The stochastic modeling of water demand requires knowledge of the statistical features of the demand time series at different spatial and temporal aggregations. The observation of real data has revealed the presence of a nontrivial scaling of the second-order moments with the number of customers. The main objective of this paper is to analyze the spatial and temporal features of the demand at different spatial aggregation scales and sampled with different temporal resolutions, deriving appropriate scaling laws for the first- and second-order moments. In this context the analytical expressions of the scaling laws are first derived, pointing out the role of the space–time correlation. Then the scaling laws are empirically derived for two data sets of real indoor water demand data of two different case studies, sampled with different metering techniques. This enables the evaluation of the relevance of this effect on real data. A further analysis is carried out to understand how the sampling time affects the ...

Flow diverter stents (FDS) are increasingly used for the treatment of complex intracranial aneurysms such as fusiform, giant, or wide-neck aneurysms. The primary goal of these devices is to reconstruct the diseased vascular segment by diverting blood flow from the aneurysm. The resulting intra-aneurysmal flow reduction promotes progressive aneurysm thrombosis and healing of the disease. In the present study, a numerical investigation was performed for modeling blood flow inside a patient-specific intracranial aneurysm virtually treated with FDS. The aim of the study is to investigate the effects of FDS placement prior to the actual endovascular treatment and to compare the effectiveness of devices differing in porosity. Numerical simulations were performed under pulsatile flow conditions, taking into account the non-Newtonian behavior of blood. Two possible post-operative conditions with virtual stent deployment were simulated. Hemodynamic parameters were calculated and compared between the pre-operative (no stent placement) and post-operative (virtual stent placement) aneurysm models. FDS placement significantly reduced intra-aneurysmal flow velocity and increased the Relative Residence Time (RRT) on the aneurysm, thus promoting thrombus formation within the dilatation and aneurysm occlusion. The results highlighted an increase in the effectiveness of FDS as its porosity increased. The proposed analysis provides pre-operative knowledge on the impact of FDS on intracranial hemodynamics, allowing the selection of the most effective treatment for the specific patient.

The knowledge of the simultaneous nodal pressure values in a water distribution network (WDN) can favor its correct management, with advantages for both water utilities and end users, and guarantee higher sustainability in the use of the water resource. However, monitoring pressure in all the nodes is not feasible, so it can be useful to develop methods that allow us to estimate the whole pressure field based on data from a limited number of nodes. For this purpose, the work employed an artificial neural network (ANN) as a machine-learning regression algorithm. Uncertainty of water demand is modeled through scaling laws, linking demand statistics to the number of users served by each node. Three groups of demand scenarios are generated by using a Latin Hypercube Random Sampling with three different cross-correlations matrices of the nodal demands. Each of the corresponding groups of pressure scenarios is employed for the training of an ANN, whose performance parameter is preliminarily used to solve the sampling design for the WDN. Most of the so-derived monitoring nodes coincide in the three cases. The performance of each ANN appears to be strongly influenced by cross-correlation values, with the best results provided by the ANN relating to the most correlated demands.

Hemodynamics plays a crucial role in the growth of an abdominal aortic aneurysm (AAA) and its possible rupture. Due to the serious consequences that arise