Identification of Pipeline Resistance Coefficient Based on Intelligent Algorithm

For network modeling, resistance coefficients in pipes can deviate from their theoretical design values due to long-term usage, wear, blockages, and other factors. This paper proposes a method for identifying pipeline resistance coefficients using the Crested Porcupine Optimizer (CPO) algorithm. This approach transforms the model parameter identification problem into a multi-objective nonlinear optimization problem with complex constraints. It allows for the calculation and accuracy analysis of resistance coefficients in the network using a limited number of observation nodes and measurement values. The identified resistance coefficients closely match the actual parameters under network operation, reduced the influence of the deviation of the drag coefficient on the ac-curacy of the simulation.