Wall roughness produces a downward shift of the mean streamwise velocity profile in the log region, known as the roughness function. The dependence of the roughness function on the height and arrangement of roughness elements has been confirmed in several studies where regular rough walls were analysed; less attention has been paid to non-regular rough walls. Here, a numerical analysis of turbulent flows over irregularly shaped rough walls is performed, clearly identifying the importance of a parameter, called the effective slope (ES) of the wall corrugations, in characterizing the geometry of non-smooth irregular walls. The effective slope proves to be one of the fundamental geometric parameters for scaling the roughness function. Specifically, for a moderate range of roughness heights, both in the transitionally and in the fully rough regime, ES appears to scale the roughness function for a wide range of irregular rough geometric configurations. The effective slope determines the relative importance of friction drag and pressure drag. For ES ~ 0.15 we find that the friction contribution to the total wall stress is nearly in balance with the pressure-drag contribution. This value separates the region where the roughness function ΔU⁺ = f(ES) is linear from that where a smooth nonlinear behaviour is observed. In the cases investigated, value ES ~ 0.15 also separates the transitionally rough regime from the fully rough regime.

In many countries, private tanks are acquired by users to reduce their vulnerability to intermittent supply. The presence of these local reservoirs modifies the user demand pattern and usually increases user water demand at the beginning of the service period depending on the tank filling process. This practice is thus responsible for the inequality that occurs among users: those located in advantaged positions of the network are able to obtain water resources soon after the service period begins, while disadvantaged users have to wait much longer, after the network is full. This dynamic process requires the development of ad hoc models in order to obtain reliable results. This paper discusses a numerical model used for evaluating this complex process as well as the application of model to an Italian case study. The model agreed with calibration data and provided interesting insights into the network filling process.

In complex networks characterized by the presence of private tanks, water managers usually apply intermittent distribution, thus reducing the water volumes supplied to the users, or use Pressure Reduction Valves (PRV) for controlling pressure in the network. The application of Pump As Turbines (PATs) appears as an alternative and sustainable solution to either control network pressure as well as to produce energy. In the present paper, the hydrodynamic model, already presented by De Marchis et al. (2011) was further developed introducing the dynamic analysis of PATs. The model was applied to a district of Palermo network (Italy) characterized by intermittent distribution and by inequities among the user in term of water supply

In many countries, users acquire private tanks to reduce their vulnerability to water scarcity. In such conditions, water managers often apply intermittent distribution in order to reduce the water volumes supplied to the users. This practice modifies the hydraulic behaviour of the network and determines competition among users that need to collect enough water resource for their uses. Intermittent distribution is thus responsible for the inequality that can occur among users: those located in advantaged positions of the network are able to obtain water resources soon after the service period begins, while others have to wait much longer, after the network is full. This paper analyses the inequalities that take part when intermittent distribution is applied in water scarcity scenarios. Considering the complexity of the process, the analysis was performed by means of an unsteady numerical model. The model was applied to a real case study which provided interesting insights into the network filling process, helping to highlight the advantaged and disadvantaged areas of the network in different water scarcity scenarios.