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Aggregation and fragmentation dynamics in random flows: From tracers to inertial aggregates.
Ksenia Guseva1, Ulrike Feudel1
1Theoretical Physics/Complex Systems, ICBM, University of Oldenburg, 26129 Oldenburg, Germany.
Physical Review. E
|July 16, 2017
Summary
We studied how particles aggregate and fragment in random flows, finding that their size distributions change with flow conditions and particle properties. Scaling laws apply, but break down when particles exhibit mixed behaviors.
Area of Science:
- Fluid Dynamics
- Particle Physics
- Chemical Engineering
Background:
- Particle aggregation and fragmentation are crucial in various natural and industrial processes.
- Understanding these dynamics in random flows is complex due to advection and inertial effects.
- Hydrodynamic stress drives fragmentation, balancing aggregation and influencing final particle size distributions.
Purpose of the Study:
- To investigate aggregation and fragmentation dynamics of tracers and inertial aggregates in random flows.
- To elucidate the impact of advection dynamics and inertial effects on aggregation rates.
- To compare steady-state size distributions of tracers versus inertial aggregates.
Main Methods:
- Utilized an individual-particle-based model to simulate particle behavior.
- Tracked individual aggregate position, velocity, and size.
- Analyzed steady-state size distributions under varying conditions.
Main Results:
- Observed that size distribution shapes vary with suspension dilution rate.
- Demonstrated that size distributions for dense inertial particles and tracer monomers can be rescaled using a characteristic size.
- Identified a crossover in scaling when aggregates exhibit both tracer-like and inertial-like behaviors.
Conclusions:
- Particle aggregation and fragmentation in random flows lead to steady-state size distributions.
- Inertial effects and advection dynamics significantly influence these distributions.
- Rescaling is possible for certain particle types, but fails for mixed-behavior aggregates, indicating complex scaling relationships.
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