Steady State Hydraulic and Thermal Simulation Model of Natural Gas Pipeline Network Based on Logistic Regression and Newton Rapson Adaptive Iteration Algorithm

Abstract Natural gas pipeline simulation technology is the core technology for pipeline management departments to acquire and monitor the operational status of pipelines. It enables managers to observe changes in the pipeline network, thereby improving the safety and reliability of pipeline operations. When solving large-scale and complex natural gas pipeline models using the traditional Newton-Raphson algorithm, issues such as slow convergence speed and low computational efficiency may arise. In this paper, an improved Newton-Raphson adaptive iteration algorithm based on Logistic Regression is proposed. Based on the variation law of iteration error when solving the model using the Newton-Raphson algorithm, an adaptive iteration step size control and convergence judgment mechanism are introduced based on the Logistic Regression function to improve the convergence speed and stability of the algorithm. The improved algorithm effectively improves the solution efficiency while maintaining high accuracy. Computational results show that when using the improved algorithm to solve steady-state simulation models of pipelines of different scales and complexities, it can maintain the same level of accuracy and stability as commercial software SPS. Compared with the traditional Newton-Raphson algorithm, the improved algorithm can achieve an acceleration effect of over 12% in terms of solution time.