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A numerical model of turbulent transient flow is used to study the dynamics of turbulence during different periods of water hammer in a polymeric pipe. The governing equations of the transient flow are solved by using the finite difference (FD) method, and the effects of viscoelasticity are modeled by means of a two-dimensional (2D) Kelvin–Voigt model. The experimental data with the Ghidaoui parameter P in the order of one are chosen in which the generated shear wave propagates toward the center of the pipe, while the pressure wave passes the length of the pipe. By studying the turbulence shear force during different times, it is shown that the turbulence structure changes considerably in the first cycle of water hammer. In the accelerated phases, the dominant feature is the creation of a shear wave near the wall, and in the decelerated phases the dominant feature is the propagation of the shear wave created in the accelerated phase.

Inverse transient analysis is known to be a powerful approach for leak detection and calibration of friction factors in pressurised pipes. In order to use inverse transient analysis, a transient state is generated in the system by opening or closing a regulating valve. This excitation and its location in a pipe network significantly influence the success of inverse-transient-analysis-based methods; this issue is the focus of this work. The paper defines a mathematical programming model to produce rapid transient variations in the system subject to allowable high and low pressures. The transient generation point in the network, the time interval and the magnitude of the excitation are decision variables. Using the method of characteristics, a numerical model is developed to analyse transients and evaluate the objective function. Based on genetic algorithms, an optimisation solver is developed to solve the problem. To demonstrate the efficiency of the method, a water distribution network of 34 pipes (with unknown friction factors) and 22 junctions (with unknown leaks) is defined and analysed. The results show that the proposed method makes a remarkable contribution to the success of applied inverse transient analysis.