Comparison of subgrid-scale models for large-eddy simulation of hydrodynamic and magnetohydrodynamic channel flows. - In: International journal of numerical methods for heat & fluid flow, ISSN 1758-6585, Bd. 29 (2019), 7, S. 2224-2236
Purpose - This paper aims to address the performance of different subgrid-scale models (SGS) for hydro- (HD) and magnetohydrodynamic (MHD) channel flows within a collocated finite-volume scheme. Design/methodology/approach - First, the SGS energy transfer is analyzed by a priori tests using fully resolved DNS data. Here, the focus lies on the influence of the magnetic field on the SGS energy transport. Second, the authors performed a series of 18 a posteriori model tests, using different grid resolutions and SGS models for HD and MHD channel flows. Findings - From the a priori analysis, the authors observe a quantitative reduction of the SGS energy transport because of the action of the magnetic field depending on its orientation. The a posteriori model tests show a clear improvement because of the use of mixed-models within the numerical scheme. Originality/value - This study demonstrates the necessity of improved SGS modeling strategies for magnetohydrodynamic channel flows within a collocated finite-volume scheme.
https://doi.org/10.1108/HFF-09-2018-0500
Visual exploration of circulation rolls in convective heat flows. - In: 2019 IEEE Pacific Visualization Symposium, (2019), S. 202-211
We present techniques to improve the understanding of pattern forming processes in Rayleigh-Bénard-type convective heat transport, through visually guided exploration of convection features in timeaveraged turbulent flows. To enable the exploration of roll-like heat transfer pathways and pattern-forming anomalies, we combine feature extraction with interactive visualization of particle trajectories. To robustly determine boundaries between circulation rolls, we propose ridge extraction in a z-averaged temperature field, and in the extracted ridge network we automatically classify topological point defects hinting at pattern forming instabilities. An importance measure based on the circular movement of particles is employed to automatically control the density of 3D trajectories and, thus, enable insights into the heat flow in the interior of rolls. A quantitative analysis of the heat transport within and across cell boundaries, as well as investigations of pattern instabilities in the vicinity of defects, is supported by interactive particle visualization including instant computations of particle density maps. We demonstrate the use of the proposed techniques to explore direct numerical simulations of the 3D Boussinesq equations of convection, giving novel insights into Rayleigh-Bénard-type convective heat transport. Keywords: flow visualization, particle-based visualzation, convective heat transport
https://doi.org/10.1109/PacificVis.2019.00031
Combined measurement of velocity and temperature in liquid metal convection. - In: Journal of fluid mechanics, ISSN 1469-7645, Bd. 876 (2019), S. 1108-1128
Combined measurements of velocity components and temperature in a turbulent Rayleigh-Bénard convection flow at a low Prandtl number of Pr = 0.029 and Rayleigh numbers of 10^6 ≤ Ra ≤ 6 × 10^7 are conducted in a series of experiments with durations of more than a thousand free-fall time units. Multiple crossing ultrasound beam lines and an array of thermocouples at mid-height allow for a detailed analysis and characterization of the complex three-dimensional dynamics of the single large-scale circulation roll in a cylindrical convection cell of unit aspect ratio which is filled with the liquid metal alloy GaInSn. We measure the internal temporal correlations of the complex large-scale flow and distinguish between short-term oscillations associated with a sloshing motion in the mid-plane as well as varying orientation angles of the velocity close to the top/bottom plates and the slow azimuthal drift of the mean orientation of the roll as a whole that proceeds on a time scale up to a hundred times slower. The coherent large-scale circulation drives a vigorous turbulence in the whole cell that is quantified by direct Reynolds number measurements at different locations in the cell. The velocity increment statistics in the bulk of the cell displays characteristic properties of intermittent small-scale fluid turbulence. We also show that the impact of the symmetry-breaking large-scale flow persists to small-scale velocity fluctuations thus preventing the establishment of fully isotropic turbulence in the cell centre. Reynolds number amplitudes depend sensitively on beam-line position in the cell such that different definitions have to be compared. The global momentum and heat transfer scalings with Rayleigh number are found to agree with those of direct numerical simulations and other laboratory experiments.
https://doi.org/10.1017/jfm.2019.556
Simultaneous measurements of velocity and temperature fields in large aspect ratio Rayleigh-Bénard convection. - In: 13th International Symposium on Particle Image Velocimetry, (2019), S. 457-467
https://athene-forschung.unibw.de/128915
On the challenges for reliable measurements of convection in large aspect ratio Rayleigh-Bénard cells in air and sulfur-hexafluoride. - In: Experimental thermal and fluid science, Volume 109 (2019), article 109841
https://doi.org/10.1016/j.expthermflusci.2019.109841
The evolution of the large-scale flow in magnetoconvection. - In: DPG-Frühjahrstagung 2019 (DPG Spring Meeting 2019) of the Condensed Matter Section (SKM) together with the Division Radiation and Medical Physics and the Working Groups Equal Opportunities, Industry and Business, Young DPG; Symposia, exhibition of scientific instruments and literature, (2019), DY 5.5
Experimental analysis of superstructures in large-aspect-ratio Rayleigh Bénard convection. - In: DPG-Frühjahrstagung 2019 (DPG Spring Meeting 2019) of the Condensed Matter Section (SKM) together with the Division Radiation and Medical Physics and the Working Groups Equal Opportunities, Industry and Business, Young DPG; Symposia, exhibition of scientific instruments and literature, (2019), DY 5.3
Transport and mixing properties of passive and active scalar with and without phase changes. - Ilmenau, 2019. - viii, 135 Seiten
Technische Universität Ilmenau, Dissertation 2019
Turbulentes Mischen und sogenanntes Entrainment erfolgt in vielen Prozessen in der Natur und der Atmosphäre. Die Dynamik von Wolken und deren Lebensdauer wird durch turbulentes Entrainment bestimmt, wo meist ungesättigte, nicht turbulente Luft in die turbulente Wolke eindringt. Ziel der vorliegenden Arbeit ist es, Mischungsprozesse aktiver und passiver Skalarfelder mithilfe rechenintensiver, direkter numerischer Simulationen näher zu untersuchen. Ein kombiniertes Euler-Lagrange Verfahren bildet hierbei die Grundlage. Im ersten Teil wird die Lagrangesche und die Eulersche Darstellung des passiven Skalars für den simplen Fall eines zerfallenden Skalars bei hohen Schmidtzahlen direkt miteinander verglichen. In dem Lagrangebild werden zahlreiche masselose Tracer mit der Strömung advektiert und die Diffusion mit Brownscher Bewegung modelliert. In dem Eulerbild wird der Skalar wie ein Kontinuum behandelt. Die durchschnittliche, charakteristische Mischungszeit wird im Lagrangebild separat durch die Berechnung des Spektrums der finiten Ljapunow-Eponenten ermittelt. Diese Zeit gibt an, ab wann die Diffusion der skalaren Schichten einsetzt. Über mehrere Mischungszeiten hinweg ergibt sich eine gute Übereinstimmung der Varianz und der Wahrscheinlichkeitsverteilung der beiden Darstellungen des Skalars. Die Berechnung der Ljapunow-Exponenten ermöglicht eine qualitative Vorhersage der Verteilung der skalaren Konzentration für unterschiedliche Zeiten. Die Lagrangesche Darstellung vom passiven Skalar hat hierbei viele Vorteile im Vergleich zu der üblichen Eulerschen Darstellung wie z.B. komplexere Randbedingungen und Modelle für die Zugabe und Umverteilung des Skalars. Der zweite Teil der Arbeit widmet sich dem Mischen des aktiven Skalars einschließlich möglicher Phasenumwandlungen. Eine direkte Anwendung dafür ist das Mischen am Rand einer Kumuluswolke. Hierbei werden in der Lagrangeschen Darstellung masselose Tracer durch Wolkentropfen ersetzt. Das Einfangen der trockenen Luft durch die turbulente Wolke bewirkt eine Verdampfung dieser Tröpfchen und dadurch lokal eine Änderung der Temperatur und des Dampfgehaltes. Beide Felder sind aktive Skalare und verändern die Strömung aufgrund ihrer Schwankungen. Hierbei werden zwei Parametereinflüsse näher untersucht, einerseits der Einfluss des gewählten Simulationsvolumens und andererseits der Einfluss der gewählten Anfangsbedingungen mit einer Variation des Turbulenzgrades der Umgebungsluft. Es stellt sich insgesamt heraus, dass während dem transientem Mischungsprozess größtenteils der Auftrieb für die Ausbildung der Mischungsschicht verantwortlich ist und anfangs zum Abwind am Wolkenrand und zum Aufwind innerhalb der Wolke führt. Statistiken individueller Tröpfchen belegen, dass das Mischen empfindlicher auf großskalige Strukturen reagiert und der Turbulenzgrad eher eine untergeordnete Rolle spielt. Eine zusätzliche systematische Untersuchung, bei der das Simulationsgebiet und dadurch die Anzahl der Freiheitsgrade erhöht wird, zeigt, dass die Art des inhomogenen Mischens am Wolkenrand überwiegend durch große Skalen bestimmt wird.
Comparison of Lagrangian and Eulerian frames of passive scalar turbulent mixing. - In: Physical review fluids, ISSN 2469-990X, Bd. 4 (2019), 4, 044607, insges. 21 S.
https://doi.org/10.1103/PhysRevFluids.4.044607
Deep learning in turbulent convection networks. - In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 1091-6490, Bd. 116 (2019), 18, S. 8667-8672
We explore heat transport properties of turbulent Rayleigh-Bénard convection in horizontally extended systems by using deep-learning algorithms that greatly reduce the number of degrees of freedom. Particular attention is paid to the slowly evolving turbulent superstructures - so called because they are larger in extent than the height of the convection layer - which appear as temporal patterns of ridges of hot upwelling and cold downwelling fluid, including defects where the ridges merge or end. The machine-learning algorithm trains a deep convolutional neural network (CNN) with U-shaped architecture, consisting of a contraction and a subsequent expansion branch, to reduce the complex 3D turbulent superstructure to a temporal planar network in the midplane of the layer. This results in a data compression by more than five orders of magnitude at the highest Rayleigh number, and its application yields a discrete transport network with dynamically varying defect points, including points of locally enhanced heat flux or "hot spots". One conclusion is that the fraction of heat transport by the superstructure decreases as the Rayleigh number increases (although they might remain individually strong), correspondingly implying the increased importance of small-scale background turbulence.
https://doi.org/10.1073/pnas.1900358116