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Direct numerical simulations of spiral Taylor–Couette turbulence

Published online by Cambridge University Press:  28 January 2020

Pieter Berghout*
Affiliation:
Physics of Fluids Group and Max Planck Center Twente, MESA+ Institute and J. M. Burgers Centre for Fluid Dynamics, University of Twente, P.O. Box 217, 7500AEEnschede, Netherlands
Rick J. Dingemans
Affiliation:
Physics of Fluids Group and Max Planck Center Twente, MESA+ Institute and J. M. Burgers Centre for Fluid Dynamics, University of Twente, P.O. Box 217, 7500AEEnschede, Netherlands
Xiaojue Zhu
Affiliation:
Physics of Fluids Group and Max Planck Center Twente, MESA+ Institute and J. M. Burgers Centre for Fluid Dynamics, University of Twente, P.O. Box 217, 7500AEEnschede, Netherlands Center of Mathematical Sciences and Applications, and School of Engineering and Applied Sciences, Harvard University, Cambridge, MA02138, USA
Roberto Verzicco
Affiliation:
Physics of Fluids Group and Max Planck Center Twente, MESA+ Institute and J. M. Burgers Centre for Fluid Dynamics, University of Twente, P.O. Box 217, 7500AEEnschede, Netherlands Dipartimento di Ingegneria Industriale, University of Rome ‘Tor Vergata’, Via del Politecnico 1, Roma00133, Italy Gran Sasso Science Institute - Viale F. Crispi, 7, 67100L’Aquila, Italy
Richard J. A. M. Stevens
Affiliation:
Physics of Fluids Group and Max Planck Center Twente, MESA+ Institute and J. M. Burgers Centre for Fluid Dynamics, University of Twente, P.O. Box 217, 7500AEEnschede, Netherlands
Wim van Saarloos
Affiliation:
Instituut-Lorentz, Universiteit Leiden, Postbus 9506, 2300 RA, Leiden, The Netherlands
Detlef Lohse*
Affiliation:
Physics of Fluids Group and Max Planck Center Twente, MESA+ Institute and J. M. Burgers Centre for Fluid Dynamics, University of Twente, P.O. Box 217, 7500AEEnschede, Netherlands Max Planck Institute for Dynamics and Self-Organisation, Am Fassberg 17, 37077Göttingen, Germany
*
Email addresses for correspondence: p.berghout@utwente.nl, d.lohse@utwente.nl
Email addresses for correspondence: p.berghout@utwente.nl, d.lohse@utwente.nl

Abstract

We perform direct numerical simulations of spiral turbulent Taylor–Couette (TC) flow for $400\leqslant Re_{i}\leqslant 1200$ and $-2000\leqslant Re_{o}\leqslant -1000$, i.e. counter-rotation. The aspect ratio $\unicode[STIX]{x1D6E4}=\text{height}/\text{gap width}$ of the domain is $42\leqslant \unicode[STIX]{x1D6E4}\leqslant 125$, with periodic boundary conditions in the axial direction, and the radius ratio $\unicode[STIX]{x1D702}=r_{i}/r_{o}=0.91$. We show that, with decreasing $Re_{i}$ or with decreasing $Re_{o}$, the formation of a turbulent spiral from an initially ‘featureless turbulent’ flow can be described by the phenomenology of the Ginzburg–Landau equations, similar as seen in the experimental findings of Prigent et al. (Phys. Rev. Lett., vol. 89, 2002, 014501) for TC flow at $\unicode[STIX]{x1D702}=0.98$ an $\unicode[STIX]{x1D6E4}=430$ and in numerical simulations of oblique turbulent bands in plane Couette flow by Rolland & Manneville (Eur. Phys. J., vol. 80, 2011, pp. 529–544). We therefore conclude that the Ginzburg–Landau description also holds when curvature effects play a role, and that the finite-wavelength instability is not a consequence of the no-slip boundary conditions at the upper and lower plates in the experiments. The most unstable axial wavelength $\unicode[STIX]{x1D706}_{z,c}/d\approx 41$ in our simulations differs from findings in Prigent et al., where $\unicode[STIX]{x1D706}_{z,c}/d\approx 32$, and so we conclude that $\unicode[STIX]{x1D706}_{z,c}$ depends on the radius ratio $\unicode[STIX]{x1D702}$. Furthermore, we find that the turbulent spiral is stationary in the reference frame of the mean velocity in the gap, rather than the mean velocity of the two rotating cylinders.

Information

Type
JFM Papers
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2020
Figure 0

Figure 1. Simplified phase space of low Reynolds TC flow. The blue line is the stability boundary at $\unicode[STIX]{x1D702}=r_{i}/r_{o}=0.91$, as considered in this study, calculated with equation (8) of Esser & Grossmann (1996). The intermittent, spiral turbulence, and ‘featureless turbulent’ regimes are schematics, indicating the approximate locations of the phases at $\unicode[STIX]{x1D702}=0.91$, similar to the phase diagram at lower radius ratio ($\unicode[STIX]{x1D702}=0.84$) in Andereck, Liu & Swinney (1986). The horizontal and vertical dashed-dotted lines represent the simulations that are performed in this paper. The inset shows the stability boundaries for varying $\unicode[STIX]{x1D702}$.

Figure 1

Figure 2. Snapshots of the azimuthal velocity $u_{\unicode[STIX]{x1D703}}$, close to the inner cylinder ($r=r_{i}+d/4$), at the centre ($r=r_{i}+d/2$) and close to the outer cylinder ($r=r_{i}+3d/4$) for $Re_{o}=-1200$ and for different $Re_{i}=800$ (ac), 750 (df), 700 (gi) and 524 (jl). The horizontal axis gives the angle $\unicode[STIX]{x1D703}$. On the vertical axis the axial coordinate $z$ is normalized with the gap width. From a chaotic turbulent base flow (ac), the finite-wavelength instability forms (panels df and in particular gi). Further away from the transition an isolated stripe (spiral) breaks down in connected and isolated turbulent spots (jl).

Figure 2

Figure 3. Streamlines overlay snapshots of the azimuthal velocity $u_{\unicode[STIX]{x1D703}}$ in the meridional plane for $Re_{o}=-1200$. The thickness of the streamlines represents the norm of the velocity vector ($u_{r},u_{z}$). We observe chaotic motion for all $r$ in the ‘featureless turbulent’ flow (a). With decreasing $Re_{i}$, laminarization occurs from the outer cylinder towards the inner cylinder (b,c). The vertical dashed lines at $(r-r_{i})/d\approx 0.4$ give the location of the nodal plane of neutral stability. The meridional snapshots where obtained at $\unicode[STIX]{x1D703}=\unicode[STIX]{x03C0}$.

Figure 3

Figure 4. (a) Dimensionless angular velocity transport $Nu_{\unicode[STIX]{x1D714}}$ versus the wavelength $\unicode[STIX]{x1D706}_{z}/d$ of the turbulent spiral for different Reynolds numbers. $Nu_{\unicode[STIX]{x1D714}}$ decreases linearly with increasing wavelength, which is attributed to the decrease in turbulence fraction in the domain, with increasing axial wavelengths of the spiral. (b) The friction factor $C_{f}$ for different Reynolds numbers versus $\unicode[STIX]{x1D706}_{z}/d$. (c) $C_{f}$ versus the inner cylinder Reynolds number $Re_{i}$ for $Re_{o}=-1200$. An increase of $C_{f}$ indicates transitional behaviour from the laminar to the turbulent regime.

Figure 4

Figure 5. Angular location $\unicode[STIX]{x1D703}$ of the turbulent spiral versus time $\hat{t}$. The vertical axis represents the angular position of the maximum turbulent intensity at $z=L/2$ and $r=r_{i}+d/2$. The horizontal axis represents dimensionless time $\hat{t}=t/T$. $\langle \hat{\unicode[STIX]{x1D714}}\rangle _{r,\unicode[STIX]{x1D703},z}$ is the calculated mean angular velocity in the domain. Excellent agreement between $\unicode[STIX]{x0394}\unicode[STIX]{x1D703}/\unicode[STIX]{x0394}\hat{t}=-0.355$ and $\langle \hat{\unicode[STIX]{x1D714}}\rangle _{r,\unicode[STIX]{x1D703},z}=-0.354$ reveals that the spiral is stationary in the reference frame of the mean angular velocity $\langle \hat{\unicode[STIX]{x1D714}}\rangle _{\unicode[STIX]{x1D703},z,r,t}$. Note that ${\textstyle \frac{1}{2}}(\hat{\unicode[STIX]{x1D714}}_{i}+\hat{\unicode[STIX]{x1D714}}_{o})=-0.326$, solid black line, does not match $\unicode[STIX]{x0394}\unicode[STIX]{x1D703}/\unicode[STIX]{x0394}\hat{t}$.

Figure 5

Figure 6. Definition of the amplitude $A$. (a) Plot of the square root of the radial velocity component squared $\sqrt{u_{r}(\unicode[STIX]{x1D703},z,r,t)^{2}}$ versus the angular position at $r=r_{i}+d/2$ and $z=L/2$ at an arbitrary instant in time when the flow is statistically stationary. The amplitude is defined as $A(z,r,t)=(\sqrt{u_{r}(z,r,t)^{2}})_{max}-(\sqrt{u_{r}(z,r,t)^{2}})_{min}$. (b) The time dependent signal. To obtain the converged amplitude, we employ time averaging and we average over spatial coordinates $(r_{i}+d/4) and for all $z$. This particular signal is acquired for $Re_{i}=660$ and $Re_{o}=-1200$.

Figure 6

Figure 7. Amplitude scaling of the turbulent spiral with varying $\unicode[STIX]{x1D716}$. (a) Scaling of the amplitude squared $A^{2}$ with varying $Re_{i}(Re_{o}=-1200)$. The fit highlights that $A^{2}\propto \unicode[STIX]{x1D716}^{1}$, with $\unicode[STIX]{x1D716}=(Re_{i,c}-Re_{i})/Re_{i,c}$, as predicted by the GL equations. Extrapolation of the fit gives $Re_{i,c}=863$, which compares very well with the experimental results in Prigent et al. (2002) for $\unicode[STIX]{x1D702}=0.98$. (b) Scaling of $A^{2}$ with varying $Re_{o}(Re_{i}=680)$. We observe an identical scaling for $A^{2}$; $A^{2}\propto \unicode[STIX]{x1D716}^{1}$, now with $\unicode[STIX]{x1D716}=(Re_{o,c}-Re_{o})/Re_{o,c}$ and $Re_{o,c}=-713$. Error bars represent the standard deviation of $\langle A^{2}\rangle _{z,r}(t)$.

Figure 7

Figure 8. Amplitude $A$ of the finite-wavelength instability versus wavenumber $\hat{k}$ for varying reduced thresholds $\unicode[STIX]{x1D716}=(Re_{i,c}-Re_{i})/Re_{i,c}$. We include all simulations, with varying aspect ratios of $42\leqslant \unicode[STIX]{x1D6E4}\leqslant 125$. Note that for large aspect ratios multiple spirals exist. Therefore, we cannot simulate smaller wavenumbers than indicated in the graph. We observe consistent behaviour of the amplitude with increasing $\unicode[STIX]{x1D716}$, following the phenomenology of a finite-wavelength instability. We fit a second-order polynomial through the data. From this fit we obtain the most unstable wavelength for each $\unicode[STIX]{x1D716}$ – thus five values in total. We obtain $38.91d<\unicode[STIX]{x1D706}_{c}<42.52d$ with $\text{mean}(\unicode[STIX]{x1D706}_{c})=41.38d$ and $\text{var}(\unicode[STIX]{x1D706}_{c})=1.74d$. As we do not observe a systematic trend in the difference between the data and the fit, but rather find that the error is of similar order for all wavenumbers $\hat{k}$, we conclude that a parabolic fit is justified.