TRAIL part 2: A comprehensive assessment of ice particle fall speed parametrisations

McCorquodale, Mark W.; Westbrook, C. D.

doi:10.1002/qj.3936

Cited by 13 publications

(32 citation statements)

References 68 publications

(142 reference statements)

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“…Clearly, such an offset can also be expected to occur in a host of practical applications, where particle properties are rarely ever uniform. This concerns, for example, the falling of dandelion seeds [30] and snowflakes [31][32][33][34][35], the sedimentation behavior of sand grains and stones [36,37], chemical and biological reactors with (inverse) fluidized beds [38], as well as the transport of microplastic in the oceans [39]. Moreover, the practical relevance is rooted in the fact that we find that even small values of γ can affect the kinematics and dynamics of spherical particles significantly.…”

mentioning

confidence: 94%

Rising and Sinking in Resonance: Mass Distribution Critically Affects Buoyancy-Driven Spheres via Rotational Dynamics

Will

Krug

2021

Phys. Rev. Lett.

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confidence: 94%

Rising and Sinking in Resonance: Mass Distribution Critically Affects Buoyancy-Driven Spheres via Rotational Dynamics

Will

Krug

2021

Phys. Rev. Lett.

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“…It is important to demonstrate that SCARLET-1.0 also produces as output the STL file of the final aggregate: this means that the virtual aggregate can be potentially 3D printed. This makes an innovative link between simulations in the virtual reality and experiences in the real world, such as laboratory investigations of the drag force exerted on complex aggregates (McCorquodale and Westbrook, 2021).…”

Section: Introductionmentioning

confidence: 99%

SCARLET-1.0: SpheriCal Approximation for viRtuaL aggrEgaTes

Rossi

Bonadonna

2021

Geosci. Model Dev.

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Abstract. Aggregation of particles occurs in a large variety of settings and is therefore the focus of many disciplines, e.g., Earth and environmental sciences, astronomy, meteorology, pharmacy, and the food industry. In particular, in volcanology, ash aggregation deeply influences the sedimentation of volcanic particles in the atmosphere during and after a volcanic eruption, affecting the accuracy of model predictions and the evaluation of hazard and risk assessments. It is thus very important to provide an exhaustive description of the outcome of an aggregation process, starting from its basic geometrical features such as the position in space of its components and the overall porosity of the final object. Here we present SCARLET-1.0, a MATLAB package specifically created to provide a 3D virtual reconstruction for volcanic ash aggregates generated in central collision processes. In centrally oriented collisions, aggregates build up their own structure around the first particle (the core), acting as a seed. This is appropriate for aggregates generated in turbulent flows in which particles show different degrees of coupling with respect to the turbulent eddies. SCARLET-1.0 belongs to the class of sphere-composite algorithms, a family of algorithms that approximate 3D complex shapes in terms of a set of sphere-composite nonoverlapping spheres. The conversion of a 3D surface to its equivalent sphere-composite structure then allows for an analytical detection of the intersections between different objects that aggregate together. Thus, provided a list of colliding sizes and shapes, SCARLET-1.0 places each element in the vector around the core, minimizing the distances between their centers of mass. The user can play with different parameters that control the minimization process. Among them the most important ones are the cone of investigation (Ω), the number of rays per cone (Nr), and the number of orientations of the object (No). All the 3D shapes are described using the Standard Triangulation Language (STL) format, which is the current standard for 3D printing. This is one of the key features of SCARLET-1.0, which results in an unlimited range of applications of the package. The main outcome of the code is the virtual representation of the object, its size, porosity, density, and the associated STL file. In addition, the object can be potentially 3D printed. As an example, SCARLET-1.0 has been applied here to the investigation of ellipsoid–ellipsoid collisions and to a more specific analysis of volcanic ash aggregation. In the first application we show that the final porosity of two colliding ellipsoids is less than 20 % if flatness and elongation are greater than or equal to 0.5. Higher values of porosities (up to 40 %–50 %) can instead be found for ellipsoids with needle-like or extremely flat shapes. In the second application, we reconstruct the evolution in time of the porosity of two different aggregates characterized by different inner structures. We find that aggregates whose population of particles is characterized by a narrow distribution of sizes tend to rapidly reach a plateau in the porosity. In addition, to reproduce the observed densities, almost no compaction is necessary in SCARLET-1.0, which is a result that suggests how ash aggregates are not well described in terms of the maximum packing condition.

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“…For large snow particles (D max > 100 µm), the C D − Re relation becomes non-linear, resulting from a complex interaction between the particle and the surrounding air (Re 1, where Re = u t D max /ν is the particle Reynolds number, D max is the particle maximum extension orthogonal to the flow direction [m], u t the snowflake terminal velocity [m/s] and ν the kinematic viscosity of air [m 2 /s]), and one cannot rely on Stokesian dynamics (valid for Re < 1) [Happel and Brenner 1983;Westbrook 2008;Zeugin et al 2020]. Further complexity is added to snow particle falling motion because of their irregular shapes, which produce unsteady wake flow, and intricate falling trajectories, such as tumbling, oscillations and fluttering [Gunn and Marshall 1957;Nemes et al 2017;McCorquodale and Westbrook 2021b;Zeugin et al 2020].…”

Section: Introductionmentioning

confidence: 99%

“…The objective of this paper is to gain a better understanding on the influence of wake flow and particle shape on drag and their potential impact on the falling motion of complex-shaped snow particles. With this aim, a DDES model, validated for the drag coefficient prediction with experimental data of 3D-printed falling snowflakes [McCorquodale and Westbrook 2021b;Tagliavini et al 2021], is used. It solves for the airflow past a fixed, irregular snowflakes at both low (Re = 50, 75, 100) and moderate/high Reynolds numbers (Re = 500, 1000, 1500).…”

Section: Introductionmentioning

confidence: 99%

Numerical analysis of the wake of complex-shaped snow particles at moderate Reynolds number

et al. 2021

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Climate model parametrization relies strongly on the prediction of snow precipitation, which in turn depends upon the snowflakes falling motion in air. The falling attitudes of such particles are elaborate because of the particles' irregular shapes, which produce meandering and turbulent wakes and give rise to convoluted trajectories. This has also an impact on the drag experienced by the particle. Especially for large snow particles falling close to the ground, Stokesian dynamics is not applicable and the dependency of drag coefficient on Reynolds number becomes non-linear. This trend arises from the complex interaction between snowflakes and the surrounding air. We investigate the wake of complex-shaped snow particles using a validated Delayed-Detached Eddy Simulation (DDES) model of airflow around a fixed snowflake, combined with experimental observations of free-falling, 3D-printed snowflakes analogs. This novel approach allows us to analyze the wake topology and decompose its momentum flux to investigate the influence of shape and wake flow on the drag coefficient and its implications on falling attitudes by comparison with experiments. At low Re, the presence of separated vortex rings is connected to particle porosity and produces an increase of the drag coefficient. At moderate flow regimes, the particle flatness impacts the shear layers separation and the momentum loss in the wake, while at high Re the drag coefficient has almost the same value among the tested geometries, although the contribution of different momentum flux terms differs. These results represent a further step towards a deeper understanding the drag of complex-shaped particles.

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TRAIL part 2: A comprehensive assessment of ice particle fall speed parametrisations

Cited by 13 publications

References 68 publications

Rising and Sinking in Resonance: Mass Distribution Critically Affects Buoyancy-Driven Spheres via Rotational Dynamics

Rising and Sinking in Resonance: Mass Distribution Critically Affects Buoyancy-Driven Spheres via Rotational Dynamics

SCARLET-1.0: SpheriCal Approximation for viRtuaL aggrEgaTes

Numerical analysis of the wake of complex-shaped snow particles at moderate Reynolds number

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