M. Breuer

Enhanced injection method for synthetically generated turbulence within the flow domain of eddy-resolving simulations

Abstract The quality of eddy-resolving turbulence simulations strongly depends on appropriate inflow conditions. In most cases they have to be time-dependent and satisfy certain conditions for the first (mean velocities) and second-order moments (Reynolds stresses) as well as concerning suitable length scales. To mimic a physically realistic incoming flow, synthetically generated turbulent velocity fluctuations superimposed on the mean velocity field are a valuable solution. However, the resolution of the grid near the inlet has to be sufficiently fine to avoid excessive damping of the turbulence intensity. In order to circumvent this problem, the injection of synthetically generated inflow data not at the inlet itself but inside the flow domain near the area of interest, where the grid is typically much finer, is an elegant loophole. In the present study two different injection techniques based on a source-term formulation are analyzed and evaluated. In addition to these techniques the injected data are weighted by a Gaussian distribution defining the influence area. In the recent work the definition of the influence area is enhanced compared to the initial version of Schmidt and Breuerxa0(2017) extending the application range. The case of a rather small influence area in comparison with the grid cell size is now tackled which is often relevant for industrial applications. The flow past a wall-mounted hemisphere is chosen as test case. The bluff body is exposed to a thick turbulent boundary layer at Re = 50,000. The generation of the turbulent velocity fluctuations in the present investigation relies on the digital filter concept, but the injection techniques evaluated are not restricted to this inflow generator. The synthetic turbulent velocity fluctuations are injected about one diameter upstream of the hemisphere. Wall-resolved large-eddy simulations are carried out for two grid resolutions and the corresponding results are analyzed and compared with the reference measurements by Wood et al.xa0(2016). Finally, one injection technique is found to be clearly superior to the other, since it guarantees the correct level of the velocity fluctuations and the reproduction of the autocorrelations.

Particle Agglomeration in Turbulent Flows: A LES Investigation Based on a Deterministic Collision and Agglomeration Model

Dry, electrostatically neutral particles in a turbulent flow are a common example for disperse particle–laden flows playing a dominant role in many technical devices such as pulverized coal firing systems or cyclone separators.

Particle-Image Velocimetry and the Assignment Problem

The Particle-Image Velocimetry (PIV) is a standard optical contactless measurement technique to determine the velocity field of a fluid flow for example around an obstacle such as an airplane wing. Tiny density neutral and light-reflecting particles are added to the otherwise invisible fluid flow. Then two consecutive images (A and B) of a thin laser illuminated light sheet are taken by a CCD camera with a time-lag of a few milliseconds. From these two images one tries to estimate the local shift of the particles, for which it is common to use a cross-correlation function. Based on the displacement of the tracers and the time-lag, the local velocities can be determined. This method requires a high level of experience by its user, fine tuning of several parameters, and multiple pre- and post-processing steps of the data in order to obtain meaningful results. We present a new approach that is based on the matching problem in bipartite graphs. Ideally, each particle in image A is assigned to exactly one particle in image B, and in an optimal assignment, the sum of shift distances of all particles in A to particles in B is minimal. However, the real-world situation is far from being ideal, because of inhomogeneous particle sizes and shapes, inadequate illumination of the images, or particle losses due to a divergence out of the two-dimensional light sheet area into the surrounding three-dimensional space, to name just a few sources of imperfection. Our new method is implemented in MATLAB with a graphical user interface. We evaluate and compare it with the cross-correlation method using real measured data. We demonstrate that our new method requires less interaction with the user, no further post-processing steps, and produces less erroneous results. This article is based on the master thesis [5], written by the first coauthor, and supervised by all other coauthors.

Fluid–Structure Interaction of a Flexible Structure in a Turbulent Flow using LES

A structure placed in a fluid flow is always affected by the pressure and shear forces acting on the surface leading to structural deformations or deflections. Partially these can be neglected and such a rigid body assumption strongly reduces the complexity of a numerical simulation setup. However, in many circumstances this assumption does not hold and fluid–structure interaction (FSI) becomes of major interest. Technical applications are numerous such as artificial heart valves, lightweight roofage or tents. Therefore, a need for appropriate numerical simulation tools exists for such coupled problems and this is the objective of the present study.