arXiv subjects

Markus Weyrauch

Publications and source records attributed to Markus Weyrauch.

3 recordsLinked to original sources

arXiv2026

Towards PR-DNS of scour around a wall-mounted cylinder in turbulent open channel flow

Particle-resolved direct numerical simulation (PR-DNS) is performed for turbulent open channel flow over a smooth horizontal wall with a vertical cylinder and a dilute set of mobile, heavy, spherical particles. At the chosen parameter point (which matches a previous study without a cylinder) the particles are mostly translating in the horizontal plane while remaining in contact with the wall. It is shown that the presence of the cylinder leads to the generation of intense vortical structures, enhanced turbulence intensity in the wake region, and to strong modifications of the local wall shear stress. These cylinder-induced perturbations have direct consequences for the average particle concentration: preferential accumulation/depletion in different parts of the wake region occurs, while the wall-normal transport of particles (against gravity) is significantly enhanced. A second simulation which adds roughness elements on the wall reveals an additional effect upon the wall-normal distribution of particles. It turns out that the configuration with wall-roughness and a wall-mounted cylinder features the largest fraction of entrained particles, even far from the wall.

physics.flu-dyn↗

arXiv2026

Investigating the mechanism by which finite-size heavy particles are entrained in turbulent open channel flow over a smooth surface

The dynamics of entrainment of finite-size heavy particles in a turbulent open channel flow over a smooth surface are analyzed. Three types of simulations, namely with freely moving, rotation-constrained, and spanwise-motion-constrained particles, were conducted using particle-resolved direct numerical simulations. With the aid of a relative velocity suitably defined in the vicinity of the finite-size particle, we decompose the hydrodynamic force into drag and lift contributions and evaluate the local wall-normal shear rate around the particles. By means of coherent structure eduction techniques, we investigate flow structures before and during lift-off events. Rotation-constrained simulations revealed the insignificance of particle rotation in the entrainment mechanism. Spanwise-motion-constrained simulations revealed the importance of particle location with respect to flow structures with apparent changes in entrainment frequency, duration of the entrainment process, wall-normal shear around the particles, and distance to the nearest vortical structures during lift-off. The contribution of lift to the wall-normal force is found to be responsible for the initiation of particle entrainment, which is induced by a high-shear event associated with fast-moving fluid. The presence of quasi-streamwise vortices is shown to be an important ingredient for the entrainment of particles into the bulk flow. The results show that, at marginal Shields number values, a high wall-normal shear rate and the proximity of an intense quasi-streamwise vortex are essential elements of the entrainment mechanism.

physics.flu-dyn↗

arXiv2025

State estimation in homogeneous isotropic turbulence using super-resolution with a 4DVar training algorithm

Variational data assimilation and machine-learning based super-resolution are two alternative approaches to state estimation in turbulent flows. The former is an optimisation problem featuring a time series of coarse observations, the latter usually requires a library of high-resolution 'ground truth' data. We show that the classic '4DVar' data assimilation algorithm can be used to train neural networks for super-resolution in three-dimensional isotropic turbulence without the need for high-resolution reference data. To do this, we adapt a pseudo-spectral version of the fully-differentiable JAX-CFD solver (Kochkov et al, Proc. Nat. Acad. Sci. 118, 2021) to three-dimensional flows and combine it with a convolutional neural network for super-resolution. As a result we are able to include entire trajectories in our loss function which is minimised with gradient-based optimisation to define the neural network weights. We show that the resulting neural networks outperform 4DVar for state estimation at initial time over a wide variety of metrics, though 4DVar leads to more robust predictions towards the end of its assimilation window. We also present a hybrid approach in which the trained neural network output is used to initialise 4DVar. The resulting performance is more than twice as accurate as other state estimation strategies for all times and performs well even beyond known limiting lengthscales, all without requiring access to high-resolution measurements at any point.

physics.flu-dyn↗