2.1. Basic equations

In two dimensions, the mass conservation equations can be written as

(2.1)\begin{equation} \frac{{\partial {u_i}}}{{\partial x}} + \frac{{\partial {w_i}}}{{\partial z}} = 0\quad (i = 1,2), \end{equation}

where $u$ and $w$ are the horizontal and vertical velocity components, respectively, $i=1$ stands for the lower-fluid layer and $i=2$ stands for the upper-fluid layer.

The momentum conservation equations for the upper- and lower-fluid layers can be written as

(2.2a)$$\begin{gather} \frac{{\partial {u_i}}}{{\partial t}} + {u_i}\,\frac{{\partial {u_i}}}{{\partial x}} + {w_i}\,\frac{{\partial {u_i}}}{{\partial z}} ={-} \frac{1}{{{\rho _i}}}\,\frac{{\partial {p_i}}}{{\partial x}}\quad (i = 1,2), \end{gather}$$

(2.2b)$$\begin{gather}\frac{{\partial {w_i}}}{{\partial t}} + {u_i}\,\frac{{\partial {w_i}}}{{\partial x}} + {w_i}\,\frac{{\partial {w_i}}}{{\partial z}} ={-} \frac{1}{{{\rho _i}}}\,{\frac{{\partial {p_i}}}{{\partial z}}} -g\quad (i = 1,2), \end{gather}$$

where $t$ stands for time, $\rho _i$ is the mass density, $p_i$ is the pressure, and $g$ is the gravitational acceleration.

The kinematic boundary condition at the upper surface of the upper-fluid layer (rigid lid) can be written as

(2.3)\begin{equation} {w_2} = 0\quad \text{at}\ z=h. \end{equation}

The kinematic boundary conditions at the interface between the two fluid layers can be written as

(2.4)\begin{equation} {w_i} = \frac{{\partial \eta }}{{\partial t}} + {u_i}\,\frac{{\partial \eta }}{{\partial x}}\quad (i = 1,2)\quad \text{at}\ z=\eta(x,t). \end{equation}

The kinematic boundary condition at the bottom of the lower-fluid layer can be written as

(2.5)\begin{equation} {u_1}=0,\quad {w_1} = 0,\quad \text{when}\ z\rightarrow-\infty. \end{equation}

The dynamic boundary condition at the interface between the two fluid layers can be written as

(2.6)\begin{equation} {\bar p_2} = {\hat p_1}\quad \text{at}\ z=\eta(x,t), \end{equation}

where $\bar p_2$ is the pressure at the lower surface of the upper-fluid layer, and $\hat p_1$ is the pressure at the upper surface of the lower-fluid layer.

Equations (2.1)–(2.6) are the basic equations for the two-layer fluid system in the deep configuration.

2.2. Equations for the deep-water two-layer HLGN model

In the traditional two-layer HLGN model (Zhao et al. Reference Zhao, Ertekin, Duan and Webster2016, Reference Zhao, Wang, Duan, Ertekin, Hayatdavoodi and Zhang2020), the undisturbed depths of the two fluid layers are both assumed to be finite. The velocity fields are expressed approximately by

(2.7a,b)\begin{equation} {u_i}(x,z,t) = \sum_{n = 0}^{{K_i}} {{u_{n,i}}(x,t)\,z^n},\quad {w_i}(x,z,t) = \sum_{n = 0}^{{K_i}} {{w_{n,i}}(x,t)\,z^n}\quad (i = 1,2), \end{equation}

where $u_{n,i}$ and $w_{n,i}$ are the unknown horizontal and vertical velocity coefficients, and $K_i$ is a positive integer that represents the level used for each layer.

In the present HLGN model, the undisturbed depth of the upper-fluid layer is still assumed to be finite. Hence a polynomial function is still utilized as the shape function to approximate the velocity field for the upper-fluid layer.

Since the undisturbed depth of the lower-fluid layer is assumed to be infinite, the product of the exponentials and polynomials is utilized as the shape function to approximate the velocity field for the lower-fluid layer. This shape function has been utilized to study one-layer deep-water problems efficiently (Webster & Kim Reference Webster and Kim1991; Zheng et al. Reference Zheng, Zhao, Duan, Ertekin and Chen2016).

The velocity fields are therefore expressed by

(2.8a)$$\begin{gather} {u_{2}}(x,z,t) = \sum_{n = 0}^{{K_2}} {{u_{n,2}}(x,t)}\,{z^n},\quad {w_{2}}(x,z,t) = \sum_{n = 0}^{{K_2}} {{w_{n,2}}(x,t)}\,{z^n}, \end{gather}$$

(2.8b)$$\begin{gather}{u_1}(x,z,t) = \sum_{n = 0}^{{K_1}} {{u_{n,1}}(x,t)}\,{{\rm e}^{{k}z}}\,{z^n},\quad {w_1}(x,z,t) = \sum_{n = 0}^{{K_1}} {{w_{n,1}}(x,t)}\,{{\rm e}^{{k}z}}\,{z^n}, \end{gather}$$

where $k$ is the representative wavenumber that should be predetermined for a given case. We will discuss the selection of $k$ for calculating internal solitary waves later, in § 4.

We first consider the upper-fluid layer. Substituting (2.8a) into (2.1) with $i=2$, we obtain

(2.9a)$$\begin{gather} {u_{{K_2},2}} = 0, \end{gather}$$

(2.9b)$$\begin{gather}{w_{n,2}} ={-} \frac{1}{n}\,\frac{{\partial {u_{n - 1,2}}}}{{\partial x}}\quad (n = 1,2,\ldots,K_2). \end{gather}$$

Substituting (2.8a) into (2.2a) and (2.2b) with $i=2$, multiplying each term by $z^n$ ($n=0,1,\ldots, K_2$) and integrating from $h$ to $\eta$, will result in

(2.10)\begin{equation} {M_{n,2}} + \frac{{\left( {{\eta ^n} - {h^n}} \right)}}{{{\rho _2}}}\,\frac{{\partial {{\bar p}_2}}}{{\partial x}} = 0\quad (n = 1,2, \ldots ,{K_2}), \end{equation}

where

(2.11a)$$\begin{gather} {M_{n,2}} = \frac{\partial }{{\partial x}}\left[ {{G_{n,2}} + g\left( {\int_h^\eta {{z^n}} \,{\rm{d}}z} \right)} \right] + n{E_{n - 1,2}} - {h^n}\left[ {{G_{0,2}} + g( {\eta - h} )} \right], \end{gather}$$

(2.11b)$$\begin{gather}{E_{n,2}} = \sum_{m = 0}^{{K_2}} {\left\{ {\begin{array}{l} {\displaystyle\dfrac{{\partial {u_{m,2}}}}{{\partial t}}\left( {\int_h^\eta {{z^{m + n}}\,{\rm{d}}z} } \right) + \dfrac{{\partial {u_{m,2}}}}{{\partial x}}\left[ {\sum_{r = 0}^{{K_2}} {{u_{r,2}}\left( {\int_h^\eta {{z^{m + r + n}}\,{\rm{d}}z} } \right)} } \right]}\\ {\quad {}+ {u_{m,2}}\left[ {\displaystyle\sum_{r = 0}^{{K_2}} {{w_{r,2}}} \left( {m\int_h^\eta {{z^{m + r + n - 1}}\,{\rm{d}}z} } \right)} \right]} \end{array}} \right\}}, \end{gather}$$

(2.11c)$$\begin{gather}{G_{n,2}} = \sum_{m = 0}^{{K_2}} {\left\{ {\begin{array}{l} {\displaystyle\dfrac{{\partial {w_{m,2}}}}{{\partial t}}\left( {\int_h^\eta {{z^{m + n}}\,{\rm{d}}z} } \right) + \dfrac{{\partial {w_{m,2}}}}{{\partial x}}\left[ {\sum_{r = 0}^{{K_2}} {{u_{r,2}}\left( {\int_h^\eta {{z^{m + r + n}}\,{\rm{d}}z} } \right)} } \right]}\\ {\quad {}+ {w_{m,2}}\left[ {\displaystyle\sum_{r = 0}^{{K_2}} {{w_{r,2}}} \left( {m\int_h^\eta {{z^{m + r + n - 1}}\,{\rm{d}}z} } \right)} \right]} \end{array}} \right\}} . \end{gather}$$

We can eliminate $\bar p_2$ by use of (2.10) with two different values of $n$, e.g. $n=1$ and $n=2$, $n=1$ and $n=3$, to obtain

(2.12)\begin{equation} ( {{\eta } - h} ){M_{n,2}} - ( {{\eta ^n} - h^n} ){M_{1,2}} = 0\quad (n = 2,3,\ldots,K_2). \end{equation}

Substituting (2.8a) into (2.3), we obtain

(2.13)\begin{equation} {w_{0,2}} ={-} \sum_{n = 1}^{{K_2}} h^n{{w_{n,2}}}. \end{equation}

Substituting (2.8a) into (2.4) with $i=2$, we obtain

(2.14)\begin{equation} \frac{{\partial \eta }}{{\partial t}} = \sum_{n = 0}^{{K_2}} {\eta}^{n}{\left( {{w_{n,2}} - \frac{{\partial \eta }}{{\partial x}}\,{u_{n,2}}} \right)}. \end{equation}

Next, we consider the lower-fluid layer. Substituting (2.8b) into (2.1) with $i=1$, we obtain

(2.15a)$$\begin{gather} {u_{{{K_1},1}}} = {w_{{{K_1},1}}} = 0, \end{gather}$$

(2.15b)$$\begin{gather}{w_{n,1}} ={-} \frac{1}{{{k}}}\left[ {\frac{{\partial {u_{n,1}}}}{{\partial x}} + ( {n + 1} ){w_{n + 1,1}}} \right]\quad (n={K_1}-1,{K_1}-2,\ldots,0). \end{gather}$$

Substituting (2.8b) into (2.2a) and (2.2b) with $i=1$, multiplying each term by ${\rm e}^{kz}\,z^n$ ($n=0,1,\ldots, K_1-1$) and integrating from $-\infty$ to $\eta$, will result in

(2.16)\begin{equation} {M_{n,1}} + \frac{{{{\rm e}^{k\eta }}\,{\eta ^n}}}{{{\rho _1}}}\,\frac{{\partial {{\hat p}_1}}}{{\partial x}} = 0\quad (n = 0,1, \ldots ,{K_1} - 1), \end{equation}

where

(2.17a)$$\begin{gather} {M_{n,1}} = \frac{\partial }{{\partial x}}\left[ {{G_{n,1}} + g\left( {\int_{ - \infty }^\eta {{{\rm e}^{kz}}\,{z^n}\,{\rm{d}}z} } \right)} \right] + k{E_{n,1}} + n{E_{n - 1,1}}, \end{gather}$$

(2.17b)$$\begin{gather}{E_{n,1}} = \sum_{m = 0}^{{K_1} - 1} {\left\{\!{\begin{array}{@{}l@{}} {\displaystyle\dfrac{{\partial {u_{m,1}}}}{{\partial t}}\left( {\int_{ - \infty }^\eta {{{\rm e}^{2kz}}}\,{z^{m + n}}\,{\rm{d}}z} \right) + \dfrac{{\partial {u_{m,1}}}}{{\partial x}}\left[ {\displaystyle\sum_{r = 0}^{{K_1} - 1} {{u_{r,1}}\left( {\int_{ - \infty }^\eta {{{\rm e}^{3kz}}}\,{z^{m + r + n}}\,{\rm{d}}z} \right)} } \right]}\\ {\quad {}+ {u_{m,1}}\left[ {\displaystyle\sum_{r = 0}^{{K_1} - 1} {{w_{r,1}}\left( {\int_{ - \infty }^\eta {{{\rm e}^{3kz}}\left( {k{z^{m + r + n}} + m{z^{m + r + n - 1}}} \right)} \,{\rm{d}}z} \right)} } \right]}\end{array}} \right\}}, \end{gather}$$

(2.17c)$$\begin{gather}{G_{n,1}} = \sum_{m = 0}^{{K_1} - 1} {\left\{\!{\begin{array}{@{}l@{}} {\displaystyle \dfrac{{\partial {w_{m,1}}}}{{\partial t}}\left( {\int_{ - \infty }^\eta {{{\rm e}^{2kz}}}\,{z^{m + n}}\,{\rm{d}}z} \right) + \dfrac{{\partial {w_{m,1}}}}{{\partial x}}\left[ {\displaystyle\sum_{r = 0}^{{K_1} - 1} {{u_{r,1}}\left( {\int_{ - \infty }^\eta {{{\rm e}^{3kz}}}\,{z^{m + r + n}}\,{\rm{d}}z} \right)} } \right]}\\ {\quad {}+ {w_{m,1}}\left[ {\displaystyle \sum_{r = 0}^{{K_1} - 1} {{w_{r,1}}\left( {\int_{ - \infty }^\eta {{{\rm e}^{3kz}}\left( {k{z^{m + r + n}} + m{z^{m + r + n - 1}}} \right)} \,{\rm{d}}z} \right)} } \right]}\end{array}} \right\}} . \end{gather}$$

We can eliminate $\hat p_1$ by use of (2.16) with two different values of $n$, e.g. $n=0$ and $n=1$, $n=0$ and $n=2$, to obtain

(2.18)\begin{equation} {M_{n,1}} - {\eta ^n}{M_{0,1}} = 0\quad (n=1,2,\ldots,{K_1}-1). \end{equation}

Substituting (2.8b) into (2.4) with $i=1$, we obtain

(2.19)\begin{equation} \frac{{\partial \eta }}{{\partial t}} = \sum_{n = 0}^{{K_1}} {{{\rm e}^{{k}\eta }}\,{\eta ^n}\left( {{w_{n,1}} - \frac{{\partial \eta }}{{\partial x}}\,{u_{n,1}}} \right)}. \end{equation}

We note that (2.5) always holds under the velocity field approximations in (2.8b).

Next, we couple the upper- and lower-fluid layers. Based on (2.6), we set $n=1$ in (2.10) and set $n=0$ in (2.16) to obtain

(2.20)\begin{equation} {\rho _2}\,{{\rm e}^{{k}\eta }}\,{M_{1,2}} - {\rho _1}( {\eta - {h}} ){M_{0,1}} = 0. \end{equation}

Finally, (2.12), (2.14) and (2.18)–(2.20) are the equations of the HLGN model. We can use (2.9b) and (2.13) to eliminate ${w_{n,2}}$ ($n=0,1,\ldots,K_2$), and use (2.15b) to eliminate ${w_{n,1}}$ ($n=0,1,\ldots,K_1-1$). As a result, the unknowns are ${u_{n,2}}$ ($n=0,1,\ldots,K_2-1$), ${u_{n,1}}$ ($n=0,1,\ldots,K_1-1$) and $\eta$. The numbers of the equations and the unknowns are both $K_2+K_1+1$. Therefore, this system of equations is closed.

It should be noted that different values of $K_2$ and $K_1$ in (2.8a) and (2.8b) correspond to different levels of the present HLGN model. For example, if we select $K_2=3$ and $K_1=5$, then we will establish the HLGN-P3E5 model, where ‘P’ is short for ‘polynomial’, and ‘E’ is short for ‘exponential’. When $K_1$ or $K_2$ increases, the dispersion performance of the present HLGN model becomes better, and this will be presented in the next subsection.

In this paper, a polynomial is utilized as the shape function for the upper-fluid layer of finite depth, and the product of polynomial and exponential is utilized as the shape function for the lower-fluid layer of infinite depth. If we rewrite the velocity approximations for the upper- and lower-fluid layers in a general form as

(2.21a,b)\begin{gather} \qquad {u_i}(x,z,t) = \sum_{n = 0}^{{K_i}} {{u_{n,i}}(x,t)\,{\sigma _{n,i}}(z)},\quad {w_i}(x,z,t) = \sum_{n = 0}^{{K_i}} {{w_{n,i}}(x,t)\,{\sigma _{n,i}}(z)}\quad (i = 1,2), \end{gather}

where ${\sigma _{n,i}}(z)$ is the general shape function utilized for each fluid layer, then one can establish the corresponding HLGN model following the derivation process presented in this paper. For example, the orthogonal polynomials (e.g. the Legendre function used for the upper-fluid layer of finite depth, and the Laguerre function used for the lower-fluid layer of infinite depth) can be utilized to further simplify the formulation due to their orthogonality and recursive properties, i.e. the terms related to the integrals of two shape function multiplications, $\int {{\sigma _{m,i}}(z)\,{\sigma _{n,i}}(z)\,{\rm {d}}z}$, can be simplified, but the much larger number of terms related to the integrals of three shape function multiplications, $\int {{\sigma _{m,i}}(z)\,{\sigma _{r,i}}(z)\,{\sigma _{n,i}}(z)\,{\rm {d}}z}$, cannot be simplified significantly.

2.3. Linear dispersion relations of the deep-water HLGN models for different levels

To validate the application range, the linear dispersion relations of the present HLGN models for different levels are studied in this subsection. Substituting the linear solutions of the horizontal-velocity coefficients $u_{n,i}$ ($n=0,1,\ldots,K_i-1$, $i=1,2$) and internal-wave profile $\eta$ to (2.12), (2.14) and (2.18)–(2.20), we can obtain the linear dispersion relations of the present HLGN models for different levels. We refer the reader to Webster et al. (Reference Webster, Duan and Zhao2011) for the derivation of the linear dispersion relations of the one-layer HLGN model for different levels in detail. Here, we fix $K_2=3$ since we find that it is enough to accurately describe the velocity fields for the upper-fluid layer of the internal solitary waves in the absence of shear-current considered in this paper (i.e. $K_2=3$ is the converged level for the upper-fluid layer).

Expressions for the linear dispersion relations of the first five levels of the HLGN models are given below.

HLGN-P3E1 model:

(2.22)\begin{equation} {\bar c^2} = \frac{{\left( {1 - \bar \rho } \right)}}{{\dfrac{1}{{15}}\,\bar \rho \left( {\dfrac{{{{\bar{k'}}^6} + 135{{\bar{k'}}^4} + 2880{{\bar{k'}}^2} + 6300}}{{{{\bar{k'}}^4} + 52{{\bar{k'}}^2} + 420}}} \right) + \dfrac{1}{2}\left( {\dfrac{{{{\bar k}^2} + {{\bar{k'}}^2}}}{{\bar k}}} \right)}}. \end{equation}

HLGN-P3E2 model:

(2.23)\begin{equation} {\bar c^2} = \frac{{\left( {1 - \bar \rho } \right)}}{{\dfrac{1}{{15}}\,\bar \rho \left( {\dfrac{{{{\bar{k'}}^6} + 135{{\bar{k'}}^4} + 2880{{\bar{k'}}^2} + 6300}}{{{{\bar{k'}}^4} + 52{{\bar{k'}}^2} + 420}}} \right) + \dfrac{1}{4}\left[ {\dfrac{{{{\bar k}^4} + 6{{\bar k}^2}{{\bar{k'}}^2} + {{\bar{k'}}^4}}}{{\bar k\left( {{{\bar k}^2} + {{\bar{k'}}^2}} \right)}}} \right]}}. \end{equation}

HLGN-P3E3 model:

(2.24)\begin{align} {\bar c^2} = \frac{{\left( {1 - \bar \rho } \right)}}{{\dfrac{1}{{15}}\,\bar \rho \left( {\dfrac{{{{\bar{k'}}^6} + 135{{\bar{k'}}^4} + 2880{{\bar{k'}}^2} + 6300}}{{{{\bar{k'}}^4} + 52{{\bar{k'}}^2} + 420}}} \right) + \dfrac{1}{2}\left[ {\dfrac{{{{\bar k}^6} + 15{{\bar k}^4}{{\bar{k'}}^2} + 15{{\bar k}^2}{{\bar{k'}}^4} + {{\bar{k'}}^6}}}{{\bar k\left( {3{{\bar k}^2} + {{\bar{k'}}^2}} \right)\left( {{{\bar k}^2} + 3{{\bar{k'}}^2}} \right)}}} \right]}}. \end{align}

HLGN-P3E4 model:

(2.25)\begin{align} {\bar c^2} = \frac{{\left( {1 - \bar \rho } \right)}}{{\dfrac{1}{{15}}\,\bar \rho \left( {\dfrac{{{{\bar{k'}}^6} + 135{{\bar{k'}}^4} + 2880{{\bar{k'}}^2} + 6300}}{{{{\bar{k'}}^4} + 52{{\bar{k'}}^2} + 420}}} \right) + \dfrac{1}{8}\left[ {\dfrac{{{{\bar k}^8} + 28{{\bar k}^6}{{\bar{k'}}^2} + 70{{\bar k}^4}{{\bar{k'}}^4} + 28{{\bar k}^2}{{\bar{k'}}^6} + {{\bar{k'}}^8}}}{{\bar k\left( {{{\bar k}^2} + {{\bar{k'}}^2}} \right)\left( {{{\bar k}^4} + 6{{\bar k}^2}{{\bar{k'}}^2} + {{\bar{k'}}^4}} \right)}}} \right]}}. \end{align}

HLGN-P3E5 model:

(2.26)\begin{align} {\bar c^2} = \frac{{\left( {1 - \bar \rho } \right)}}{{\dfrac{1}{{15}}\,\bar \rho \left( {\dfrac{{{{\bar{k'}}^6} + 135{{\bar{k'}}^4} + 2880{{\bar{k'}}^2} + 6300}}{{{{\bar{k'}}^4} + 52{{\bar{k'}}^2} + 420}}} \right) + \dfrac{1}{2}\left[ {\dfrac{{\left( {{{\bar k}^2} + {{\bar{k'}}^2}} \right)\left( {{{\bar k}^8} + 44{{\bar k}^6}{{\bar{k'}}^2} + 166{{\bar k}^4}{{\bar{k'}}^4} + 44{{\bar k}^2}{{\bar{k'}}^6} + {{\bar{k'}}^8}} \right)}}{{\bar k\left( {5{{\bar k}^4} + 10{{\bar k}^2}{{\bar{k'}}^2}{\rm{ + }}{{\bar{k'}}^4}} \right)\left( {{{\bar k}^4} + 10{{\bar k}^2}{{\bar{k'}}^2}{{ + 5}}{{\bar{k'}}^4}} \right)}}} \right]},} \end{align}

where the dimensionless linear-wave speed $\bar c$, wavenumber $\bar {k'}$, representative wavenumber ${\bar k}$ and mass density $\bar \rho$ are

(2.27)\begin{equation} \bar c = \frac{c}{{\sqrt {gh} }},\quad \bar{k'} = {k'}h,\quad {{\bar k}} = {k}h,\quad \bar \rho = \frac{{{\rho _2}}}{{{\rho _1}}}. \end{equation}

Similar principles apply in obtaining the linear dispersion relations of the present HLGN models for higher levels (not shown here due to the complexity of the final relations).

The exact linear dispersion relation for internal gravity waves in this two-layer deep configuration is (Lamb Reference Lamb1932)

(2.28)\begin{equation} \bar c_{exact}^2 = \frac{{1 - \bar \rho }}{{\bar{k'}\left( {1 + \bar \rho \coth \bar{k'}} \right)}}. \end{equation}

Figure 2 shows the relationship between the normalized wave speed squared ${{\bar c}^2}/{\bar c_{exact}^2}$ and the wavenumber $\bar{k'}$ for the linear dispersion relations of the present HLGN models for different levels for the case $\bar \rho =0.952$ (Debsarma et al. Reference Debsarma, Das and Kirby2010). We observe that when $\bar k$ is not as close to $\bar {k'}$, such as when it is small (e.g. $\bar k/\bar {k'}=0.25$ shown in figure 2a) or large (e.g. $\bar k/\bar {k'}=4$ shown in figure 2e), the present HLGN models for lower levels have quite small application ranges. Under this condition, the present HLGN models for higher levels are required. When $\bar k$ is closer to $\bar {k'}$ (e.g. $\bar k/\bar {k'}=0.5$ shown in figure 2(b) and $\bar k/\bar {k'}=2$ shown in figure 2d), the application ranges of the present HLGN models for lower levels become significantly wider. Meanwhile, we observe that the HLGN-P3E3 model almost has the same application range compared with those of the HLGN-P3E4 and HLGN-P3E5 models. In particular, when $\bar k = \bar {k'}$, shown in figure 2(c), the HLGN models for different levels have the same and optimal application range. Meanwhile, from (2.22)–(2.26), we see that ${\bar {c}}^2 > 0$ since ${\bar {\rho }} < 1$, and when $\bar {k'}\rightarrow +\infty$, ${\bar {c}}^2\rightarrow 0$.

Figure 2.

Relationship between the normalized wave speed squared ${{\bar c}^2}/{\bar c_{exact}^2}$ and the wavenumber $\bar{k'}$ for the linear dispersion relations of the present HLGN models for different levels, $\bar \rho =0.952$: (a) $k/ {k'}=0.25$, (b) $k/ {k'}=0.5$, (c) $k/ {k'}=1$, (d) $k/ {k'}=2$, (e) $k/ {k'}=4$.

Furthermore, we compare the relationship between the normalized wave speed squared ${{\bar c}^2}/{\bar c_{exact}^2}$ and the wavenumber $\bar{k'}$ for the linear dispersion relations of the HLGN-P3E5 model and other strongly nonlinear models, including the CC model (Choi & Camassa Reference Choi and Camassa1996a) and the DDK model (Debsarma et al. Reference Debsarma, Das and Kirby2010), for the case ${\bar \rho }=0.952$. The linear dispersion relations of the two strongly nonlinear models are as follows (see e.g. Debsarma et al. Reference Debsarma, Das and Kirby2010).

CC model:

(2.29)\begin{equation} {{\bar c}^2} = \frac{{1 - \bar \rho }}{{\bar \rho + \bar{k'}}}. \end{equation}

DDK model:

(2.30)\begin{equation} {\bar c^2} = \frac{{1 - \bar \rho }}{{\bar \rho \left( {\dfrac{{6{{\bar{k'}}^2} + 15}}{{{{\bar{k'}}^2} + 15}}} \right) + \bar{k'}}}. \end{equation}

The comparison is shown in figure 3, where we observe that the application range of the CC model is $\bar {k'}<0.74$ if the tolerable absolute error is $1-|{{\bar c}^2}/{\bar c_{exact}^2}| \leq 10\,\%$. The DDK model expands the application range to $\bar {k'}<5.15$. The application range of the HLGN-P3E5 model with $\bar k=\bar {k'}$ reaches $\bar {k'}<12.58$, and it has the widest application range among these models.

Figure 3.

Relationship between the normalized wave speed squared ${{\bar c}^2}/{\bar c_{exact}^2}$ and the wavenumber $\bar{k'}$ for the linear dispersion relations of the HLGN-P3E5 model and other strongly nonlinear models, $\bar \rho =0.952$.

4.1. The effect of the representative wavenumber $k$

In this subsection, we focus on the selection of the representative wavenumber $k$ of the present HLGN model for calculating internal solitary waves. Here, we define the representative wavelength $\lambda = 2{\rm \pi} / k$. Koop & Butler (Reference Koop and Butler1981) introduced an integral measure of the effective wavelength, $\lambda _e$, which was defined as

(4.1)\begin{equation} {\lambda _e} = \left| {\frac{1}{a}\int_0^\infty {\eta (X)\,{\rm{d}}X} } \right|. \end{equation}

Many studies utilized (4.1) to obtain the relationship between the wave amplitude and effective wavelength (see e.g. Choi & Camassa Reference Choi and Camassa1999; Camassa et al. Reference Camassa, Choi, Michallet, Rusas and Sveen2006; Choi, Zhi & Barros Reference Choi, Zhi and Barros2020; Choi Reference Choi2022). Since, for a given case, the internal solitary wave profile $\eta _{MCC}$ obtained by the MCC model can be calculated by solving (3.51) in Choi & Camassa (Reference Choi and Camassa1999), we can instantly obtain the effective wavelength $\lambda _{e\text {-}MCC}$ by use of (4.1), and set it as a reference value. According to this reference value, we can choose $\lambda$ of the present HLGN model.

Here, we consider some numerical cases whose parameters are given by Choi & Camassa (Reference Choi and Camassa1999) and Debsarma et al. (Reference Debsarma, Das and Kirby2010) as follows: the density ratio between the two fluid layers is $\rho _2/\rho _1=0.78$, and the internal solitary-wave amplitude is $a/h=1.7955$. We note that only no-current cases are considered in this subsection. Under these parameters, we obtain $\lambda _{e\text {-}MCC}/h\approx 24$ for a very deep configuration where the depth ratio of the upper-fluid layer to the lower-fluid layer is $1/99$.

We first select $\lambda /\lambda _{e\text {-}MCC}= 2$. Figure 7 shows the wave profile and velocity profile at the maximum interface displacement for different levels of the present HLGN model. In figure 7(b), $c_0$ is the linear long-wave speed in a two-layer fluid system ($\rho _2<\rho _1$) for the deep configuration under the rigid-lid approximation, which is defined as

(4.2)\begin{equation} {c_0} = \sqrt {gh\left( {\frac{{{\rho _1}}}{{{\rho _2}}} - 1} \right)}. \end{equation}

It is noted that through our calculations, we find that $K_2=3$ (given in (2.8a)) can provide sufficiently accurate velocity fields for the upper-fluid layer of finite depth in the absence of background shear-current. As a result, we change the value of $K_1$ (given in (2.8b)) only to perform the convergence test for the no-current cases.

Figure 7.

Convergence test of the present HLGN models for different levels, $\lambda = 2\lambda _{e\text {-}MCC}$, $\rho _2/{\rho _1}=0.78$ and $a/h=1.7955$: (a) wave profile; (b) velocity profile at the maximum interface displacement.

As shown in figure 7, we observe that the results of the wave profile and velocity profile at the maximum interface displacement obtained by the HLGN-P3E3 and HLGN-P3E1 models show some differences with those obtained by the HLGN-P3E5 and HLGN-P3E7 models, while the results obtained by the HLGN-P3E5 model and the results of the HLGN-P3E7 model show very good agreement. Thus it is concluded that when we select $\lambda /\lambda _{e\text {-}MCC}= 2$, the HLGN-P3E5 model can provide the converged results for this case.

We also select $\lambda /\lambda _{e\text {-}MCC}= 1$ and $4$ to perform the convergence tests of the present HLGN models for different levels. It is demonstrated that the HLGN-P3E9 model can provide converged results under the two different values of $\lambda$ for this case. Comparisons between the converged results of the present HLGN models for different $\lambda$ on wave profile and velocity profile at the maximum interface displacement are shown in figures 8(a) and 8(b), respectively.

Figure 8.

Comparisons between the converged results of the present HLGN models with different representative wavelength $\lambda$, $\rho _2/{\rho _1}=0.78$ and $a/h=1.7955$: (a) wave profile; (b) velocity profile at the maximum interface displacement.

From figure 8, we observe that the converged results of present HLGN model show very good agreement for different values of $\lambda$. On the other hand, if we select the suitable one, $\lambda /\lambda _{e\text {-}MCC}= 2$, then we can obtain the converged results of the present HLGN model for the lower level (i.e. the HLGN-P3E5 model).

To further verify the suitable selection of $\lambda /\lambda _{e\text {-}MCC}= 2$, we calculate an internal solitary wave with a larger amplitude $a/h=5$ (maximum amplitude considered in the present study) when the density ratio is still $\rho _2 / \rho _1 =0.78$. Under these parameters, we obtain $\lambda _{e\text {-}MCC}/h\approx 30$ for the deep configuration where the depth ratio of the upper-fluid layer to the lower-fluid layer is $1/99$. The convergence test of the present HLGN models for different levels on wave profile and velocity profile at the maximum interface displacement when $\lambda /\lambda _{e\text {-}MCC}= 2$ is shown in figure 9, where we again observe that the HLGN-P3E5 model can provide the convergence results.

Figure 9.

Convergence test of the present HLGN models for different levels, $\lambda = 2\lambda _{e\text {-}MCC}$, $\rho _2/{\rho _1}=0.78$ and $a/h=5$: (a) wave profile; (b) velocity profile at the maximum interface displacement.

In general, we find that the HLGN-P3E5 model with $\lambda /\lambda _{e\text {-}MCC}= 2$ (i.e. $kh={\rm \pi} h/\lambda _{e\text {-}MCC}$) can provide the converged results for the no-current cases considered in this paper. In the next subsection, we will use the present HLGN-P3E5 model for the calculations.

4.2. Comparison between the present HLGN model and other models for internal solitary waves in the absence of background shear-current

In this subsection, we apply the present HLGN model to large-amplitude internal solitary waves in the absence of background shear-current in the deep configuration with two density ratios. The first case is $\rho _2 / \rho _1 = 0.952$, which is used by Debsarma et al. (Reference Debsarma, Das and Kirby2010), and the other case is $\rho _2 / \rho _1 = 0.78$, which is used by Choi & Camassa (Reference Choi and Camassa1999) and Debsarma et al. (Reference Debsarma, Das and Kirby2010). The wave profile, velocity profile at the maximum interface displacement and wave speed are studied. For comparison purposes, results of the HLGN model are compared with those obtained by the DDK model and the CC model. We also use the numerical code developed by Rusas (Reference Rusas2000) to obtain Euler's solution. The algorithm of this numerical code is given in Grue et al. (Reference Grue, Jensen, Rusas and Sveen1999). The ratio of the upper-fluid layer depth to the lower-fluid layer depth is $1/99$ in all Euler's solutions shown in the following cases.

4.2.1. Case I: $\rho _2 / \rho _1 = 0.952$

We first consider the case $\rho _2 / \rho _1 = 0.952$. The relationships between the wave speed and wave amplitude obtained by the HLGN model, the DDK model, the CC model and Euler's solution are shown in figure 10.

Figure 10.

Relationship between the wave speed and wave amplitude, $\rho _2/{\rho _1}=0.952$.

From figure 10, we observe that the CC model underestimates the wave speed for a given amplitude compared with Euler's solution. When the amplitude increases, the calculation error of the CC model becomes larger. The DDK model can describe the wave speed accurately when the wave amplitude is small. When the wave amplitude becomes larger, the DDK model overestimates the wave speed. In general, the wave speed calculated by the present HLGN model match Euler's solution very well even when the wave amplitude is large.

Next, we focus on the wave profile. Following Debsarma et al. (Reference Debsarma, Das and Kirby2010), we consider the internal solitary waves with amplitudes $a/h= 0.875$ and $1.884$. Results of the present HLGN model, the DDK model, the CC model and Euler's solution on wave profile are shown in figure 11.

Figure 11.

Wave profile, $\rho _2/{\rho _1}=0.952$: (a) $a/h=0.875$, (b) $a/h=1.884$.

From figure 11, we observe that the wave profile obtained by the present HLGN model show very good agreement with Euler's solution. On the other hand, the CC model obtains the narrower wave profile, and the DDK model obtains the wider profile compared with Euler's solution.

The velocity profiles at the maximum interface displacement obtained by the HLGN model and Euler's solution are shown in figure 12. From figure 12, we observe that the HLGN results match Euler's solution very well, for both the upper-fluid layer and the lower-fluid layer.

Figure 12.

Velocity profile at the maximum interface displacement, $\rho _2/{\rho _1}=0.952$: (a) $a/h=0.875$, (b) $a/h=1.884$.

4.2.2. Case II: $\rho _2 / \rho _1 = 0.78$

Now we consider the case $\rho _2 / \rho _1 = 0.78$. The relationships between the wave speed and wave amplitude obtained by different models are shown in figure 13.

Figure 13.

Relationship between the wave speed and wave amplitude, $\rho _2/{\rho _1}=0.78$.

From figure 13, we observe again that the DDK model overestimates the wave speed and the CC model underestimates the wave speed when the amplitude is relatively large. The wave speed obtained by the HLGN model matches Euler's solution well even when the wave amplitude reaches $a/h=4.5$.

Then we consider the profiles of internal solitary waves with amplitudes $a/h= 1.7955$ and $5$. Due to the absence of the CC and DDK results in the existing literature for amplitude $a/h = 5$, to be used for comparisons, we have written the code to obtain the numerical results of the CC and DDK models. We first validate the accuracy of the code that we developed for this purpose for the CC and DDK models. Figure 14 shows the comparison between the present numerical results of the CC and DDK models, and the results that we capture from Debsarma et al. (Reference Debsarma, Das and Kirby2010) for the case $a/h = 1.7955$ and $\rho _2/{\rho _1}=0.78$, where perfect agreement is observed. This is an indication that our code for the CC and DDK models is accurate.

Figure 14.

Comparison between the present numerical results of the CC model and the DDK model and the results given by Debsarma et al. (Reference Debsarma, Das and Kirby2010), $a/h = 1.7955$ and $\rho _2/{\rho _1}=0.78$.

Results of the HLGN model, the DDK model, the CC model and Euler's solution on wave profile are shown in figure 15, where we observe that the present HLGN model shows better agreement with Euler's solution compared with the results of the DDK and CC models.

Figure 15.

Wave profile, $\rho _2/{\rho _1}=0.78$: (a) $a/h=1.7955$, (b) $a/h=5$.

Figure 16 shows the relationship between the effective wavelength and the wave amplitude, which can be obtained by using (4.1). The results of the HLGN model again match Euler's solution very well even when the amplitude reaches $a/h=5$. For further discussion on the relationship between the effective wavelength and the amplitude when the amplitude approaches the maximum value, we refer the reader to Camassa et al. (Reference Camassa, Choi, Michallet, Rusas and Sveen2006).

Figure 16.

Relationship between the effective wavelength and wave amplitude, $\rho _2/{\rho _1}=0.78$.

Comparisons between the results of the HLGN model and Euler's solution for the velocity profile at the maximum interface displacement of two amplitudes of internal solitary waves are shown in figures 17(a) and 17(b), respectively. We again observe that the HLGN results match Euler's solution remarkably well.

Figure 17.

Velocity profile at the maximum interface displacement, $\rho _2/{\rho _1}=0.78$: (a) $a/h=1.7955$, (b) $a/h=5$.

To demonstrate the cost of running different models, we apply the HLGN-P3E5 model, the CC model, the DDK model and the numerical method for solving Euler's equations to calculate the internal solitary wave with $a/h = 1.7955$ when $\rho _2/{\rho _1}=0.78$. All calculations are performed on a computer equipped with a Xeon(R) W-11855M CPU @ 3.20 GHz and 32 GB of memory. The Newton–Raphson method, as mentioned in § 3, is applied to solve the nonlinear equations. The number of discrete nodes for describing the half-profile of the internal solitary wave is $300$. The HLGN-P3E5 model, the CC model and the DDK model are implemented by Mathematica using our own code, while Euler's solution is obtained by Matlab in Rusas (Reference Rusas2000). It should be noted that the results provided by the CC model should be obtained in advance for the DDK model (Debsarma et al. Reference Debsarma, Das and Kirby2010). Table 1 shows the CPU times for these models.

Table 1.

CPU times for the different models for calculating internal solitary waves, $a/h=1.7955$ and $\rho _2/{\rho _1}=0.78$.

From table 1, it is demonstrated that the CPU time of the CC model and the DDK model is relatively short, while the HLGN model requires longer computational time. The HLGN model, however, can be used to study the wave–current interaction, wave–wave interaction and wave–topography interaction, which go beyond the capabilities of Euler's solution (Rusas Reference Rusas2000) used in this comparison.

4.3. Comparison between the present HLGN model and the DJL equation for internal solitary waves in the presence of background shear-current

In this subsection, we will conduct some numerical tests to assess the accuracy of the HLGN model for the internal solitary waves in the presence of background shear-current for the deep configuration. The density ratio between the two fluid layers is selected as $\rho _2/\rho _1=0.952$. The amplitude of the internal solitary wave is fixed at $a/h=0.5$. Under these parameters, we determine the representative wavenumber as $kh = {\rm \pi}h/{\lambda _{e\text {-}MCC}} \approx 0.133$. We select the velocity profile of the background shear-current as the exponential form

(4.3)\begin{equation} {u_c}(Z) = U\,{{\rm e}^{{k_c}Z}}, \end{equation}

where $U$ is the current speed at the undisturbed interface between the two fluid layers, and $k_c$ is a real number.

We first consider the case where $U/c_0=1$ and $k_c/k=1$ (i.e. $k_ch=0.133$). We apply (3.4) with $\tilde {K}_2=3$ and $\tilde {K}_1=1$ to fit this velocity profile of the current, which can be written as

(4.4)\begin{equation} {u_c}(Z)= {c_0}\,{{\rm e}^{0.133({Z}/{h})}} \approx \left\{ \begin{array}{@{}ll@{}} {c_0}\left( {1 + 0.133\,\dfrac{Z}{h}+0.00885\,\dfrac{Z^2}{h^2}} \right), & \text{for}\ 0\leq Z \leq h,\\ {c_0}\,{{\rm e}^{0.133({Z}/{h})}}, & \text{for} \ {-\infty} < Z \leq 0. \end{array} \right. \end{equation}

Comparisons of the velocity profile between the fitted background shear-current and the desired background shear-current for the upper- and lower-fluid layers are shown in figures 18(a) and 18(b), respectively, where good agreement is observed.

Figure 18.

Comparisons of the velocity profile between the fitted background shear-current and the desired background shear-current in the two-layer fluid system, $U/c_0=1$ and $k_ch=0.133$: (a) upper-fluid layer; (b) lower-fluid layer.

We apply the HLGN-P5E7 model to obtain the converged results of the HLGN model for all the cases in this section. For comparison purposes, we also obtain the results of the DJL equation by using the open-source solver given by Dunphy, Subich & Stastna (Reference Dunphy, Subich and Stastna2011). It should be noted that in the DJL open-source solver, the velocity profile of the background shear-current is not necessarily fitted. Meanwhile, since the DJL equation considers the continuous density-stratified condition, we set the depth of the pycnocline as very small (i.e. 0.1 % of the total depth), and the depth ratio between the upper-fluid layer and lower-fluid layer as approximately 1/99.

The wave profile and the velocity profile at the maximum interface displacement obtained by the HLGN model and the DJL equation are shown in figures 19(a) and 19(b), respectively. We observe that the results obtained by the HLGN model show good agreement with those obtained by the DJL equation.

Figure 19.

Comparison between the results obtained by the HLGN model and the DJL equation, $\rho _2/{\rho _1}=0.952$, $a/h=0.5$, $U/c_0=1$ and $k_ch=kh=0.133$: (a) wave profile; (b) velocity profile at the maximum interface displacement.

Figures 20(a) and 20(b) show the wave profile and the velocity profile at the maximum interface displacement obtained by the HLGN model and the DJL equation for the case $U/c_0=-1$, where good agreement is also found. It is also shown that the wave profile is much narrower for the case $U/c_0=-1$ than that for the case $U/c_0=1$.

Figure 20.

Comparison between the results obtained by the HLGN model and the DJL equation, $\rho _2/{\rho _1}=0.952$, $a/h=0.5$, $U/c_0=-1$ and $k_ch=kh=0.133$: (a) wave profile; (b) velocity profile at the maximum interface displacement.

Then we apply the same approach to study the case $k_c=2k$ (i.e. $k_ch=0.266$). Taking the case $U/c_0=1$ as an example, we use (3.4) with $\tilde {K}_2=3$ and $\tilde {K}_1=3$ to fit this velocity profile of the current, which can be written as

(4.5)\begin{align} {u_c}(Z)={c_0}\,{{\rm e}^{0.266({Z}/{h})}} \approx \left\{ \begin{array}{@{}ll@{}} {c_0}\left( {1 + 0.266\,\dfrac{Z}{h}+0.03538\,\dfrac{Z^2}{h^2}} \right), & \text{for}\ 0\leq Z \leq h,\\ {c_0}\,{{\rm e}^{0.133({Z}/{h})}}\left( {0.96442{{ + }}0.09891\,\dfrac{{Z}}{h} + 0.00264\,\dfrac{{{Z^2}}}{{{h^2}}}} \right), & \text{for} \ {-\infty} < Z \leq 0. \end{array} \right. \end{align}

Comparisons of the velocity profile between the fitted background shear-current and the desired background shear-current for the upper- and lower-fluid layers are shown in figures 21(a) and 21(b), respectively, where we observe that the fitted results still match the desired results well.

Figure 21.

Comparisons of the velocity profile between the fitted background shear-current and the desired background shear-current in the two-layer fluid system, $U/c_0=1$ and $k_ch=0.266$: (a) upper-fluid layer; (b) lower-fluid layer.

Figures 22 and 23 show the results obtained by the HLGN model and the DJL equation for the cases $U/c_0=1$ and $U/c_0=-1$, respectively. Good agreement is found between the HLGN results and the DJL results in general.

Figure 22.

Comparison between the results obtained by the HLGN model and the DJL equation, $\rho _2/{\rho _1}=0.952$, $a/h=0.5$, $U/c_0=1$ and $k_ch=2kh=0.266$: (a) wave profile; (b) velocity profile at the maximum interface displacement.

Figure 23.

Comparison between the results obtained by the HLGN model and the DJL equation, $\rho _2/{\rho _1}=0.952$, $a/h=0.5$, $U/c_0=-1$ and $k_ch=2kh=0.266$: (a) wave profile; (b) velocity profile at the maximum interface displacement.