Pipe Flow-resistance Characteristic Coefficient Identification Using Genetic Optimization

The validity of a hydraulic network model depends not only on the accuracy of its physical and geometric data but also on the accuracy of certain parametric data such as pipe flow-resistance characteristic coefficient(PFRCC). Difficulties associated with economical and reliable measurements for PFRCC often require estimation of the parameter through model parametric identification. By the law of conservation of mass and energy, a hydraulic equation model has been established at the very beginning. Then two different numerical calculating methods have been compared. This paper introduces basic steps about establishing hydraulic network model. The difficulties that exist in the modelling process come from the simplification and hypothesis. The necessary and sufficient conditions for analytical approach which based one group of observation data have been discussed. In the end the limitation of analytical approach is pointed out.Identification of PFRCC is an inverse problem of hydraulic calculation. This paper describes an optimization approach to identify a network model for PFRCC. The proposed model obtains an optimal solution by minimizing a nonlinear objective function subject to a set of linear and nonlinear constraints using a powerful search technique based on a genetic algorithm. The optimization problem can be simplified by using hydraulic network model. After that it can be solved by using MATLAB genetic algorithm Toolbox.The identification method is verified by the simulation and experimental results in the last two sections of this thesis. Firstly a looped artificial network has been designed to provide hypothetical observation datas from different hydraulic conditions. Then the fitness function has been created by using these hypothetical observation datas. Application of the optimal identification model to water distribution systems using synthetic data demonstrates capabilities of the proposed algorithm to generate good solutions in an efficient and robust manner. This paper also analyzes various calculation errors about different pipes by using sensitivity analysis method. This method can be able to predict the accuracy of the PFRCC in different pipes. Real observation datas have also been used to support identification theory. The calculating results are in agreement with the measured data.