Roberto Magini

Residential water demand exhibits stochastic variability across all spatial and temporal scales, making probabilistic approaches essential for realistic modelling. Using scaling laws enables the derivation of statistics on aggregated demand from single-user data while maintaining information about spatial correlation. This study highlights how scaling laws define the dependence of mean and variance on the number of users and also reveals multiscale dynamics. In particular, variance growth at variable exponents and the emergence of multimodal distributions are shown. Furthermore, the Poisson Rectangular Pulse (PRP) model can reproduce these features by introducing frequency-use patterns that reflect the non-homogeneous nature of water demand.

Urban rivers represent a highly vulnerable environment due to the intense anthropogenic pressures and the restricted self‐depuration capacity. The current monitoring programs often lack to provide a full knowledge of all contaminant sources and processes governing their behaviour due to the complexity and variability of the conditions in urban water bodies and the increasing occurrence of new pollutants such as the Contaminants of Emerging concern (CECs). This called for an increasing regulatory attention and the need to carry out the revision of the Water Framework Directive (WFD), as it is currently ongoing. This study proposes an integrated new approach for urban water quality assessment, which combines traditional parameters, emerging contaminants, (i.e. CECs and Microplastics-MPs), isotopic tracing and modelling, with the aim of improving source discrimination and advancing the understanding of contaminant dynamics in heavily modified urban rivers. The new approach was tested on a selected stretch of the Tiber river, within the city of Rome. Data of pollutant concentrations, measured along the water body close by the point source releases, were combined with the hydraulic information through a mass‐balance approach with the aim to quantify contributions of different processes. The stable isotope ratios were evaluated as tracers for identifying contaminant origins and potential transformation processes, representing an innovative application in the context of urban river monitoring. Additionally, the Qual2K model was employed to simulate water‐quality dynamics under varying hydrological and meteorological conditions, focusing on the behaviour of CECs.

Residential water demand exhibits marked variability over time and across users, with direct implications for the sustainable and robust design of water distribution networks. In this study, a high-resolution experimental dataset is analysed to characterise the statistical structure of hourly consumption, deriving mean and variance scaling laws, cross-correlations between user groups, and probability density functions (PDFs) of aggregated demand. The results show that demand does not behave as an independent process. During the morning peak (07:00–08:00), the distribution does not converge to a unimodal shape as aggregation increases, but exhibits a clear bimodality for aggregation levels larger than approximately N ≈ 400 users. This behaviour indicates the presence of two synchronised consumption regimes and a non-negligible average correlation. In contrast, during the evening and night slots, unimodal distributions (Gamma or Lognormal) emerge, consistent with largely independent contributions and limited synchronisation. For comparison, a simplified Poisson Rectangular Pulse (PRP) model is evaluated. While this model reproduces the mean flow rate, it does not capture the observed variance, underscoring the need for models that account for heterogeneity and user correlations. The scaling laws, correlations, and empirical PDFs derived in this study provide a quantitative basis for generating probabilistic demand scenarios, supporting the sustainable, resilient, and robust design of water distribution networks.

Water scarcity is a critical issue exacerbated by climate change, urbanization, and population growth, particularly in regions with insufficient water infrastructure. Rainwater harvesting (RWH) systems offer a sustainable solution to mitigate water shortages by collecting and storing rainwater for non-potable uses. This study focuses on the design, efficiency, and reliability of RWH systems in residential environments, with an emphasis on optimizing the sizing of storage volumes and collection areas. Using a behavioural simulation model, we generate nomograms that facilitate the design of RWH systems by analyzing the interactions among storage capacity, collection area, rainfall patterns, and water demand. Specifically, this paper evaluates the effectiveness of RWH systems through efficiency and reliability metrics such as water savings, mains reliance, overflow discharge, and system reliability. The proposed procedure integrates stochastic rainfall and water demand data, including a detailed analysis of toilet usage, in order to simulate the performance of RWH systems across different time scales. Case studies in Italy and Denmark are used to assess the influence of climatic differences on system performance. The findings provide a comprehensive methodology for RWH system design, and offer valuable insights into improving a sustainable water management strategy.

Hemodynamics in intracranial aneurysm strongly depends on the non-Newtonian blood behavior due to the large number of suspended cells and the ability of red blood cells to deform and aggregate. However, most numerical investigations on intracranial hemodynamics adopt the Newtonian hypothesis to model blood flow and predict aneurysm occlusion. The aim of this study was to analyze the effect of the blood rheological model on the hemodynamics of intracranial aneurysms in the presence or absence of endovascular treatment. A numerical investigation was performed under pulsatile flow conditions in a patient-specific aneurysm with and without the insertion of an appropriately reconstructed flow diverter stent (FDS). The numerical simulations were performed using Newtonian and non-Newtonian assumptions for blood rheology. In all cases, FDS placement reduced the intra-aneurysmal velocity and increased the relative residence time (RRT) on the aneurysmal wall, indicating progressive thrombus formation and aneurysm occlusion. However, the Newtonian model largely overestimated RRT values and consequent aneurysm healing with respect to the non-Newtonian models. Due to the non-Newtonian blood properties and the large discrepancy between Newtonian and non-Newtonian simulations, the Newtonian hypothesis should not be used in the study of the hemodynamics of intracranial aneurysm, especially in the presence of endovascular treatment.

Demand in a water distribution network (WDN) is an aleatory variable, owing to the unpredictable behaviors of water users. Therefore, it is one of the main reasons for uncertainty in the design of this infrastructure. The increasing number of water demand data sets offers opportunities to improve the traditional deterministic design approaches of WDNs by combining statistical and optimization methods. Robust optimization (RO) takes demand uncertainty into account by studying solutions that perform well under any possible demand scenario, that is, any possible realizations of this variable in the lifetime of a WDN. The right choice of scenario is therefore essential to ensure the reliability of the designed network. This paper presents a statistical methodology for generating scenarios to be used to solve a robust design optimization problem. It involves three steps: (a) descriptive analytics of historical data to derive the marginal distributions of peak hour demand in each node of the WDN, (b) generation of a very large number of snapshots by stratified sampling from the correlated marginal distributions of nodal peak demand, (c) generation of the peak demand scenarios by reducing the number of snapshots. Two heuristic techniques are proposed to reduce the number of snapshots, and for each of them, two different numbers of scenarios are derived. Two multi‐objective RO models are solved: the first model includes cost minimization and a mean‐variance Generalized Resilience and Failure index maximization objectives, and the second one additionally considers the minimization of the maximum undelivered demand, formalized using a regret function.

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.

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