, many approaches have been made for establishing a Unified Flow Model to predict accurately the mentioned properties with an outstanding advance made by Zhang et al. (2003b). This paper proposes a novel methodol-ogy for selecting closure relationships from the models included in the Unified Flow Model developed by the previously cited authors. A tree based model is built based on a data driven methodology developed from a 27670 points data set and later tested for flow pattern prediction in a set made of 9224 observations. The clo-sure relationship selection model achieved a 74% accuracy in classifying flow regimes for a wide range of two-phase flow conditions.