3.1. Green's functions and the periodic half-plane potential

A key ingredient in our boundary integral formulation will be the $2L$-periodic (in $x$) half-plane Green's functions for the Laplace and Stokes equations. For the Laplace equation we have the following expression for the $2L$-periodic Green's function (see, for example, Linton Reference Linton1999)

(3.1)\begin{equation} G(\boldsymbol{x},\boldsymbol{x}_0)=-\frac{1}{2{\rm \pi}}\ln\left(2\left|\sin\left(\frac{\rm \pi}{2L}(z-z_0)\right)\right|\right), \end{equation}

where $\boldsymbol {x}=(x,y)\in H$, $z=x+\textrm {i}y$, $\boldsymbol {x}_0=(x_0,y_0)\in \overline{H}$ and $z_0=x_0+\textrm {i}y_0$. The over-bar notation is used to represent the closure of the set, here meaning the union of $H$ and its boundary $y=0$. It follows from the method of images that the periodic Green's function on $H$ may be written

(3.2)\begin{equation} G^H(\boldsymbol{x},\boldsymbol{x}_0)=G(\boldsymbol{x},\boldsymbol{x}_0)-G\left(\boldsymbol{x},\boldsymbol{x}_0'\right), \end{equation}

where $\boldsymbol {x}_0'=(x_0,-y_0)$ is the mirror image of $\boldsymbol {x}_0$ in $\varGamma _0$.

We also consider the problem (2.1)–(2.5) in the absence of an interface, which will be useful for the boundary integral reformulation of the potential problem introduced in the following section. In this case the problem reduces to the Laplace equation on $\varOmega _1=H$, with boundary conditions comprising $2L$-periodic Dirichlet data $f$ along $\varGamma _0$ (2.4), and (2.5). Using Green's representation formula for $\boldsymbol {x}\in H$, the solution of this half-plane problem $\phi ^H$ may be expressed in the form (Chappell Reference Chappell2015)

(3.3)\begin{equation} \phi^H(\boldsymbol{x})=\int_{\varGamma_0}\frac{\partial G^H}{\partial y_0}(\boldsymbol{x},\boldsymbol{x}_0)f(x_0)\,\mathrm{d}x_0. \end{equation}

In the sequel we make use of the following reduced notation $A:=G(\boldsymbol {x},\boldsymbol {x}_0)$ and $A':=G(\boldsymbol {x},\boldsymbol {x}'_0)$. For the Stokes equation, the $2L$-periodic Green's function is given by the matrix (Pozrikidis Reference Pozrikidis1992)

(3.4)\begin{equation} \boldsymbol{\mathsf{G}}(\boldsymbol{x},\boldsymbol{x}_0)= \left[\begin{matrix} -A-\lambda(y-y_0)A_y-\dfrac{1}{2{\rm \pi}} & \lambda(y-y_0)A_x\\ \lambda(y-y_0)A_x & -A+\lambda(y-y_0)A_y \end{matrix}\right], \end{equation}

where the wavenumber $\lambda ={\rm \pi} /L$ and the subscripts $x$ and $y$ denote partial derivatives with respect to the variables $\lambda x$ and $\lambda y$. In this case, the periodic Green's function on the half-space $H$ is given by (Pozrikidis Reference Pozrikidis1992)

(3.5)\begin{equation} \boldsymbol{\mathsf{G}}^H(\boldsymbol{x},\boldsymbol{x}_0)=\boldsymbol{\mathsf{G}}(\boldsymbol{x},\boldsymbol{x}_0)-\boldsymbol{\mathsf{G}}\left(\boldsymbol{x},\boldsymbol{x}_0'\right)-2\lambda^2y_0^2\boldsymbol{\mathsf{G}}^D\left(\boldsymbol{x},\boldsymbol{x}_0'\right)-2\lambda y_0\boldsymbol{\mathsf{G}}^S\left(\boldsymbol{x},\boldsymbol{x}_0'\right), \end{equation}

where

(3.6)\begin{equation} \boldsymbol{\mathsf{G}}^D(\boldsymbol{x},\boldsymbol{x}_0')= \left[\begin{matrix} A'_{yy} & A'_{xy}\\ -A'_{xy} & A'_{yy}\end{matrix}\right] \end{equation}

and

(3.7)\begin{equation} \boldsymbol{\mathsf{G}}^S\left(\boldsymbol{x},\boldsymbol{x}'_0\right)= \left[\begin{matrix} -\lambda(y+y_0)A'_{yy} & -A'_x-\lambda(y+y_0)A'_{xy}\\ -A'_x+\lambda(y+y_0)A'_{xy} & -\lambda(y+y_0)A'_{yy} \end{matrix}\right]. \end{equation}

In addition to the Green's functions $G^H$ and $\boldsymbol{\mathsf{G}}^H$, our boundary integral formulations will also require the 2$L$-periodic normal stress term

(3.8)\begin{align} \boldsymbol{\mathsf{T}}(\boldsymbol{x},\boldsymbol{x}_0)&=2\nu_x\left[\begin{matrix} -2A_x-\lambda(y-y_0)A_{xy} & \lambda(y-y_0)A_{xx}-A_y\\ \lambda(y-y_0)A_{xx}-A_y & \lambda(y-y_0)A_{xy} \end{matrix}\right]\nonumber\\ &\quad +2\nu_y\left[\begin{array}{ccc} \lambda(y-y_0)A_{xx}-A_y & \lambda(y-y_0)A_{xy}\\ \lambda(y-y_0)A_{xy} & -\lambda(y-y_0)A_{xx}-A_y \end{array}\right]. \end{align}

The periodic normal stress on the half-space $H$ is then given by (Pozrikidis Reference Pozrikidis1992)

(3.9)\begin{equation} \boldsymbol{\mathsf{T}}^H(\boldsymbol{x},\boldsymbol{x}_0)=\boldsymbol{\mathsf{T}}(\boldsymbol{x},\boldsymbol{x}_0)-\boldsymbol{\mathsf{T}}(\boldsymbol{x},\boldsymbol{x}_0')-2\lambda^2 y_0^2\boldsymbol{\mathsf{T}}^D(\boldsymbol{x},\boldsymbol{x}_0')-2\lambda y_0\boldsymbol{\mathsf{T}}^S(\boldsymbol{x},\boldsymbol{x}_0'), \end{equation}

where

(3.10)\begin{equation} \boldsymbol{\mathsf{T}}^D(\boldsymbol{x},\boldsymbol{x}_0')=\nu_x\left[\begin{matrix} 2A'_{yyx} & 2A'_{xyx} \\ A'_{yyy}-A'_{xyx} & 2A'_{xyy}\end{matrix}\right]+\nu_y\left[\begin{matrix} A'_{yyy}-A'_{xyx} & 2A'_{xyy} \\ -2A'_{xyy} & 2A'_{yyy} \end{matrix}\right] \end{equation}

and

(3.11)\begin{align} \boldsymbol{\mathsf{T}}^S(\boldsymbol{x},\boldsymbol{x}'_0)&=\nu_x\left[\begin{matrix} -2A'_{xy}-2\lambda(y+y_0)A'_{yyx} & 4A'_{yy}-2\lambda(y+y_0)A'_{xyx}\\ \lambda(y+y_0)(A'_{xyx}-A'_{yyy}) & -2A'_{xy}-2\lambda(y+y_0)A'_{xyy} \end{matrix}\right]\nonumber\\ &\quad +\nu_y\left[\begin{matrix} \lambda(y+y_0)(A'_{xyx}-A'_{yyy}) & -2A'_{xy}-2\lambda(y+y_0)A'_{xyy}\\ -2A'_{xy}+2\lambda(y+y_0)A'_{xyy} & -2\lambda(y+y_0)A'_{yyy} \end{matrix}\right]. \end{align}

3.2. Boundary integral model

We now detail the interface-only boundary integral formulation of (2.2)–(2.14). The advantages of such a formulation are that it provides a significant increase in computational efficiency though reducing the size of the domain to be discretised, as well as alleviating problems due to near singularities associated with boundary integral methods in long slender domains.

For the fluid problem (2.6)–(2.14), the simplest formulation is an indirect model using a single layer potential solution ansatz (Pozrikidis Reference Pozrikidis1992, chap. 5). In addition to giving a relatively simple interface-only formulation of the problem, this approach also has the advantage that the same equation can be used to determine the fluid velocity on both sides of the interface $\varGamma _I$. This is a consequence of the continuity of the Green's function (3.5) across the interface. We therefore assume that for $\boldsymbol {x}\in \overline{\Omega} _{\alpha }$ ($\alpha =1,2$), the fluid velocity $\boldsymbol {u}_{\alpha }$ may be expressed in the form (Pozrikidis Reference Pozrikidis1992)

(3.12)\begin{equation} \boldsymbol{u}_{\alpha}(\boldsymbol{x})=\int_{\varGamma_{\alpha}}\boldsymbol{\mathsf{G}}^H(\boldsymbol{x},\boldsymbol{x}_0)\boldsymbol{q}(\boldsymbol{x}_0)\,\mathrm{d}\varGamma_{\alpha}(\boldsymbol{x}_0)=\int_{\varGamma_I}\boldsymbol{\mathsf{G}}^H(\boldsymbol{x},\boldsymbol{x}_0)\boldsymbol{q}(\boldsymbol{x}_0)\,\mathrm{d}\varGamma_{I}(\boldsymbol{x}_0), \end{equation}

where the second equality follows from the property that $\boldsymbol{\mathsf{G}}^H(\boldsymbol {x},\boldsymbol {x}_0)$ vanishes for $\boldsymbol {x}_0\in \varGamma _0$. The vector $\boldsymbol {q}$ is an unknown density term to be computed by solving the second-kind boundary integral equation

(3.13)\begin{equation} -\boldsymbol{q}(\boldsymbol{x})+\frac{(\mu_2-\mu_1)}{(\mu_1+\mu_2)}\int_{\varGamma_I}\boldsymbol{\mathsf{T}}^H(\boldsymbol{x},\boldsymbol{x}_0)\boldsymbol{q}(\boldsymbol{x}_0)\,\mathrm{d}\varGamma_{I}(\boldsymbol{x}_0)=\frac{(\boldsymbol{\sigma}_v^2-\boldsymbol{\sigma}_v^1)\boldsymbol{\cdot}\boldsymbol{\nu}}{{2{\rm \pi}}(\mu_1+\mu_2)}, \end{equation}

with $\boldsymbol {x}\in \varGamma _I$. The existence and uniqueness of solutions to (3.13) are discussed in Pozrikidis (Reference Pozrikidis1992, § 5.4), and holds provided both $\mu _1,\ \mu _2>0$. We now make use of the symmetry of $\boldsymbol {\sigma }_v^{\alpha }$ ($\alpha =1,2$), the definitions ((2.10) and (2.11)) and the interface conditions ((2.8) and (2.9)) to rewrite the right-hand side of (3.13) via

(3.14)\begin{equation} \left(\boldsymbol{\sigma}_v^2-\boldsymbol{\sigma}_v^1\right)\boldsymbol{\cdot}\boldsymbol{\nu}=(\boldsymbol{\nu}\boldsymbol{\cdot}(\boldsymbol{\sigma}_M^1-\boldsymbol{\sigma}_M^2)\boldsymbol{\cdot}\boldsymbol{\nu}-\gamma\kappa)\boldsymbol{\nu}. \end{equation}

Here, we have made use of the fact that $\boldsymbol {\tau }\boldsymbol {\cdot }(\boldsymbol {\sigma }_M^1-\boldsymbol {\sigma }_M^2)\boldsymbol {\cdot }\boldsymbol {\nu }=0$ due to the interface conditions ((2.2) and (2.3)), which provides

(3.15)\begin{equation} \boldsymbol{\nu}\boldsymbol{\cdot}\left(\boldsymbol{\sigma}_M^1-\boldsymbol{\sigma}_M^2\right)\boldsymbol{\cdot}\boldsymbol{\nu}=\frac{\epsilon_0}{2\epsilon_2}(\epsilon_2-\epsilon_1)\left(\epsilon_1\left(\frac{\partial\phi_1}{\partial \boldsymbol{\nu}}\right)^2+\epsilon_2\left(\frac{\partial\phi_1}{\partial \boldsymbol{\tau}}\right)^2\right). \end{equation}

The final expression for the right-hand side of (3.13) is therefore

(3.16)\begin{equation} \frac{1}{{4{\rm \pi}}(\mu_1+\mu_2)}\left(\frac{\epsilon_0}{\epsilon_2}(\epsilon_2-\epsilon_1)\left(\epsilon_1\left(\frac{\partial\phi_1}{\partial \boldsymbol{\nu}}\right)^2+\epsilon_2\left(\frac{\partial\phi_1}{\partial \boldsymbol{\tau}}\right)^2\right)-\gamma\kappa\right)\boldsymbol{\nu}. \end{equation}

For the electrical potential problem (2.1)–(2.5), a single layer potential solution ansatz fails because $G^H$ vanishes on $\varGamma _0$ where the boundary condition $f$ is prescribed. A double layer potential solution ansatz would lead to a more complicated equation for computing the Neumann data on the interface $\varGamma _I$ and so we instead turn to a direct formulation based on Green's representation formula, as derived in Chappell (Reference Chappell2015). This has the additional advantage that the unknowns appearing in the boundary integral equations are the physical quantities (potentials) that we wish to compute. In order to obtain an interface-only formulation in this case we make use of the half-space solution $\phi ^H$. We obtain the following second-kind Fredholm integral equation for the potential $\phi =\phi _1=\phi _2$ at a point $\boldsymbol {x}\in \varGamma _I$:

(3.17)\begin{equation} \phi(\boldsymbol{x})-2\frac{(\epsilon_2-\epsilon_1)}{(\epsilon_1+\epsilon_2)}\int_{\varGamma_I}\frac{\partial G^H}{\partial\boldsymbol{\nu}_0}(\boldsymbol{x},\boldsymbol{x}_0)\phi(\boldsymbol{x}_0)\,\mathrm{d}\varGamma_I(\boldsymbol{x}_0)=\frac{2\epsilon_1}{(\epsilon_1+\epsilon_2)}\phi^H(\boldsymbol{x}). \end{equation}

The notation $\boldsymbol {\nu }_0$ is used to specify that the normal derivative in this case is taken at $\boldsymbol {x}_0$, rather than at $\boldsymbol {x}$ as before. Clearly the case $\epsilon _1=\epsilon _2$ reduces to $\phi =\phi ^H$ as expected. Note that here we are effectively treating $\phi ^H$ as our boundary data on $\varGamma _I$ and that, since $\phi ^H$ is harmonic, it is analytic. In the examples considered later, a closed form expression will be available for $\phi ^H$; however, in general it may be necessary to approximate $\phi ^H$ by, for example, a truncated Fourier series (if solving the half-plane problem via separation of variables) or quadrature (if computing $\phi ^H$ directly from the boundary integral formula (3.3)). It is shown in Chappell (Reference Chappell2015) that the integral equation (3.17) has a unique bounded solution for any $\epsilon _1,\ \epsilon _2>0$.

In order to combine the above-described fluid and electric potential models, we note that the fluid equations depend on the normal and tangential derivatives of $\phi$ on $\varGamma _I$ (see (3.16)), rather than on $\phi$ itself. The tangential derivative may be computed from $\phi$ relatively simply using interpolation of $\varGamma _I$ by trigonometric polynomials as discussed later. The normal derivative will be computed from the result for $\phi$ by making use of the Dirichlet-to-Neumann operator (Chappell Reference Chappell2015), which results from solving the following first-kind integral equation for $\partial \phi _{\alpha }/\partial \boldsymbol {\nu }_0$, $\alpha =1,2$:

(3.18)\begin{equation} \frac{\epsilon_{\alpha}}{\epsilon_2}\int_{\varGamma_I}G^H(\boldsymbol{x},\boldsymbol{x}_0)\frac{\partial \phi_{\alpha}}{\partial\boldsymbol{\nu}_0}(\boldsymbol{x}_0)\,\mathrm{d}\varGamma_I(\boldsymbol{x}_0)=-\frac{1}{2}\phi(\boldsymbol{x})+\int_{\varGamma_I}\frac{\partial G^H}{\partial\boldsymbol{\nu}_0}(\boldsymbol{x},\boldsymbol{x}_0)\phi(\boldsymbol{x}_0)\,\mathrm{d}\varGamma_I(\boldsymbol{x}_0). \end{equation}

In Chappell (Reference Chappell2015) it is shown that (3.18) has a unique bounded solution in the Sobolev space $H^{-1/2}(\varGamma _I)$ – see for example Kress (Reference Kress1989, § 8.2) for an introduction to Sobolev spaces.

The solution procedure is thus to first find the potential $\phi$ correponding to the initially flat interface $y=h_0$ by solving (3.17), and to compute the required normal derivative via (3.18). The associated flow $\boldsymbol {u}_1$ along $\varGamma _I$ is computed via (3.13) and the formula (3.12), and the kinematic boundary condition (2.14) provides the updated interfacial position $h$. This process is then repeated to obtain the time evolution of the interface $h(x,t)$.

4.1. Thin-film flow model

We first non-dimensionalise the governing equations by introducing the following scalings:

(4.1)\begin{equation} \left. \begin{aligned} (x,y) & =L(\tilde{x},\delta\tilde{h}(x,t)\tilde{y}),\quad t=\mu_1 L\gamma^{-1}\delta^{-3}\tilde{t},\ \phi_1 = \Phi\tilde{\phi_1},\\ (u_1,v_1) & = \delta^3\gamma\mu_1^{-1}(\tilde{u}_1,\delta\tilde{v}_1),\quad p_1=\gamma\delta L^{-1}\tilde{p}_1, \end{aligned} \right\} \end{equation}

where tildes denote dimensionless quantities and $\Phi$ denotes the amplitude of the applied voltage $f(x)$ appearing in (2.4). We highlight that we have chosen a frame of reference where the interface is fixed (following Secomb Reference Secomb1978; O'Dea & Waters Reference O'Dea and Waters2006); in particular, the free interface $\varGamma _I$ has been mapped to the flat surface at $\tilde {y}=1$. The dimensionless parameter $\delta =h_0/L$ denotes the (mean) aspect ratio of the thin viscous film domain $\varOmega _1$; to simplify the governing equations, we adopt the long-wavelength limit, setting $0<\delta \ll 1$. We assume, consistent with the oil–air interface application of interest, that the viscosity and pressure in the thin film dominate those in the surrounding fluid ($\mu _1\gg \mu _2$, $p_1\gg p_2$), under which scaling the interface motion may be expressed entirely in terms of variables defined in $\varOmega _1$ and so we do not consider scalings for the dependent variables associated with $\varOmega _2$ here.

At leading order, the dimensionless versions of (2.6), (2.7) governing the flow in $\varOmega _1$ are

(4.2)\begin{equation} \tilde{h}\frac{\partial\tilde{u}_1}{\partial\tilde{x}}-\tilde{y}\frac{\partial \tilde{h}}{\partial\tilde{x}}\frac{\partial\tilde{u}_1}{\partial\tilde{y}}+\frac{\partial\tilde{v}_1}{\partial\tilde{y}}=0, \end{equation}

(4.3)\begin{equation} \frac{\partial\tilde{p}_1}{\partial\tilde{x}}=\frac{1}{\tilde{h}^2}\frac{\partial^2\tilde{u}_1}{\partial\tilde{y}^2},\quad \frac{\partial\tilde{p}_1}{\partial\tilde{y}}=0.\end{equation}

The leading-order dimensionless boundary conditions (2.8), (2.9), (2.12) and (2.14) read

(4.4)\begin{gather} \frac{\partial^2 \tilde{h}}{\partial \tilde{x}^2}=KM_1-\tilde{p}_{{1}} \quad \text{at}\ \tilde{y}=1, \end{gather}

(4.5)\begin{gather} \frac{\partial \tilde{u}_1}{\partial \tilde{y}}=0 \quad \text{at}\ \tilde{y}=1, \end{gather}

(4.6)\begin{gather} \left(\tilde{u}_1,\tilde{v}_1\right)=\boldsymbol{0} \quad \text{at}\ \tilde{y}=0, \end{gather}

(4.7)\begin{gather} \frac{\partial \tilde{h}}{\partial \tilde{t}}+\tilde{u}_1\frac{\partial \tilde{h}}{\partial\tilde{x}}-\tilde{v}_1=0 \quad \text{at}\ \tilde{y}=1. \end{gather}

Here, $M_1$ is the contribution from the Maxwell stress, given by

(4.8)\begin{equation} M_1=\frac{\epsilon_1}{\delta^2\tilde{h}^2}\left(\frac{\partial\tilde{\phi}_1}{\partial \tilde{y}}\right)^2+\epsilon_2\left(\frac{\partial\tilde{\phi}_1}{\partial \tilde{x}}-\frac{\tilde{y}}{\tilde{h}}\frac{\partial \tilde{h}}{\partial \tilde{x}}\frac{\partial\tilde{\phi}_1}{\partial \tilde{y}}\right)^2 \end{equation}

evaluated at $\tilde {y}=1$, and the corresponding coefficient $K$ is

(4.9)\begin{equation} K=\frac{\epsilon_0(\epsilon_2-\epsilon_1)\Phi^2}{2\gamma\epsilon_2 h_0}. \end{equation}

Note that the apparent $O(\delta ^{-2})$ term in (4.8) is actually $O(1)$ since, as we shall see in the next section, $\partial \tilde {\phi }_1/\partial \tilde {y}=O(\delta )$. We note that in (4.8) we have used the interface conditions ((2.2) and (2.3)) for the electrostatic problem to express $M_1$ in terms of the potential in $\varOmega _1$ only; this follows on from the procedure outlined in the previous section for the boundary integral model (see (3.15) and (3.16)).

The second of (4.3) provides $\tilde {p}_1 = \tilde {p}_1(\tilde {x},\tilde {t})$, in view of which, we may integrate (4.2) and the first of (4.3), imposing the relevant no-slip and interface conditions, to obtain

(4.10)\begin{gather} \tilde{u}_1\left(\tilde{\boldsymbol{x}},t\right)=\tilde{h}^2\frac{\partial\tilde{p}_1}{\partial\tilde{x}}\left(\frac{\tilde{y}^2}{2}-\tilde{y}\right), \end{gather}

(4.11)\begin{gather} \tilde{v}_1\left(\tilde{\boldsymbol{x}},t\right)=-\tilde{h}\frac{\partial}{\partial\tilde{x}}\left(\tilde{h}^2\frac{\partial\tilde{p}_1}{\partial\tilde{x}}\right)\left(\frac{\tilde{y}^3}{6}-\frac{\tilde{y}^2}{2}\right)+\tilde{h}^2\frac{\partial \tilde{h}}{\partial\tilde{x}}\frac{\partial\tilde{p}_1}{\partial\tilde{x}}\left(\frac{\tilde{y}^3}{3}-\frac{\tilde{y}^2}{2}\right), \end{gather}

where $\tilde {\boldsymbol {x}}=(\tilde {x},\tilde {y}).$

The dimensionless kinematic boundary condition (4.7), exploiting (4.10) and (4.11) at $\tilde {y}=1$, gives

(4.12)\begin{equation} \frac{\partial \tilde{h}}{\partial \tilde{t}}=\frac{\partial}{\partial\tilde{x}}\left(\frac{\tilde{h}^3}{3}\frac{\partial\tilde{p}_1}{\partial\tilde{x}}\right). \end{equation}

Applying the normal stress condition (4.4) gives a PDE for the interface position $\tilde {h}$ in terms of input data from the solution of the electrostatic problem (2.1)–(2.5), that is

(4.13)\begin{equation} \frac{\partial \tilde{h}}{\partial \tilde{t}}=\frac{\partial}{\partial\tilde{x}}\left(\frac{\tilde{h}^3}{3}\left(K\frac{\partial M_1}{\partial\tilde{x}}-\frac{\partial^3 \tilde{h}}{\partial\tilde{x}^3}\right)\right). \end{equation}

The discretisation and numerical solution of (4.13) will be described in the subsequent sections.

4.2. Reduced electrostatic model

We now consider a correspondingly reduced asymptotic model for the electrostatic problem (2.1)–(2.5), in order to obtain an analytical expression for $\tilde {\phi }_1$ and thereby decouple the numerical solution of (4.13) from the electrostatic problem.

Consider first the electrostatic problem in $\varOmega _1$. Assuming an asymptotic expansion for the potential of the form $\tilde {\phi }_1=\tilde {\phi }_{10}+\delta \tilde {\phi }_{11}+\ldots\,$, under the scalings (4.1), (2.1) and (2.5) at leading order and at $O(\delta )$ read

(4.14)\begin{equation} \frac{\partial^2\tilde{\phi}_{1i}}{\partial\tilde{y}^2}=0 \end{equation}

for $0<\tilde {y}<1$, $i=0,1$;

(4.15a,b)\begin{equation} \tilde{\phi}_{10}(\tilde{x},0)=\tilde{f}(\tilde{x}), \quad \tilde{\phi}_{11}(\tilde{x},0)=0. \end{equation}

We hence obtain

(4.16)\begin{gather} \tilde{\phi}_{10}(\tilde{x},\tilde{y}) = \tilde{f}(\tilde{x}) + \alpha(\tilde{x})\tilde{y},\end{gather}

(4.17)\begin{gather} \tilde{\phi}_{11}(\tilde{x},\tilde{y}) = \beta(\tilde{x})\tilde{y}, \end{gather}

wherein $\tilde {f}=f/\Phi$ is the dimensionless applied potential.

In (4.16) and (4.17), $\alpha$ and $\beta$ are determined from the interface conditions ((2.2) and (2.3)) on specification of $\tilde {\phi }_2=\phi _2/\Phi$, which we now consider. Firstly, we note that the thin-film scaling is not appropriate for the potential model in $\varOmega _2$, apart from in a thin layer near the interface; instead we consider an alternative non-dimensionalisation $y=L\tilde {Y}$. Under this scaling, and since the oil film $\varOmega _1$ is thin ($\delta \ll 1$), the interfacial conditions are now applied at $\tilde {Y}=0$; $\varOmega _1$ and $\varOmega _2$ are ‘inner’ and ‘outer’ layers, the connection between which occurring at $\tilde {y}=1$ or $\tilde {Y}=\delta \tilde {h}$. To leading order in $\delta$ the electrostatic problem in $\varOmega _2$ is therefore

(4.18)\begin{equation} \frac{\partial^2\tilde{\phi}_2}{\partial\tilde{x}^2}+\frac{\partial^2\tilde{\phi}_2}{\partial \tilde{Y}^2}=0 \end{equation}

for $\tilde {Y}>0$,

(4.19)\begin{equation} \frac{1}{L}\frac{\partial\tilde{\phi}_2}{\partial \tilde{Y}}\rightarrow 0 \end{equation}

as $\tilde {Y}\rightarrow \infty$, with interface conditions applied at $\tilde {Y}=0$.

Applying the interface continuity condition yields $\tilde {\phi }_2(\tilde {x},0)=\tilde {f}(\tilde {x})$ and hence $\tilde {\phi }_2$ is the half-plane solution $\phi ^{H}$ from (3.3) written in $(\tilde {x},\tilde {Y})$ coordinates

(4.20)\begin{equation} \tilde{\phi}_2(\tilde{x},\tilde{Y}) = \frac{1}{2}\int_{-1}^1\frac{\sinh({\rm \pi}\tilde{Y})\tilde{f}(\tilde{x}_0)}{\cosh({\rm \pi}\tilde{Y})-\cos({\rm \pi}(\tilde{x}-\tilde{x}_0))}\,\mathrm{d}\tilde{x}_0. \end{equation}

Expressed in their respective coordinates for clarity, the normal derivative jump condition reads

(4.21)\begin{equation} \frac{\epsilon_1}{\delta \tilde{h}}\left.\frac{\partial\tilde{\phi}_1}{\partial \tilde{y}}\right|_{\tilde{y}=1}=\epsilon_2\left.\frac{\partial\tilde{\phi}_2}{\partial \tilde{Y}}\right|_{\tilde{Y}=0}, \end{equation}

and hence $\alpha =0$, with $\beta$ determined from $\tilde {\phi }_2$ via (4.21) as follows:

(4.22)\begin{equation} \beta = \frac{\epsilon_2\tilde{h}}{\epsilon_1} \left.\frac{\partial\tilde{\phi}_{2}}{\partial \tilde{Y}}\right|_{\tilde{Y}=0}. \end{equation}

We thereby obtain

(4.23)\begin{equation} \tilde{\phi_1}(\tilde{x},\tilde{y})=\tilde{f}(\tilde{x}) +\frac{{\rm \pi}\epsilon_2\delta\tilde{h}\tilde{y}}{2\epsilon_1}\int_{-1}^1\frac{\tilde{f}(\tilde{x}_0)}{1-\cos({\rm \pi}(\tilde{x}-\tilde{x}_0))}\,\mathrm{d}\tilde{x}_0, \end{equation}

and substitution into the expression for $M_1$ (4.8), provides, to leading order in $\delta$,

(4.24)\begin{equation} M_1=\frac{\epsilon_2^2{\rm \pi}^2}{4\epsilon_1} \left( \int_{-1}^1\frac{\tilde{f}(\tilde{x}_0)}{1-\cos({\rm \pi}(\tilde{x}-\tilde{x}_0))}\,\mathrm{d}\tilde{x}_0 \right)^2+\epsilon_2\left(\frac{\mathrm{d}\tilde{f}}{\mathrm{d}\tilde{x}}\right)^2. \end{equation}

The expression (4.24) may then be applied in the equation for the interface position (4.13) to model the evolution of the interface geometry.

We remark in passing that straightforward analytical progress can be made by taking the additional limit of small amplitude variations in the applied voltage; e.g. $\tilde {f}(\tilde {x})=1+\varepsilon \mathcal {F}(\tilde {x})$, for some $|\varepsilon |\ll 1$. Here, one may solve the potential problem, and the associated linear fourth-order PDE for $\tilde {h}$ directly; however, this additional linearisation procedure results in simplified interfacial dynamics that does not reflect that observed experimentally (in particular, the amplitude growth rate obtained is independent of the applied potential amplitude) and so we do not pursue this here.

5.1. Discretisation for the full boundary integral approach

The discretisation of the integral equations (3.17) and (3.18) using spectrally convergent Nyström methods is detailed in Chappell (Reference Chappell2015); here we give a brief summary. Let us first consider the second-kind Fredholm equation (3.17).

Under the assumption of an infinitely differentiable interface (in space), we also have an infinitely differentiable kernel in (3.17) and as discussed in § 3.2, the data term $\phi ^H$ is also infinitely differentiable. In this situation a simple application of the trapezoidal rule yields a super-algebraically convergent method (Preston et al. Reference Preston, Chamberlain and Chandler-Wilde2011; Atkinson Reference Atkinson1997). To implement this scheme we note that $\boldsymbol {x}=(x,h(x,t))$ on $\varGamma _I$, and hence the trapezoidal rule approximation to the integral over $\varGamma _I$ of a function $F(\boldsymbol {x})=\hat {F}(x)$ may be written

(5.2)\begin{equation} \left. \begin{aligned} \int_{\varGamma_I}F(\boldsymbol{x})\,\mathrm{d}\varGamma_I(\boldsymbol{x}) & =\int_{-L}^{L}\hat{F}(x)\sqrt{1+\left(\frac{\partial h}{\partial x}{(x,t)}\right)^2}\,\mathrm{d}x\\ & \approx\frac{2L}{n}\sum_{j=1}^{n}\hat{F}\left(x_j\right)\sqrt{1+\left(\frac{\partial h}{\partial x}{(x_j,t)}\right)^2}, \end{aligned} \right\} \end{equation}

where $x_j=-L+2L(j-1)/n$ denotes a uniform spatial discretisation of the (dynamic) interface $\varGamma _I$, and we highlight that throughout this work we suppress the time dependence of $\varGamma _I$ for concision. Note that because we have assumed above that $h$ is given by trigonometric interpolation, its derivative $\partial h/\partial x$ may be computed simply by differentiating its Fourier components as described in Preston et al. (Reference Preston, Chamberlain and Chandler-Wilde2011). Applying the formula (5.2) to the integral in (3.17) yields the following approximation:

(5.3)\begin{equation} \int_{\varGamma_I}\frac{\partial G^H}{\partial\boldsymbol{\nu}_0}(\boldsymbol{x},\boldsymbol{x}_0)\phi(\boldsymbol{x}_0)\,\mathrm{d}\varGamma_I(\boldsymbol{x}_0)\approx\frac{2L}{n}\sum_{j=1}^{n}k_{i,j}\hat{\phi}(x_j)\sqrt{1+\left(\frac{\partial h}{\partial x}{(x_j,t)}\right)^2}, \end{equation}

where

(5.4)\begin{equation} k_{i,j}=\frac{1}{\sqrt{1+\left(\dfrac{\partial h}{\partial x}{(x_j,t)}\right)^2}}\left.\left(\dfrac{\partial G^H}{\partial y_0}(\boldsymbol{x},\boldsymbol{x}_0)-\dfrac{\partial h}{\partial x}{(x_j,t)}\dfrac{\partial G^H}{\partial x_0}(\boldsymbol{x},\boldsymbol{x}_0)\right)\right|_{(\boldsymbol{x}=\boldsymbol{x}_i,\ \boldsymbol{x}_0=\boldsymbol{x}_j)} \end{equation}

for $i\neq j$ and $\hat {\phi }(x_j):=\phi (\boldsymbol {x}_j)$. In addition, $\boldsymbol {x}_i=(x_i,h(x_i,t))$ (and similarly for $\boldsymbol {x}_j$) and $k_{i,j}$ corresponds to the directional derivative of $G_H$ (3.2) with respect to $\boldsymbol {\nu }$ at the quadrature node $\boldsymbol {x}_j$. When $i=j$, the formula (5.4) contains an apparent singularity at $x_i=x_j$ (see (3.1) and (3.2)). However, one may utilise the smoothness of the interface profile $h$ to derive the following (non-singular) formula for $k_{j,j}$ in terms of $h$ and its spatial derivatives (for similar derivations see Preston et al. (Reference Preston, Chamberlain and Chandler-Wilde2011) and Atkinson (Reference Atkinson1997, chap. 7)):

(5.5)\begin{equation} k_{j,j}=\frac{\dfrac{\partial^2 h}{\partial x^2}{(x_j,t)}}{4{\rm \pi}\left(1+\left(\dfrac{\partial h}{\partial x}{(x_j,t)}\right)^2\right)}+\frac{1}{4L\sqrt{1+\left(\dfrac{\partial h}{\partial x}{(x_j,t)}\right)^2}}\coth\left(\frac{{\rm \pi} }{L}h(x_j,t)\right). \end{equation}

As with the first derivative, $\partial ^2 h/\partial x^2$ may be computed simply by differentiation of the Fourier components for $h$. Applying the approximation (5.3) to the second-kind integral equation (3.17) leads to the following Nyström scheme for the approximate solution $\phi ^n$:

(5.6)\begin{equation} \phi^{n}(x_i)+\frac{4L(\epsilon_1-\epsilon_2)}{n(\epsilon_1+\epsilon_2)}\sum_{j=1}^{n}k_{i,j}\phi^{n}(x_j)\sqrt{1+\left(\frac{\partial h}{\partial x}{(x_j,t)}\right)^2}=\frac{2\epsilon_1\phi^H(x_i,h(x_i,t))}{(\epsilon_1+\epsilon_2)} \end{equation}

for $i=1,\ldots ,n$. The super-algebraic convergence of $\phi ^n$ to $\phi$ for increasing $n$ is a consequence of Preston et al. (Reference Preston, Chamberlain and Chandler-Wilde2011, theorem 3.12).

We now consider a Nyström method for the solution of (3.18), which is a first-kind integral equation for $\partial \phi _{\alpha }/\partial \nu _0$, $\alpha =1,2$. In particular, we note that the kernel function $G^H$ contains a logarithmic singularity and (3.18) falls into the class of first-kind equations analysed in Kress & Sloan (Reference Kress and Sloan1993). The approach here is therefore based on the scheme presented in Kress & Sloan (Reference Kress and Sloan1993) (see also Kress (Reference Kress1989) and references therein). Super-algebraic convergence rates will then be attained due to Kress & Sloan (Reference Kress and Sloan1993, theorem 2.3) if the right-hand side of (3.18) is infinitely differentiable. We note that $\phi$ will be replaced by the numerical solution $\phi ^n$ in the right-hand side of (3.18) and therefore trigonometric polynomials will be used to obtain an infinitely differentiable interpolant. The right-hand side of (3.18) will then be infinitely differentiable for $h(\cdot ,t)\in C^{\infty }([-L,L])$ for all $t\geq 0$ (Chappell Reference Chappell2015).

We now outline the quadrature rule we employ to approximate the integral on the left-hand side of (3.18). We first define

(5.7)\begin{equation} V(\boldsymbol{x},\boldsymbol{x}_0):=\frac{1}{4{\rm \pi}}\ln\left(4\sin^2\left(\frac{\rm \pi}{2L}(x-x_0)\right)\right)+G(\boldsymbol{x},\boldsymbol{x}_0) \end{equation}

and note that on $\varGamma _I$ where $y=h(x,t)$ and $h(\cdot ,t)\in C^{\infty }([-L,L])$, then $V$ is infinitely differentiable with respect to both $x$ and $x_0$ (Chappell Reference Chappell2015). We then decompose the integral on the left-hand side of (3.18) as

(5.8)\begin{align} &\int_{\varGamma_I}G^H(\boldsymbol{x},\boldsymbol{x}_0)\frac{\partial \phi_{\alpha}}{\partial\boldsymbol{\nu}_0}(\boldsymbol{x}_0)\,\mathrm{d}\varGamma_I(\boldsymbol{x}_0) = \int_{\varGamma_I}(V(\boldsymbol{x},\boldsymbol{x}_0)-G(\boldsymbol{x},\boldsymbol{x}'_0))\frac{\partial \phi_{\alpha}}{\partial\boldsymbol{\nu}_0}(\boldsymbol{x}_0)\,\mathrm{d}\varGamma_I(\boldsymbol{x}_0) \nonumber\\ &\qquad -\frac{1}{4{\rm \pi}}\int_{\varGamma_I}\ln\left(4\sin^2\left(\frac{\rm \pi}{2L}(x-x_0)\right)\right)\frac{\partial \phi_{\alpha}}{\partial\boldsymbol{\nu}_0}(\boldsymbol{x}_0)\,\mathrm{d}\varGamma_I(\boldsymbol{x}_0), \end{align}

where the logarithmic singularity of the integral on the left-hand side is only present in the final integral on the right-hand side. In fact, $V(\boldsymbol {x},\boldsymbol {x}_0)-G(\boldsymbol {x},\boldsymbol {x}'_0)$ is infinitely differentiable with respect to both $x$ and $x_0$ on $\varGamma _I$ and so the first integral on the right of (5.8) may be well approximated using the trapezoidal rule as before. For the term containing the logarithmic singularity we employ a quadrature rule of the form

(5.9)\begin{equation} \frac{-1}{4{\rm \pi}}\int_{-L}^{L}\ln\left(4\sin^2\left(\frac{\rm \pi}{2L}(x-x_0)\right)\right)\hat{F}(x_0)\,\mathrm{d}x_0\approx\sum_{j=1}^{2N}R_{j}(x)\hat{F}\left(x_j\right), \end{equation}

for positive integer $N=n/2$ (assuming $n$ is even), with $x_j=-L+L(j-1)/n$, $j=1,\ldots ,n$, as before. The quadrature weight function $R_{j}(x)$ is given by

(5.10)\begin{equation} R_{j}(x)=\frac{L}{2{\rm \pi} n}\left\{\sum_{m=1}^{N-1}\frac{1}{m}\cos\left(\frac{m{\rm \pi}}{L}(x-x_j)\right)+\frac{1}{n}\cos\left(\frac{N{\rm \pi}}{L}(x-x_j)\right)\right\}. \end{equation}

This choice of quadrature computes the integral in (5.9) exactly when $\hat {F}$ has been replaced by its trigonometric interpolation polynomial. To see this replace $\hat {F}$ in (5.9) by the Lagrange trigonometric polynomial of order $j$, then the formula (5.10) may be derived for the integral on the left-hand side; see Kress & Sloan (Reference Kress and Sloan1993) and Kress (Reference Kress1989, p. 208) for details.

We therefore arrive at the following approximation for the integral on the left-hand side of (3.18) evaluated at the $i$th quadrature node for any $i=1,\ldots ,n$:

(5.11)\begin{align} & \int_{\varGamma_I} G^H(\boldsymbol{x}_i,\boldsymbol{x}_0)\frac{\partial \phi_{\alpha}}{\partial\boldsymbol{\nu}_0}(\boldsymbol{x}_0)\,\mathrm{d}\varGamma_I(\boldsymbol{x}_0)\nonumber\\ &\qquad \approx\sum_{j=1}^{n}\frac{\partial\phi_{\alpha}}{\partial\boldsymbol{\nu}_0}(\boldsymbol{x}_j)\left(R_{j}(x_i)+\frac{2L}{n}s_{i,j}\right)\sqrt{1+\left(\frac{\partial h}{\partial x}{(x_j,t)}\right)^2} \end{align}

for $\alpha =1,2$. Here, $s_{i,j}=V(\boldsymbol {x}_i,\boldsymbol {x}_j)-G(\boldsymbol {x}_i,\boldsymbol {x}'_j)$ and $\boldsymbol {x}'_j=(x_j,-h(x_j,t))$. For the diagonal case this may be reduced to

(5.12)\begin{equation} s_{j,j}=\frac{-1}{4{\rm \pi}}\left\{\ln\left(1+\left(\frac{\partial h}{\partial x}{(x_j,t)}\right)^2\right)-\ln\left(4\sinh^2\left(\frac{\rm \pi}{L}h(x_j,t)\right)\right)\right\}. \end{equation}

Applying the approximation (5.11) to the first-kind integral equation (3.18) leads to the following Nyström scheme for the approximate solution $\partial \phi ^n_{\alpha }/\partial \nu _0$:

(5.13)\begin{align} &\sum_{j=1}^{n}\frac{\partial\phi^n_{\alpha}}{\partial\nu_0}(x_j)\left(R_{j}(x_i)+\frac{2L}{n}s_{i,j}\right)\sqrt{1+\left(\frac{\partial h}{\partial x}{(x_j,t)}\right)^2}\nonumber\\ &\qquad =\frac{\epsilon_2}{\epsilon_{\alpha}}\left(\frac{-\phi^n(x_i)}{2}+\frac{2L}{n}\sum_{j=1}^{n}k_{i,j}\phi^{n}(x_j)\sqrt{1+\left(\frac{\partial h}{\partial x}{(x_j,t)}\right)^2}\right), \end{align}

for $i=1,\ldots ,n$ and $\alpha =1,2$.

The vectorial boundary integral equation (3.13) may also be discretised via the Nyström method following approaches analogous to that for (3.17) described above. Instead of the directional derivative of $G^H=A-A'$ with respect to $\boldsymbol {\nu }_0$ found in (3.17), the matrix kernel function $\boldsymbol{\mathsf{T}}^{H}$ (3.9) contains derivatives of $A$ and $A'$ between first and third order with respect to both $\lambda x$ and $\lambda y$, as well as mixed derivatives. We note that in most cases there are no singularities to consider in the integrand in (3.13); in the expression (3.9), the latter three terms are all evaluated at $\boldsymbol {x}'_0$, which is the reflection of the point $\boldsymbol {x}_0$ in $\varGamma _0$ and, as such, can never coincide with the point $\boldsymbol {x}\in \varGamma _I$. In these cases we base our Nyström method discretisation on the standard trapezoidal rule as outlined above. For the first term in (3.9), however, we must take care of the singularities at $\boldsymbol {x}=\boldsymbol {x}_0$ in the quadrature scheme for $\int _{\varGamma _I}\boldsymbol{\mathsf{T}}(\boldsymbol {x},\boldsymbol {x}_0)\boldsymbol {q}(\boldsymbol {x}_0)\mathrm {d}\varGamma _I(\boldsymbol {x}_0)$. In particular, we need to evaluate $A_x$, $A_y$, $\lambda (y-y_0)A_{xx}$ and $\lambda (y-y_0)A_{xy}$ in the case $i=j$, where $i$ is the index of the quadrature node at which the solution is sought and $j$ is the index of the quadrature node for the numerical integration over $\varGamma _I$ as above. To do so, we utilise the following relations:

(5.14)\begin{gather} \left.\left( A_x\right)\right|_{(\boldsymbol{x}=\boldsymbol{x}_j,\,\boldsymbol{x}_0=\boldsymbol{x}_j)}=\frac{1}{\lambda}\left.\left(\frac{\partial G}{\partial x}(\boldsymbol{x},\boldsymbol{x}_0)\right)\right|_{(\boldsymbol{x}=\boldsymbol{x}_j,\,\boldsymbol{x}_0=\boldsymbol{x}_j)}=0, \end{gather}

(5.15)\begin{gather} \left.\left( A_y\right)\right|_{(\boldsymbol{x}=\boldsymbol{x}_j,\,\boldsymbol{x}_0=\boldsymbol{x}_j)}=\frac{1}{\lambda}\left.\left(\frac{\partial G}{\partial y}(\boldsymbol{x},\boldsymbol{x}_0)\right)\right|_{(\boldsymbol{x}=\boldsymbol{x}_j,\,\boldsymbol{x}_0=\boldsymbol{x}_j)}=\frac{\dfrac{\partial^2 h}{\partial x^2}{(x_j,t)}}{4{\rm \pi}\lambda\left(1+\left(\dfrac{\partial h}{\partial x}{(x_j,t)}\right)^2\right)}, \end{gather}

(5.16)\begin{gather} \left.\left(\lambda(y-y_0) A_{xx}\right)\right|_{(\boldsymbol{x}=\boldsymbol{x}_j,\,\boldsymbol{x}_0=\boldsymbol{x}_j)}=\frac{1}{\lambda}\left.\left((y-y_0)\frac{\partial^2 G}{\partial x^2}(\boldsymbol{x},\boldsymbol{x}_0)\right)\right|_{(\boldsymbol{x}=\boldsymbol{x}_j,\,\boldsymbol{x}_0=\boldsymbol{x}_j)}=0, \end{gather}

(5.17)\begin{gather} \left.\left(\lambda(y-y_0) A_{xy}\right)\right|_{(\boldsymbol{x}=\boldsymbol{x}_j,\,\boldsymbol{x}_0=\boldsymbol{x}_j)}=\frac{1}{\lambda}\left.\left((y-y_0)\frac{\partial^2 G}{\partial x\partial y}(\boldsymbol{x},\boldsymbol{x}_0)\right)\right|_{(\boldsymbol{x}=\boldsymbol{x}_j,\,\boldsymbol{x}_0=\boldsymbol{x}_j)}=0. \end{gather}

For the case $i\neq j$, we simply employ a standard trapezoidal rule to evaluate $\int _{\varGamma _I}\boldsymbol{\mathsf{T}}(\boldsymbol {x},\boldsymbol {x}_0)\boldsymbol {q}(\boldsymbol {x}_0)\mathrm {d}\varGamma _I(\boldsymbol {x}_0)$. For reasons of brevity we omit to write out the numerical scheme for (3.13) in full, but instead note simply that it is analogous to the procedure for the scalar equation (3.17), except using the relations above for the diagonal case.

Once (3.13) has been solved for $\boldsymbol {q}$, the solution for the fluid velocity $\boldsymbol {u}$ on $\varGamma _I$ may be evaluated using the formula (3.12). We evaluate this formula using a quadrature rule that is consistent with the Nyström method scheme applied to solve (3.13). In particular, the trapezoidal rule is applied, together with a treatment of singularities that is analogous the scheme for (3.18) detailed above. We note that as for $\boldsymbol{\mathsf{T}}^H$, it is only in the first of the four terms ($\boldsymbol{\mathsf{G}}$) that are summed to form the matrix $\boldsymbol{\mathsf{G}}^H$ (3.5) (the kernel function in (3.12)) that singularities can occur. For the four entries of the matrix $\boldsymbol{\mathsf{G}}$ we need to evaluate $A$, $\lambda (y-y_0)A_x$ and $\lambda (y-y_0)A_y$ in the case $i=j$, where $i$ and $j$ are as above. For the latter two terms, it is clear from the above relations for (3.13) that we have

(5.18)\begin{gather} \left.\left( \lambda(y-y_0)A_x\right)\right|_{(\boldsymbol{x}=\boldsymbol{x}_j,\,\boldsymbol{x}_0=\boldsymbol{x}_j)}=\left.\left((y-y_0)\frac{\partial G}{\partial x}(\boldsymbol{x},\boldsymbol{x}_0)\right)\right|_{(\boldsymbol{x}=\boldsymbol{x}_j,\,\boldsymbol{x}_0=\boldsymbol{x}_j)}=0, \end{gather}

(5.19)\begin{gather} \left.\left( \lambda(y-y_0)A_y\right)\right|_{(\boldsymbol{x}=\boldsymbol{x}_j,\,\boldsymbol{x}_0=\boldsymbol{x}_j)}=\left.\left((y-y_0)\frac{\partial G}{\partial y}(\boldsymbol{x},\boldsymbol{x}_0)\right)\right|_{(\boldsymbol{x}=\boldsymbol{x}_j,\,\boldsymbol{x}_0=\boldsymbol{x}_j)}=0. \end{gather}

For the terms containing $A$, we need to evaluate

(5.20)\begin{equation} \int_{\varGamma_I}G(\boldsymbol{x},\boldsymbol{x}_0)q(\boldsymbol{x}_0)\,\mathrm{d}\varGamma_I(\boldsymbol{x}_0), \end{equation}

where the function $q$ can be either entry from the vector function $\boldsymbol {q}$. This can be done using an analogous splitting to the one in (5.8) as follows:

(5.21)\begin{align} &\int_{\varGamma_I}G(\boldsymbol{x},\boldsymbol{x}_0)q(\boldsymbol{x}_0)\,\mathrm{d}\varGamma_I(\boldsymbol{x}_0) =\int_{\varGamma_I}V(\boldsymbol{x},\boldsymbol{x}_0)q(\boldsymbol{x}_0)\,\mathrm{d}\varGamma_I(\boldsymbol{x}_0)\nonumber\\ &\qquad -\frac{1}{4{\rm \pi}} \int_{\varGamma_I}\ln\left(4\sin^2\left(\frac{\rm \pi}{2L}(x-x_0)\right)\right)q(\boldsymbol{x}_0)\,\mathrm{d}\varGamma_I(\boldsymbol{x}_0). \end{align}

Following the same steps as for (3.18) leads to the following approximation for the integral (5.20) evaluated at the $i$th quadrature node for any $i=1,\ldots ,n$:

(5.22)\begin{align} &\int_{\varGamma_I}G(\boldsymbol{x}_i,\boldsymbol{x}_0) q(\boldsymbol{x}_0)\,\mathrm{d}\varGamma_I(\boldsymbol{x}_0)\nonumber\\ &\qquad \approx\sum_{j=1}^{n}q(\boldsymbol{x}_j)\left(R_{j}(x_i)+\frac{2L}{n}V(\boldsymbol{x}_i,\boldsymbol{x}_j)\right)\sqrt{1+\left(\frac{\partial h}{\partial x}{(x_j,t)}\right)^2}. \end{align}

For the diagonal case $i=j$ we note that

(5.23)\begin{equation} V(\boldsymbol{x}_j,\boldsymbol{x}_j)=\frac{-1}{4{\rm \pi}}\ln\left(1+\left(\frac{\partial h}{\partial x}{(x_j,t)}\right)^2\right). \end{equation}

Finally, we describe the time evolution of the interface $h(x,t)$ via a discretised form of (2.14) with time step ${\rm \Delta} t$ and spatial mesh size ${\rm \Delta} x$. We denote $h_i^k=h(x_i,t_k)$ for $i=1,\ldots ,n$, $k=1,2,\ldots$ and apply Euler's method to approximate $h_i^{k+1}=h(x_i,t_{k+1})$ as follows:

(5.24)\begin{equation} h_i^{k+1}=h_i^k+{\rm \Delta} t\left.\left(u\frac{\partial h}{\partial x}-v \right)\right|_{(x_i,t_k)}. \end{equation}

Note that the flow $\boldsymbol {u}_1=\boldsymbol {u}=(u,v)^{\textrm {T}}$ corresponding to the interface position at $t=t_k$ is obtained as described above, where the spatial variation of $h$ at each time point is given by trigonometric interpolation and hence its spatial derivative may be computed simply by differentiating its Fourier components. We further remark that since the velocity terms on the right-hand side of (5.24) are only known at the current time then we are somewhat restricted in terms of our choice of time discretisation method. In the next section, we will discuss the additional discretisation methods employed for the two asymptotic reductions introduced above.

5.2. Discretisation for the asymptotic approaches

In this section we describe two approaches for the discretisation of the interface evolution equation (4.13).

First, we consider an explicit Euler scheme as follows:

(5.25)\begin{equation} \tilde{h}_i^{k+1}=\tilde{h}_i^k+{\rm \Delta} t\left.\left(\frac{\partial}{\partial \tilde{x}}\left(\frac{\tilde{h}^3}{3}\left(K\frac{\partial M_1}{\partial \tilde{x}}-\frac{\partial^3 \tilde{h}}{\partial \tilde{x}^3}\right) \right)\right)\right|_{(\tilde{x}_i,\tilde{t}_k)}, \end{equation}

with corresponding notation to that employed in (5.24), but with the tildes denoting the non-dimensional variables in (4.13) as before. Note that the spatial derivatives of $M_1$ are computed either by Fourier interpolation in the case of scheme 2, or analytically in the case of the (fully asymptotic) scheme 3.

The presence of a ‘stiff’ fourth-order spatial derivative on the right-hand side of (4.13) places restrictive stability constraints on the time-stepping scheme. For example, a standard von Neumann stability analysis reveals that an explicit finite difference discretisation with $n$ spatial mesh points leads to a requirement of the form ${\rm \Delta} t\propto n^{-4}$. We remark that such an analysis is presented in, for example, Momoniat, Harley & Adlem (Reference Momoniat, Harley and Adlem2010), in which the generalised lubrication equation (i.e. $K=0$ in (4.13)) is considered; however, the result can be extended to the inhomogeneous case under the assumption of an upper bound on the maximal film height. We also find in practice that the scheme (5.25) requires a time step ${\rm \Delta} t\propto n^{-4}$ for stability, even when the spatial derivatives of $\tilde {h}$ are computed by Fourier interpolation in place of finite difference formulæ. Nonetheless, the relevant terms may be evaluated relatively quickly, particularly in the case of scheme 3 (fully asymptotic), meaning that it is feasible to compute solutions within the confines of the stability constraint, provided not too fine a spatial grid is required. Generally this should be the case provided that the applied voltage $f$ is smooth and periodic, since then the interpolation by trigonometric polynomials should converge spectrally.

In order to circumvent the stability constraint when necessary, we additionally consider the implicit–explicit finite difference scheme presented in Momoniat et al. (Reference Momoniat, Harley and Adlem2010), which treats the $\partial ^3\tilde {h}/\partial \tilde {x}^3$ term in (4.13) implicitly. Alternative approaches include Crank–Nicolson based schemes for both driven (Ha, Kim & Myers Reference Ha, Kim and Myers2008) and interface relaxation problems (Momoniat et al. Reference Momoniat, Harley and Adlem2010; Li & Kim Reference Li and Kim2013) or finite difference schemes that preserve the positivity of the interface height (Zhornitskaya & Bertozzi Reference Zhornitskaya and Bertozzi1999). In higher-dimensional settings it may be preferable to consider finite element approximations for nonlinear fourth-order degenerate PDEs, see for example Barrett, Blowey & Garcke (Reference Barrett, Blowey and Garcke1998).

Our implicit–explicit finite difference scheme may be derived by first approximating the outer spatial derivative using a standard second-order central difference formula with step size ${\rm \Delta} x$, while evaluating the third-order spatial derivative at the advanced time as follows:

(5.26)\begin{align} \tilde{h}_i^{k+1}&=\tilde{h}_i^k+\frac{K{\rm \Delta} t}{6{\rm \Delta} x}\left(\left(\tilde{h}_{i+1}^k\right)^3-\left(\tilde{h}_{i-1}^k\right)^3\right)\left.\frac{\partial M_1}{\partial \tilde{x}}\right|_{(\tilde{x}_i,\tilde{t}_k)}\nonumber\\ &\quad -\frac{{\rm \Delta} t}{6{\rm \Delta} x}\left(\left(\tilde{h}_{i+1}^k\right)^3\frac{\partial^3\tilde{h}_{i+1}^{k+1}}{\partial\tilde{x}^3}-\left(\tilde{h}_{i-1}^k\right)^3\frac{\partial^3\tilde{h}_{i-1}^{k+1}}{\partial\tilde{x}^3}\right). \end{align}

Recall that the spatial derivatives of $M_1$ are computed either by Fourier interpolation in the case of scheme 2, or analytically in the case of scheme 3 and thus finite difference approximations of these derivatives are not required. The fully discrete scheme is then obtained on application of the second-order central difference formula

(5.27)\begin{equation} \frac{\partial^3\tilde{h}_{i}^{k+1}}{\partial\tilde{x}^3}\approx\frac{\tilde{h}_{i+2}^{k+1}-2\tilde{h}_{i+1}^{k+1}+2\tilde{h}_{i-1}^{k+1}-\tilde{h}_{i-2}^{k+1}}{2{\rm \Delta} x^3}, \end{equation}

adjusted for the appropriate spatial grid positions. We note that this scheme is shown to be unconditionally stable in the case of the homogeneous switch-off model ($K=0$) for the relaxation of an initially disturbed interface in Momoniat et al. (Reference Momoniat, Harley and Adlem2010), providing a significant improvement over an explicit scheme. However, in our case this comes at the cost of a lower precision approximation of the spatial derivatives (second order instead of spectral convergence). In the next section we consider the three numerical solution approaches outlined above in order to understand the extent of the validity of the asymptotic models and to perform tests with parameters corresponding to those used in typical experimental set-ups (Brown et al. Reference Brown, Wells, Newton and McHale2009, Reference Brown, McHale and Mottram2011).

6.1. Investigation of the asymptotic models

In this section we first perform a series of experiments to investigate the validity of the asymptotic models derived in § 4. We test our models using the same boundary condition for (2.4) as employed in Brown et al. (Reference Brown, McHale and Mottram2011) for a static applied potential; namely: $f(x)=\Phi \cos ({\rm \pi} x/L)$, where $\Phi$ is a constant amplitude term. Note that one can then derive the half-space solution $\phi ^H$, either using separation of variables or directly from the boundary integral formula (3.3), to obtain

(6.1)\begin{equation} \phi^{H}(\boldsymbol{x})=\Phi\cos({\rm \pi} x/L)\exp(-{\rm \pi} y/L). \end{equation}

One may also evaluate the asymptotic formula for the Maxwell stress $M_1$ (4.24) as

(6.2)\begin{equation} M_1=\epsilon_2{\rm \pi}^2\left(\frac{\epsilon_2}{\epsilon_1}\cos^2({\rm \pi}\tilde{x}) +\sin^2({\rm \pi}\tilde{x})\right). \end{equation}

In addition we set the parameter $\epsilon _1=8$ and $\epsilon _2=1$ to reflect typical values for oil and air in our application of interest (Brown et al. Reference Brown, McHale and Mottram2011).

We use the methods described in the previous section to approximate the evolution of the film profile $h(x,t)$ from an initial constant profile $h(x,0)=h_0$ until the profile reaches a steady-state wrinkle where the effect of the applied potential is exactly balanced by the forces arising from the surface tension on a curved interface profile. Figure 2 shows the amplitude of the final steady-state wrinkle estimated by all three models: full boundary integral approach for a Stokes’ flow, hybrid long-wavelength asymptotic approach coupled to the boundary integral solution of the electrostatic problem and finally the fully asymptotic approach. The effect of different force terms is assessed by varying the magnitude of the surface tension $\gamma$, which enters the loading term in both the Stokes flow model (3.16) and the asymptotic model via the parameter $K$ in (4.13). In particular, a larger surface tension will lead to lower steady-state amplitudes by amplifying the curvature driven motion of the interface relative to the dielectrophoretic motion. We fix the parameters $L=1$, $\Phi =1$, $\mu _1=\epsilon _0$ and $\mu _2=\mu _1\times 10^{-4}$ so that the assumption $\mu _1\gg \mu _2$ introduced in § 4.1 is valid for these results.

Figure 2. Comparison of the results from all three models (full boundary integral approach for a Stokes flow, hybrid long-wavelength asymptotic approach coupled to the boundary integral solution of the electrostatic problem and fully asymptotic approach) for the final steady-state wrinkle amplitude with three different force terms: case 1 with $\gamma =\epsilon _0/50$, case 2 with $\gamma =\epsilon _0/500$ and case 3 with $\gamma =\epsilon _0/5000$.

For all three forcing cases tested, one observes convergence of the numerical and hybrid asymptotic approaches to the fully asymptotic (leading order in $\delta$) result as $\delta \rightarrow 0$ and the leading-order approximation becomes reasonable in the regime $\delta <0.05$. The fully numerical (Stokes flow) simulation loses accuracy as $\delta$ becomes smaller, and the issue is particularly evident for larger force terms. In these situations, a large number of quadrature nodes may be required in the Nyström discretisation to obtain convergence to a steady state. This imposed a practical limitation on the minimum $\delta$ value that could be simulated in the full numerical model for each of the three forcing cases ranging from $\delta =0.025$ for the lightest forcing (case 3 in figure 2) to $\delta =0.1$ for the strongest forcing (case 1 in figure 2). A similar limitation is present for the hybrid approach, although the computation of each step is much quicker due to only employing the boundary integral method for the electrostatic part of the simulation. The main computational limitation of this approach is rather the very small time steps necessary for stability of the explicit time-stepping scheme (5.25). In this case, the situation deteriorates further when using the implicit–explicit scheme (5.26) due to a build up of numerical errors and the lower precision of the finite difference approximation for the spatial derivatives; the situation could potentially be improved with the development of a semi-implicit spectral approach in future work. Stable calculations for the fully asymptotic approach can, however, be obtained using the implicit–explicit scheme (5.26) without the strong time-step limitation and this was by far the most computationally efficient approach of the three.

In figure 3 we investigate the thin-film cases more closely in order to examine not only how close the final amplitudes are, but also the growth behaviour over time and the match of the steady-state profile. In addition, we investigate the case of two viscous fluids of comparable viscosity, replacing $\mu _2=\mu _1\times 10^{-4}$ with $\mu _2=\mu _1/2$. Note that the two asymptotic approaches are both independent of $\mu _2$, being derived under the assumption that $\mu _1\gg \mu _2$, and so only one new curve appears on each panel labelled ‘Stokes viscous’. In all cases we observe a similar final wrinkle profile for all four experiments with the largest discrepancy occurring in the stronger forcing case (case 1) for the numerical boundary integral simulation with a more viscous fluid in $\varOmega _2$; here, the greater viscosity leads to a slightly larger final wrinkle amplitude. The differences between the simulations are more evident in the amplitude evolution plots in the left column. However, for the lighter forcing case with $\delta =0.025$, we still see a good agreement between the two asymptotic predictions and the fully numerical results with both $\mu _2$ values tested. For the stronger forcing case with $\delta =0.1$ we now observe clear differences in not only the time taken to reach the steady state, but also the steady-state amplitude (due to the finer scale on the vertical axis compared with the final profile plots). These differences can mainly be attributed to the assumptions under which the asymptotic models are derived not being fully satisfied for $\delta =0.1$, and additionally for $\mu _2=\mu _1/2$ in the ‘Stokes Viscous’ case.

Figure 3. The time evolution of the wrinkle amplitude from flat to steady state $(a{,}c)$ and the final steady-state wrinkle profile $(b{,}d)$. The rows represent the thinnest-film experiment shown in figure 2 for two of the three forcing cases: case 1 with $\gamma =\epsilon _0/50$ (a,b) and case 3 with $\gamma =\epsilon _0/5000$ (c,d). The dash-dot curve (Stokes viscous) shows the effect of reducing the viscosity ratio between $\varOmega _1$ and $\varOmega _2$ in the Stokes flow boundary integral model from $10^4$ to $2$.

A perhaps surprising observation on the results in figure 2 is how closely the wrinkle amplitudes predicted by the numerically simulated Stokes flow model and the hybrid asymptotic approach agree for thick films, suggesting that it is the additional asymptotic reduction of the electrostatic model that is more limiting. We investigate this further in figure 4 where the growth behaviour of a thick film ($\delta =0.5$) over time and the match of the steady-state profile predicted by the numerically simulated Stokes flow model and the hybrid asymptotic approach are compared. The results show that both methods predict essentially the same steady-state result, but the hybrid asymptotic approach is not able to correctly capture the time dynamics and predicts a much faster onset of the steady state than the full model. However, if only the steady state itself were of interest then either method could reasonably be used.

Figure 4. The time evolution of the wrinkle amplitude from flat to steady state $(a)$ and the final steady-state wrinkle profile $(b)$ for a thick film with $\delta =0.5$ and $\gamma =\epsilon _0/5000$.

To summarise, between the three approaches tested, at least one can give reasonably accurate results for any possible $\delta$ value and for $\delta <0.05$ one can use the fully asymptotic method for maximal computational efficiency. Note that here we have only presented results where the steady-state amplitude has converged to three significant figures. We have not reported any verification of the discretisation methods or their convergence properties. For the boundary integral model of the electrostatic problem, such an analysis can be found in Chappell (Reference Chappell2015). We have also independently verified that the boundary integral model employed for the fluid dynamical simulations here is in agreement with the solution of Orchard (Reference Orchard1963) for the surface tension driven levelling of an initially perturbed oil–air interface. Finally, the agreement between the three different models presented here for appropriate parameter choices provides verification of their correctness.

6.2. Application to an experimental set-up

In this section we present results for a more experimentally realistic system where the applied potential originates from an array of interdigitated electrodes. In particular, we base our model set up on the experimental work presented in Brown et al. (Reference Brown, Wells, Newton and McHale2009) for the dynamics of an oil–air interface using a thin film of hexadecane. Therein, an interdigital array of striped coplanar electrodes was shown to spread a droplet of oil into a thin uniform film at low voltage and that increasing the voltage drop between neighbouring electrodes gave rise to a periodic undulation at the surface of the oil. A list of the relevant material parameters for this set-up is provided in table 1; we direct the reader to Brown et al. (Reference Brown, Wells, Newton and McHale2009) for a detailed description of the experimental set-up. We apply a modified form of the potential model for interdigitated electrodes from den Otter (Reference den Otter2002), but with a minor modification in order to better model the electrode configuration in Brown et al. (Reference Brown, Wells, Newton and McHale2009). Explicitly, we take

(6.3)\begin{equation} f(x)=\frac{2\Phi}{\rm \pi}\sum_{j=1}^{\infty}\frac{1}{2j-1}J_0\left(\frac{(2j-1){\rm \pi}}{4}\right)\left(1+\sin\left(\frac{(2j-1){\rm \pi} x}{L}\right)\right), \end{equation}

as shown in figure 5 with $\Phi =400$ and $L=240\ \mathrm {\mu }\text {m}$. The infinite sum in (6.3) has been truncated at $N=1,3$ and $11$ terms for the different curves in the plot. For $N=1$ we have a simple (raised) sine curve and would expect similar profiles to those obtained in the previous section and not the more interesting profiles observed experimentally in Brown et al. (Reference Brown, Wells, Newton and McHale2009). We may further obtain that

(6.4)\begin{equation} \phi^H(\boldsymbol{x})=\frac{2\Phi}{\rm \pi}\sum_{j=1}^{\infty}\frac{1}{j_{{o}}}J_0\left(\frac{\rm \pi}{4}\right)\left(1+\sin\left(\frac{j_{{o}}{\rm \pi} x}{L}\right)\exp\left(-\frac{j_{{o}}{\rm \pi} y}{L}\right)\right), \end{equation}

where the notation $j_{{o}}=2j-1$ has been introduced for brevity and $J_0$ is the zeroth-order Bessel function of the first kind as usual. The corresponding Maxwell stress is then be derived as

(6.5)\begin{equation} M_1=4\epsilon_2\left(\left(\sum_{j=1}^{\infty} J_0\left(\frac{j_{{o}}{\rm \pi}}{4}\right)\cos(\,j_{{o}}{\rm \pi} \tilde{x})\right)^2+\frac{\epsilon_2}{\epsilon_1}\left(\sum_{j=1}^{\infty} J_0\left(\frac{j_{{o}}{\rm \pi}}{4}\right)\sin(\,j_{{o}}{\rm \pi} \tilde{x})\right)^2\right). \end{equation}

Figure 5. Modelling the electric potential from an array of interdigitated electrodes using (6.3) with parameters $\Phi =400\ \textrm {V}$, $L=240\ \mathrm {\mu }\textrm {m}$ and truncating the sum after $N$ terms as specified in the legend.

Table 1. Parameters used in the hexadecane oil–air interface model.

Figure 6 shows the steady-state wrinkle profiles for the set-up described above with electrode pitch $L=240\ \mathrm {\mu }\textrm {m}$ and a variety of different applied voltages $\Phi$ between 275 V and 550 V using (6.3) with the sums truncated after three terms. The small parameter $\delta$ for the asymptotic models ranges from 0.025 with $h_0=6\ \mathrm {\mu }\textrm {m}$ up to 0.125 with $h_0=30\ \mathrm {\mu }\textrm {m}$ and so the results in the previous section suggest that the fully asymptotic model should at least be appropriate in the former case. The plots are all scaled consistently, but their position on the vertical axis has been shifted for ease of comparison between the plots for different values of $h_0$. Note that as before, convergence issues arise in the fully numerical Stokes flow code for thin films in combination with relatively large applied voltages leading to prohibitive computational costs in these cases. For this reason, the profiles in this case are only calculated for the thickest film ($30\ \mathrm {\mu }\textrm {m}$) and for a limited range of amplitudes for the $16.5\ \mathrm {\mu }\textrm {m}$ film. The fully asymptotic model has only been plotted for the thinnest-film case since this is the only case where the leading order in $\delta$ result is approximately valid. The hybrid numerical-asymptotic model can be applied to compute the steady-state wrinkle profile throughout the range of applied voltages and different film thickness values, but we note that as before, we only expect the growth behaviour with respect to time to be well approximated for thinner films.

Figure 6. The steady-state wrinkle profile of a hexadecane oil–air interface predicted by $(a)$ the Stokes flow numerical model, $(b)$ the hybrid numerical-asymptotic model and $(c)$ the fully asymptotic model. The mean film height $h_0$ is chosen as $h_0= 6\ \mathrm {\mu }\text {m},\ 16.5\ \mathrm {\mu }\text {m},\ 30\ \mathrm {\mu }\text {m}$, corresponding to a small parameter $\delta$ ranging from $0.025$ to $0.125$, and results for each model are presented in appropriate regimes (see text). A simulated array of interdigitated electrodes is applied to excite the system via (6.3) and truncating the sum after three terms. The applied potential is indicated in the legend of (c), and is consistent throughout all panels. The plots are all scaled consistently, but their position on the vertical axis has been shifted for ease of comparison between different values of $h_0$.

The results in figure 6 show that the steady-state profile predicted by the hybrid numerical asymptotic model (figure 6b) agrees reasonably closely with both the numerical Stokes flow model for the thicker films (figure 6a) and the fully asymptotic model for the thinnest film with $h_0=6\ \mathrm {\mu }\textrm {m}$ at lower applied potentials (figure 6c). Some discrepancies can also be observed, with the hybrid model typically predicting slightly lower wrinkle amplitudes than the other two models; note that this observation is consistent with the results from the previous subsection as shown in figure 2. The fully asymptotic model also predicts larger amplitudes and a stronger influence of the higher-order expansion terms in (6.3), which can be observed through the deeper trough in the wrinkle profiles at $x=0$ in panel (c). The reason for this can be found by inspection of the asymptotic formula for the Maxwell stress term $M_1$ in (6.5) and recalling that inhomogeneous part of the thin film (4.13) depends on $\partial M_1/\partial \tilde {x}$ and $\partial ^2 M_1/\partial \tilde {x}^2$. After differentiation with respect to $\tilde {x}$, the sums in (6.5) become divergent and hence the match between the fully asymptotic model and the other two will deteriorate if we include more terms in the sum (6.3) than the three considered here. The other two models depend instead on the half-space solution (6.4) via (3.17), in which the higher-order terms in (6.3) have increasing exponential decay rates.

Finally, we note that the results in figure 6 bear a strong similarity to the experimental findings published in figure 5 of Brown et al. (Reference Brown, Wells, Newton and McHale2009), both in terms of the observed profile shapes and amplitudes. In particular, the experimental results also show a simple sinusoidal profile when $h_0=30\ \mathrm {\mu }\textrm {m}$ and a flattening of the peak at $x=0$, followed by the increasing emergence of a trough as $h_0$ is decreased.