Giorgia Tagliavini


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Tagliavini

First Name

Giorgia

ORCID

0000-0003-2090-8190

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2022 - 20254
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Publications 1 - 4 of 4
  • Computational investigation of complex-shaped snow particle aerodynamics: drag prediction and wake characteristics
    Item type: Doctoral Thesis
    Tagliavini, Giorgia (2022)
    Snow crystal shapes and falling behavior play a paramount role in snow precipitation. The knowledge of snow precipitation microstructure is fundamental for many applications such as precipitation remote sensing, polarimetric measurements, and climate model parametrization. Moreover, snowfalls influence snow distribution on the ground, which plays a pivotal role in Earth’s climate regulation due to its high albedo. Therefore, understanding snow particles falling behavior is crucial. The irregular shape of snowflakes makes their falling attitudes elaborate, gives rise to convoluted trajectories, and may trigger meandering and turbulent wakes. This complex interaction between snowflake shape and the surrounding air impacts the particle drag coefficient and its settling velocity, but it is far from being fully understood. In the first part of this study, a Delayed-Detached Eddy Simulation model is developed to predict the drag coefficient of snowflakes falling at Reynolds number (Re) between 50 and 2200. The model results are then compared against laboratory experiments using 3D-printed snowflake analogs of the same shape, falling at the same Reynolds number. The first objective is to assess the capability of the numerical model to predict the drag coefficient of complex-shaped particles when their orientation is known a posteriori. Close agreement in the drag coefficient value is found in cases where the particles fall steadily, while a more complex behavior is observed in cases where the flow is unsteady. Secondly, a method to estimate the drag coefficient when the orientation of the particle is not known a posteriori is proposed. A suitable average of two orientations corresponding to the minimum and maximum eigenvalues of the inertia tensor provides a good estimate of the particle drag coefficient. Meanwhile, existing correlations for the drag of non-spherical particles produce large errors (≈ 50%). The same approach in then used to evaluate the snow particle terminal velocity. Subsequently, the previously validated Delayed-Detached Eddy Simulations (DDES), combined with experimental observations of free-falling, 3D-printed snowflakes analogs, are employed to analyze the wake topology and momentum flux and investigate the influence of shape and wake flow on the drag coefficient, together with its implications on falling attitudes by comparison with experiments. At low Re, the presence of separated vortex rings is related to particle porosity and increased drag coefficient. With regard to the momentum flux, the contribution of the mean velocity term in the wake momentum deficit is the largest for all particles. At moderate flow regimes, the particle roundness impacts the shear layers separation and the momentum loss in the wake, with increasing contribution by the fluctuating velocity term. At high Re, even though the drag coefficient does not change much among the different geometries, although the contribution of the fluctuating velocity term in momentum flux differs significantly. Finally, to cross-validate and assess the limitation of the numerical and experimental approaches used in this thesis for snowflake wake characterization, 4D-Particle Tracking Velocimetry experiments of free-falling, 3D-printed snowflakes analogs and Delayed-Detached Eddy Simulations of fixed snow particles are compared analyzing time- and space-averaged flow quantities in the snowflake wake. Firstly, the two approaches are cross-validated for low Re cases where close agreement is found and, secondly, we investigate how strongly the wake of freely falling particles deviates from a fixed particle wake at high Re. At low Reynolds numbers (steady falling behavior), the fixed-particle model can properly represent the wake of freely falling particles, while at moderate/high flow regimes (unsteady falling motion), the comparison highlights much larger differences. Accounting for the movement of the particle by applying a co-moving frame to the laboratory data or filtering the numerical data on larger grids partially reduces these differences, implying that an unsteady fall significantly alters the average wake structure as compared to a fixed particle model.
  • Biophysical properties at patch scale shape the metabolism of biofilm landscapes
    Item type: Journal Article
    Depetris, Anna; Tagliavini, Giorgia; Peter, Hannes; et al. (2022)
    npj Biofilms and Microbiomes
    Phototrophic biofilms form complex spatial patterns in streams and rivers, yet, how community patchiness, structure and function are coupled and contribute to larger-scale metabolism remains unkown. Here, we combined optical coherence tomography with automated O2 microprofiling and amplicon sequencing in a flume experiment to show how distinct community patches interact with the hydraulic environment and how this affects the internal distribution of oxygen. We used numerical simulations to derive rates of community photosynthetic activity and respiration at the patch scale and use the obtained parameter to upscale from individual patches to the larger biofilm landscape. Our biofilm landscape approach revealed evidence of parallels in the structure-function coupling between phototrophic biofilms and their streambed habitat.
  • Modal analysis reveals imprint of snowflake shape on wake flow structures
    Item type: Journal Article
    Tagliavini, Giorgia; Holzner, Markus; Corso, Pascal (2025)
    International Journal of Multiphase Flow
    This study investigates the complex interplay of wake flow dynamics, particle shape, and falling behavior of snowflakes through advanced flow analysis. We employ Proper Orthogonal Decomposition and Dynamic Mode Decomposition to analyze the wake flow patterns of three distinct snowflake geometries at a Reynolds number of 1500: a dendrite crystal, a columnar crystal, and a rosette-like particle. Proper Orthogonal Decomposition reveals that spatial resolution significantly impacts the capture of flow structures, particularly for particles with more intricate wake flow structure, corresponding to unstable falling motion. Dynamic Mode Decomposition demonstrates high sensitivity to temporal resolution, with data of the forces exerted on the snowflake incorporated in the matrix prior to the decomposition mitigating information loss at lower sampling rates. We establish a linear relationship between snowflake shape porosity and minimum and maximum Dynamic Mode Decomposition eigenfrequencies, absolute decay or growth rates, and the wavenumber of the most energetic mode, linking particle geometry to wake flow characteristics. Higher porosity corresponds to more stable, small-scale flow structures and steady falling motion, while lower porosity promotes larger, unstable structures and falling trajectories with random particle orientations. These findings reveal the interdependence of snowflake geometry, wake flow dynamics, and falling behavior and highlight the importance of considering both spatial and temporal resolutions when dealing with modal analysis. This research contributes to improved predictions of snowflake falling behavior, with potential applications in meteorology and climate science.
  • Wake characteristics of complex-shaped snow particles: Comparison of numerical simulations with fixed snowflakes to time-resolved particle tracking velocimetry experiments with free-falling analogs
    Item type: Journal Article
    Tagliavini, Giorgia; Khan, Majid Hassan; McCorquodale, Mark; et al. (2022)
    Physics of Fluids
    Experimental and numerical approaches have their own advantages and limitations, in particular, when dealing with complex phenomena such as snow particles falling at moderate Reynolds numbers (Re). Time-resolved, three-dimensional particle tracking velocimetry (4D-PTV) experiments of free-falling, three-dimensional (3D)-printed snowflakes' analogs shed light on the elaborate falling dynamics of irregular snow particles but present a lower resolution (tracer seeding density) and a limited field of view (domain size) to fully capture the wake flow. Delayed-detached eddy simulations of fixed snow particles do not realistically represent all the physics of a falling ice particle, especially for cases with unsteady falling attitudes, but accurately predict the drag coefficient and capture the wake characteristics for steadily falling snowflakes. In this work, we compare both approaches on time- and space-averaged flow quantities in the snowflake wake. First, we cross validate the two approaches for low Re cases, where close agreement of the wake features is expected, and second, we assess how strongly the unsteady falling motion perturbs the average wake pattern as compared to a fixed particle at higher Re. For steadily falling snowflakes, the fixed-particle model can properly represent the wake flow with errors within the experimental uncertainty (+/- 15%). At moderate/high Re (unsteady falling motion), larger differences are present. Applying a co-moving frame to the experimental data to account for the particle movement or filtering the numerical data on larger grids reduces these differences only to some extent, implying that an unsteady fall significantly alters the average wake structure as compared to a fixed particle model.
Publications 1 - 4 of 4