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Systems analysis of hybrid, multi-scale complex flow simulations using Newton-GMRES

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Abstract

We present a methodology to analyze the stationary states and stability of complex fluid flows by using hybrid, discrete, and/or continuum multi-scale simulations. Building on existing theories, our scheme extracts dynamical and equilibrium characteristics from carefully chosen time integrations of these multi-scale evolution equations. Two canonical problems are presented to demonstrate the ability and accuracy of the formalism. The first is an investigation of flow-induced transitions seen in homogeneous, hard- rod liquid crystal suspensions subjected to a linear shear flow. In the second problem, we study the phenomenon of draw resonance, a dynamical instability in an isothermal fiber-spinning process, by using a multi-scale hybrid simulation that incorporates both stochastic and continuum models.

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Notes

  1. Similar assumptions also underpin the theory of inertial manifolds (Mass and Pope 1992), approximate inertial manifolds (Gear et al. 2005), and many singularly perturbed systems that arise in engineering modeling.

  2. Assuming that the long-term dynamics of the time-stepper are dictated by p slow moments, where p ≪ M, it can be shown that the GMRES iteration converges in at most p + 1 iterations, whereas for continuation, the dimension of the slow sub-space of moments increases from p to at most p + 2 (Kelley et al. 2004).

  3. Consider the director rotated by a small angle \(\boldsymbol{\varphi}\) so that n goes to \({\bf n}+{\boldsymbol{\varphi}} \times {\bf n}\). Since \(f_{0} = f_{0}({\bf u}\boldsymbol{\cdot}{\bf n})\) and \(f_{0}^{R} = f_{0}({\bf u}\boldsymbol{\cdot} ({\bf n}+{\boldsymbol{\varphi}} \times {\bf n}))\) both satisfy Eq. (9), it follows that at linear order, \( \Psi(\boldsymbol{\varphi} \boldsymbol{\cdot} ({\bf n} \times {{\partial{f_{0}} \over \partial{\bf n}}}))=0, \) Ψ being the linearized operator corresponding to the right side of Eq. 9. Steady solutions, thus, have a neutral eigenvalue λ R  = 0, characterizing the soft mode (rotation of director) with corresponding eigenvector \(\boldsymbol{\varphi} \boldsymbol{\cdot} ({\bf n} \times {\partial{f_{0}}/\partial{\bf n}})\). In terms of the Fourier coefficients, setting all the b k  = 0 for k ≥ 1 is equivalent to pinning the director and removing this rotational degeneracy.

  4. Configuration variables Q z,i and Q r,i are functions of both space and time. The standard Wiener processes W z,i and W r,i are functions of time only.

  5. In an experimental situation, the flow rate is generally given but the initial area is unknown because of the extrudate swell and uncertainty as to the precise location of the coordinate origin.

  6. Finite element calculations of Keunings et al. (1983) have shown that this ratio monotonically goes from − 0.5 to 0 at the point where the stresses become radially uniform, as the tension in the fiber is increased. Also, it has been shown (see Denn 1974 for instance) that the fiber-spinning equations presented here are insensitive to this stress ratio as long as it is in the range from − 0.5 to 0.

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Correspondence to Zubair Anwar.

Appendix: Numerical discretization

Appendix: Numerical discretization

The partial differential equations for the fiber-spinning process are solved by discretizing the computational domain z ∈ [0, 1] into N elements and time domain t ∈ [0,T f ] in N T steps. We then use the finite element method to discretize spatially the mass and momentum balances and the equation for the velocity gradient G. Continuous, quadratic basis functions φ i are used for A and v z , whereas continuous linear basis functions ψ i are used for G. In contrast, the polymer contributions to the stress tensor, τ p, zz and τ p, rr , and configuration fields, Q z, i and Q r, i , are discretized in space by using the discontinuous Galerkin method with local linear discontinuous basis functions \(\psi_{\text{DG},\,l}\). For time discretization, all terms are treated explicitly to yield the following algorithm.

  1. 1.

    Given A k, \(\tau_{p,\,zz}^k\), \(\tau_{p,\,rr}^k\), and the initial guess \(v_z^k\) and G k, update the velocity and velocity gradient by solving the following nonlinear problem for the weak formulation

    $$\begin{array}{lll} \int_0^1\left(\frac{\partial A^k}{\partial z}\left[-3\frac{\partial v_z^{k+1}}{\partial z}+3(1-\beta)G^{k+1}\right.\right. \\ \quad\qquad\qquad+\left.(\tau_{p,\,zz}^k-\tau_{p,\,rr}^k)\vphantom{\frac{\partial v_z^{k+1}}{\partial z}}\right] \\ \quad\quad+\;A^k\left[-3\frac{\partial^2v_z^{k+1}}{\partial z^2}+3(1-\beta)\frac{\partial G^{k+1}}{\partial z}\right. \\ \qquad\qquad\quad+\left.\left.\frac{\partial}{\partial z}\left[\tau_{p,\,zz}^k-\tau_{p,\,rr}^k\right]\vphantom{\frac{\partial^2v_z^{k+1}}{\partial z^2}}\right]\right)\phi_i\,\textrm{d}z = 0 \\ \int_0^1\Bigl(G^{k+1}-\frac{\partial v_z^{k+1}}{\partial z}\Bigr)\psi_i\,\textrm{d}z = 0 \end{array}$$
    (29)
  2. 2.

    Given the new velocity field update the area as follows

    $$\begin{array}{lll} \int_0^1A^{k+1}\phi_i\,\textrm{d}z \notag\\ \qquad\qquad= \int_0^1A^k\phi_i\,\textrm{d}z-\Delta t\int_0^1\left(v_z^{k+1}\frac{\partial A^k}{\partial z} +A^k\frac{\partial v_z^{k+1}}{\partial z}\right)\phi_i\,\textrm{d}z\end{array}$$
    (30)
  3. 3.

    Update the polymer contributions to the stress tensor for the Oldroyd-B model by using the following weak formulations over each element [z j ,z j + 1] of the domain

    $$\begin{array}{lll} \int_{z_j}^{z_{j+1}}\text{De}\tau^{k+1}_{p,\,zz}\psi_{\text{DG},\,l}\,\textrm{d}z\\ \qquad= \int_{z_j}^{z_{j+1}}\text{De}\tau^k_{p,\,zz}\psi_{\text{DG},\,l}\,\textrm{d}z\\ \qquad\qquad +\;\Delta t\int_{z_j}^{z_{j+1}}\left(-2(1-\beta)G^{k+1} -\tau^k_{p,\,zz}\vphantom{\frac{\partial\tau^k_{p,\,zz}}{\partial z}}\right.\\ \qquad\qquad\qquad +\;2\text{De}G^{k+1}\tau^k_{p,\,zz}\\ \qquad\qquad\qquad-\left.v_z^{k+1}\frac{\partial\tau^k_{p,\,zz}}{\partial z}\right)\psi_{\text{DG},\,l}\,\:\textrm{d}z\:+ {\mathcal{Z}}_{1}\\ \qquad\times\begin{cases} {\mathrm{For}}\:\:\: v_z^{k+1}\Bigr\lvert_{z_j}\:\:\:\:>0 \\[6pt] {\mathcal{Z}}_{1} = \Delta t\:\text{De}\left({\psi_{\text{DG},\,l}}v_z^{k+1}(\tau_{p,\,zz}^{k,\text{ex}}-\tau_{p,\,zz}^{k,\text{in}})\right)\Bigr\lvert_{z_j}\\[6pt] {\mathrm{For}}\:\:\: v_z^{k+1}\Bigr\lvert_{z_{j+1}}<0\\[6pt] {\mathcal{Z}}_{1} = -\Delta t\:\text{De}\left({\psi_{\text{DG},\,l}}v_z^{k+1}(\tau_{p,\,zz}^{k,\text{ex}}-\tau_{p,\,zz}^{k,\text{in}})\right)\Bigr\lvert_{z_{j+1}} \end{cases}\\ \end{array}$$
    (31)
    $$\begin{array}{lll} \int_{z_j}^{z_{j+1}}\text{De}\tau^{k+1}_{p,\,rr}\psi_{\text{DG},\,l}\,\textrm{d}z\\ \;\;= \int_{z_j}^{z_{j+1}}\text{De}\tau^k_{p,\,rr}\psi_{\text{DG},\,l}\,\textrm{d}z\\ \qquad +\Delta t\int_{z_j}^{z_{j+1}}\left((1-\beta)G^{k+1} -\tau^k_{p,\,rr}-\text{De}G^{k+1}\tau^k_{p,\,rr}\vphantom{\frac{\partial\tau^k_{p,\,rr}}{\partial z}}\right.\\ \qquad\qquad\qquad-\left.v_z^{k+1}\frac{\partial\tau^k_{p,\,rr}}{\partial z}\right)\psi_{\text{DG},\,l}\,\textrm{d}z\:+\:{\mathcal{Z}}_{2}\\ \qquad\times\begin{cases} {\mathrm{For}} \:\:\: v_z^{k+1}\Bigr\lvert_{z_j}\:\:\:\:>0 \\[4pt] {\mathcal{Z}}_{2} = \Delta t\:\text{De}\left({\psi_{\text{DG},\,l}}v_z^{k+1}(\tau_{p,\,rr}^{k,\text{ex}}-\tau_{p,\,rr}^{k,\text{in}})\right)\Bigr\lvert_{z_j}\\[4pt] {\mathrm{For}}\:\:\:v_z^{k+1}\Bigr\lvert_{z_{j+1}}<0 \\[4pt] {\mathcal{Z}}_{2} = -\Delta t\:\text{De}\left({\psi_{\text{DG},\,l}}v_z^{k+1}(\tau_{p,\,rr}^{k,\text{ex}}-\tau_{p,\,rr}^{k,\text{in}})\right)\Bigr\lvert_{z_{j+1}} \end{cases} \end{array}$$
    (32)

    For the Hookean dumbbell model, instead, we lift \(\tau_{p,\,zz}^k\) and \(\tau_{p,\,rr}^k\) to a consistent ensemble of molecular configurations \(\{Q^k_{z,\,i},Q^k_{r,\,i}\}\) that are then evolved using the following weak formulations

    $$\begin{array}{lll} \int_{z_j}^{z_{j+1}}Q^{k+1}_{z,\,i}\psi_{\text{DG},\,l}\,\textrm{d}z\\ \;\;=\int_{z_j}^{z_{j+1}}Q^{k}_{z,\,i}\psi_{\text{DG},\,l}\,\textrm{d}z\\ \quad+\;\Delta t\int_{z_j}^{z_{j+1}}\left[-v_z^{k+1}\frac{\partial Q^k_{z,\,i}}{\partial z}+G^{k+1}Q^k_{z,\,i} \right.\\ \qquad\qquad\qquad-\left.\frac{1}{2\text{De}}Q^k_{z,\,i}\vphantom{\frac{\partial Q^k_{z,\,i}}{\partial z}}\right]\psi_{\text{DG},\,l}\,\textrm{d}z \\ \quad+\Delta W_{z,\,i}\int_{z_j}^{z_{j+1}}\psi_{\text{DG},\,l}\,\textrm{d}z\:+\:{\mathcal{Z}}_{3}\\ \quad\times\begin{cases} {\mathrm{For}}\:\:\:v_z^{k+1}\Bigr\lvert_{z_j}>0 \\ {\mathcal{Z}}_{3} = \Delta t\:\left({\psi_{\text{DG},\,l}}v_z^{k+1}(Q^{k,\text{ex}}_{z,\,i}-Q^{k,\text{in}}_{z,\,i})\right)\Bigr\lvert_{z_j}\\ {\mathrm{For}}\:\:\:v_z^{k+1}\Bigr\lvert_{z_{j+1}}<0 \\ {\mathcal{Z}}_{3} = -\Delta t\:\left({\psi_{\text{DG},\,l}}v_z^{k+1}(Q^{k,\text{ex}}_{z,\,i}-Q^{k,\text{in}}_{z,\,i})\right)\Bigr\lvert_{z_{j+1}} \end{cases} \end{array}$$
    (33)
    $$\begin{array}{lll} \int_{z_j}^{z_{j+1}}Q^{k+1}_{r,\,i}\psi_{\text{DG},\,l}\,\textrm{d}z\\ \;\;=\int_{z_j}^{z_{j+1}}Q^{k}_{r,\,i}\psi_{\text{DG},\,l}\,\textrm{d}z\\ \quad +\Delta t\int_{z_j}^{z_{j+1}}\left[-v_z^{k+1}\frac{\partial Q^k_{r,\,i}}{\partial z}-\frac{G^{k+1}}{2}Q^k_{r,\,i} \right.\\ \qquad\qquad\qquad-\left.\frac{1}{2\text{De}}Q^k_{r,\,i}\vphantom{\frac{\partial Q^k_{r,\,i}}{\partial z}}\right]\psi_{\text{DG},\,l}\,\textrm{d}z\\ \quad +\Delta W_{r,\,i}\int_{z_j}^{z_{j+1}}\psi_{\text{DG},\,l}\,\textrm{d}z\:+\: {\mathcal{Z}}_{4}\\ \quad\times\begin{cases} {\mathrm{For}}\:\:\:v_z^{k+1}\Bigr\lvert_{z_j}>0 \\[6pt] {\mathcal{Z}}_{4} = \Delta t \:\left({\psi_{\text{DG},\,l}}v_z^{k+1}(Q^{k,\text{ex}}_{r,\,i}-Q^{k,\text{in}}_{r,\,i})\right) \Bigr\lvert_{z_j}\\[6pt] {\mathrm{For}}\:\:\:v_z^{k+1}\Bigr\lvert_{z_{j+1}}<0 \\[6pt] {\mathcal{Z}}_{4} = -\Delta t\:\left({\psi_{\text{DG},\,l}}v_z^{k+1}(Q^{k,\text{ex}}_{r,\,i}-Q^{k,\text{in}}_{r,\,i})\right)\Bigr\lvert_{z_{j+1}} \end{cases}\\ \end{array}$$
    (34)

    For the Hookean dumbbell simulation, another ensemble of equilibrium configurations \(\{\hat{Q}^k_{z,\,i},\hat{Q}^k_{r,\,i}\}\) are also initialized that have the same initial configuration as \(\{Q^k_{z,\,i},Q^k_{r,\,i}\}\). These are then subjected to the same Wiener process and evolved according to a similar weak form with \(v_z^{k+1}\) and G k + 1 equal to zero. The evolved configurations for the flow field and quiescent state are then used to evaluate the updated stress field, \(\tau_{p,\,zz}^{k+1}\) and \(\tau_{p,\,rr}^{k+1}\).

  4. 4.

    Repeat steps 1–3 with A k + 1, \(\tau_{p,\,zz}^{k+1}\), \(\tau_{p,\,rr}^{k+1}\), \(v_z^{k+1}\), and G k + 1 until t = T f .

In the discontinuous Galerkin method, the weak form of the advection term has been replaced with the sum of an integral over the element [z j ,z j + 1] and a jump term that is only evaluated at inflow boundaries. Specifically, terms labeled \(\tau_{p,\,rr}^{k,\text{ex}}\) are evaluated upstream of a given element, while terms labeled \(\tau_{p,\,rr}^{k,\text{in}}\) are evaluated within the element itself. If a boundary, z j or z j + 1 is not an inflow boundary, the jump term at that boundary is equal to zero. Also, in the weak formulation for the Hookean dumbbell configuration, the same Wiener process (ΔW z, i or ΔW r, i ) is applied to all points in space. In writing the weak formulation for the dumbbell configuration, the Wiener process has been scaled by \(\sqrt{\Delta t/\text{De}}\).

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Anwar, Z., Gopinath, A. & Armstrong, R.C. Systems analysis of hybrid, multi-scale complex flow simulations using Newton-GMRES. Rheol Acta 51, 849–866 (2012). https://doi.org/10.1007/s00397-012-0645-7

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