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Small-scale shear layers in isotropic turbulence of viscoelastic fluids

Published online by Cambridge University Press:  26 February 2025

Tomoaki Watanabe*
Affiliation:
Department of Mechanical Engineering and Science, Kyoto University, Kyoto 615-8540, Japan
Hugo Abreu
Affiliation:
LAETA, IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
Koji Nagata
Affiliation:
Department of Mechanical Engineering and Science, Kyoto University, Kyoto 615-8540, Japan
Carlos B. da Silva
Affiliation:
LAETA, IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
*
Email address for correspondence: watanabe.tomoaki.8x@kyoto-u.ac.jp
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Abstract

Small-scale shear layers arising from the turbulent motion of viscoelastic fluids are investigated through direct numerical simulations of statistically steady, homogeneous isotropic turbulence in a fluid described by the FENE-P model. These shear layers are identified via a triple decomposition of the velocity gradient tensor. The viscoelastic effects are examined through the Weissenberg number ($\textit{Wi}$), representing the ratio of the longest polymer relaxation time scale to the Kolmogorov time scale. The mean flow around these shear layers is analysed within a local reference frame that characterises shear orientation. In both Newtonian and viscoelastic turbulence, shear layers appear in a straining flow, featuring stretching in the shear vorticity direction and compression in the layer normal direction. Polymer stresses are markedly influenced by the shear and strain, which enhance kinetic energy dissipation due to the polymers. The shear layers in viscoelastic turbulence exhibit a high aspect ratio, undergoing significant characteristic changes once $\textit{Wi}$ exceeds approximately 2. As $\textit{Wi}$ increases, the extensive strain weakens, diminishing vortex stretching. This change coincides with an imbalance between extension and compression in the straining flow. In the shear layer, the interaction between vorticity and polymer stress causes the destruction and production of enstrophy at low and high $\textit{Wi}$ values, respectively. Enstrophy production at high $\textit{Wi}$ is induced by normal polymer stress oriented along the shear flow, associated with the diminished extensive strain. The $\textit{Wi}$-dependent behaviour of these shear layers aligns with the overall flow characteristics, underscoring their pivotal roles in vorticity dynamics and kinetic energy dissipation in viscoelastic turbulence.

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JFM Papers
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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1. Introduction

The addition of a very small fraction of macromolecules into a Newtonian solvent (typically water) can lead to massive drag reductions of up to 80 % (Toms Reference Toms1948). This has led to much practical research due to its potential engineering applications, and many studies have focused on understanding the physical mechanisms associated with the drag reduction. Although the majority of the investigations have focused on wall-bounded flows (e.g. Li & Graham Reference Li and Graham2007; White & Mungal Reference White and Mungal2008; Graham Reference Graham2014), many recent investigations have focused on homogeneous isotropic turbulence (HIT) in order to focus on the details of the energy cascade mechanism in these flows (De Angelis et al. Reference De Angelis, Casciola, Benzi and Piva2005; Ouellette, Xu & Bodenschatz Reference Ouellette, Xu and Bodenschatz2009). In particular, detailed investigations have been devoted to the kinetic energy flux within the modified energy cascade mechanism, and the shape of the kinetic energy spectrum in turbulent flows of viscoelastic fluids (Valente, da Silva & Pinho Reference Valente, da Silva and Pinho2014, Reference Valente, da Silva and Pinho2016). The classical (nonlinear) energy cascade flux decreases in inertio-elastic turbulence, with a concomitant increase of the kinetic energy dissipated directly by the polymers. At high Weissenberg numbers, which are defined as the ratio between the maximum polymer relaxation time scale and the Kolmogorov time scale, the polymers initiate a polymer-induced energy cascade from large to small scales. This process alters the mechanisms of energy transfer and is accompanied by modifications in the shape of the energy spectra. Equivalent modifications can also be examined in real space using velocity structure functions. Alterations in the spectra and structure functions due to polymers have been observed in direct numerical simulations (DNS) of isotropic turbulence (Valente et al. Reference Valente, da Silva and Pinho2014, Reference Valente, da Silva and Pinho2016) and in experiments on turbulence generated by a grid (Vonlanthen & Monkewitz Reference Vonlanthen and Monkewitz2013) or by counter-rotating baffled discs (Ouellette et al. Reference Ouellette, Xu and Bodenschatz2009). The detailed definition of the Weissenberg number $\textit{Wi}$ is given in § 2. The same definition of $\textit{Wi}$ is used consistently throughout the paper. The modification of the energy cascade caused by the polymers results in a reduced solvent dissipation rate compared to Newtonian turbulence, since part of the classical energy flux within the solvent eventually finds its way into the polymer molecules, where the kinetic energy is stored. Since the drag in a pipe or a channel can be related to the dissipation rate within the fluid, this dissipation reduction observed for isotropic turbulence can be seen as a drag reduction, as described in previous studies (Kalelkar, Govindarajan & Pandit Reference Kalelkar, Govindarajan and Pandit2005; Perlekar, Mitra & Pandit Reference Perlekar, Mitra and Pandit2006; Cai, Li & Zhang Reference Cai, Li and Zhang2010; Ferreira, Pinho & da Silva Reference Ferreira, Pinho and da Silva2016).

The small-scale turbulent structures are important in understanding the energy dissipation process in turbulence (Tsinober Reference Tsinober2009). The structures are also explored for understanding complex turbulent flows in terms of simplified structural models (Adrian, Meinhart & Tomkins Reference Adrian, Meinhart and Tomkins2000). Small-scale turbulent motions are often studied by analysing the velocity gradient tensor $\boldsymbol {\nabla } \boldsymbol {u}$ . Throughout the paper, subscript indices are used to specify components of tensors and vectors. The velocity gradient tensor is often analysed with decompositions by which particular types of local fluid motion are extracted. In a classical double decomposition, it is split into symmetric and antisymmetric components as $(\boldsymbol {\nabla } {\boldsymbol {u}})_{\textit{ij}}={\unicode{x1D61A}}_{\textit{ij}}+{\mathsf{\Omega}} _{\textit{ij}}$ , with the rate-of-strain tensor ${\unicode{x1D61A}}_{\textit{ij}}=(\partial u_i/\partial x_j+\partial u_j/\partial x_i)/2$ and the rate-of-rotation tensor ${\mathsf{\Omega}} _{\textit{ij}}=(\partial u_i/\partial x_j-\partial u_j/\partial x_i)/2$ . Turbulence is characterised by vortices with rotating motion, which contributes to ${\mathsf{\Omega}} _{\textit{ij}}$ (Corrsin & Kistler Reference Corrsin and Kistler1955). Small-scale vortices, in particular, have received significant attention in turbulence research. Early investigations of vortices used enstrophy $\omega ^2/2={\mathsf{\Omega}} _{\textit{ij}}{\mathsf{\Omega}} _{\textit{ij}}$ for vortex identification. Here, successive indices imply summation. Flow regions with large enstrophy frequently exhibit a tubular shape (Jiménez et al. Reference Jiménez, Wray, Saffman and Rogallo1993). These small-scale tubular vortices are called vortex tubes, worms or vortex filaments, and have been studied extensively in previous works, where the scalings of the diameter, azimuthal velocity, stretching rate and other properties have been revealed for various turbulent flows (Siggia Reference Siggia1981; Vincent & Meneguzzi Reference Vincent and Meneguzzi1991; Jiménez & Wray Reference Jiménez and Wray1998; Tanahashi, Iwase & Miyauchi Reference Tanahashi, Iwase and Miyauchi2001; Ganapathisubramani, Lakshminarasimhan & Clemens Reference Ganapathisubramani, Lakshminarasimhan and Clemens2008; Kang & Meneveau Reference Kang and Meneveau2008; da Silva, Dos Reis & Pereira Reference da Silva, Dos Reis and Pereira2011; Jahanbakhshi, Vaghefi & Madnia Reference Jahanbakhshi, Vaghefi and Madnia2015; Watanabe et al. Reference Watanabe, da Silva, Nagata and Sakai2017; Ghira, Elsinga & da Silva Reference Ghira, Elsinga and da Silva2022).

The vortical structures in turbulent flows of viscoelastic fluids have been analysed in several works. The drag reduction observed in wall-bounded flows is associated with the weakening and suppressing of the streamwise vortices (Kim et al. Reference Kim, Li, Sureshkumar, Balachandar and Adrian2007; Li & Graham Reference Li and Graham2007). Guimarães et al. (Reference Guimarães, Pimentel, Pinho and da Silva2020) and Guimarães, Pinho & da Silva (Reference Guimarães, Pinho and da Silva2022) reported that the large-scale eddies in turbulent jets and wakes of viscoelastic fluids are substantially different from the ones observed in Newtonian free shear flows: they appear to be more elongated and showing more sheet-like shapes as the Weissenberg number increases. It is noteworthy that the large-scale eddies obtained in the simulations by Guimarães et al. (Reference Guimarães, Pimentel, Pinho and da Silva2020, Reference Guimarães, Pinho and da Silva2022) closely resemble the experimental visualisations reported in Yamani et al. (Reference Yamani, Raj, Zaki, McKinley and Bischofberger2023). Compared to turbulent free flows of Newtonian fluids, a significant decrease of the entrainment rate observed in these flows is caused by the depletion of small-scale structures due to the viscoelasticity (Abreu, Pinho & da Silva Reference Abreu, Pinho and da Silva2022). Vortical structures in HIT of viscoelastic fluids have been analysed extensively, as noted in various studies, including Perlekar et al. (Reference Perlekar, Mitra and Pandit2006) and Cai et al. (Reference Cai, Li and Zhang2010). It has been observed that intense vorticity structures in DNS of HIT in viscoelastic fluids typically manifest as sheets rather than tubes. However, the characteristics of these structures have yet to be investigated in much detail, except in Horiuti, Matsumoto & Fujiwara (Reference Horiuti, Matsumoto and Fujiwara2013), where the observed drag reduction reported for viscoelastic fluids is explained in relation to the dynamics of these structures. In high drag-reduction cases, the creation of vortex tubes due to the roll-up of sheets is hindered by tensile forces.

When vortical structures in Newtonian fluids are identified from an enstrophy field, sheet-like structures are also observed together with the vortex tubes (Vincent & Meneguzzi Reference Vincent and Meneguzzi1991; Jiménez et al. Reference Jiménez, Wray, Saffman and Rogallo1993). These structures are known as vortex sheets. In recent studies, they have been simply named shear layers because of intense shear in the layer structures (Eisma et al. Reference Eisma, Westerweel, Ooms and Elsinga2015; Watanabe, Tanaka & Nagata Reference Watanabe, Tanaka and Nagata2020). The present study also names these structures as shear layers because they are identified with a quantity characterising shearing motion. Since ${\mathsf{\Omega}} _{\textit{ij}}$ and ${\unicode{x1D61A}}_{\textit{ij}}$ are significant for local fluid motion within the shear layers, both vortex tubes and shear layers have been observed in enstrophy visualisation. Various identification methods to detect vortex tubes have been proposed in previous studies. Several identification schemes for vortex tubes rely on the three-dimensional or two-dimensional distribution of quantities associated with these structures, such as the second invariant of the velocity gradient tensor (Chong, Perry & Cantwell Reference Chong, Perry and Cantwell1990; Jeong & Hussain Reference Jeong and Hussain1995). Recent advances in this topic have successfully identified vortex tubes as vortical Lagrangian coherent structures, as demonstrated by Neamtu-Halic et al. (Reference Neamtu-Halic, Krug, Haller and Holzner2019), based on the Lagrangian-averaged vorticity deviation (Haller et al. Reference Haller, Hadjighasem, Farazmand and Huhn2016). The Lagrangian coherent structures approach in the detection of turbulent structures has the advantage of defining the boundary of each structure (Green, Rowley & Haller Reference Green, Rowley and Haller2007; Haller Reference Haller2023). Once a vortex tube is identified, it is easy to determine two characteristic orientations of the structure, namely a vortex axis and a radial direction. Thus vortex tubes are often studied with a radial profile of flow variables (Jiménez et al. Reference Jiménez, Wray, Saffman and Rogallo1993; Jahanbakhshi et al. Reference Jahanbakhshi, Vaghefi and Madnia2015), which has helped the investigation of these structures. On the other hand, most studies of shear layers have relied on flow visualisations (Horiuti & Fujisawa Reference Horiuti and Fujisawa2008; Buxton & Ganapathisubramani Reference Buxton and Ganapathisubramani2010; Bhatt & Tsuji Reference Bhatt and Tsuji2021). This is likely due to the fact that the common identification method for shear layers uses a scalar quantity to determine the location of these layers (Horiuti & Takagi Reference Horiuti and Takagi2005), but fails to determine their orientation, which is essential in the analysis of these structures.

New identification schemes for vortex tubes have been proposed because of the difficulty in distinguishing vortex tubes and shear layers in the enstrophy field. This difficulty arises from a shear contribution to ${\mathsf{\Omega}} _{\textit{ij}}$ . A parallel shear flow has large ${\mathsf{\Omega}} _{\textit{ij}}$ and ${\unicode{x1D61A}}_{\textit{ij}}$ , although there is no rotating motion typical of vortex tubes. Therefore, recent vortex identification schemes rely on new decompositions of $\boldsymbol {\nabla } \boldsymbol {u}$ . To prevent local shearing motion from blurring a quantity used to identify vortex tubes, the new decompositions often extract a shear component from $\boldsymbol {\nabla } \boldsymbol {u}$ . Examples are the triple decomposition (Kolář Reference Kolář2007) and Rortex-based decomposition (Liu et al. Reference Liu, Gao, Tian and Dong2018). These two decompositions are related, although the decomposition algorithms are different. In addition, the triple decomposition can be expressed with a real Schur form, which provides an efficient decomposition algorithm (Kronborg & Hoffman Reference Kronborg and Hoffman2023). The mathematical properties of these decompositions have been studied extensively in previous works, including Galilean invariance (Wang, Gao & Liu Reference Wang, Gao and Liu2018). These decompositions can define an alternative vorticity vector representing rigid-body rotation by removing the shear contribution (Maciel, Robitaille & Rahgozar Reference Maciel, Robitaille and Rahgozar2012; Šístek et al. Reference Šístek, Kolář, Cirak and Moses2012; Kolář & Šístek Reference Kolář and Šístek2014).

The shear component of $\boldsymbol {\nabla } \boldsymbol {u}$ in these new decomposition schemes is shown to be helpful in identifying the shear layers in turbulent flows (Eisma et al. Reference Eisma, Westerweel, Ooms and Elsinga2015; Nagata et al. Reference Nagata, Watanabe, Nagata and da Silva2020; Jiang et al. Reference Jiang, Lefauve, Dalziel and Linden2022). For this purpose, the triple decomposition has been applied to two- or three-dimensional velocity data from DNS or experiments with particle image velocimetry. Both experiments and DNS have successfully identified shear layers with consistent characteristics (Eisma et al. Reference Eisma, Westerweel, Ooms and Elsinga2015; Watanabe et al. Reference Watanabe, Tanaka and Nagata2020, Reference Watanabe, Mori, Ishizawa and Nagata2024; Fiscaletti, Buxton & Attili Reference Fiscaletti, Buxton and Attili2021; Hayashi, Watanabe & Nagata Reference Hayashi, Watanabe and Nagata2021a ). Eisma et al. (Reference Eisma, Westerweel, Ooms and Elsinga2015) identified a thin layer structure with intense shear in a turbulent boundary layer. Unlike early studies on the shear layers based on the visualisation, the shear component of $\boldsymbol {\nabla } \boldsymbol {u}$ can identify both the location and orientation of shear layers (Watanabe et al. Reference Watanabe, Tanaka and Nagata2020). The shear layer location is identified with a scalar quantity related to the intensity of shearing motion, which does not vary with respect to coordinate transformation. For example, the visualisation of the shear intensity in different reference frames is provided in Hayashi et al. (Reference Hayashi, Watanabe and Nagata2021a ), where the same shear layer is identified in two frames. This allows us to calculate the statistics conditionally taken in a local reference frame that characterises the shear layer orientation. The mean velocity profiles near the shear layers have been investigated in turbulent boundary layers, jets, mixing layers and HIT (Eisma et al. Reference Eisma, Westerweel, Ooms and Elsinga2015; Watanabe et al. Reference Watanabe, Tanaka and Nagata2020; Fiscaletti et al. Reference Fiscaletti, Buxton and Attili2021; Hayashi et al. Reference Hayashi, Watanabe and Nagata2021a ). The velocity in the layer-parallel direction exhibits a drastic jump across the shear layer. For HIT and turbulent free shear flows, the Kolmogorov scales characterise the velocity jump and thickness of shear layers (Watanabe et al. Reference Watanabe, Tanaka and Nagata2020; Fiscaletti et al. Reference Fiscaletti, Buxton and Attili2021; Hayashi et al. Reference Hayashi, Watanabe and Nagata2021a ). In addition, the shear layers have been shown to form in a biaxial strain field. This local flow topology of the shear and strain agrees with the Burgers vortex layer (Davidson Reference Davidson2004). In wall-bounded shear flows, these shear layers possibly separate uniform momentum zones, whose interaction may occur across the shear layers (Eisma et al. Reference Eisma, Westerweel, Ooms and Elsinga2015; Fan et al. Reference Fan, Xu, Yao and Hickey2019; Gul, Elsinga & Westerweel Reference Gul, Elsinga and Westerweel2020; Chen, Chung & Wan Reference Chen, Chung and Wan2021). Besides these characteristics of shear layers, their roles in turbulence have been explored in recent studies. The viscous dissipation of turbulent kinetic energy occurs primarily within shear layers because the rate-of-strain tensor is dominated by shearing motion (Pirozzoli, Bernardini & Grasso Reference Pirozzoli, Bernardini and Grasso2010; Das & Girimaji Reference Das and Girimaji2020; Nagata et al. Reference Nagata, Watanabe, Nagata and da Silva2020). Shear layers typically form within a straining flow, which induces vortex stretching, leading to predominant enstrophy production within these layers (Pirozzoli et al. Reference Pirozzoli, Bernardini and Grasso2010; Watanabe et al. Reference Watanabe, Tanaka and Nagata2020). Shear layers are inherently unstable, and their roll-up, accompanied by enstrophy amplification, leads to the formation of vortex tubes (Vincent & Meneguzzi Reference Vincent and Meneguzzi1994; Watanabe & Nagata Reference Watanabe and Nagata2023). This instability is promoted by velocity fluctuations with scales slightly larger than the layer thickness (Watanabe & Nagata Reference Watanabe and Nagata2023). Thus understanding this instability is crucial for comprehending the scale-by-scale interaction of turbulent motion, which may be connected to the energy cascade process and energy dissipation at the smallest scales. Indeed, Enoki, Watanabe & Nagata (Reference Enoki, Watanabe and Nagata2023) has demonstrated that the velocity fluctuations associated with shearing motion are responsible for the nonlinear energy cascade.

A mean flow pattern around the shear layers reported in the studies mentioned above resembles that observed in a so-called strain eigenframe (Elsinga & Marusic Reference Elsinga and Marusic2010; Elsinga et al. Reference Elsinga, Ishihara, Goudar, da Silva and Hunt2017; Sakurai & Ishihara Reference Sakurai and Ishihara2018). The strain eigenframe is a reference frame defined with the eigenvectors of ${\unicode{x1D61A}}_{\textit{ij}}$ . The averages in the strain eigenframe are taken from an entire flow field. Nonetheless, the results exhibit a shear layer pattern, as also observed with the averages solely taken for the shear layer region. This also suggests that various turbulence characteristics are explained by the properties of shear layers. For example, the shear layer pattern successfully predicts the $-5/3$ law of an energy spectrum and particle transport (Elsinga & Marusic Reference Elsinga and Marusic2016; Goudar & Elsinga Reference Goudar and Elsinga2018). The agreement between the statistics obtained in the strain eigenframe and near the shear layers has been explained by the dominance of shearing motion in turbulence. Analysis of HIT and planar jets has confirmed that shearing motions can appear anywhere in a turbulent flow field. In the near region of the shear layer, shearing motions are highly intense, whereas outside this region, the flow is characterised by moderately intense or weak shear (Watanabe et al. Reference Watanabe, Tanaka and Nagata2020; Hayashi et al. Reference Hayashi, Watanabe and Nagata2021a ). Therefore, the mean flow observed in the strain eigenframe is effectively explained by the characteristics of the shear layers. These studies have thus highlighted the importance of shear layers in understanding the behaviour of turbulent flows.

These recent studies of small-scale shear layers motivate further investigation into the characteristics of shear layers in viscoelastic turbulence. Given that shear layers dominate various important phenomena in turbulence, the viscoelastic effects on these layers are expected to elucidate several of the overall modifications induced by viscoelasticity in these flows. Shear layers in viscoelastic turbulence have also been investigated by Horiuti et al. (Reference Horiuti, Matsumoto and Fujiwara2013). They found that the shear layers become stable due to the tensile forces from the polymers acting on the shear layers. However, these previous investigations focused on flow visualisation techniques. The aim of the present paper is centred on the statistical analysis of the shear layers, facilitated by a recently developed method for detecting shear layers, as discussed earlier. In this work, DNS are carried out to investigate the mean flow properties of shear layers in HIT in a viscoelastic fluid. This fluid consists of a homogeneous dilute solution of long-chained (polymer) molecules dissolved in a Newtonian fluid, which is well described by the FENE-P rheological model (where the FENE-P model is a finitely extensible nonlinear elastic constitutive model closed with the Peterlin approximation). Given that the shear layer is a small-scale structure with a thickness close to the Kolmogorov scale, one of the crucial parameters is expected to be the Weissenberg number $\textit{Wi}$ . The present analysis will show that the shear layers significantly influence the behaviour of polymer molecules in the nearby fluid regions. In addition, the $\textit{Wi}$ dependence of a mean flow field around the shear layers is shown to be important for the vorticity dynamics and kinetic energy dissipation caused by the polymer molecules. The interest of the present work also lies in elucidating the modified viscous dissipation mechanism in isotropic turbulence for viscoelastic fluids, which can be linked to the mechanism of drag reduction observed in pipes and channels, as discussed in previous studies (Kalelkar et al. Reference Kalelkar, Govindarajan and Pandit2005; Perlekar et al. Reference Perlekar, Mitra and Pandit2006; Cai et al. Reference Cai, Li and Zhang2010; Ferreira et al. Reference Ferreira, Pinho and da Silva2016).

The paper is organised as follows. Section 2 describes the DNS database used in this study. Section 3 presents the identification method of small-scale shear layers based on the triple decomposition and the conditional averaging procedure used for the statistical analysis. Section 4 discusses local fluid motions described by the triple decomposition and the mean flow field and polymer stress near the shear layers. The role of the shear layers in the vorticity dynamics and kinetic energy dissipation is also revealed based on the mean flow around the shear layers. Finally, the paper is summarised in § 5.

2. The DNS of statistically stationary HIT

The fluid analysed in this study consists of a dilute polymeric solution, formed when a Newtonian solvent carries a very small fraction of long-chain polymer molecules such that interaction between polymer chains is negligible. The fluid is modelled using the FENE-P model (Bird, Dotson & Johnson Reference Bird, Dotson and Johnson1980). The flow field is incompressible, satisfying the continuity equation

(2.1) \begin{equation} \frac{\partial u_j}{\partial x_j} = 0,\end{equation}

and the momentum equations

(2.2) \begin{equation} \frac{\partial u_i}{\partial t} + u_j\, \frac{\partial u_i}{\partial x_j} = \frac{1}{\rho}\,\frac{\partial p}{\partial x_i} + \frac{1}{\rho}\,\frac{\partial {\mathsf{\sigma}}_{\textit{ij}}}{\partial x_j},\end{equation}

where $u_i(\boldsymbol {x},t)$ is the velocity vector, $\rho$ is the fluid density, and $p(\boldsymbol {x},t)$ is the pressure. The stress tensor ${\mathsf{\sigma}} _{\textit{ij}}$ represents the sum of the Newtonian (solvent) and polymer contributions, and is written as

(2.3) \begin{equation} {\mathsf{\sigma}}_{\textit{ij}} = {\mathsf{\sigma}}^{[S]}_{\textit{ij}} + {\mathsf{\sigma}}^{[P]}_{\textit{ij}},\end{equation}

where the Newtonian stresses are given by

(2.4) \begin{equation} {\mathsf{\sigma}}^{[S]}_{\textit{ij}} = 2 \rho \nu^{[S]} {\unicode{x1D61A}}_{\textit{ij}},\end{equation}

with the Newtonian kinematic viscosity $\nu ^{[S]}$ . The polymer contribution to the zero-shear-rate kinematic viscosity, $\nu ^{[P]}$ , can be related to the ratio between the solvent and the total zero-shear-rate viscosity of the solution, $\beta = \nu ^{[S]} / ( \nu ^{[P]} + \nu ^{[S]} )$ . The ensembles of polymer chains are represented by a dumbbell model, where two beads are connected by a nonlinear spring. These beads represent subsets of chains, while the spring accounts for their interactions (Bird et al. Reference Bird, Curtiss, Armstrong and Hassager1987). The polymeric stresses are then computed as

(2.5) \begin{equation} {\mathsf{\sigma}}^{[P]}_{\textit{ij}} = \frac{\rho \nu^{[P]}}{\tau_P}\, [f({\unicode{x1D60A}}_{kk})\,{\unicode{x1D60A}}_{\textit{ij}} - {\mathsf{\delta}}_{\textit{ij}} ],\end{equation}

where ${\mathsf{\delta}} _{\textit{ij}}$ is the Kronecker symbol, and ${\unicode{x1D60A}}_{\textit{ij}}$ is the conformation tensor, characterising an ensemble of polymer chains. Here, ${\unicode{x1D60A}}_{\textit{ij}}$ represents the second-order moment of the end-to-end vector connecting the ends of a polymer chain, normalised by the square of its equilibrium length $\langle r^2 \rangle _0$ , and is given by

(2.6) \begin{equation} {\unicode{x1D60A}}_{\textit{ij}} = \frac{\langle r_i r_j \rangle}{\langle r^2 \rangle_0}, \end{equation}

where $\langle r^2 \rangle$ is the ensemble-averaged squared length of the polymers. The FENE-P model uses the longest relaxation time of the polymer molecules $\tau _P$ , and the Peterlin function defined by

(2.7) \begin{equation} f({\unicode{x1D60A}}_{kk}) = \frac{(L_{\!P})^2 - 3}{(L_{\!P})^2 - {\unicode{x1D60A}}_{kk}},\end{equation}

where ${\unicode{x1D60A}}_{kk}$ is the trace of the conformation tensor, and $L_{\!P}$ is the maximum extensibility parameter defined as the polymer chain length at the fully extended state normalised by the root mean square (r.m.s.) end-to-end distance of polymer chain at the equilibrium state. The square root of the trace of the conformation tensor, $\sqrt {{\unicode{x1D60A}}_{kk}}$ , represents its normalised extension length. Finally, the conformation tensor is computed by its governing transport equation:

(2.8) \begin{equation} \frac{D{\unicode{x1D60A}}_{\textit{ij}}}{D t} \equiv \frac{\partial{\unicode{x1D60A}}_{\textit{ij}}}{\partial t} + u_k\,\frac{\partial{\unicode{x1D60A}}_{\textit{ij}}}{\partial x_k} ={\unicode{x1D60A}}_{jk}\,\frac{\partial u_i}{\partial x_k} +{\unicode{x1D60A}}_{ik}\,\frac{\partial u_j}{\partial x_k} -\frac{1}{\tau_P}\,[f({\unicode{x1D60A}}_{kk})\,{\unicode{x1D60A}}_{\textit{ij}} -{\mathsf{\delta}}_{\textit{ij}} ].\end{equation}

In (2.8), the left-hand side represents the material derivative, while the first two terms on the right-hand side represent the polymer stretching/distortion, and the last term is associated with the storage of elastic energy by the polymer molecules.

The present study analyses the DNS database of statistically steady HIT of viscoelastic fluids described by the FENE-P model in Abreu et al. (Reference Abreu, Pinho and da Silva2022). In the present DNS code, the momentum equations are integrated using pseudo-spectral methods (de-aliased with the $2/3$ rule) and a third-order Runge–Kutta scheme for temporal advancement. Specifically, for (2.2), the diffusive terms involving the shear and polymer-stress tensors are computed in Fourier space, while the nonlinear terms are evaluated in physical space (using de-aliasing). Similarly, for the conformation tensor, temporal advancement employs the same Runge–Kutta scheme as is used for the momentum equations, with all terms computed using pseudo-spectral methods for spatial differencing, except for the convective term. This term is calculated using the central-differences algorithm proposed by Vaithianathan et al. (Reference Vaithianathan, Robert, Brasseur and Collins2006), based on the Kurganov–Tadmor method, which ensures that the conformation tensor remains symmetric and positive definite, avoiding the need to add artificial diffusion in (2.8). The statistically steady state is achieved by using a forcing scheme (Alvelius Reference Alvelius1999). More details of the DNS code are given in Ferreira et al. (Reference Ferreira, Pinho and da Silva2016), Silva, Zecchetto & da Silva (Reference Silva, Zecchetto and da Silva2018), Abreu et al. (Reference Abreu, Pinho and da Silva2022), and references therein. The present algorithm and its implementation have been validated extensively in a series of papers, beginning with Valente et al. (Reference Valente, da Silva and Pinho2014). Moreover, the values of $\tau _p$ , $L_p$ and $\beta$ used in this study approximately match those in the experimental study by Ouellette et al. (Reference Ouellette, Xu and Bodenschatz2009). The experiments utilised polyacrylamide in concentration up to 45 p.p.m. in weight (equivalent to $\beta \geq 0.8$ ), which is considered dilute. The validations and comparisons with experiments and other DNS were reported in our previous studies (Valente et al. Reference Valente, da Silva and Pinho2014; Guimarães et al. Reference Guimarães, Pimentel, Pinho and da Silva2020, Reference Guimarães, Pinho and da Silva2022; Guimarães, Pinho & da Silva Reference Guimarães, Pinho and da Silva2023). Additionally, it is also important to note that the Kolmogorov scale is much larger than the maximum extension of the polymer molecules (Valente et al. Reference Valente, da Silva and Pinho2014).

Table 1. DNS databases of HIT in Newtonian (Newt.) and viscoelastic fluids: Weissenberg number ( $\textit{Wi}$ ); the maximum relaxation time of the polymer molecules ( $\tau _P$ ); turbulent Reynolds number ( $Re_\lambda$ ); r.m.s. velocity fluctuations ( $u_{0}$ ); solvent mean viscous dissipation rate ( $\langle \varepsilon _s \rangle$ ); polymer mean dissipation rate ( $\langle \varepsilon _p \rangle$ ); dissipation reduction (DR); Taylor microscale ( $\lambda$ ); Kolmogorov microscale ( $\eta$ ); Kolmogorov time scale ( $\tau _\eta$ ); Kolmogorov velocity scale ( $u_\eta$ ); the maximum effective wavenumber normalised by the Kolmogorov scale ( $k_{max}\eta$ ).

Table 1 summarises the physical parameters of the DNS databases, which are the same as in Abreu et al. (Reference Abreu, Pinho and da Silva2022) and include reference Newtonian DNS and a total of four viscoelastic simulations. The simulations use a triply periodic domain of size $(2{\rm \pi} \times 2{\rm \pi} \times 2{\rm \pi} )$ , and $N^3=768^3$ collocation points. The statistics reported in this work are obtained with the combined ensemble and volume averages, denoted by $\langle f\rangle$ . The kinematic viscosity of the solvent is $\nu ^{[S]}=0.0023$ in all simulations, and the viscoelastic simulations differ by the value of the maximum relaxation time of the polymer molecules, $\tau _P$ , which varies between $0.025$ and $0.2$ . The ratio between the solvent and the total zero-shear-rate viscosities of the solution and the maximum extensibility of the polymers are equal to $\beta =0.8$ and $(L_{\!P})^{2}=100^2$ , respectively, in all the viscoelastic simulations. The Kolmogorov length, velocity and time scales are defined as $\eta =({\nu ^{[S]}}/\langle \varepsilon _\nu \rangle ^{1/3})^{3/4}$ , $u_\eta =({\nu ^{[S]}}\langle \varepsilon _\nu \rangle )^{1/4}$ and $\tau _{\eta }=({\nu ^{[S]}}/\langle \varepsilon _\nu \rangle )^{1/2}$ , respectively, where $\varepsilon _\nu =2\nu ^{[S]}{\unicode{x1D61A}}_{\textit{ij}}{\unicode{x1D61A}}_{\textit{ij}}$ is the solvent viscous dissipation rate of kinetic energy. Notice that the present definitions for the Kolmogorov (length, velocity and time) scales characterise the smallest existing scales of motion for the solvent. However, it is clear that these are not the characteristic scales of motion for viscoelastic turbulent flows in the classical sense. Specifically, the statistics of the flow do not collapse when normalised by these scales (Valente et al. Reference Valente, da Silva and Pinho2014). The Weissenberg number is $\textit{Wi}=\tau _P/\tau _\eta$ , which is the time scale ratio between the maximum relaxation time of the polymer molecules and the Kolmogorov time scale. The range of values of $\textit{Wi}$ used in these simulations covers the three regimes previously observed in isotropic turbulence of viscoelastic fluids as described in previous studies (Valente et al. Reference Valente, da Silva and Pinho2014; Ferreira et al. Reference Ferreira, Pinho and da Silva2016). For very low $\textit{Wi}$ , viscoelastic effects manifest as an additional (small) viscosity without any other additional effects. At higher $\textit{Wi}$ values, the viscoelastic effects significantly alter the energy cascade, causing deviations of the kinetic energy spectra from the classical $-5/3$ Kolmogorov–Obukhov law. At even higher $\textit{Wi}$ , the polymers and solvent become decoupled, and the $-5/3$ law is observed once again. The transition between the latter two regimes is observed at approximately $\textit{Wi}=2$ (Ferreira et al. Reference Ferreira, Pinho and da Silva2016). The turbulent Reynolds number $Re_\lambda =u_0\lambda /\nu ^{[S]}$ is defined with the r.m.s. value of velocity fluctuations of HIT, $u_{0}=\sqrt {\langle u^2\rangle +\langle v^2\rangle +\langle w^2\rangle /3}$ , and the Taylor micro-scale $\lambda =\sqrt {15\nu ^{[S]}u_0^2/\langle \varepsilon _\nu \rangle }$ . Table 1 also shows the mean dissipation rates caused by the solvent and by the polymers, denoted as $\langle \varepsilon _s \rangle$ and $\langle \varepsilon _p \rangle = \langle {\mathsf{\sigma}} ^{[P]}_{\textit{ij}} {\unicode{x1D61A}}_{\textit{ij}} \rangle$ , respectively. Additionally, it includes the drag reduction (also referred to as dissipation reduction), which is defined as $\textrm {DR} = \varepsilon _p / (\varepsilon _s + \varepsilon _p)$ , following the definitions used in Cai et al. (Reference Cai, Li and Zhang2010) and Ferreira et al. (Reference Ferreira, Pinho and da Silva2016). The results in table 1 indicate that DR values of approximately $80\,\%$ are observed for the viscoelastic simulations at the highest value of $\textit{Wi}$ . The maximum effective wavenumber $k_{max}$ normalised by $\eta$ is higher than 1.5 for all the simulations, attesting that the smallest scales of motion are always finely resolved. The ensemble averages are taken for 10 snapshots saved with a time interval of the Kolmogorov time scale. To estimate statistical errors, we employ a method based on subdividing the computational domain. Specifically, the entire computational domain is divided into two equal halves, generating two separate datasets. We then calculate statistical quantities independently within each of these datasets. The measure of statistical error for a given statistical quantity is determined by comparing its value as calculated within each half-domain against the value obtained for the whole domain. As shown below, the statistical errors are negligible for the shear layer analysis.

3. Statistical analysis of small-scale shear layers

3.1. The triple decomposition

The triple decomposition of the velocity gradient tensor is used to investigate shearing motion in turbulence (Kolář Reference Kolář2007). Several algorithms have been proposed for the triple decomposition (Kolář Reference Kolář2007; Nagata et al. Reference Nagata, Watanabe, Nagata and da Silva2020; Hayashi et al. Reference Hayashi, Watanabe and Nagata2021a ; Kronborg & Hoffman Reference Kronborg and Hoffman2023). The present study uses the one presented in Hayashi et al. (Reference Hayashi, Watanabe and Nagata2021a ). The explanation given below follows these early studies of the triple decomposition, which can be referred to for the details of the physical meaning and algorithm of the decomposition.

The triple decomposition splits $\boldsymbol {\nabla } {\boldsymbol {u}}$ into three components of (i) shear, (ii) rigid-body rotation and (iii) irrotational strain (elongation), as $\boldsymbol {\nabla } {\boldsymbol {u}}= \boldsymbol {\nabla } {\boldsymbol {u}}_S +\boldsymbol {\nabla } {\boldsymbol {u}}_R +\boldsymbol {\nabla } {\boldsymbol {u}}_E$ , where $S$ , $R$ and $E$ stands for shear, rotation and elongation, respectively. The decomposition formula below has to be applied in a specific reference frame, called the basic reference frame, which is chosen from many reference frames defined with three sequential rotational transformations ${\unicode{x1D64C}}(\theta _{1},\theta _{2},\theta _{3})$ :

(3.1) \begin{equation}{\unicode{x1D64C}} = \left( \begin{array}{@{}ccc@{}}\cos\theta_{1}\cos\theta_{2}\cos\theta_{3} -\sin\theta_{1}\sin\theta_{3} &\sin\theta_{1}\cos\theta_{2}\cos\theta_{3} +\cos\theta_{1}\sin\theta_{3} &-\sin\theta_{2}\cos\theta_{3}\\-\cos\theta_{1}\cos\theta_{2}\sin\theta_{3} -\sin\theta_{1}\cos\theta_{3} &-\sin\theta_{1}\cos\theta_{2}\sin\theta_{3} +\cos\theta_{1}\cos\theta_{3} & \sin\theta_{2}\sin\theta_{3}\\ \cos\theta_{1}\sin\theta_{2} &\sin\theta_{1}\sin\theta_{2} & \cos\theta_{2} \end{array}\right)\!,\end{equation}

with angles $0^{\circ }\leq \theta _{1}\leq 180^{\circ }$ , $0^{\circ }\leq \theta _{2}\leq 180^{\circ }$ and $0^{\circ }\leq \theta _{3}\leq 90^{\circ }$ . The reference frame of DNS, ${\boldsymbol {x}}$ , is transformed to the rotated reference frame ${\boldsymbol {x}}^{*}$ as ${\boldsymbol {x}}^{*}={\unicode{x1D64C}}{\boldsymbol {x}}$ . In this frame, the velocity gradient tensor is expressed as $(\boldsymbol {\nabla } \boldsymbol {u})^{*}={\unicode{x1D64C}}(\boldsymbol {\nabla } \boldsymbol {u} ){\unicode{x1D64C}}^{\rm T}$ . Here, superscript $*$ represents a quantity in the rotated reference frame. The basic reference frame assumes that an interaction scalar $I^{*}= |{\mathsf{\Omega}} ^{*}_{12}{\unicode{x1D61A}}^{*}_{12}| +|{\mathsf{\Omega}} ^{*}_{23}{\unicode{x1D61A}}^{*}_{23}| +|{\mathsf{\Omega}} ^{*}_{31}{\unicode{x1D61A}}^{*}_{31}|$ becomes the largest among all reference frames. The basic reference frame can be determined by evaluating $I^{*}$ in many reference frames for discrete sets of $(\theta _{1},\theta _{2},\theta _{3})$ . First, $I^{*}$ is evaluated for ${\boldsymbol {x}}^{*}={\unicode{x1D64C}}{\boldsymbol {x}}$ with

(3.2) \begin{gather} \theta_1=0, 45^{{\circ}},\ldots, 180^{{\circ}}, \end{gather}
(3.3) \begin{gather}\theta_2=0, 45^{{\circ}},\ldots, 180^{{\circ}}, \end{gather}
(3.4) \begin{gather}\theta_3=0, 45^{{\circ}}, 90^{{\circ}}. \end{gather}

From these sets of $(\theta _{1},\theta _{2},\theta _{3})$ , one can find the angles that provide the largest $I^{*}$ . These angles are denoted by $(\theta _{1}^{(1)},\theta _{2}^{(1)},\theta _{3}^{(1)})$ . Then the largest $I^{*}$ is searched for ${\boldsymbol {x}}^{*}={\unicode{x1D64C}}{\boldsymbol {x}}$ with

(3.5) \begin{gather} \theta_i=\theta_i^{(min)}, \theta_i^{(min)}+15^{{\circ}},\ldots, \theta_i^{(max)}, \end{gather}
(3.6) \begin{gather}\theta_i^{(min)}=\theta_i^{(1)}-45^{{\circ}}/2, \end{gather}
(3.7) \begin{gather}\theta_i^{(max)}=\theta_i^{(1)}+45^{{\circ}}/2, \end{gather}

for $i=1,2,3$ . Again, the angles for the largest $I^{*}$ are denoted by $(\theta _{1}^{(2)},\theta _{2}^{(2)},\theta _{3}^{(2)})$ . Finally, the basic reference frame is determined with the angles with the largest $I^{*}$ among

(3.8) \begin{gather} \theta_i=\theta_i^{(min)}, \theta_i^{(min)}+5^{{\circ}},\ldots, \theta_i^{(max)}, \end{gather}
(3.9) \begin{gather}\theta_i^{(min)}=\theta_i^{(2)}-15^{{\circ}}/2, \end{gather}
(3.10) \begin{gather}\theta_i^{(max)}=\theta_i^{(2)}+15^{{\circ}}/2. \end{gather}

The angles with the largest $I^{*}$ are denoted by $(\theta _{1}^{(B)},\theta _{2}^{(B)},\theta _{3}^{(B)})$ , by which the basic reference frame is obtained with $\boldsymbol {x}^{(B)}={\unicode{x1D64C}}(\theta _{1}^{(B)},\theta _{2}^{(B)},\theta _{3}^{(B)})\,{\boldsymbol {x}}$ . The velocity gradient tensor in the basic reference frame is also calculated as $(\boldsymbol {\nabla } \boldsymbol {u})^{(B)}={\unicode{x1D64C}}(\boldsymbol {\nabla } \boldsymbol {u} ){\unicode{x1D64C}}^{\rm T}$ . Then the triple decomposition calculates the decomposed tensors as

(3.11) \begin{gather} (\boldsymbol{\nabla} {\boldsymbol{u}}_{\textit{RES}})^{(B)}_{\textit{ij}}= \mathrm{sgn}[(\boldsymbol{\nabla} {\boldsymbol{u}})^{(B)}_{\textit{ij}}] \min[|(\boldsymbol{\nabla} {\boldsymbol{u}})^{(B)}_{\textit{ij}}|, |(\boldsymbol{\nabla} {\boldsymbol{u}})^{(B)}_{\textit{ji}}|], \end{gather}
(3.12) \begin{gather}(\boldsymbol{\nabla} {\boldsymbol{u}}_{S})^{(B)}_{\textit{ij}}= (\boldsymbol{\nabla} {\boldsymbol{u}})^{(B)}_{\textit{ij}}-(\boldsymbol{\nabla} {\boldsymbol{u}}_{\textit{RES}})^{(B)}_{\textit{ij}}, \end{gather}
(3.13) \begin{gather}(\boldsymbol{\nabla} {\boldsymbol{u}}_{R})^{(B)}_{\textit{ij}}= [(\boldsymbol{\nabla} {\boldsymbol{u}}_{\textit{RES}})^{(B)}_{\textit{ij}}- (\boldsymbol{\nabla} {\boldsymbol{u}}_{\textit{RES}})^{(B)}_{\textit{ji}}]/2, \end{gather}
(3.14) \begin{gather}(\boldsymbol{\nabla} {\boldsymbol{u}}_{E})^{(B)}_{\textit{ij}}= [(\boldsymbol{\nabla} {\boldsymbol{u}}_{\textit{RES}})^{(B)}_{\textit{ij}}+(\boldsymbol{\nabla} {\boldsymbol{u}}_{\textit{RES}})^{(B)}_{\textit{ji}}]/2, \end{gather}

for $i,j=1,2,3$ , where sgn is a sign function. These tensors are evaluated in the basic reference frame. Finally, the decomposed tensors in the original reference frame, $\boldsymbol {\nabla } {\boldsymbol {u}}_S$ , $\boldsymbol {\nabla } {\boldsymbol {u}}_R$ and $\boldsymbol {\nabla } {\boldsymbol {u}}_E$ , are calculated by applying the inverse transformation of ${\unicode{x1D64C}}(\theta _{1}^{(B)},\theta _{2}^{(B)},\theta _{3}^{(B)})$ to the corresponding tensors in the basic reference frame. The intensities of the three motions are evaluated as $I_\alpha =\sqrt {2\,(\boldsymbol {\nabla } {\boldsymbol {u}}_\alpha )_{\textit{ij}}\,(\boldsymbol {\nabla } {\boldsymbol {u}}_\alpha )_{\textit{ij}}}$ for $\alpha =S, R, E$ . The interaction scalar $I^{*}$ represents an effective pure shearing motion extracted by the decomposition formula. The effect of the extraction of the shear tensor $\boldsymbol {\nabla } {\boldsymbol {u}}$ is also maximised in the basic reference frame with the largest $I^{*}$ . This is because the norm of $\boldsymbol {\nabla } {\boldsymbol {u}}$ is written as $\|(\boldsymbol {\nabla } {\boldsymbol {u}})^{*}\|^{2}=\|(\boldsymbol {\nabla } {\boldsymbol {u}}_{\textit{RES}})^{*}\|^2 + 4I^{*}$ in any reference frame (Kolář Reference Kolář2007). The original triple decomposition is formulated as an optimisation problem of searching the basic reference frame. Later, it is shown that the solution of this maximisation problem for $I^{*}$ corresponds to the standardised real Schur form of the velocity gradient tensor (Kronborg & Hoffman Reference Kronborg and Hoffman2023).

3.2. Conditional statistics of small-scale shear layers

In the present work, shear layers are analysed statistically by detecting them through the triple decomposition formalism described above. Within this framework, statistics are conditionally calculated in a local reference frame defined for each detected shear layer, with the same method as outlined in Watanabe & Nagata (Reference Watanabe and Nagata2022, Reference Watanabe and Nagata2023). The detailed algorithm for the conditional averages has been described in these papers (and references therein), and here only a brief explanation is given on the procedure used to evaluate the statistics of these shear layers.

The small-scale shear layers arising from turbulent velocity fluctuations can be detected with the shear intensity $I_S$ . The shear layer locations are identified with the local maxima of $I_S$ , which the Hessian matrix of $I_S$ can determine uniquely. The gradient of $I_S$ is denoted by $\partial _{x} I_S=\partial I_S/\partial x$ , $\partial _{y} I_S=\partial I_S/\partial y$ and $\partial _{z} I_S=\partial I_S/\partial z$ . The shear layer analysis is conducted for the local maxima of $I_S$ , which satisfy

(3.15) \begin{gather} \partial_{x} I_S(x-\Delta x,y,z)>0, \quad \partial_{x} I_S(x+\Delta x,y,z)<0, \end{gather}
(3.16) \begin{gather}\partial_{y} I_S(x,y-\Delta y,z)>0,\quad \partial_{y} I_S(x,y+\Delta y,z)<0, \end{gather}
(3.17) \begin{gather}\partial_{z} I_S(x,y,z-\Delta z)>0,\quad \partial_{z} I_S(x,y,z+\Delta z)<0, \end{gather}

with $\Delta x$ , $\Delta y$ and $\Delta z$ ranging between 0 and $2\varDelta$ , where $\varDelta$ is the computational grid size. These conditions follow Hayashi et al. (Reference Hayashi, Watanabe and Nagata2021a ) and discard the local maxima of $I_S$ associated with noise-like patterns from the analysis.

For each detected shear layer, a local reference frame $(\zeta _1, \zeta _2, \zeta _3)$ is introduced for the conditional statistics. This reference frame is defined with the shear orientation and referred to as a shear coordinate (shown in figure 6 below). The procedure to determine the shear coordinate follows Watanabe et al. (Reference Watanabe, Tanaka and Nagata2020) and is explained briefly here. An example of a flow field observed in the shear coordinate is also presented in § 4.2. The velocity vector in this reference frame is denoted by $(u_{\zeta _1}, u_{\zeta _2}, u_{\zeta _3})$ . The shear coordinate assumes that the shear is predominantly represented by $\partial u_{\zeta _3}/\partial \zeta _2$ . The unit vectors in the $\zeta _1$ , $\zeta _2$ and $\zeta _3$ directions are denoted by ${\boldsymbol {n}}_{\zeta _1}$ , ${\boldsymbol {n}}_{\zeta _2}$ and ${\boldsymbol {n}}_{\zeta _3}$ , respectively. First, ${\boldsymbol {n}}_{\zeta _1}$ is taken in the direction of the shear vorticity vector $({\boldsymbol {\omega }}_S)_i=\epsilon _{\textit{ijk}}(\boldsymbol {\nabla } {\boldsymbol {u}}_S)_{jk}$ as ${\boldsymbol {n}}_{\zeta _1}={\boldsymbol {\omega }}_S/|{\boldsymbol {\omega }}_S|$ , where $\epsilon _{\textit{ijk}}$ is the Levi-Civita symbol. The other two unit vectors are determined by a method similar to that used to identify the basic reference frame, by which the shear coordinate is chosen from $N_n=4000$ orthogonal coordinates where the $x$ component of ${\boldsymbol {n}}_2$ is given by $({\boldsymbol {n}}_2)_{x}=2(n/N_n-0.5)$ , with an integer $n=0,\ldots,N_n$ . For each value of $n$ , ${\boldsymbol {n}}_2$ and ${\boldsymbol {n}}_3$ can be determined from the conditions ${\boldsymbol {n}}_i\boldsymbol {\cdot }{\boldsymbol {n}}_j=0$ for $i\neq j$ , $|{\boldsymbol {n}}_i|=1$ for $i=1, 2, 3$ , and ${\boldsymbol {n}}_{\zeta _1}={\boldsymbol {\omega }}_S/|{\boldsymbol {\omega }}_S|$ . The coordinate transformation tensor from $(x,y,z)$ to the new reference frame can be obtained with ${\boldsymbol {n}}_{\zeta _1}$ , ${\boldsymbol {n}}_{\zeta _2}$ and ${\boldsymbol {n}}_{\zeta _3}$ . Then the shear component $\boldsymbol {\nabla } {\boldsymbol {u}}_S$ evaluated in the new reference frame is denoted by $(\boldsymbol {\nabla } {\boldsymbol {u}}_S)^{(n)}$ . The shear coordinate is determined with $n$ that yields the largest value of $(\boldsymbol {\nabla } {\boldsymbol {u}}_S)^{(n)}_{32}$ .

For each shear layer, variables defined on the DNS grid are interpolated on the shear coordinate, which is also represented by discrete points. Various vectors and tensors are evaluated in the shear coordinate, where the components are denoted with subscript $\zeta _i$ , e.g. $\omega _{\zeta _i}$ for the vorticity vector, and ${\mathsf{\sigma}} _{\zeta _i\zeta _j}^{[P]}$ for the polymer-stress tensor. The interpolation is repeated for all detected shear layers. Finally, the ensemble averages of the shear layers are taken as functions of $(\zeta _1, \zeta _2, \zeta _3)$ . This average is denoted by $\bar {f}$ . In all cases, the number of samples utilised for ensemble averaging exceeds $3\times 10^5$ , which is in line with similar shear layer analyses conducted for turbulent jets (Hayashi et al. Reference Hayashi, Watanabe and Nagata2021a ; Hayashi, Watanabe & Nagata Reference Hayashi, Watanabe and Nagata2021b ). The statistical convergence test in Hayashi et al. (Reference Hayashi, Watanabe and Nagata2021b ) indicates that $3\times 10^5$ samples are sufficient to yield reliable statistical data for shear layers. To further assess the statistical accuracy, additional examinations of statistical errors are conducted for the present DNS databases below.

The present study focuses on examining the mean properties of shear layers. Watanabe & Nagata (Reference Watanabe and Nagata2023) conducted an investigation into the characteristics of individual shear layers in Newtonian HIT. They revealed that the probability density functions (p.d.f.s) of various properties of shear layers, such as thickness, velocity jump and local Reynolds number, typically exhibit a single peak. This peak aligns with the shear layer characteristics deduced from the mean flow pattern around the shear layers in other studies (Watanabe et al. Reference Watanabe, Tanaka and Nagata2020; Fiscaletti et al. Reference Fiscaletti, Buxton and Attili2021; Hayashi et al. Reference Hayashi, Watanabe and Nagata2021a ). Given these findings, employing ensemble averages of shear layers emerges as a valuable approach to explore the characteristics of these structures.

4. Results and discussion

4.1. Shear and rotating motions in viscoelastic turbulence

Figure 1 shows contours of the intensities of shear and rigid-body rotation, $I_S$ and $I_R$ , on two-dimensional planes in the Newtonian case and viscoelastic cases with $\textit{Wi}=2.0$ and 4.6. All figures show regions with $(400\eta )^2$ . These quantities can be used to detect shear layers and vortex tubes in turbulence (Nagata et al. Reference Nagata, Watanabe, Nagata and da Silva2020). Circular patterns of large $I_R$ are the cross-sections of vortex tubes, while their long patterns are related to the axes of in-plane vortex tubes. On the other hand, large $I_S$ is often concentrated in thin layers, which are the shear layers due to turbulent fluctuations. A comparison between the Newtonian and viscoelastic cases shows that the geometry of these structures changes as $\textit{Wi}$ increases. Here, the shear layers in the viscoelastic simulations are longer than in the Newtonian case. Additionally, the vortex tubes appear less frequently in the viscoelastic case than in the Newtonian case. Figure 2 displays the isosurfaces of $I_S$ and $I_R$ in a region $400\eta \times 400\eta \times 150\eta$ . As also shown in the two-dimensional visualisation, the aspect ratio of shear layers becomes large as $\textit{Wi}$ increases. The instability of the shear layers results in the formation of vortex tubes (Vincent & Meneguzzi Reference Vincent and Meneguzzi1994; Watanabe & Nagata Reference Watanabe and Nagata2023; Watanabe Reference Watanabe2024), and their length scales are related to each other. Because of the larger size of the shear layers at higher $\textit{Wi}$ , the size of the vortex tubes also increases in the viscoelastic simulations.

Figure 1. Two-dimensional profiles of intensities of (ac) rigid-body rotation and (df) shear: (a,d) Newtonian case; viscoelastic cases with (b,e) $\textit{Wi}=2.0$ and (c,f) $\textit{Wi}=4.6$ . Only a small part of the computational domain ( $400\eta \times 400\eta$ ) is shown here.

Figure 2. Shear layers (white) and vortex tubes (orange), which are visualised by the isosurfaces of intensities of shear, $I_S=2\langle I_S\rangle$ , and rigid-body rotation, $I_R=4\langle I_R\rangle$ : (a) Newtonian case; viscoelastic cases with (b) $\textit{Wi}=2.0$ and (c) $\textit{Wi}=4.6$ . Only a small part of the computational domain ( $400\eta \times 400\eta \times 150\eta$ ) is shown here.

Figure 3 shows the temporal evolution of shear layers and vortex tubes in a Newtonian fluid over a duration $3\tau _\eta$ . The sequence progresses from figure 3(a) to figure 3(d) at constant intervals $\tau _\eta$ . During this time, the formation of vortices from shear layers is distinctly observable. In figure 3(a), a shear layer, labelled S, already encompasses a vortex V1 that has formed within it. As this layer evolves, additional vortices V2 and V3 emerge from the same layer, as seen in figures 3(b,c). Eventually, this shear layer fragments and partially disappears in figure 3(d). Such vortex formation processes in shear layers are explained by the shear instability, and are also documented in other studies (Vincent & Meneguzzi Reference Vincent and Meneguzzi1994; Watanabe et al. Reference Watanabe, Tanaka and Nagata2020; Watanabe & Nagata Reference Watanabe and Nagata2023).

Figure 3. Temporal evolution of shear layers (white) and vortex tubes (orange) for the reference Newtonian simulation, which are visualised by the same isosurfaces as those in figure 2(a). The progression from (a) to (d) represents the advancement of time in intervals of the Kolmogorov time scale $\tau _\eta$ . Only a small part of the computational domain ( $100\eta \times 100\eta \times 50\eta$ ) is shown here.

Figure 4 presents a similar visualisation but over a longer duration $7\tau _\eta$ for the viscoelastic simulation with $\textit{Wi}=4.6$ . Even in the viscoelastic case, the vortex formation occurs: a shear layer marked as S produces a vortex V. However, this process unfolds more gradually compared to the Newtonian case. Additionally, many shear layers in the viscoelastic fluid do not readily form vortices, in contrast to the vortex generation within shear layers in the Newtonian fluid. Horiuti et al. (Reference Horiuti, Matsumoto and Fujiwara2013) have demonstrated that shear layers in viscoelastic turbulence tend to stabilise due to the tensile forces exerted by polymers on the shear layers by conducting DNS with the Johnson–Segalman model for the polymer stress. This stabilisation inhibits the fragmentation of large shear layers into smaller ones, resulting in shear layers with a low aspect ratio in Newtonian turbulence. Consequently, this inhibited instability in viscoelastic fluids accounts for the observation of larger and flatter shear layers, as shown in figure 2(c).

Figure 4. The same as figure 3 but for the viscoelastic simulation with $\textit{Wi}=4.6$ . Temporal evolution is visualised over $7\tau _\eta$ from (a) to (h).

Figure 5 shows the p.d.f.s of $I_S$ , $I_R$ and $I_E$ normalised by the Kolmogorov time scale. The distributions of the p.d.f.s are similar to those in turbulent jets and HIT of Newtonian fluids (Hayashi et al. Reference Hayashi, Watanabe and Nagata2021a ). The peaks of the p.d.f.s appear at $I_S/\tau _\eta ^{-1}\approx 0.5$ , $I_R=0$ and $I_E/\tau _\eta ^{-1}\approx 0.03$ , indicating that most of the flow is dominated by shearing motion. This is also confirmed in figure 1, where $I_S$ has non-zero value even if it is small, while $I_R\approx 0$ is observed except for regions occupied by vortex tubes. Thus rigid-body rotation and elongation are highly intermittent in space. The insets show the p.d.f.s for moderately large intensities of these motions. As $\textit{Wi}$ increases, the p.d.f.s for large $I_S$ increase, and those for large $I_R$ and $I_E$ decrease. Therefore, shearing motion becomes more dominant in local fluid motion than the other motions in a viscoelastic fluid. The vorticity vector can be decomposed into two components of shear and rigid-body rotation as ${\boldsymbol {\omega }}={\boldsymbol {\omega }}_S+{\boldsymbol {\omega }}_R$ , with $({\boldsymbol {\omega }}_S)_i=\epsilon _{\textit{ijk}}(\boldsymbol {\nabla } {\boldsymbol {u}}_S)_{jk}$ and $({\boldsymbol {\omega }}_R)_i=\epsilon _{\textit{ijk}}(\boldsymbol {\nabla } {\boldsymbol {u}}_R)_{jk}$ (Kolář Reference Kolář2007). The structures related to ${\boldsymbol {\omega }}_S$ and ${\boldsymbol {\omega }}_R$ are shear layers and vortex tubes, respectively (Eisma et al. Reference Eisma, Westerweel, Ooms and Elsinga2015; Nagata et al. Reference Nagata, Watanabe, Nagata and da Silva2020; Fiscaletti et al. Reference Fiscaletti, Buxton and Attili2021). Therefore, the $\textit{Wi}$ dependence of the p.d.f.s suggests that the vorticity is related more to shear layers than vortex tubes in viscoelastic turbulence. The DNS of viscoelastic turbulence have also shown that more sheet-like structures are identified in visualisation with vorticity as $\textit{Wi}$ increases (Watanabe & Gotoh Reference Watanabe and Gotoh2014; Ferreira et al. Reference Ferreira, Pinho and da Silva2016). This tendency is well explained by the dominance of shearing motion over rotating motion at high $\textit{Wi}$ because the shear vorticity ${\boldsymbol {\omega }}_S$ becomes more important in the total vorticity ${\boldsymbol {\omega }}$ as $\textit{Wi}$ increases.

Figure 5. The p.d.f.s of intensities of (a) shear, (b) rigid-body rotation and (c) elongation, normalised by the Kolmogorov time scale $\tau _\eta$ . The insets show the p.d.f.s with moderately large values of the intensities.

4.2. Mean flow field of small-scale shear layers

This subsection discusses the mean flow field evaluated in the shear coordinate, sketched in figure 6(a). Shearing motion is not usually observed in the reference frame of DNS because the shear layers have no preference in the orientation with respect to $x$ , $y$ and $z$ . However, in the local shear coordinate, a local shear-flow pattern is easily observed in the $\zeta _2$ $\zeta _3$ plane. This is confirmed in figure 6(b), where a flow around one of the shear layers is visualised on the $\zeta _2$ $\zeta _3$ plane at $\zeta _1=0$ . The thin shear layer with large $I_S$ is extended in the $\zeta _3$ direction. Flows in the $\pm \zeta _3$ directions are observed on the sides of the shear layer, generating shear in the layer structure. The conditional averages taken as functions of $(\zeta _1, \zeta _2, \zeta _3)$ reveal the mean flow characteristics of the shear layers shown in figure 6(b).

Figure 6. (a) A schematic of a shear coordinate. (b) A shear layer observed in the shear coordinate for $\textit{Wi}=3.0$ . The shear intensity $I_S$ and two-dimensional velocity vectors are shown on the $\zeta _2$ $\zeta _3$ plane at $\zeta _1=0$ . The velocity vectors relative to the velocity at $(\zeta _1,\zeta _2,\zeta _3)=(0,0,0)$ are shown here. The length of a vector corresponds to the vector magnitude.

Figure 7 shows the mean flow field around the shear layer with the mean shear intensity $\overline {I_S}$ and mean velocity vectors on the visualised planes for the Newtonian case. The three planes intersecting the centre of the shear layers are visualised in each plot. Large $\overline {I_S}$ is observed in a layer that is thin in the $\zeta _2$ direction and long in the other two directions. On the $\zeta _2$ $\zeta _3$ plane, the mean flows in the $\zeta _3$ and $-\zeta _3$ directions appear for $\zeta _2>0$ and $\zeta _2<0$ , respectively, representing local shearing motion. The mean velocity vectors on the $\zeta _1$ $\zeta _2$ plane exhibit a biaxial strain with stretching in the $\zeta _1$ direction, and compression in the $\zeta _2$ direction, which are confirmed by $\partial \overline {u_{\zeta _1}}/\partial \zeta _1 >0$ and $\partial \overline {u_{\zeta _2}}/\partial \zeta _2 <0$ . The $\zeta _1$ $\zeta _3$ plane is parallel to the shear layer, and the $\zeta _3$ dependence of the mean flow on this plane is relatively weak compared to the dependence in the other directions. These mean flow patterns are independent of flows and Reynolds numbers under Kolmogorov normalisation (Watanabe et al. Reference Watanabe, Tanaka and Nagata2020; Fiscaletti et al. Reference Fiscaletti, Buxton and Attili2021; Hayashi et al. Reference Hayashi, Watanabe and Nagata2021a ), and the present results agree with previous studies.

Figure 7. Mean shear intensity $\overline {I_S}$ and mean velocity vectors on (a) the $\zeta _2$ $\zeta _3$ plane at $\zeta _1=0$ , (b) the $\zeta _1$ $\zeta _2$ plane at $\zeta _3=0$ and (c) the $\zeta _1$ $\zeta _3$ plane at $\zeta _2=0$ , for the Newtonian case. The length of a vector corresponds to the vector magnitude.

Figures 8 and 9 present the mean flows near the shear layers in the viscoelastic simulations with $\textit{Wi}=2.0$ and 4.6. The shear and straining flows observed on the $\zeta _2$ $\zeta _3$ and $\zeta _1$ $\zeta _2$ planes also appear in the viscoelastic cases. However, the large- $\overline {I_S}$ region is extended for larger $|\zeta _1|$ and $|\zeta _3|$ as $\textit{Wi}$ increases from the Newtonian case. The layer thickness in the $\zeta _2$ direction is thinner in the viscoelastic cases than in the Newtonian case. Therefore, the aspect ratio of the shear layers increases in the viscoelastic turbulence, as also observed for the visualised shear layers in figure 2.

Figure 8. The same as figure 7 but for $\textit{Wi}=2.0$ .

Figure 9. The same as figure 7 but for $\textit{Wi}=4.6$ .

Figures 10 and 11 present the three-dimensional visualisations of the streamlines of mean flow around shear layers for Newtonian and viscoelastic cases, respectively. A shear flow with compressive motion in the direction normal to the layer is observed on either side of the shear layer, as indicated by the streamlines contracting towards the layer. Furthermore, the streamlines within the shear layer are oriented in the $\zeta _1$ direction, where extensive strain facilitates the stretching of shear vorticity. The inherent vorticity of the shear layer with a finite aspect ratio causes the streamlines to exhibit partially rotating patterns at the edges of the shear layers. In fact, the mean profile of the intensity of rigid-body rotation supports the presence of vortices associated with rigid-body rotation at the edges of the shear layer (Watanabe & Nagata Reference Watanabe and Nagata2022). The observed flow pattern, characterised by a combination of shear, stretching in the $\zeta _1$ direction, and compression in the $\zeta _2$ direction, is typical of small-scale shear layers in turbulent flows. This pattern is crucial in defining the structures within turbulent flows in terms of fluid particle motion.

Figure 10. Mean streamlines around the shear layer for the Newtonian case: (a) diagonal view; (b) top view from the $\zeta _1$ direction. The isosurface of $\overline {I_S}/\tau _\eta ^{-1}=1.1$ (white) visualises the shear layer. The streamlines that pass the line connecting $(\zeta _1/\eta, \zeta _2/\eta, \zeta _3/\eta )=(-5, 60, 30)$ and $(5, -60, -30)$ are visualised in a spherical domain with radius $80\eta$ . The arrows indicate the flow direction.

Because of the shear and strain acting on the layer, mean velocity jumps are observed for $\overline {u_{\zeta _1}}$ on the $\zeta _1$ axis, and $\overline {u_{\zeta _2}}$ and $\overline {u_{\zeta _3}}$ on the $\zeta _2$ axis. The corresponding mean velocity profiles are shown in figure 12 for all simulations. For $\overline {u_{\zeta _1}}$ , the mean velocity difference between $\zeta _1>0$ and $\zeta _1<0$ becomes large as $\textit{Wi}$ increases. However, the velocity jump is observed over a longer distance at higher $\textit{Wi}$ , resulting in smaller $\partial \overline {u_{\zeta _1}}/\partial \zeta _1$ within the shear layer. For $\overline {u_{\zeta _2}}$ and $\overline {u_{\zeta _3}}$ , the mean velocity differences also increase with $\textit{Wi}$ . However, viscoelasticity has virtually no effect on the mean velocity gradient within the shear layer.

Figure 13 presents an analysis of statistical errors by displaying the mean velocity and shear intensity profiles across the shear layers, as derived from two separate datasets described in § 3.2. Each dataset represents half of the computational domain. The comparison of these profiles reveals minimal differences in their distributions, demonstrating a high degree of statistical convergence. It indicates that even with half the number of statistical samples for the shear layers, the results remain consistent. Therefore, the conclusions drawn from the statistical analysis of the shear layer are robust and not skewed by statistical errors.

Figure 11. The same as figure 10 but for $\textit{Wi}=4.6$ .

Figure 12. Mean velocity profiles across the shear layer: (a) $\overline {u_{\zeta _1}}$ on the $\zeta _1$ axis; (b) $\overline {u_{\zeta _2}}$ on the $\zeta _2$ axis; and (c) $\overline {u_{\zeta _3}}$ on the $\zeta _2$ axis. The profiles are shown along the lines that pass the centre of the shear layer, $(\zeta _1,\zeta _2,\zeta _3)=(0,0,0)$ .

Figure 13. The mean velocity and shear intensity profiles across the shear layer obtained from two distinct datasets, each representing one half of the computational domain (Newtonian case). Their differences provide a measure of statistical errors.

The velocity jumps of shear layers are further examined by plotting the maximum values of the mean velocity gradients against $\textit{Wi}$ in figure 14(a). The error bars indicate the statistical errors by illustrating the values obtained from two separate datasets. Both datasets yield almost identical values for the mean velocity gradients, and the statistical errors are negligible. As shown in figure 12, $\partial \overline {u_{\zeta _1}}/\partial \zeta _1$ and $\partial \overline {u_{\zeta _3}}/\partial \zeta _2$ become the largest at $\zeta _1=0$ and $\zeta _2=0$ , while $\partial \overline {u_{\zeta _2}}/\partial \zeta _2$ has negative peaks. The mean velocity gradient related to shearing motion, $\partial \overline {u_{\zeta _3}}/\partial \zeta _2$ , normalised by the Kolmogorov time scale, is similar for Newtonian and viscoelastic cases, and hardly varies with $\textit{Wi}$ . The extensive strain $\partial \overline {u_{\zeta _1}}/\partial \zeta _1$ becomes weak as $\textit{Wi}$ increases. Similarly, the compressible strain $\partial \overline {u_{\zeta _2}}/\partial \zeta _2$ becomes weak as $\textit{Wi}$ increases, although the decrease in $|\partial \overline {u_{\zeta _2}}/\partial \zeta _2|$ with $\textit{Wi}$ is not as significant as that in $\partial \overline {u_{\zeta _1}}/\partial \zeta _1$ for high $\textit{Wi}$ .

Figure 14. The Weissenberg number ( $\textit{Wi}$ ) dependence of (a) the mean velocity gradients associated with shear and strain in the shear layer, $\partial \overline {u_{\zeta _1}}/\partial \zeta _1$ , $|\partial \overline {u_{\zeta _2}}/\partial \zeta _2|$ and $\partial \overline {u_{\zeta _3}}/\partial \zeta _2$ , and (b) the length scales of the shear layer in the $\zeta _1$ , $\zeta _2$ and $\zeta _3$ directions, which are denoted by $\delta _{S1}$ , $\delta _{S2}$ and $\delta _{S3}$ , respectively. The Kolmogorov length ( $\eta$ ) and time ( $\tau _\eta$ ) scales are used for normalisation. The maximum values of the mean velocity gradients near the shear layers are plotted in (a), where $|\partial \overline {u_{\zeta _2}}/\partial \zeta _2|$ is shown instead of $\partial \overline {u_{\zeta _2}}/\partial \zeta _2$ for the usage of the logarithmic scale. In (a), the error bars represent the statistical errors estimated with the two datasets, generated by dividing the computational domain into two equal halves.

The constant values of $\partial \overline {u_{\zeta _3}}/\partial \zeta _2$ within the shear layer are observed when normalised by the Kolmogorov time scale, which is defined based on the solvent dissipation rate of kinetic energy. In Newtonian turbulence, the dissipation of kinetic energy is predominantly driven by shearing motion, specifically related to $\partial u_{\zeta _3}/\partial \zeta _2$ in the shear layers (Das & Girimaji Reference Das and Girimaji2020; Nagata et al. Reference Nagata, Watanabe, Nagata and da Silva2020). Mathematically, irrotational strain without shear, represented by $\boldsymbol {\nabla } \boldsymbol {u}_{E}$ in the triple decomposition, can also contribute to dissipation. However, it has been demonstrated that this type of motion does not significantly contribute to dissipation as compared to shearing motion in Newtonian turbulence (Das & Girimaji Reference Das and Girimaji2020; Nagata et al. Reference Nagata, Watanabe, Nagata and da Silva2020). Consequently, $\partial \overline {u_{\zeta _3}}/\partial \zeta _2$ normalised by the Kolmogorov time scale tends to remain constant across different Reynolds number flows (Watanabe et al. Reference Watanabe, Tanaka and Nagata2020). The present results indicate that in viscoelastic cases, the physical mechanism underlying the constancy of normalised $\partial \overline {u_{\zeta _3}}/\partial \zeta _2$ for different $\textit{Wi}$ values is the predominance of shearing motion in the dissipation by the solvent. Below, the $\textit{Wi}$ dependence of the mean solvent dissipation in turbulence is well explained by changes in dissipation due to shear layers, further confirming that shear layer structures are responsible for solvent dissipation even in viscoelastic turbulence. On the other hand, $\partial \overline {u_{\zeta _1}}/\partial \zeta _1$ and $|\partial \overline {u_{\zeta _2}}/\partial \zeta _2|$ show a clear dependence on $\textit{Wi}$ even when normalised by the Kolmogorov time scale. These components are associated with the straining flow around shear layers. In Newtonian turbulence, the configuration analysis of vortex tubes and shear layers has suggested that the straining flow acting on shear layers is partially induced by nearby vortex tubes (Watanabe & Nagata Reference Watanabe and Nagata2022). Therefore, the observed suppression of vortex formations from shear layers in viscoelastic turbulence, as discussed in figures 10 and 11, is likely related to the weakened straining flow observed as the decreases in $\partial \overline {u_{\zeta _1}}/\partial \zeta _1$ and $|\partial \overline {u_{\zeta _2}}/\partial \zeta _2|$ at higher $\textit{Wi}$ values. Consistently, the weakened irrotational strain is further confirmed by the $\textit{Wi}$ dependency of $I_E$ in figure 5(c).

The $\textit{Wi}$ dependence of $\partial \overline {u_{\zeta _1}}/\partial \zeta _1$ and $\partial \overline {u_{\zeta _3}}/\partial \zeta _2$ has an important implication for vorticity dynamics. The interaction between the shear and biaxial strain causes large enstrophy production (Watanabe et al. Reference Watanabe, Tanaka and Nagata2020; Hayashi et al. Reference Hayashi, Watanabe and Nagata2021a ). This is explained by the vorticity arising from large $\partial \overline {u_{\zeta _3}}/\partial \zeta _2$ of shearing motion, which is stretched by the extensive strain in the $\zeta _1$ direction with large $\partial \overline {u_{\zeta _1}}/\partial \zeta _1$ . The vorticity due to shear is unlikely to be influenced by viscoelasticity, as attested by the weak $\textit{Wi}$ dependence of $\partial \overline {u_{\zeta _3}}/\partial \zeta _2$ , whereas the extensive strain $\partial \overline {u_{\zeta _1}}/\partial \zeta _1$ becomes weaker with increasing $\textit{Wi}$ . Therefore, the enstrophy production due to shear and surrounding straining motion is weakened as the viscoelastic effects become significant. This is also confirmed below with the enstrophy budget near the shear layer.

Another interesting behaviour is in the balance between $\partial \overline {u_{\zeta _1}}/\partial \zeta _1$ and $\partial \overline {u_{\zeta _2}}/\partial \zeta _2$ . The incompressible condition imposes $\partial {u_{\zeta _1}}/\partial \zeta _1+\partial {u_{\zeta _2}}/\partial \zeta _2+\partial {u_{\zeta _3}}/\partial \zeta _3=0$ . In the Newtonian case, $\partial \overline {u_{\zeta _1}}/\partial \zeta _1+\partial \overline {u_{\zeta _2}}/\partial \zeta _2\approx 0$ is satisfied in the shear layer, suggesting that the two-dimensional strain is acting on the shear layer with approximately equal strength of the extension and compression. However, the extensive strain $\partial \overline {u_{\zeta _1}}/\partial \zeta _1$ becomes weaker than the compressive strain $\partial \overline {u_{\zeta _2}}/\partial \zeta _2$ at high $\textit{Wi}$ . This can cause an additional extensive strain $\partial \overline {u_{\zeta _3}}/\partial \zeta _3>0$ in the shear-flow direction. The magnitude of the mean velocity vectors in figures 7–9 is expressed with the lengths of the arrows. In the Newtonian case, the mean velocity in the $\zeta _3$ direction is negligibly small within the shear layer, which is along the $\zeta _3$ axis at $\zeta _2=0$ in figure 7(a). However, in figure 9(a) with $\textit{Wi}=4.6$ , the mean velocities with $\overline {u_{\zeta _3}}>0$ and $\overline {u_{\zeta _3}}<0$ are observed for $\zeta _3>0$ and $\zeta _3<0$ in the shear layer, respectively, confirming that the extensive strain is indeed acting in the $\zeta _3$ direction. As discussed below, this $\textit{Wi}$ dependence of $\overline {u_{\zeta _3}}$ is important for the polymer stress within the shear layers.

Figure 14(b) shows the $\textit{Wi}$ dependence of the size of the shear layers in the three directions evaluated with the mean shear intensity $\overline {I_S}$ . Here, a normalised mean shear intensity $\hat {I}_S$ is defined as $\hat {I}_S(\zeta _1,\zeta _2,\zeta _3)=[\overline {I_S}(\zeta _1,\zeta _2,\zeta _3)-\langle I_S\rangle ]/[\overline {I_S}(0,0,0)-\langle I_S\rangle ]$ . Because $\overline {I_S}=\langle I_S\rangle$ at large $|\zeta _i|$ , $\hat {I}_S$ is equal to 1 at the shear layer centre, and decreases to 0 as $|\zeta _i|$ increases. The length scale of the shear layer in the $\zeta _1$ direction, $\delta _{S1}$ , is quantified as the distance between two points of $\hat {I}_S(\zeta _1,0,0)=0.1$ on the $\zeta _1$ axis at $(\zeta _2, \zeta _3)=(0,0)$ . Similarly, the length scales in the $\zeta _2$ and $\zeta _3$ directions, $\delta _{S2}$ and $\delta _{S3}$ , are quantified with the locations $\hat {I}_S(0,\zeta _2,0)=0.1$ and $\hat {I}_S(0,0,\zeta _3)=0.1$ , respectively. In figure 14(b), $\delta _{S1}$ and $\delta _{S3}$ increase, and $\delta _{S2}$ decreases, as $\textit{Wi}$ becomes large. Thus the shear layers become larger in the layer-parallel directions and thinner in the normal direction as the viscoelastic effects become more important. This geometrical change also agrees with the visualised shear layers in figure 2. The shear layer is one of the smallest-scale vortical structures, manifesting with a sheet-like geometry. In Newtonian turbulence, the thickness of these shear layers scales with $\eta$ (Watanabe et al. Reference Watanabe, Tanaka and Nagata2020; Fiscaletti et al. Reference Fiscaletti, Buxton and Attili2021; Hayashi et al. Reference Hayashi, Watanabe and Nagata2021a ). However, as $\textit{Wi}$ increases, the thickness of these layers, when normalised by $\eta$ , decreases. This observation indicates that the Kolmogorov scale does not adequately characterise the length scale of vortical structures at the smallest scales in viscoelastic turbulence.

The above viscoelastic effects on the mean properties of the shear layers are significant at high $\textit{Wi}$ . However, the shear layers in viscoelastic turbulence at low $\textit{Wi}$ behave similarly to Newtonian turbulence. In figure 14(a), the mean velocity gradients of the biaxial strain hardly change due to the viscoelastic effects up to $\textit{Wi}\approx 2$ , whereas the gradient in the $\zeta _1$ direction begins to decrease for $\textit{Wi}>2$ . Similarly, the shear layer thickness $\delta _{S2}$ is influenced by viscoelasticity for $\textit{Wi}>2$ . These results imply that the shear layers in viscoelastic turbulence are similar to those in Newtonian turbulence until $\textit{Wi}$ becomes sufficiently large. Once $\textit{Wi}$ exceeds a certain criterion at $\textit{Wi}\approx 2$ , viscoelasticity drastically influences the shear layers, as is also observed as the changes in the kinetic energy spectra (Ferreira et al. Reference Ferreira, Pinho and da Silva2016).

4.3. Polymer-stress distribution near the shear layers

The velocity gradients due to the shear and strain are expected to affect the polymer molecules near the shear layers. The present study examines the polymer-stress tensor expressed in the shear coordinate, ${\mathsf{\sigma}} _{\zeta _i\zeta _j}^{[P]}$ , whose averages taken around the shear layer are $\overline {{\mathsf{\sigma}} _{\zeta _i\zeta _j}^{[P]}}$ . Here, the normalised mean stresses are defined as $\hat {{\mathsf{\sigma}} }_{\zeta _i\zeta _j}^{[P]}=\overline {{\mathsf{\sigma}} _{\zeta _i\zeta _j}^{[P]}}/(\rho u_\eta ^{2})$ . Figure 15 shows six independent components of the mean polymer-stress tensor for $\textit{Wi}=1.3$ and 4.6 across the shear layer. The mean polymer stresses vary significantly near the shear layer, suggesting that the shear layers impact the extension of the polymer molecules. Here, spatial variations near the shear layer are observed for $\hat {{\mathsf{\sigma}} }_{\zeta _1\zeta _1}^{[P]}$ , $\hat {{\mathsf{\sigma}} }_{\zeta _2\zeta _2}^{[P]}$ , $\hat {{\mathsf{\sigma}} }_{\zeta _3\zeta _3}^{[P]}$ and $\hat {{\mathsf{\sigma}} }_{\zeta _2\zeta _3}^{[P]}$ , which are strongly influenced by the shear layers, whereas the remaining components are almost zero. The polymer-stress tensor is defined with the conformation tensor evolving according to (2.8), which is helpful to understand the relation between the conformation tensor in the shear coordinate, ${\unicode{x1D60A}}_{\zeta _i\zeta _j}$ , and the velocity jumps around shear layers. The extensive strain with $\partial \overline {u_{\zeta _1}}/\partial \zeta _1>0$ and compressive strain with $\partial \overline {u_{\zeta _2}}/\partial \zeta _2<0$ appear in the governing equations of ${\unicode{x1D60A}}_{\zeta _1\zeta _1}$ and ${\unicode{x1D60A}}_{\zeta _2\zeta _2}$ as ${\unicode{x1D60A}}_{\zeta _1\zeta _1}(\partial {u_{\zeta _1}}/\partial \zeta _1)$ and ${\unicode{x1D60A}}_{\zeta _2\zeta _2}(\partial {u_{\zeta _2}}/\partial \zeta _2)$ , respectively. For ${\unicode{x1D60A}}_{\zeta _1\zeta _1}$ , the extensive strain causes stretching of the polymer molecules, which results in large ${\unicode{x1D60A}}_{\zeta _1\zeta _1}$ . For the same reason, the compressive strain decreases ${\unicode{x1D60A}}_{\zeta _2\zeta _2}$ . These contributions of the straining flow to ${\unicode{x1D60A}}_{\zeta _1\zeta _1}$ and ${\unicode{x1D60A}}_{\zeta _2\zeta _2}$ explain large $\hat {{\mathsf{\sigma}} }_{\zeta _1\zeta _1}^{[P]}$ and small $\hat {{\mathsf{\sigma}} }_{\zeta _2\zeta _2}^{[P]}$ within the shear layers. Similarly, $\partial \overline {u_{\zeta _3}}/\partial \zeta _2>0$ due to the shear causes the polymer distortion, and contributes to an increase of ${\unicode{x1D60A}}_{\zeta _2\zeta _3}={\unicode{x1D60A}}_{\zeta _3\zeta _2}$ . For ${\unicode{x1D60A}}_{\zeta _2\zeta _3}$ , the compressive strain in the $\zeta _2$ direction may contribute to a reduction of ${\unicode{x1D60A}}_{\zeta _2\zeta _3}$ . However, figure 14(a) has shown that the shear causes a larger velocity gradient than the strain, namely $\partial {u_{\zeta _3}}/\partial \zeta _2>|\partial {u_{\zeta _2}}/\partial \zeta _2|$ . Therefore, an increase of ${\unicode{x1D60A}}_{\zeta _2\zeta _3}$ due to the shear can surpass a decrease due to the compressive strain. Thus ${\unicode{x1D60A}}_{\zeta _2\zeta _3}$ , namely $\hat {{\mathsf{\sigma}} }_{\zeta _2\zeta _3}^{[P]}$ , is locally large near the shear layer. A comparison between $\textit{Wi}=1.3$ and $\textit{Wi}=4.6$ suggests that the relative importance of $\hat {{\mathsf{\sigma}} }_{\zeta _3\zeta _3}^{[P]}$ compared to the other components depends on $\textit{Wi}$ : $\hat {{\mathsf{\sigma}} }_{\zeta _3\zeta _3}^{[P]}$ within the shear layer ( $\zeta _2\approx 0$ ) is as large as $\hat {{\mathsf{\sigma}} }_{\zeta _1\zeta _1}^{[P]}$ at $\textit{Wi}=4.6$ , whereas $\hat {{\mathsf{\sigma}} }_{\zeta _3\zeta _3}^{[P]}$ is much smaller than $\hat {{\mathsf{\sigma}} }_{\zeta _1\zeta _1}^{[P]}$ for $\textit{Wi}=1.3$ . This $\textit{Wi}$ dependence is explained by $\partial \overline {u_{\zeta _3}}/\partial \zeta _3$ , which increases from zero as $\textit{Wi}$ increases because of the imbalance between the extension and compression of the straining flow in figure 14(a). For $\textit{Wi}>2$ , the extensive strain in the $\zeta _3$ direction with $\partial \overline {u_{\zeta _3}}/\partial \zeta _3>0$ begins to act in the shear layer. This strain causes an increase of ${\unicode{x1D60A}}_{\zeta _3\zeta _3}$ due to the polymer stretching, ${\unicode{x1D60A}}_{\zeta _3\zeta _3}(\partial {u_{\zeta _3}}/\partial \zeta _3)>0$ , which appears in the governing equation for ${\unicode{x1D60A}}_{\zeta _3\zeta _3}$ . This additional extensive strain becomes important only for sufficiently high $\textit{Wi}$ . Therefore, the large peak of $\hat {{\mathsf{\sigma}} }_{\zeta _3\zeta _3}^{[P]}$ within the shear layer is observed at $\textit{Wi}=4.6$ but not at $\textit{Wi}=1.3$ .

Figure 15. Mean polymer stresses $\overline{{{\mathsf{\sigma}}}^{[P]}_{\zeta_{i}{\zeta_j}}}$ near the shear layer for (a) $\textit{Wi}=1.3$ and (b) $\textit{Wi}=4.6$ . The results are shown for the non-dimensionalised stresses $\hat {{\mathsf{\sigma}} }^{[P]}_{\zeta _{i}{\zeta _j}} =\overline {{{\mathsf{\sigma}} }^{[P]}_{\zeta _{i}{\zeta _j}}}/(\rho u_\eta ^{2})$ , and are plotted against $\zeta _2/\eta$ at $(\zeta _1,\zeta _3)=(0,0)$ . The stress tensor is evaluated in the shear coordinate $(\zeta _1, \zeta _2, \zeta _3)$ .

Figures 16 and 17 visualise the two-dimensional profiles of $\hat {{\mathsf{\sigma}} }_{\zeta _i\zeta _j}^{[P]}$ on the $\zeta _2$ $\zeta _3$ plane at $\zeta _1=0$ for $\textit{Wi}=1.3$ and 3.0. White lines are the isolines of $\overline {I_S}$ , which mark the location of the shear layer. Qualitative differences between the large and small $\textit{Wi}$ cases are observed for $\hat {{\mathsf{\sigma}} }_{\zeta _3\zeta _3}^{[P]}$ and $\hat {{\mathsf{\sigma}} }_{\zeta _2\zeta _3}^{[P]}$ . The difference in $\hat {{\mathsf{\sigma}} }_{\zeta _3\zeta _3}^{[P]}$ is due to the stretching in the $\zeta _3$ direction arising from the imbalance in the strain, as discussed above. As a result, $\hat {{\mathsf{\sigma}} }_{\zeta _3\zeta _3}^{[P]}$ for $\textit{Wi}=3.0$ becomes large within the shear layer. However, large $\hat {{\mathsf{\sigma}} }_{\zeta _3\zeta _3}^{[P]}$ appears on the sides of the shear layer for $\textit{Wi}=1.3$ . A similar difference is found for $\hat {{\mathsf{\sigma}} }_{\zeta _2\zeta _3}^{[P]}$ , which is large within the shear layer at $\textit{Wi}=3.0$ . In particular, very large $\hat {{\mathsf{\sigma}} }_{\zeta _2\zeta _3}^{[P]}$ appears along the $\zeta _3$ axis at $\zeta _2=0$ above and below the centre of the shear layer. The governing equation of ${\unicode{x1D60A}}_{\zeta _2\zeta _3}$ contains ${\unicode{x1D60A}}_{\zeta _2\zeta _3}(\partial \overline {u_{\zeta _3}}/\partial \zeta _3)$ , which causes an increase of ${\unicode{x1D60A}}_{\zeta _2\zeta _3}$ at high $\textit{Wi}$ because of the $\textit{Wi}$ dependence of $\partial \overline {u_{\zeta _3}}/\partial \zeta _3$ explained above. As discussed below, the $\textit{Wi}$ dependence of ${{\mathsf{\sigma}} }_{\zeta _3\zeta _3}^{[P]}$ is crucial in the vorticity dynamics of shear layers.

Figure 16. Non-dimensionalised mean polymer stresses $\hat {{\mathsf{\sigma}} }^{[P]}_{\zeta _{i}\zeta _{j}}$ near the shear layer on the $\zeta _2$ $\zeta _3$ plane at $\zeta _1=0$ for $\textit{Wi}=1.3$ : (a) $\hat {{\mathsf{\sigma}}}^{[P]}_{\zeta _1\zeta _1}$ , (b) $\hat {{\mathsf{\sigma}}}^{[P]}_{\zeta _2\zeta _2}$ , (c) $\hat {{\mathsf{\sigma}}}^{[P]}_{\zeta _3\zeta _3}$ and (d) $\hat {{\mathsf{\sigma}}}^{[P]}_{\zeta _2\zeta _3}$ .

Figure 17. The same as figure 16 but for $\textit{Wi}=3.0$ .

4.4. The relevance of shear layers to vorticity dynamics and kinetic energy dissipation

The transport equation of enstrophy for viscoelastic turbulence is written as

(4.1) \begin{equation} \frac{{\rm D} \omega^2/2}{{\rm D} t} =\underbrace{ \omega_i{\unicode{x1D61A}}_{\textit{ij}}\omega_j }_{P_{\omega}} +\underbrace{ \nu^{[S]}\,\frac{\partial^2 \omega^2/2}{\partial x_j\,\partial x_j} }_{D_{\omega}} - \underbrace{ \nu^{[S]}\,\frac{\partial \omega_i}{\partial x_j}\, \frac{\partial \omega_i}{\partial x_j} }_{\varepsilon_{\omega}} +\underbrace{ \omega_i\epsilon_{nji}\,\frac{\partial^2 {\mathsf{\sigma}}^{[P]}_{mj}}{\partial x_m\,\partial x_n} }_{V_{\omega}}, \end{equation}

where $P_\omega$ is the production term, $D_\omega$ is the viscous diffusion term, $\varepsilon _\omega$ is the viscous dissipation term, and $V_\omega$ represents an enstrophy change due to the interaction between vorticity and polymer stresses. The last term is absent in Newtonian fluids. The average of ${\rm D}(\omega ^2/2)/{\rm D} t$ , i.e. $\langle {\rm D}(\omega ^2/2)/{\rm D}t\rangle$ , is zero in HIT because the statistical stationarity and homogeneity assume $\langle \partial f/\partial t\rangle =0$ and $\langle \partial f/\partial x_i\rangle =0$ for any variable $f$ . However, when the average of ${\rm D}(\omega ^2/2)/{\rm D}t$ is taken conditionally for certain regions in HIT, such as shear layers or vortex tubes, the conditional average of ${\rm D}(\omega ^2/2)/{\rm D}t$ is not necessarily zero because the enstrophy amplification or attenuation may occur in a specific region in HIT.

Figure 18(a) shows the enstrophy budget across the shear layer in Newtonian turbulence, with the conditional averages of each term plotted against $\zeta _2/\eta$ at $(\zeta _1,\zeta _3)=(0,0)$ . The enstrophy production $P_\omega$ becomes large within the shear layer because of the stretching of vorticity arising from the shear by the extensive strain in the $\zeta _1$ direction. The enstrophy is locally large within the shear layer. Therefore, the diffusion term $D_\omega$ reduces and increases the enstrophy inside and outside the shear layer, respectively, transferring the enstrophy from the shear layer to the outside. The dissipation $\varepsilon _\omega$ is also large near the shear layer and has negative peaks at the locations where the enstrophy growth due to the viscous diffusion occurs. These results of the enstrophy budget in HIT are consistent with those in a turbulent planar jet (Hayashi et al. Reference Hayashi, Watanabe and Nagata2021b ).

Figure 18. The enstrophy budget near the shear layer for (a) the Newtonian case, (b) $\textit{Wi}=1.3$ and (c) $\textit{Wi}=3.0$ . The averages of (4.1), $\overline {P_\omega }$ , $\overline {D_\omega }$ , $\overline {\varepsilon _\omega }$ and $\overline {V_\omega }$ , normalised by the Kolmogorov time scale $\tau _\eta$ , are plotted against $\zeta _2/\eta$ at $(\zeta _1,\zeta _3)=(0,0)$ .

Figures 18(b,c) present the enstrophy budget in viscoelastic cases. The negative peak of the diffusion term $D_\omega$ at $\zeta _2=0$ for $\textit{Wi}=3.0$ is larger than in the Newtonian case. The diffusion is related to the enstrophy gradient on both sides of the shear layer. Because the shear layer thickness decreases with $\textit{Wi}$ , the local enstrophy gradient in the $\zeta _2$ direction also becomes large, resulting in the large negative peak of $\overline {D_\omega }$ in the shear layer. On the other hand, the peak of the production term $P_\omega$ decreases as $\textit{Wi}$ increases from the Newtonian case. Large $P_\omega$ within the shear layer is attributed to shear $\partial {u_{\zeta _3}}/\partial \zeta _2$ and extensive strain $\partial {u_{\zeta _1}}/\partial \zeta _1$ . The shear contributes to the vorticity vector in the $\zeta _1$ direction, $\omega _{\zeta _1}$ . Production $P_\omega$ has a term $\omega _{\zeta _1}{\unicode{x1D61A}}_{\zeta _1\zeta _1}\omega _{\zeta _1}$ to which the shear and extensive strain contribute in the form $(\partial {u_{\zeta _3}}/\partial \zeta _2)^2(\partial {u_{\zeta _1}}/\partial \zeta _1)$ . Thus the large velocity gradients $\partial {u_{\zeta _3}}/\partial \zeta _2$ and $\partial {u_{\zeta _1}}/\partial \zeta _1$ of the shear layer cause the large enstrophy production. Figure 14(a) has already shown that as $\textit{Wi}$ increases, the extensive strain $\partial {u_{\zeta _1}}/\partial \zeta _1$ becomes weak, especially when $\textit{Wi}$ exceeds approximately 2. For this reason, the enstrophy production becomes small at high $\textit{Wi}$ . In addition to this change in the enstrophy production, the vorticity/polymer interaction term $V_\omega$ changes its role in the vorticity dynamics within the shear layer. At $\textit{Wi}=1.3$ , $\overline {V_\omega }$ is negative in the shear layer and acts as a destruction term of enstrophy. However, it has a large positive peak comparable to $P_\omega$ at $\textit{Wi}=3.0$ , and acts as an additional production term. This transition implies that shearing motion changes from an inertia-dominated state to an inertio-elasticity dominated state. These observations are consistent with those reported by Abreu et al. (Reference Abreu, Pinho and da Silva2022), where a similar transition from a sink to a source role for the term $V_\omega$ was noted with increasing values of $\textit{Wi}$ , even though their analysis was not focused exclusively on shear layers. In wall-bounded turbulent shear flows, the interaction between vorticity and polymers introduces a sink term for the streamwise component of enstrophy, leading to a reduction in streamwise vorticity (Kim et al. Reference Kim, Li, Sureshkumar, Balachandar and Adrian2007). Conversely, when considering enstrophy defined for the mean velocity, namely the squared mean vorticity, this interaction acts as a source term for its streamwise component near the wall (Song et al. Reference Song, Lin, Liu, Lu and Khomami2021).

The $\textit{Wi}$ dependence of the production and destruction of enstrophy due to the polymers, $V_\omega$ , is examined by considering the following decomposition based on the vorticity vector in the shear coordinate:

(4.2) \begin{equation} V_\omega = \underbrace{\omega_{\zeta_1}\epsilon_{\zeta_n\zeta_j\zeta_1}\,\frac{\partial^2{\mathsf{\sigma}}^{[P]}_{\zeta_m\zeta_j}}{\partial x_{\zeta_m}\,\partial x_{\zeta_n}} }_{V_{\omega_{\zeta1}}}+\underbrace{\omega_{\zeta_2}\epsilon_{\zeta_n\zeta_j\zeta_2}\,\frac{\partial^2{\mathsf{\sigma}}^{[P]}_{\zeta_m\zeta_j}}{\partial x_{\zeta_m}\,\partial x_{\zeta_n}} }_{V_{\omega_{\zeta2}}}+\underbrace{\omega_{\zeta_3}\epsilon_{\zeta_n\zeta_j\zeta_3}\,\frac{\partial^2{\mathsf{\sigma}}^{[P]}_{\zeta_m\zeta_j}}{\partial x_{\zeta_m}\,\partial x_{\zeta_n}} }_{V_{\omega_{\zeta3}}},\end{equation}

which represents the interaction of the vorticity in the $\zeta _i$ direction with the polymer stresses. Figures 19(a,b) show the averages of these decomposed terms for $\textit{Wi}=1.3$ and 3.0. The $\zeta _1$ vorticity component has a dominant contribution to this term, whereas the averages of the other terms are negligibly small. Thus the polymer stress interaction with the $\zeta _1$ vorticity, that is, a vorticity due to shear, destroys and produces enstrophy for $\textit{Wi}=1.3$ and 3.0, respectively.

Figure 19. The mean profiles of decomposed vorticity/polymer interaction terms, (4.2), in the enstrophy transport equation across the shear layer for (a) $\textit{Wi}=1.3$ and (b) $\textit{Wi}=3.0$ . (c) The mean profile of a component of the interaction between $\omega _{\zeta _1}$ and ${\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _3}$ , the third term $\overline {V_{\omega _{\zeta 1}}}({\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _3})$ in (4.3), across the shear layer. The results taken at $(\zeta _1,\zeta _3)=(0,0)$ are plotted against $\zeta _2/\eta$ .

To further examine the $\textit{Wi}$ dependence of $V_\omega$ , $V_{\omega _{\zeta 1}}$ is decomposed into six terms with different components of the polymer-stress tensor, as

(4.3) \begin{align}V_{\omega_{\zeta1}} &= \omega_{\zeta_1}\,\frac{\partial^2{\mathsf{\sigma}}^{[P]}_{\zeta_1\zeta_3}} {\partial x_{\zeta_1}\,\partial x_{\zeta_2}}+\omega_{\zeta_1}\,\frac{\partial^2{\mathsf{\sigma}}^{[P]}_{\zeta_2\zeta_3}} {\partial x_{\zeta_2}\,\partial x_{\zeta_2}}+\omega_{\zeta_1}\,\frac{\partial^2{\mathsf{\sigma}}^{[P]}_{\zeta_3\zeta_3}} {\partial x_{\zeta_3}\,\partial x_{\zeta_2}}\nonumber\\ &\quad-\omega_{\zeta_1}\,\frac{\partial^2{\mathsf{\sigma}}^{[P]}_{\zeta_1\zeta_2}} {\partial x_{\zeta_1}\,\partial x_{\zeta_3}}-\omega_{\zeta_1}\,\frac{\partial^2{\mathsf{\sigma}}^{[P]}_{\zeta_2\zeta_2}} {\partial x_{\zeta_2}\,\partial x_{\zeta_3}}-\omega_{\zeta_1}\,\frac{\partial^2{\mathsf{\sigma}}^{[P]}_{\zeta_3\zeta_2}} {\partial x_{\zeta_3}\,\partial x_{\zeta_3}}.\end{align}

The distributions of the stresses near the shear layer in figure 15 suggest that the terms with ${\mathsf{\sigma}} ^{[P]}_{\zeta _2\zeta _2}$ , ${\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _3}$ and ${\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _2}$ have dominant contributions to $V_{\omega _{\zeta 1}}$ . We have examined all the decomposed terms near the shear layer for all simulations with different $\textit{Wi}$ , and noticed that the third term with ${\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _3}$ , denoted by $V_{\omega _{\zeta 1}}({\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _3})$ , is responsible for the $\textit{Wi}$ dependence of $V_{\omega }$ observed in figure 18. Figure 19(c) presents the mean profile of $V_{\omega _{\zeta 1}}({\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _3})$ across the shear layer. For small $\textit{Wi}$ , this term is small even within the shear layer. However, a large positive peak appears for $\textit{Wi}=3.0$ and 4.6. Because of this $\textit{Wi}$ dependence of the interaction between the vorticity in the $\zeta _1$ direction with the normal polymer stress in the $\zeta _3$ direction, the role of $V_\omega$ changes with $\textit{Wi}$ . The shear expressed as $\partial {u_{\zeta _3}}/\partial \zeta _2>0$ contributes to large positive $\omega _{\zeta _1}$ . Therefore, the sign of $V_{\omega _{\zeta 1}}({\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _3})$ depends on $\partial ^2 {\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _3}/\partial x_{\zeta _3}\partial x_{\zeta _2}$ . The distributions of ${\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _3}$ for low and high $\textit{Wi}$ have been presented in figures 16(c) and 17(c), respectively. For high $\textit{Wi}$ , large $\overline {{\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _3}}$ distributes from the first to third quadrants along the shear layer, which is slightly tilted from the $\zeta _3$ direction. Therefore, an instantaneous profile of ${\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _3}$ often has a positive peak in the first or third quadrant, which can cause positive $\partial ^2 {\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _3}/\partial x_{\zeta _3}\partial x_{\zeta _2}$ at $(\zeta _2, \zeta _3)=(0,0)$ , namely the viscoelastic enstrophy production at high $\textit{Wi}$ . On the other hand, large $\overline {{\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _3}}$ at low $\textit{Wi}$ appears in the second and third quadrants, resulting in negative $\partial ^2 {\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _3}/\partial x_{\zeta _3}\partial x_{\zeta _2}$ with the enstrophy destruction. As discussed above, these differences related to ${\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _3}$ are attributed to the viscoelastic effects on the mean flow field around the shear layer, which changes significantly once $\textit{Wi}$ exceeds approximately 2.

In viscoelastic turbulence, kinetic energy dissipation rates due to viscous and polymer stresses are written as $\varepsilon _\nu =2\nu {\unicode{x1D61A}}_{\textit{ij}}{\unicode{x1D61A}}_{\textit{ij}}$ and $\varepsilon _P={\unicode{x1D61A}}_{\textit{ij}}{\mathsf{\sigma}} ^{[P]}_{\textit{ij}}$ , respectively. Figures 20(a,b) show the profiles of the mean dissipation rates $\overline {\varepsilon _\nu }$ and $\overline {\varepsilon _P}$ near the shear layer, which are normalised by the volume-averaged total dissipation rate $\langle \varepsilon \rangle =\langle \varepsilon _\nu +\varepsilon _P\rangle$ . Both dissipation rates have peaks at the centre of the shear layer. The viscous and polymer dissipation rates tend to decrease and increase, respectively, as $\textit{Wi}$ increases. As a result, the polymer stress contributes more to kinetic energy dissipation than the viscous stress for $\textit{Wi}\geq 2.0$ .

Figure 20. The mean kinetic energy dissipation rates due to (a) viscous stress $\overline {\varepsilon _\nu }$ , and (b) polymer stress $\overline {\varepsilon _P}$ , near the shear layer. (c) Contributions of different polymer-stress components to the dissipation rate (4.4) at $\textit{Wi}=3.0$ . The dissipation rates are normalised by the volume-averaged total dissipation rate $\langle \varepsilon \rangle =\langle \varepsilon _\nu +\varepsilon _P\rangle$ . The results taken at $(\zeta _1,\zeta _3)=(0,0)$ are plotted against $\zeta _2/\eta$ .

The dissipation rate due to the polymer can be decomposed into the contributions from each component of the polymer-stress tensor in the shear coordinate, as

(4.4) \begin{equation}\varepsilon_{P}=\sum_{\alpha=1}^{3}\sum_{\beta=1}^{3}{\mathsf{\varepsilon}}_{P_{\alpha\beta}}, \quad \mathrm{with}\ {\mathsf{\varepsilon}}_{P_{\alpha\beta}}={\unicode{x1D61A}}_{\zeta_{\alpha}\zeta_{\beta}}{\mathsf{\sigma}}^{[P]}_{\zeta_{\alpha}\zeta_{\beta}},\end{equation}

where the summation is not applied to the Greek indices when each component ${\mathsf{\varepsilon}}_{P_{\alpha\beta}}$ is considered. The mean profiles of velocity and polymer stress around the shear layer indicate that ${\mathsf{\varepsilon}} _{P_{11}}$ , ${\mathsf{\varepsilon}} _{P_{22}}$ , ${\mathsf{\varepsilon}} _{P_{33}}$ and ${\mathsf{\varepsilon}} _{P_{23}}$ are dominant because of the large velocity gradients and significant variations of the polymer stresses within the shear layer. Figure 20(c) shows the mean profiles of these dissipation components near the shear layer for $\textit{Wi}=3.0$ . The large dissipation rate by the polymer within the shear layer is caused by ${\mathsf{\varepsilon}} _{P_{11}}$ and ${\mathsf{\varepsilon}} _{P_{23}}$ . These components become large because of the extensive strain and shear, which also contribute to the amplification of the corresponding components of the polymer-stress tensor.

For the present case, $\overline {\varepsilon _P}$ is always positive even at large $\zeta _2$ , where $\overline {\varepsilon _P}$ asymptotically approaches the volume average of $\varepsilon _P$ . Thus the polymer stress leads to the dissipation of the turbulent kinetic energy. The large positive ${\mathsf{\varepsilon}} _{P_{11}}$ in figure 20(c) is attributed to the three-dimensional flow around the shear layers, specifically an extensional strain in the normal direction of the shear plane ( $\zeta _2$ $\zeta _3$ plane). In DNS of viscoelastic planar jets, an energy conversion to turbulent kinetic energy was observed in the potential core region, where the flow is characterised by a two-dimensional mean shear (Guimarães et al. Reference Guimarães, Pinho and da Silva2023). These studies suggest that the three-dimensionality of small-scale shear layers plays an important role in the turbulent kinetic energy dissipation associated with $\varepsilon _P$ .

The above results are obtained locally with conditional averages for the shear layers. Their contributions to the global characteristics of turbulence are discussed by comparing the conditional averages with volume averages in the entire computational domain. Figure 21(a) shows the $\textit{Wi}$ dependence of the mean production and polymer-stress terms of enstrophy in (4.1). The mean values are evaluated with the volume averages $\langle P_\omega \rangle$ and $\langle V_\omega \rangle$ , or the conditional averages at the centre of shear layers $\overline {P_\omega }$ and $\overline {V_\omega }$ . At large $\textit{Wi}$ , both terms contribute to the enstrophy growth. Therefore, these averages are normalised by the sum of two terms, $\langle P_\omega +V_\omega \rangle$ or $\overline {P_\omega +V_\omega }$ . For the shear layer, $\overline {P_\omega }$ and $\overline {V_\omega }$ respectively decrease and increase with $\textit{Wi}$ , as discussed above. The same trend is observed for the volume averages. Figure 21(b) provides a similar comparison for the kinetic energy dissipation rates due to the viscous and polymer stresses. The averages of these dissipation rates normalised by the total dissipation rate are plotted against $\textit{Wi}$ . For both averages, the viscous dissipation rate decreases with $\textit{Wi}$ , whereas the dissipation rate due to the polymer stress increases with $\textit{Wi}$ . The $\textit{Wi}$ dependence is also similar for both averages. The agreement between these two averages indicates that the $\textit{Wi}$ dependence of the volume-averaged quantities is well explained by the changes in the shear layers at high $\textit{Wi}$ , and that the shear layers are important in the global characteristics of viscoelastic turbulence. In figure 21, the error bars illustrate the range of values obtained from two separate datasets. The narrow range of these error bars suggests that the statistical errors are negligible.

Figure 21. (a) The Weissenberg number ( $\textit{Wi}$ ) dependence of averages of the production term $P_\omega$ and vorticity/polymer interaction term $V_\omega$ in the enstrophy transport equation (4.1). The averages are calculated with the conventional volume averages $\langle P_\omega \rangle$ and $\langle V_\omega \rangle$ , or the conditional averages $\overline {P_\omega }$ and $\overline {V_\omega }$ , at the centre of the shear layer $(\zeta _1, \zeta _2, \zeta _3)=(0,0,0)$ . The sum of the two terms normalises each average. (b) The $\textit{Wi}$ dependence of averages of the viscous dissipation rate $\varepsilon _\nu$ and polymer dissipation rate $\varepsilon _P$ , which are calculated with the volume averages or conditional averages at the centre of the shear layer. Each average is normalised by the average of the total dissipation $\varepsilon =\varepsilon _\nu +\varepsilon _P$ . The error bars represent the statistical errors estimated with the two datasets, generated by dividing the computational domain into two equal halves.

5. Conclusions

Small-scale shear layers arising from turbulent velocity fluctuations are investigated with DNS of statistically stationary homogeneous isotropic turbulence of Newtonian and viscoelastic fluids, where the FENE-P model describes the latter. By identifying the position and orientation of each shear layer with the triple decomposition, the mean flow field around the shear layers is analysed, with conditional averages taken in a shear coordinate defined with the shear orientation. The viscoelastic effects are discussed as functions of the Weissenberg number $\textit{Wi}$ , which is the time scale ratio between the maximum relaxation time of the polymer molecules and the Kolmogorov time scale. The present work focuses on the characteristics of local shear layer regions, which play a crucial role in the dissipation mechanisms of turbulent flows. The motivation to study the dissipation mechanism in viscoelastic turbulent flows stems from the well-known depletion of viscous dissipation rates observed in these flows. This phenomenon potentially relates to the dissipation reduction, and thus drag reduction, observed in other types of flows, such as pipe and channel flows of viscoelastic fluids.

The shear layers at high $\textit{Wi}$ have a large aspect ratio related to small thickness and large length in the layer-parallel direction. The intensities of local fluid motions described by the triple decomposition vary with $\textit{Wi}$ . As $\textit{Wi}$ increases, shearing motion becomes more significant while rigid-body rotation and elongation weaken. Rigid-body rotation is manifested as vortex tubes in turbulence. Because of this $\textit{Wi}$ dependence, the shear layers in viscoelastic turbulence are more dominant as small-scale structures than the vortex tubes. The vortex formation from shear layers, triggered by the shear instability, occurs in both Newtonian and viscoelastic fluids. This instability also results in the fragmentation of shear layers, leading to the formation of smaller structures. However, in the viscoelastic case, this process unfolds more gradually and is observed less frequently. The suppressed instability of shear layers in viscoelastic turbulence explains the prevalence of larger and flatter shear layers, as well as the dominance of shear layers over vortex tubes.

The mean flow field around the shear layers has been examined in a shear coordinate system in figure 6(a). For both Newtonian and viscoelastic turbulence, the shear layers are formed in a straining flow, with extensive strain in the vorticity direction of shear, and compressive strain in the layer-normal direction. The mean velocity gradient due to shear is independent of $\textit{Wi}$ when it is normalised by the Kolmogorov scale. However, the extensive strain becomes weak as $\textit{Wi}$ increases. Similarly, the compressive strain tends to be weak, although this variation is less significant than observed for the extensive strain. Therefore, the extensive and compressive strains are not balanced at large $\textit{Wi}$ , unlike in the Newtonian and low $\textit{Wi}$ cases. This imbalance is significant for sufficiently large $\textit{Wi}$ , which is $\textit{Wi} \gtrsim 2$ for the present DNS.

These mean velocity gradients of the shear and biaxial strain affect the polymer-stress distribution near the shear layer. Because the straining flow causes the polymer stretching and compression, the normal polymer stresses in the shear vorticity direction and the layer normal direction are locally strengthened and weakened, respectively, within the shear layer. The velocity gradient of the shear causes considerable tangential polymer stress in the shear-flow orientation. Because the strain acting on the shear layer is observed regardless of $\textit{Wi}$ , the distributions of these stress components are also similar for all $\textit{Wi}$ . However, the $\textit{Wi}$ dependence is observed for other components. The imbalance in the straining flow discussed above can cause an additional extensive strain in the layer-parallel direction only for $\textit{Wi} \gtrsim 2$ . In this case, the corresponding polymer stress, the normal stress in the shear-flow direction, becomes large within the shear layer. This component for low $\textit{Wi}$ has large values outside the shear layer. Thus its distribution near the shear layer drastically changes once $\textit{Wi}$ exceeds approximately $2$ .

The enstrophy budget and kinetic energy dissipation were also investigated near the shear layers. The enstrophy production is very active in the shear layer because the extensive strain causes the stretching of vorticity due to shear. The viscous effect transfers enstrophy from the centre of the shear layer to the outside, where the viscous dissipation of enstrophy occurs actively. The polymers also affect the enstrophy budget near the shear layers. First, a thinner shear layer at large $\textit{Wi}$ causes a greater viscous diffusion because of the large enstrophy gradient. In addition, vortex stretching due to the strain is weakened at high $\textit{Wi}$ because the extensive strain is weakened. These variations with $\textit{Wi}$ are associated with the changes in the flow field around the shear layers caused by the viscoelastic effects. The direct interaction between vorticity and polymer stresses also influences the enstrophy evolution. The role of this interaction changes with $\textit{Wi}$ : it causes the destruction and production of enstrophy within the shear layers at low and high $\textit{Wi}$ , respectively. The vorticity due to the shear dominates the enstrophy production and destruction due to the polymer stresses in the shear layer. In addition, the normal polymer stress in the shear-flow direction is related to the observed $\textit{Wi}$ dependence of the vorticity and polymer interaction. This stress component is large within and outside the shear layer at high and low $\textit{Wi}$ , respectively, and this different distribution causes different roles of the polymer stress in the enstrophy budget. The $\textit{Wi}$ dependence of the vorticity and polymer interaction is also related to the imbalance between stretching and compression of the straining flow, which is significant only at high $\textit{Wi}$ . For the kinetic energy dissipation rate within the shear layer, the contribution from the polymer stresses becomes more important as $\textit{Wi}$ becomes large. This dissipation within the shear layer is also related to the components of the polymer-stress tensor enhanced by the shear and strain.

Finally, the $\textit{Wi}$ dependences of the enstrophy production by vortex stretching, the enstrophy production/destruction due to the polymer and vorticity interaction, and the kinetic energy dissipation rates due to viscous and polymer stresses are compared for the averages within the shear layers and the averages in the entire flow region. The $\textit{Wi}$ dependence of these quantities is similar for both averages, suggesting that the shear layers dominate these phenomena. Crucially, it is suggested that both viscous and polymer kinetic energy dissipations occur predominantly within these shear layers, highlighting the significant role that shear layers play in the dissipation reduction in viscoelastic turbulence.

For Newtonian turbulence, these statistical properties of the shear layers have been compared well with the Burgers vortex layer (Watanabe et al. Reference Watanabe, Tanaka and Nagata2020; Hayashi et al. Reference Hayashi, Watanabe and Nagata2021a ). The instability of simple shear flows has also been studied with idealised models (Corcos & Lin Reference Corcos and Lin1984; Lin & Corcos Reference Lin and Corcos1984; Beronov & Kida Reference Beronov and Kida1996). For small-scale shear layers, such models can be developed based on the mean flow field observed around shear layers, as has been reported successfully for Newtonian turbulence (Watanabe & Nagata Reference Watanabe and Nagata2023). These models have demonstrated their capability to reproduce the unstable behaviour of shear layers within turbulent flows, including their response to perturbations. This aspect is crucial for flow control strategies that target small-scale turbulent structures (Watanabe Reference Watanabe2024), and for understanding the scale-by-scale interaction of turbulent motions (Watanabe & Nagata Reference Watanabe and Nagata2023). Extending these model-based studies to viscoelastic turbulence is feasible based on the present findings, providing an interesting framework to understand viscoelastic turbulence with turbulent structures.

Acknowledgements

We acknowledge PRACE for awarding us access to resource Marenostrum IV based in Spain at https://www.bsc.es. We also acknowledge Minho Advanced Computing Center for providing HPC computing and consulting resources that have contributed to the research results reported in this paper (https://macc.fccn.pt). This work was also partially supported by the Collaborative Research Project on Computer Science with High-Performance Computing in Nagoya University and the HPCI System Research Project (hp230045).

Funding

This work was supported by JSPS KAKENHI grant nos JP22K03903 and JP22H01398. The authors acknowledge Fundação para a Ciência e a Tecnologia (FCT) for its financial support via the project LAETA Base Funding (doi: 10.54499/UIDB/50022/2020).

Declaration of interests

The authors report no conflict of interest.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Footnotes

Article updated 16 June 2025.

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Figure 0

Table 1. DNS databases of HIT in Newtonian (Newt.) and viscoelastic fluids: Weissenberg number ($\textit{Wi}$); the maximum relaxation time of the polymer molecules ($\tau _P$); turbulent Reynolds number ($Re_\lambda$); r.m.s. velocity fluctuations ($u_{0}$); solvent mean viscous dissipation rate ($\langle \varepsilon _s \rangle$); polymer mean dissipation rate ($\langle \varepsilon _p \rangle$); dissipation reduction (DR); Taylor microscale ($\lambda$); Kolmogorov microscale ($\eta$); Kolmogorov time scale ($\tau _\eta$); Kolmogorov velocity scale ($u_\eta$); the maximum effective wavenumber normalised by the Kolmogorov scale ($k_{max}\eta$).

Figure 1

Figure 1. Two-dimensional profiles of intensities of (ac) rigid-body rotation and (df) shear: (a,d) Newtonian case; viscoelastic cases with (b,e) $\textit{Wi}=2.0$ and (c,f) $\textit{Wi}=4.6$. Only a small part of the computational domain ($400\eta \times 400\eta$) is shown here.

Figure 2

Figure 2. Shear layers (white) and vortex tubes (orange), which are visualised by the isosurfaces of intensities of shear, $I_S=2\langle I_S\rangle$, and rigid-body rotation, $I_R=4\langle I_R\rangle$: (a) Newtonian case; viscoelastic cases with (b) $\textit{Wi}=2.0$ and (c) $\textit{Wi}=4.6$. Only a small part of the computational domain ($400\eta \times 400\eta \times 150\eta$) is shown here.

Figure 3

Figure 3. Temporal evolution of shear layers (white) and vortex tubes (orange) for the reference Newtonian simulation, which are visualised by the same isosurfaces as those in figure 2(a). The progression from (a) to (d) represents the advancement of time in intervals of the Kolmogorov time scale $\tau _\eta$. Only a small part of the computational domain ($100\eta \times 100\eta \times 50\eta$) is shown here.

Figure 4

Figure 4. The same as figure 3 but for the viscoelastic simulation with $\textit{Wi}=4.6$. Temporal evolution is visualised over $7\tau _\eta$ from (a) to (h).

Figure 5

Figure 5. The p.d.f.s of intensities of (a) shear, (b) rigid-body rotation and (c) elongation, normalised by the Kolmogorov time scale $\tau _\eta$. The insets show the p.d.f.s with moderately large values of the intensities.

Figure 6

Figure 6. (a) A schematic of a shear coordinate. (b) A shear layer observed in the shear coordinate for $\textit{Wi}=3.0$. The shear intensity $I_S$ and two-dimensional velocity vectors are shown on the $\zeta _2$$\zeta _3$ plane at $\zeta _1=0$. The velocity vectors relative to the velocity at $(\zeta _1,\zeta _2,\zeta _3)=(0,0,0)$ are shown here. The length of a vector corresponds to the vector magnitude.

Figure 7

Figure 7. Mean shear intensity $\overline {I_S}$ and mean velocity vectors on (a) the $\zeta _2$$\zeta _3$ plane at $\zeta _1=0$, (b) the $\zeta _1$$\zeta _2$ plane at $\zeta _3=0$ and (c) the $\zeta _1$$\zeta _3$ plane at $\zeta _2=0$, for the Newtonian case. The length of a vector corresponds to the vector magnitude.

Figure 8

Figure 8. The same as figure 7 but for $\textit{Wi}=2.0$.

Figure 9

Figure 9. The same as figure 7 but for $\textit{Wi}=4.6$.

Figure 10

Figure 10. Mean streamlines around the shear layer for the Newtonian case: (a) diagonal view; (b) top view from the $\zeta _1$ direction. The isosurface of $\overline {I_S}/\tau _\eta ^{-1}=1.1$ (white) visualises the shear layer. The streamlines that pass the line connecting $(\zeta _1/\eta, \zeta _2/\eta, \zeta _3/\eta )=(-5, 60, 30)$ and $(5, -60, -30)$ are visualised in a spherical domain with radius $80\eta$. The arrows indicate the flow direction.

Figure 11

Figure 11. The same as figure 10 but for $\textit{Wi}=4.6$.

Figure 12

Figure 12. Mean velocity profiles across the shear layer: (a) $\overline {u_{\zeta _1}}$ on the $\zeta _1$ axis; (b) $\overline {u_{\zeta _2}}$ on the $\zeta _2$ axis; and (c) $\overline {u_{\zeta _3}}$ on the $\zeta _2$ axis. The profiles are shown along the lines that pass the centre of the shear layer, $(\zeta _1,\zeta _2,\zeta _3)=(0,0,0)$.

Figure 13

Figure 13. The mean velocity and shear intensity profiles across the shear layer obtained from two distinct datasets, each representing one half of the computational domain (Newtonian case). Their differences provide a measure of statistical errors.

Figure 14

Figure 14. The Weissenberg number ($\textit{Wi}$) dependence of (a) the mean velocity gradients associated with shear and strain in the shear layer, $\partial \overline {u_{\zeta _1}}/\partial \zeta _1$, $|\partial \overline {u_{\zeta _2}}/\partial \zeta _2|$ and $\partial \overline {u_{\zeta _3}}/\partial \zeta _2$, and (b) the length scales of the shear layer in the $\zeta _1$, $\zeta _2$ and $\zeta _3$ directions, which are denoted by $\delta _{S1}$, $\delta _{S2}$ and $\delta _{S3}$, respectively. The Kolmogorov length ($\eta$) and time ($\tau _\eta$) scales are used for normalisation. The maximum values of the mean velocity gradients near the shear layers are plotted in (a), where $|\partial \overline {u_{\zeta _2}}/\partial \zeta _2|$ is shown instead of $\partial \overline {u_{\zeta _2}}/\partial \zeta _2$ for the usage of the logarithmic scale. In (a), the error bars represent the statistical errors estimated with the two datasets, generated by dividing the computational domain into two equal halves.

Figure 15

Figure 15. Mean polymer stresses $\overline{{{\mathsf{\sigma}}}^{[P]}_{\zeta_{i}{\zeta_j}}}$ near the shear layer for (a) $\textit{Wi}=1.3$ and (b) $\textit{Wi}=4.6$. The results are shown for the non-dimensionalised stresses $\hat {{\mathsf{\sigma}} }^{[P]}_{\zeta _{i}{\zeta _j}} =\overline {{{\mathsf{\sigma}} }^{[P]}_{\zeta _{i}{\zeta _j}}}/(\rho u_\eta ^{2})$, and are plotted against $\zeta _2/\eta$ at $(\zeta _1,\zeta _3)=(0,0)$. The stress tensor is evaluated in the shear coordinate $(\zeta _1, \zeta _2, \zeta _3)$.

Figure 16

Figure 16. Non-dimensionalised mean polymer stresses $\hat {{\mathsf{\sigma}} }^{[P]}_{\zeta _{i}\zeta _{j}}$ near the shear layer on the $\zeta _2$$\zeta _3$ plane at $\zeta _1=0$ for $\textit{Wi}=1.3$: (a) $\hat {{\mathsf{\sigma}}}^{[P]}_{\zeta _1\zeta _1}$, (b) $\hat {{\mathsf{\sigma}}}^{[P]}_{\zeta _2\zeta _2}$, (c) $\hat {{\mathsf{\sigma}}}^{[P]}_{\zeta _3\zeta _3}$ and (d) $\hat {{\mathsf{\sigma}}}^{[P]}_{\zeta _2\zeta _3}$.

Figure 17

Figure 17. The same as figure 16 but for $\textit{Wi}=3.0$.

Figure 18

Figure 18. The enstrophy budget near the shear layer for (a) the Newtonian case, (b) $\textit{Wi}=1.3$ and (c) $\textit{Wi}=3.0$. The averages of (4.1), $\overline {P_\omega }$, $\overline {D_\omega }$, $\overline {\varepsilon _\omega }$ and $\overline {V_\omega }$, normalised by the Kolmogorov time scale $\tau _\eta$, are plotted against $\zeta _2/\eta$ at $(\zeta _1,\zeta _3)=(0,0)$.

Figure 19

Figure 19. The mean profiles of decomposed vorticity/polymer interaction terms, (4.2), in the enstrophy transport equation across the shear layer for (a) $\textit{Wi}=1.3$ and (b) $\textit{Wi}=3.0$. (c) The mean profile of a component of the interaction between $\omega _{\zeta _1}$ and ${\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _3}$, the third term $\overline {V_{\omega _{\zeta 1}}}({\mathsf{\sigma}} ^{[P]}_{\zeta _3\zeta _3})$ in (4.3), across the shear layer. The results taken at $(\zeta _1,\zeta _3)=(0,0)$ are plotted against $\zeta _2/\eta$.

Figure 20

Figure 20. The mean kinetic energy dissipation rates due to (a) viscous stress $\overline {\varepsilon _\nu }$, and (b) polymer stress $\overline {\varepsilon _P}$, near the shear layer. (c) Contributions of different polymer-stress components to the dissipation rate (4.4) at $\textit{Wi}=3.0$. The dissipation rates are normalised by the volume-averaged total dissipation rate $\langle \varepsilon \rangle =\langle \varepsilon _\nu +\varepsilon _P\rangle$. The results taken at $(\zeta _1,\zeta _3)=(0,0)$ are plotted against $\zeta _2/\eta$.

Figure 21

Figure 21. (a) The Weissenberg number ($\textit{Wi}$) dependence of averages of the production term $P_\omega$ and vorticity/polymer interaction term $V_\omega$ in the enstrophy transport equation (4.1). The averages are calculated with the conventional volume averages $\langle P_\omega \rangle$ and $\langle V_\omega \rangle$, or the conditional averages $\overline {P_\omega }$ and $\overline {V_\omega }$, at the centre of the shear layer $(\zeta _1, \zeta _2, \zeta _3)=(0,0,0)$. The sum of the two terms normalises each average. (b) The $\textit{Wi}$ dependence of averages of the viscous dissipation rate $\varepsilon _\nu$ and polymer dissipation rate $\varepsilon _P$, which are calculated with the volume averages or conditional averages at the centre of the shear layer. Each average is normalised by the average of the total dissipation $\varepsilon =\varepsilon _\nu +\varepsilon _P$. The error bars represent the statistical errors estimated with the two datasets, generated by dividing the computational domain into two equal halves.