Publikationen

Turbulence in a wall-bounded flow of supercritical fluid can be significantly modulated by nonuniform body forces. This study presents direct numerical simulations performed with a nonuniform streamwise body force varying in the wall-normal direction in a fully developed channel flow. A quasilaminar state and reorganized turbulence were obtained by changing the amplitude of the nonuniform body force which distorts the parabolic mean velocity profile and thus alters the turbulence production due to mean shear. Weak production of the flattened mean velocity profile leads to a relaminarization. In the quasilaminar state, all components of the Reynolds stress tensor are fairly weak except the streamwise fluctuations distant from the wall, which indicates the collapse of the near-wall turbulence self-sustaining cycle. The remaining streamwise fluctuations away from the wall exhibit elongated streaks, which is shown by premultiplied spectra. In the recovered turbulence regime, the Reynolds shear stress has a negative range in the bulk connected with a positive range near the wall. This corresponds to the nonmonotonic shear stress resulting from an M-shaped velocity profile. In a subsequent quadrant analysis of the Reynolds shear stress, the sweep and ejection events are found to dominate near the wall only while inward Q1 and outward Q3 motions are significant in the bulk. Both kinds of high-speed events can be attributed to the velocity maxima of the M-shaped mean velocity profile and are found to penetrate towards the wall and the channel center. Interestingly, the flow topology shows the typical teardrop shape once turbulence recovered. Our study contributes to the understanding of flow relaminarization in mixed convection and assists in developing further flow control techniques.

The physical complexity and the large number of degrees of freedom that can be resolved today by direct numerical simulations of turbulent flows, and by the most sophisticated experimental techniques, require new strategies to reduce and analyse the data so generated, and to model the turbulent behaviour. We discuss a few concrete examples for which the turbulence data have been analysed by machine learning tools. We also comment on work in neighbouring fields of physics, particularly astrophysical (and astronomical) work, where Big Data has been the paradigm for some time. We discuss unsupervised, semi-supervised and supervised machine learning methods to direct numerical simulations data of homogeneous isotropic turbulence, Rayleigh-Bénard convection, and the minimal flow unit of a turbulent channel flow; for the last case, we discuss in some detail the application of echo state networks, this being one implementation of reservoir computing. The paper also provides a brief perspective on machine learning applications more broadly.

Magnetohydrodynamic convection in a downward flow of liquid metal in a vertical duct is investigated experimentally and numerically. It is known from earlier studies that in a certain range of parameters, the flow exhibits high-amplitude pulsations of temperature in the form of isolated bursts or quasi-regular fluctuations. This study extends the analysis while focusing on the effects of symmetry introduced by two-sided rather than one-sided wall heating. It is found that the temperature pulsations are robust physical phenomena appearing for both types of heating and various inlet conditions. At the same time, the properties, typical amplitude, and range of existence in the parametric space are very different at the symmetric and asymmetric heating. The obtained data show good agreement between computations and experiments and allow us to explain the physical mechanisms causing the pulsation behavior.

Direct numerical simulations are carried out to study the flow structure and transport properties in turbulent Rayleigh-Bénard convection in a vertical cylindrical cell of aspect ratio one with an imposed axial magnetic field. Flows at the Prandtl number 0.025 and Rayleigh and Hartmann numbers up to 10^9 and 1400, respectively, are considered. The results are consistent with those of earlier experimental and numerical data. As anticipated, the heat transfer rate and kinetic energy are suppressed by a strong magnetic field. At the same time, their growth with Rayleigh number is found to be faster in flows at high Hartmann numbers. This behaviour is attributed to the newly discovered flow regime characterized by prominent quasi-two-dimensional structures reminiscent of vortex sheets observed earlier in simulations of magnetohydrodynamic turbulence. Rotating wall modes similar to those in Rayleigh-Bénard convection with rotation are found in flows near the Chandrasekhar linear stability limit. A detailed analysis of the spatial structure of the flows and its effect on global transport properties is reported.