HEAT TRANSFER CORRELATION FOR TWO PHASE FLOW IN VERTICAL PIPES USING ARTIFICIAL NEURAL NETWORK

In many industrial applications, such as the flow of natural gas and oil in flowlines and wellbores, the knowledge of nonboiling two-phase, two-component (liquid and permanent gas) heat transfer is required. Several heat transfer correlations for forced convective heat transfer during gas-liquid two-phase flow in vertical pipes have been published over the past 40 years. These correlations were developed based on limited experimental data and are only applicable to certain flow patterns and fluid combinations. Kim et al. (2000) proposed a heat transfer correlation for turbulent gas-liquid flow in vertical pipes with different flow patterns and fluid combinations. Their correlation was developed using four sets of experimental data (a total of 255 data points) for vertical pipes. The form of their correlation was based on the major nondimensional parameter