Search arXivSearch

arXiv · 1407.5781

Initial surface disturbance on a shear current: the Cauchy--Poisson problem with a twist

Abstract

We solve for the first time the classical linear Cauchy--Poisson problem for the time evolution an initial surface disturbance when a uniform shear current is present beneath the surface. The solution is general, including the effects of gravity, surface tension and constant finite depth. The particular case of an initially Gaussian disturbance of width $b$ is studied for different values of three system parameters: a "shear Froude number" $S\sqrt{b/g}$ ($S$ is the uniform vorticity), the Bond number and the depth relative to the initial perturbation width. Different phase and group velocity in different directions yield very different wave patterns in different parameter regimes when the shear is strong, and the well known pattern of diverging ring waves in the absence of shear can take on very different qualitative behaviours. For a given shear Froude number, both finite depth and nonzero capillary effects are found to weaken the influence of the shear on the resulting wave pattern. The various patterns are analysed and explained in light of the shear-modified dispersion relation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Simen Å. Ellingsen. 2014-07-22. Initial surface disturbance on a shear current: the Cauchy--Poisson problem with a twist. https://doi.org/10.1063/1.4891640

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Bayesian neural network correction of RANS turbulence models with uncertainty quantification in separated flows

Data-driven correction of turbulence models offers a promising route for improving Reynolds-averaged Navier-Stokes (RANS) predictions, but quantifying uncertainty in such corrections and ensuring generalization across flows remain key challenges. This work presents a Bayesian neural network (BNN) framework for uncertainty-aware correction of RANS models. The BNN represents both epistemic and input-dependent aleatoric uncertainty, interpreted here as irreducible scatter in the feature-to-correction relationship, with the latter propagated as spatially correlated fields through the RANS solver. The framework is trained exclusively on a periodic-hill configuration and evaluated without retraining on five unseen separated-flow configurations. The learned corrections improve the training-flow prediction, but the strong momentum-field benefit obtained from the anisotropy correction does not transfer consistently to the unseen flows. Out-of-distribution under-coverage is already present at the correction-field level, indicating that the loss of calibration is already present in transfer of the learned correction rather than being introduced primarily by CFD propagation. Overall, the framework enables joint propagation of epistemic and aleatoric uncertainty while exposing the present limitations of uncertainty calibration under distribution shift.

physics.flu-dyn

Direct numerical simulation of particle-laden flow in a linear compressor cascade: Unsteady boundary-layer effects on particle-blade interactions

We perform point-particle direct numerical simulations (PP-DNS) of particle-laden flow through a linear compressor cascade subjected to synthetic free-stream turbulence. Monodisperse particles are advanced in a one-way coupled Eulerian-Lagrangian framework with drag-only dynamics. We use PP-DNS collision statistics together with empirical deposition and erosion models to estimate particle deposition and blade erosion. Collision hotspots and regions of predicted deposition are identified near the leading edge and over the pressure side. On the pressure side, for the intermediate Stokes number, the onset of frequent collisions and predicted deposition is spatially associated with elevated boundary-layer intermittency during bypass transition. Nevertheless, for the largest particles, impacts occur farther upstream. On the suction side, sparse collisions and predicted deposition events appear only for the smallest particles and are phase-modulated by separation-induced vortex shedding. Joint distributions of impact velocity and angle show that leading-edge impacts are faster and span wider angles than pressure-side impacts. For the two larger particle sizes, the higher normal impact velocities near the leading edge result in a greater likelihood of rebound than on the pressure side. Therefore, appreciable erosion is predicted primarily near the leading edge under the present conditions. The present results highlight the role of unsteady boundary-layer dynamics in affecting particle-blade interactions in compressor cascades.

physics.flu-dyn

Diffusion Enhancement and Directional Suppression Induced by Reciprocal Flows

Reciprocal flows repeatedly return fluid elements to their initial positions, producing no net advective transport on time average. Nevertheless, their interplay with diffusion gives rise to nontrivial transport. To describe this phenomenon, we develop a general theory of effective diffusion under two-dimensional linear flows with arbitrary time dependence. By analyzing the advection-diffusion equation, we derive exact expressions for the mean square displacement and the effective diffusion coefficients for extensional, simple shear, and rotational flows within a single framework. We show that the reciprocal flows universally induce diffusion enhancement. The diffusion tensor exhibits pronounced anisotropy, which can result in directional diffusion suppression in spite of the direction-averaged diffusion enhancement. Our results provide a general framework for diffusion control by time-dependent flows and can provide new strategies for transport manipulation in microfluidic and biological systems.

physics.flu-dyn