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Alessandro Lenci

Fixed-term Researcher in Tenure Track L. 79/2022

Department of Civil, Chemical, Environmental, and Materials Engineering

Academic discipline: CEAR-01/A Hydraulics

Research

Keywords: Fluid Mechanics Environmental Hydraulics Porous and Fractured Media Heat Transport in Geothermal Systems Non-Newtonian Fluids Stochastic Modeling Upscaling and Reduced-Order Modeling Numerical Modeling Anomalous Transport

    Flow and transport in porous and fractured media
    I study flow, dispersion and transport processes in porous media and fracture networks, with emphasis on the effects of geometrical and hydraulic heterogeneity. The main objective is to link pore- or fracture-scale mechanisms with effective descriptions at the system scale.

    Anomalous transport and stochastic modeling
    I develop stochastic models to describe non-Fickian transport regimes, distributed residence times, breakthrough curves with long tails, and fracture–matrix exchange processes. My work uses Lagrangian approaches and Time-Domain Random Walk methods to derive reduced models that remain physically consistent with the underlying transport mechanisms.

    Heat transport in fractured and geothermal systems
    I investigate heat transport in fractures and fractured media, including fluid advection, matrix conduction and nonlocal thermal exchange. These models are relevant to geothermal systems, thermal tracer tests and subsurface hydrothermal applications.

    Non-Newtonian fluids in confined and fractured media
    I study the effect of complex rheology — including shear-thinning, power-law, Ellis and Herschel–Bulkley fluids — on flow and transport in heterogeneous fractures and channels. The main focus is to quantify how the constitutive law modifies hydraulic path selection and transport responses.

    Numerical modeling and upscaling
    I develop and apply numerical models for coupled flow, transport and fracture–matrix exchange problems. This activity includes high-resolution simulations, reduced-order models, Lagrangian methods and upscaling procedures aimed at obtaining computationally efficient predictive descriptions.

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