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Asymptotics of the centre-mode instability in viscoelastic channel flow: with and without inertia

Published online by Cambridge University Press:  22 August 2024

Rich R. Kerswell*
Affiliation:
DAMTP, Centre for Mathematical Sciences, University of Cambridge, CB3 0WA, UK
Jacob Page*
Affiliation:
School of Mathematics, University of Edinburgh, EH9 3FD, UK
*
†Email addresses for correspondence: rrk26@cam.ac.uk, jacob.page@ed.ac.uk
†Email addresses for correspondence: rrk26@cam.ac.uk, jacob.page@ed.ac.uk

Abstract

Motivated by the recent numerical results of Khalid et al. (Phys. Rev. Lett., vol. 127, 2021, 134502), we consider the large-Weissenberg-number ($W$) asymptotics of the centre mode instability in inertialess viscoelastic channel flow. The instability is of the critical layer type in the distinguished ultra-dilute limit where $W(1-\beta )=O(1)$ as $W \rightarrow \infty$ ($\beta$ is the ratio of solvent-to-total viscosity). In contrast to centre modes in the Orr–Sommerfeld equation, $1-c=O(1)$ as $W \rightarrow \infty$, where $c$ is the phase speed normalised by the centreline speed as a central ‘outer’ region is always needed to adjust the non-zero cross-stream velocity at the critical layer down to zero at the centreline. The critical layer acts as a pair of intense ‘bellows’ which blows the flow streamlines apart locally and then sucks them back together again. This compression/rarefaction amplifies the streamwise-normal polymer stress which in turn drives the streamwise flow through local polymer stresses at the critical layer. The streamwise flow energises the cross-stream flow via continuity which in turn intensifies the critical layer to close the cycle. We also treat the large-Reynolds-number ($Re$) asymptotic structure of the upper (where $1-c=O(Re^{-2/3})$) and lower branches of the $Re$–$W$ neutral curve, confirming the inferred scalings from previous numerical computations. Finally, we remark that the viscoelastic centre-mode instability was actually first observed in viscoelastic Kolmogorov flow by Boffetta et al. (J. Fluid Mech., vol. 523, 2005, pp. 161–170).

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JFM Papers
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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, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press.
Figure 0

Figure 1. The centre-mode neutral curve (black dashed line) for $\beta =0.9$ and the asymptotic predictions (upper/lower branch – red/blue solid lines) for $W$ (a), $k$ (b) and $1-c$ (c). Panel (d) shows how the lower branch of the neutral curve rotates around and crosses the inertialess limit of $Re=0$ as $\beta$ increases from $\beta =0.9$ (black again) through $\beta =0.98$ (green) to $\beta =0.994$ (dark red). The solid blue lines indicate the asymptotic prediction for the various lower branches (the $\beta =0.994$ prediction is reached for $Re \rightarrow -\infty$; note only $Re=O(10)$ is shown here). The dark red dot at $(W,Re)=(973.8,0)$ is the lowest point reached by the neutral curve for $Re=0$ for an Oldroyd-B fluid (Khalid et al.2021b). The $W$–$Re$ neutral curve in (a) is the channel flow equivalent of the pipe flow curve shown on the left in figure 1 of Dong & Zhang (2022).

Figure 1

Figure 2. The (asymptotic) eigenfunction on the upper branch neutral curve for $\beta =0.9$: $\hat {u}$ (red) and $\hat {v}$ (blue) in (a); $\hat {\tau }_{11}$ (blue), $\hat {\tau }_{12}$ (red) and $\hat {\tau }_{22}$ (black) in (b) (in both real/imaginary parts are solid/dashed). The eigenfunction has been normalised so that $\hat {u}(0)=1$. The calculation has been done over the domain $\hat {Y} \in [-15,0]$ for clarity but larger domains (e.g. $[-50,0]$) were used for convergence purposes.

Figure 2

Table 1. Upper branch neutral curve characteristics for $\beta =0.9$ as $Re \rightarrow \infty$. At a given finite $Re$, $\hat {W}=W/Re^{1/3}$, $\hat {k}=k/Re^{1/3}$ and $\hat {a}=(1-c)Re^{2/3}$. The bottom line shows the values given in (3.11a–c) found by solving the asymptotic problem in (3.10).

Figure 3

Table 2. Lower branch neutral curve characteristics for $\beta =0.9$ and $0.98$ as $Re \rightarrow \infty$ and for $\beta =0.994$ as $Re \rightarrow -\infty$ (hence $\hat {W}$ and $\hat {k}$ are both negative). A comparison with results from finite $Re$ calculations is shown in figure 1(d).

Figure 4

Figure 3. The (asymptotic) eigenfunctions on the lower branch neutral curve for $\beta =0.9$ (a,b) and $\beta =0.994$ (c,d): $\hat {u}$ (red) and $\hat {v}$ (blue) in (a,c); $\hat {\tau }_{11}$ (blue), $\hat {\tau }_{12}$ (red) and $\hat {\tau }_{22}$ (black) in (b,d) (in both real/imaginary parts are solid/dashed). Both eigenfunctions have been normalised so that $\hat {u}(0)=1$.

Figure 5

Figure 4. Neutral eigenfunction at $W=32\,000$ (a), 128 000 (b) and 512 000 (c), $k=1.1$ and $\varLambda \approx 4.11=W(1-\beta )$ and $c_r \approx 0.99218$. Vertical dotted line near $y=0$ is the critical layer where $U=1-y^2=c_r$. Here, $v$ is blue (real/imaginary parts solid/dashed, respectively), real part of $Dv$ is red and imaginary part is black. Notice the $O(1)$ jump in ${\rm Re} (Dv)$ (and in ${\rm Re} (D^2v)$) across the critical layer and the increasingly singular behaviour of ${\rm Im} (Dv)$ (black solid line) as $W$ increases (the line dips deeper down at the critical layer approximating the logarithmic singularity).

Figure 6

Table 3. Values of $c_r$ and $k\varLambda :=k(1-\beta )W$ as plotted in inset A of figure 2 in Khalid et al. (2021b) on the neutral curve for $k=0.1$ and $1.1$ at various large $W$ ($\pm$ indicates upper and lower parts of the neutral curve and ${}^{a}$ results computed using quadruple rather than double precision). The $\infty$ entry comes from Richardson extrapolation eliminating the leading $O(1/W)$ error. In all cases, $\varLambda$ is slower to converge than $c_r$, with the upper curve calculations on the right highlighting this.

Figure 7

Table 4. Values of $c_r$ and $k\varLambda$ found by asymptotic matching on the neutral curve for $k=0.1$ and $1.1$ with various choices of $\delta$ ($\pm$ indicates upper and lower parts of the neutral curve and $^{a}$ results computed using quadruple rather than double precision). The quadruple precision calculations were done using the multi-precision extension package ‘Advanpix’ for Matlab. The ‘$0$’ predictions are found by applying Richardson extrapolation to the last two entries assuming a leading $O(\delta )$ error. All four cases compare well with the numerical predictions marked by ‘$\infty$’ in table 3.

Figure 8

Figure 5. The ‘error’ in $c$ and $\varLambda$ as a function of finite $\delta$ compared with their $\delta =0$ limiting values for the asymptotically matched solution and for the numerical solution as a function of $1/W$ compared with the $W\rightarrow \infty$ limit for $k=1.1$ and $(c, \varLambda )=(0.999218, 4.1101)$. Circles are used for the asymptotically matched solutions (filled red circles for $c$ and open blue circles for $\varLambda$); for the numerical approximations, red $+$ is used for $c$ and blue filled squares for $\varLambda$. The ‘truth’ $(c(0),\varLambda (0))$ is estimated by assuming $c(0)=c(\delta ) +a\delta +b \delta ^2+\ldots$ and eliminating the leading error between the two most accurate predictions – i.e. $c(0):= (10c(10^{-7})-c(10^{-6}))/9$. Note $1/W \leq 1/974$ for an Oldroyd-B fluid (Khalid et al.2021b).

Figure 9

Figure 6. The outer solution at $k=1.1$ and $\varLambda \approx 4.11=W(1-\beta )$ found by applying the matching conditions (4.63)–(4.68) with $\delta =2 \times 10^{-4}$ (a) and $\delta =5 \times 10^{-6}$ (b). In (a), the numerical solution shown in figure 4 at $W=32\,000$ is plotted using thick translucent lines of the appropriate colour – $v$ is blue (real/imaginary parts solid/dashed respectively), real part of $Dv$ is red and imaginary part is black – to demonstrate that the asymptotic and numerical solutions match well. Panel (b) shows the increasingly singular behaviour in the imaginary part of $Dv$ at the critical point as $\delta$ decreases.

Figure 10

Figure 7. A comparison of the stresses $\tau _{12}$ (a,b) and $\tau _{11}$ (c,d) at $k=1.1$, $\varLambda \approx 4.11$ computed numerically at $W=32\,000$ (in blue in both (a,c) and (b,d)) with the outer solutions (a,c) and leading inner asymptotic solutions given by (4.24)–(4.25) (b,d – note the rescaling of both axes). The thick translucent red line is the numerical solution and the blue solid/dashed lines are the asymptotic approximation (as usual solid lines indicate real parts and dashed lines imaginary parts).

Figure 11

Figure 8. (a) $P(\kern0.7pt y)$ plotted against $y$ for a solution ($k=1.1$, $\varLambda =4.520468$ and $c=0.99921892$) matched across the critical layer using $\delta = 2 \times 10^{-4}$ (the position of the critical layer is shown as the vertical dashed black line). (b) $C(\kern0.7pt y)$ (green) and $D(\kern0.7pt y)$ (black dashed) plotted against $y$ for the same matched solution. These figures show that the velocity field is driven locally by the critical layer whereas the polymer perturbation is driven globally by the gradual relaxation of the streamline distortion caused by the critical layer.

Figure 12

Figure 9. Contour plots of the outer solution corresponding to $k=1.1$, $\varLambda =4.520468$ and $c=0.99921892$ matched across the critical layer using $\delta = 2 \times 10^{-4}$; (a) $u$, (b) $v$, (c) $\tau _{11}$ and (d) $\phi$. The last plot focuses on the area around the critical layer (the white gap is just the excluded critical layer region $[y^*-\delta, y^*+\delta ]$). For $u$ and $v$, 20 equally spaced contour levels between the maximum and minimum values are used. For $\tau _{11}$, the contour levels are $\pm [0,10,20,30,40,50,60,100,200,1000,2000,5000]$ and 50 equally spaced levels are used for $\phi$ with all the contours essentially contained in the shown area around the critical layer.

Figure 13

Figure 10. (a) The effect on $W_{min}$ of decreasing $\lambda$ from 1 to 0.96 (the common point on $\lambda =1$ is the undisturbed channel flow value of $W_{min}=974$): expt. 1 black dash-dot line; expt. 2 blue dashed line; expt. 3 yellow solid line; expt. 4 red solid line. (b) The base flow profile $U:=\lambda (1-y^2)+(1-\lambda ) y$ in expt. 3 (yellow line) and $U:=\lambda (1-y^2)+(1-\lambda )(-y)$ in expt. 4 (red line) for $\lambda =0.96$ showing how the former eliminates the zero-shear point whereas the latter moves it into the interior (notice only a small part of the flow domain is being shown close to the midplane. In all cases the problem is solved over $y \in [-1,0]$).

Figure 14

Figure 11. (a) Viscoelastic Kolmogorov base flow as given in (7.1) in green and channel flow $(1-y^2)$ in dashed black. Neutral vKf eigenfunctions for $k=1$ and $(W,\beta,c)=(2000, 0.99954, 0.98029)$ (b), $(W,\beta,c)=( 200, 0.99530, 0.97897)$ (c) and $(W,\beta,c)=( 20, 0.93972, 0.96726)$ (d). Here, $v$ is blue (real/imaginary parts solid/dashed respectively), real part of $Dv$ is red and imaginary part is black.

Figure 15

Table 5. Check of codes with the eigenvalue shown in figure 1 (inset) of Khalid et al. (2021b). The unstable centre mode at $\beta =0.997$, $k=0.75$ and $W=2500$ is shown there with $c_r \approx 0.9996$ and $c_i \approx 8 \times 10^{-5}$. Two shooting codes were written based on different integrators. The first used the Runge–Kutta 4th order scheme (RK4) over a uniformly spaced grid (here 50 000 points across $[-1,0]$) and the second used Matlab's ODE15s with relative and absolute tolerances set at $3\times 10^{-14}$ (runtime 8 s on an Apple M1 processor).