Publikationen

This paper presents a novel trust-region method for the optimization of multiple expensive functions. We apply this method to a biobjective optimization problem in fluid mechanics, the optimal mixing of particles in a flow in a closed container. The three-dimensional time-dependent flows are driven by Lorentz forces that are generated by an oscillating permanent magnet located underneath the rectangular vessel. The rectangular magnet provides a spatially non-uniform magnetic field that is known analytically. The magnet oscillation creates a steady mean flow (steady streaming) similar to those observed from oscillating rigid bodies. In the optimization problem, randomly distributed mass-less particles are advected by the flow to achieve a homogeneous distribution (objective function 1) while keeping the work done to move the permanent magnet minimal (objective function 2). A single evaluation of these two objective functions may take more than two hours. For that reason, to save computational time, the proposed method uses interpolation models on trust-regions for finding descent directions. We show that, even for our significantly simplified model problem, the mixing patterns vary significantly with the control parameters, which justifies the use of improved optimization techniques and their further development.

The article is devoted to an experimental study of a submerged flat jet flow in a transverse magnetic field. Two different approaches to the experimental study of jet flows are described. Detailed information about the experimental program and measuring methods presented here. The flow of a flat jet 6 mm high in a square channel with a side of 56 mm is considered. The channel is positioned so that the plane of the jet is perpendicular to the magnetic field induction. The results of measuring velocity profiles and waveforms by swivel-type probe with potential sensor are presented. Effects that can be interpreted in different ways are found: strongly unstationary flow regimes, mean flow reorganization, and development of near-wall jets. Additional experiments are prepared to obtain more detailed information about the restructuring and development of the jet. In particular, continuous measurements along the channel will be made in the presence of a slight main flow.

Reservoir computing is an efficient implementation of a recurrent neural network that can describe the evolution of a dynamical system by supervised machine learning with- out solving the underlying mathematical equations. In this work, reservoir computing is applied to model the large-scale evolution and the resulting low-order turbulence statistics of a two-dimensional turbulent Rayleigh-Bénard convection flow at a Rayleigh number Ra = 10^7 and a Prandtl number Pr = 7 in an extended spatial domain with an aspect ratio of 6. Our data-driven approach, which is based on a long-term direct numerical simulation of the convection flow, comprises a two-step procedure: (1) reduction of the original simulation data by a proper orthogonal decomposition (POD) snapshot analysis and subsequent truncation to the first 150 POD modes which are associated with the largest total energy amplitudes; (2) setup and optimization of a reservoir computing model to describe the dynamical evolution of these 150 degrees of freedom and thus the large-scale evolution of the convection flow. The quality of the prediction of the reservoir computing model is comprehensively tested by a direct comparison of the results of the original direct numerical simulations and the fields that are reconstructed by means of the POD modes. We find a good agreement of the vertical profiles of mean temperature, mean convective heat flux, and root-mean-square temperature fluctuations. In addition, we discuss temperature variance spectra and joint probability density functions of the turbulent vertical velocity component and temperature fluctuation, the latter of which is essential for the turbulent heat transport across the layer. At the core of the model is the reservoir, a very large sparse random network characterized by the spectral radius of the corresponding adjacency matrix and a few further hyperparameters which are varied to investigate the quality of the prediction. Our work demonstrates that the reservoir computing model is capable of modeling the large-scale structure and low-order statistics of turbulent convection, which can open new avenues for modeling mesoscale convection processes in larger circulation models.

We report on Rayleigh-Bénard convection with strongly varying fluid properties experimentally and theoretically. Using pressurized sulfur-hexafluoride (SF6) above its critical point, we are able to make measurements at mean temperatures (Tm) and pressures (Pm) along Prandtl-number isolines in the (T,P) parameter space. This allows us to keep the mean Rayleigh- (Ram) and Prandtl number (Prm) constant while changing the temperature dependences of the fluid properties independently, e.g., probing the liquidlike or gaslike region that are left and right of the supercritical isochore. Hence, non-Oberbeck-Boussinesq (NOB) effects can be measured and analyzed cleanly. We measure the temperature at midheight (Tc) as well as the global vertical heat flux. We observe a significant heat transport enhancement of up to 112% under strong NOB conditions. Furthermore, we develop a theoretical model for the global vertical heat flux based on ideas of Grossmann and Lohse (GL) in OB systems, adjusted for nonconstant fluid properties. In this model, the NOB effects influence the boundary layer and hence Tc, but the change of the heat flux is predominantly due to a change of the fluid properties in the bulk, in particular the heat capacity cp and density p. Predictions from our model are consistent with our experimental results as well as with previous measurements carried out in pressurized ethane and cryogenic helium.

In this work we study numerically liquid metal flow in a squareduct under the influence ofa transverse magnetic field applied in a spanwise direction (coplanar). The key interest of thepresent study is an attempt of passive control of flow regimesdeveloped under magnetic fieldand thermal loads by applying specially shaped conditions,such as swirling, at the duct inlet.In this paper, we report results of numerical simulations ofthe interaction of swirling flow andtransverse magnetic field in a square duct flow. Analysis of the obtained regimes might beimportant for the development of an experimental setup, in order to design corresponding inletsections.