L. Ridolfi | Searcharxiv

arXiv2023

Investigating the magnitude and temporal localization of inertial particle mixing in turbulent channel flows

Mixing of inertial point particles in a turbulent channel flow at Reτ = 950 is investigated by means of direct numerical simulations. We consider inertial particles, at varying Stokes number, released from pairs of sources located at different positions inside the channel and analyze the rate at which particles come into close proximity to each other. To do so, we employ a Lagrangian framework, which is suitable for the analysis of trajectories and in general for the study of mixing and dispersion problems. By varying the release position of particles along the wall-normal direction we obtain a thorough description of mixing in an anisotropic turbulent flow. Moreover, we analyze the effects of particle inertia and show that these are not univocal but also depend on the position and alignment of the sources, owing in particular to the dependence of the flow timescales on the distance from the wall.

physics.flu-dyn↗

arXiv2016

Classification of infrastructure networks by neighborhood degree distribution

A common way of classifying network connectivity is the association of the nodal degree distribution to specific probability distribution models. During the last decades, researchers classified many networks using the Poisson or Pareto distributions. Urban infrastructures, like transportation (railways, roads, etc.) and distribution (gas, water, energy, etc.) systems, are peculiar networks strongly constrained by spatial characteristics of the environment where they are constructed. Consequently, the nodal degree of such networks spans very small ranges not allowing a reliable classification using the nodal degree distribution. In order to overcome this problem, we here (i) define the neighborhood degree, equal to the sum of the nodal degrees of the nearest topological neighbors, the adjacent nodes and (ii) propose to use neighborhood degree to classify infrastructure networks. Such neighborhood degree spans a wider range of degrees than the standard one allowing inferring the probabilistic model in a more reliable way, from a statistical standpoint. In order to test our proposal, we here analyze twenty-two real water distribution networks, built in different environments, demonstrating that the Poisson distribution generally models very well their neighborhood degree distributions. This result seems consistent with the less reliable classification achievable with the scarce information using the standard nodal degree distribution.

cs.SI↗