Development of an accurate method to prognosticate choke flow coefficients for natural gas flow through nozzle and orifice type chokes

In this study, a rigorous method based on artificial intelligence is developed to predict the choke flow coefficient for subsonic natural gas flow through nozzle and orifice type chokes. Reynolds number and ratio of choke diameter to pipe diameter was utilized as input parameters. The method used in this study is radial basis function neural network coupled with genetic algorithm. The results showed great agreement with experimental data. In addition, the proposed method was compared with classic correlations. This comparison demonstrated the robustness and superiority of the GA-RBF model.