Ponente
Descripción
Abstract
Understanding tracer particle distribution in turbulent open-channel flow is highly relevant to biological systems. In natural aquatic environments, many biologically significant entities behave as passive tracers or near-tracers, including planktonic microorganisms, fish larvae, algal spores, aquatic pollen, and small neutrally buoyant seeds. Their dispersal, encounter rates, and ultimately their survival and reproduction depend critically on the turbulent structures that transport and concentrate them. Coherent turbulent features - such as streaks, vortices, and ejection-sweep events - create heterogeneous particle distributions, leading to localized hotspots where biological interactions (e.g., predation, fertilization, or nutrient uptake) are enhanced. By simulating tracer particles, we isolate the pure hydrodynamic mechanisms that shape these distributions.
Unfortunately, the distribution of particles in an open-channel turbulent flow is challenging to predict due to the complex interactions between turbulent structures and the particles. In this work, we explore the particle distribution to detect possible internal structures and, if feasible, hypothesize about the topology resulting from fluid–particle interactions. The existence of any coherent topological structure will be assessed directly from the particle distribution.
We assume that particles behave as tracers, following the fluid flow without detaching from it. A total of 1,000,000 spherical particles is considered. The fluid–particle system is simulated using direct numerical simulation (DNS) with particle tracking. The flow has a Reynolds number of 180, based on the shear velocity. The numerical method employs a second-order Adams–Bashforth scheme for time advancement and solves the Poisson equation for pressure, while particle tracking is performed using a second-order Runge–Kutta method.
The exploratory simulation reveals that coherent structures persist, suggesting the feasibility of extending the methodology to higher-density particles and investigating the dynamics of the particle distribution topology to analyze topological invariants.