The effect of nanocrystalline Ni-W coating on the tensile properties of copper

C. N. Panagopoulos; E. P. Georgiou; D.A. Lagaris; V. Antonakaki; C. N. Panagopoulos; E. P. Georgiou; D.A. Lagaris; V. Antonakaki

doi:10.3934/matersci.2016.2.324

AIMS Materials Science

2016, Volume 3, Issue 2: 324-338. doi: 10.3934/matersci.2016.2.324

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Research article Topical Sections

The effect of nanocrystalline Ni-W coating on the tensile properties of copper

1.
Laboratory of Physical Metallurgy, National Technical University of Athens, Zografos, 15780, Athens, Greece
2.
KU Leuven, Dept. Materials Engineering (MTM), Kasteelpark Arenberg 44, B-3001, Leuven, Belgium
3.
Falex Tribology NV, Wingepark 23B, 3110, Rotselaar, Belgium

Received: 08 February 2016 Accepted: 09 March 2016 Published: 15 March 2016

Nanostructured Ni-W alloy coatings containing approximately 40 wt.% tungsten were electrodeposited onto copper substrates. The effect of the coatings thickness on the surface topography, microstructure and grain size was investigated with the aid of Atomic Force Microscopy (AFM), Scanning Electron Microscopy (SEM) and X-ray Diffraction (XRD) techniques respectively. In addition, this research work aims in understanding the influence and correlation between microstructure and thickness of these Ni-W coatings with the bulk mechanical properties of coated specimens. The experimental results indicated that the micro-hardness and Ultimate Tensile Strength (UTS) of the Ni-W coated copper were higher than that of bare copper, whereas both slightly increased with increasing coating thickness up to 21 μm. On the other hand, the ductility of Ni-W coated copper decreased significantly with increasing coating thickness. Thus it could be said that when applying Ni-W coatings there are certain limitations not only in terms of their composition, but their thickness, grain size and coating structure should be also taken into consideration, in order to obtain an understanding of their mechanical behavior.

Keywords:

Citation: C. N. Panagopoulos, E. P. Georgiou, D.A. Lagaris, V. Antonakaki. The effect of nanocrystalline Ni-W coating on the tensile properties of copper[J]. AIMS Materials Science, 2016, 3(2): 324-338. doi: 10.3934/matersci.2016.2.324

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Abstract

1. Introduction

Since Hirsch and Smale^[1] proposed the necessity of structural stability, this topic has received sufficient attention from scholars. This type of research focuses on whether small disturbances in the coefficients, initial data, and geometric models in the equations will cause significant disturbances in the solutions. At the beginning, people were mainly keen on dealing with the continuous dependence and convergence of fluid in porous media defined in two-dimensional or three-dimensional bounded regions. Freitas et al. ^[2] studied the long-term behavior of porous-elastic systems and proved that solutions depend continuously on the initial data. Payne and Straughan^[3] established a prior bounds and maximum principles for the solutions and obtained the structural stability of Darcy fluid in porous media, where they assumed that the temperature satisfies Newton's cooling conditions at the boundary. Scott^[4] considered the situation where Darcy fluid undergoes exothermic reactions at the boundary and obtained the continuous dependence of the solutions on the boundary parameters. Li et al.^[5] studied the interface connection between Brinkman–Forchheimer fluid and Darcy fluid in a bounded region, and obtained the continuous dependence on the heat source and Forchheimer coefficient. For more papers, on can see ^[6,7,8,9,10].

With the continuous development of technology and progress in the field of engineering, the necessity of studying the structural stability of fluid equations on a semi-infinite cylinder is even more urgent. The semi-infinite cylinder refers to a cylinder whose generatrix is parallel to the coordinate axis and its base is located on the coordinate plane, i.e.,

$ R = \Big\{(x_1, x_2, x_3)\Big|(x_1, x_2)\in D,\ x_3\geq0\Big\}, $

where $ D $ is a bounded domain on $ x_1Ox_2 $.

Li et al. have already done some work on this topic. Li and Lin^[11] proved the continuous dependence on the Forchheimer coefficient of the Brinkman–Forchheimer equations in $ R $. Papers ^[12] and ^[13] obtained structural stability for Forchheimer fluid and temperature-dependent bidispersive flow in $ R $, respectively.

In this paper, we introduce a new cylinder with a disturbed base, which has been considered in ^[14]. Let $ D(f) $ represent the disturbed base, i.e.,

$ D(f) = \Big\{(x_1, x_2, x_3)\Big|x_3 = f(x_1, x_2)\geq0,\ (x_1, x_2)\in D\Big\}, $

where the given function $ f $ satisfies

$\begin{align*} |f(x_1, x_2)| < \epsilon,\ \epsilon > 0. \end{align*}$ $

$ \epsilon $ is called the perturbation parameter. The cylinder with a disturbed base is defined as

$ R(f) = \Big\{(x_1, x_2, x_3)\Big|(x_1, x_2)\in D,\ x_3\geq f(x_1, x_2)\geq0\Big\}. $

Different from ^[14], we study the heat conduction equation applicable to the study of layered composite materials in binary mixtures^[15]

$\begin{align} b_1u_t& = k_1\triangle u-\gamma(u-v), \ \text{in} \ R\times\{t > 0\}, \end{align}$ $

(1.1)

$\begin{align} b_2v_t& = k_2\triangle v+\gamma(u-v), \ \text{in} \ R\times\{t > 0\}, \end{align}$ $

(1.2)

$\begin{align} u& = v = 0, \ \text{on}\ \partial D\times\{x_3 > 0\}\times\{t > 0\}, \end{align}$ $

(1.3)

$\begin{align} u& = v = 0, \ \text{in}\ R\times\{t = 0\}, \end{align}$ $

(1.4)

where $ k_1, k_2, b_1, b_2 $ and $ \gamma $ are positive constants. $ u $ and $ v $ are the temperature fields in each constituent. Papers ^[16,17,18] further discussed and generalized the application of Eqs (1.1) and (1.2).

In this paper, we shall also use the notations

$ R(z) = \Big\{(x_1, x_2, x_3)\Big| (x_1, x_2)\in D, x_3\geq z\geq 0\Big\}, $

$ D(z) = \Big\{(x_1, x_2, x_3)\Big| (x_1, x_2)\in D, x_3 = z\geq 0\Big\}. $

The main work of this article investigates the continuous dependence of solutions to Eqs (1.1)–(1.4) on perturbation parameters and base data. Due to many practical constraints, it is very common for the base of the cylinder to experience minor disturbance. Therefore, studying the effects of these disturbances is essential. To this end, we assume that $ u^* $ and $ v^* $ are perturbed solutions of Eqs (1.1)–(1.4) on $ R(f) $, and then prove that the difference between the unperturbed solutions and the perturbed solutions satisfies a first-order differential inequality. By solving this inequality, we can obtain the continuous dependence of the solution.

2. A priori bounds

On the finite end $ D $, we assume that the solutions to (1.1)–(1.4) satisfy

$\begin{align} u(t, {\boldsymbol{x}}) = L_{11}(t, x_1, x_2),\ v(t, {\boldsymbol{x}})& = L_{12}(t, x_1, x_2), t > 0,\ x_3 = 0,\ (x_1, x_2)\in D(0), \end{align}$ $

(2.1)

$\begin{align} u^*(t, {\boldsymbol{x}}) = L_{21}(t, x_1, x_2),\ v^*(t, {\boldsymbol{x}})& = L_{22}(t, x_1, x_2), t > 0,\ x_3 = f(x_1, x_2),\ (x_1, x_2)\in D(0). \end{align}$ $

(2.2)

In (2.1) and (2.2), the known functions $ L_{ij} (i, j = 1, 2) $ satisfy the compatibility conditions on $ \partial D $.

We let that $ \mathcal{H}_1(t, \boldsymbol{x}) $ and $ \mathcal{H}_2(t, \boldsymbol{x}) $ are specific functions who have the same boundary conditions as $ u^* $ and $ v^* $, respectively. That is

$\begin{align} \mathcal{H}_1(t, {\boldsymbol{x}}) = L_{21}(t, x_1, x_2)\exp\{-\sigma(x_3-f)\},\ \mathcal{H}_2(t, {\boldsymbol{x}}) = L_{22}(t, x_1, x_2)\exp\{-\sigma(x_3-f)\}, \end{align}$ $

(2.3)

where $ \sigma > 0 $.

We now derive some lemmas.

Lemma 2.1. If $ L_{21}, L_{22}\in H^1([0, \infty)\times D(f)) $, then

$\begin{align*} \int_0^t\exp\{-\eta_1\tau\}&\Big[k_1||\nabla u^*(\tau)||_{L^2(R(f))}^2+k_2||\nabla v^*(\tau)||_{L^2(R(f))}^2\Big]d\tau\leq d_1(t), \end{align*}$ $

where

$\begin{align} d_1(t)& = \int_0^t\exp\{-\eta_1\tau\}\Big[k_1||\nabla \mathcal{H}_1||_{L^2(R(f))}^2+k_2||\nabla \mathcal{H}_2||_{L^2(R(f))}^2\Big]d\tau \\ &+\exp\{-\eta_1t\}\Big[b_1||\mathcal{H}_1(t)||_{L^2(R(f))}^2+b_2||\mathcal{H}_2(t)||_{L^2(R(f))}^2\Big] \\ &+\frac{1}{2}\int_0^t\exp\{-\eta_1\tau\}\Big[b_1\eta_1||\mathcal{H}_{1,\tau}(\tau)||_{L^2(R(f))}^2 +b_2\eta_1||\mathcal{H}_{2,\tau}(\tau)||_{L^2(R(f))}^2\Big]d\tau \\ &+\frac{1}{2}\gamma\int_0^t\exp\{-\eta_1\tau\}||(\mathcal{H}_1-\mathcal{H}_2)(\tau)||_{L^2(R(f))}^2d\tau. \end{align}$ $

(2.4)

Proof. Using (1.1)–(1.4), we begin with

$\begin{align} \int_0^t\int_{R(f)}\exp\{-\eta_1\tau\}\Big[b_1u^*_\tau-k_1\triangle u^*+\gamma(u^*-v^*)\Big]u^*dxd\tau& = 0, \\ \int_0^t\int_{R(f)}\exp\{-\eta_1\tau\}\Big[b_2v^*_\tau-k_2\triangle v^*-\gamma(u^*-v^*)\Big]v^*dxd\tau& = 0. \end{align}$ $

We compute

$\begin{align} \frac{1}{2}\exp\{-\eta_1t\}&\Big[b_1||u^*(t)||_{L^2(R(f))}^2+b_2||v^*(t)||_{L^2(R(f))}^2\Big] \\ &+\int_0^t\exp\{-\eta_1\tau\}\Big[b_1\eta_1||u^*(\tau)||_{L^2(R(f))}^2+b_2\eta_1||v^*(\tau)||_{L^2(R(f))}^2\Big]d\tau \\ &+\int_0^t\exp\{-\eta_1\tau\}\Big[k_1||\nabla u^*(\tau)||_{L^2(R(f))}^2+k_2||\nabla v^*(\tau)||_{L^2(R(f))}^2\Big]d\tau \\ &+\gamma\int_0^t\exp\{-\eta_1\tau\}||(u^*-v^*)(\tau)||_{L^2(R(f))}^2d\tau \\ & = -\int_0^t\int_{D(f)}\exp\{-\eta_1\tau\}\Big[k_1\frac{\partial u^*}{\partial x_3}u^*+k_2\frac{\partial v^*}{\partial x_3}v^*\Big]dAd\tau. \end{align}$ $

(2.5)

On the other hand, we use (2.3) to compute

$\begin{align} -\int_0^t\int_{D(f)}&\exp\{-\eta_1\tau\}\Big[k_1\frac{\partial u^*}{\partial x_3}u^*+k_2\frac{\partial v^*}{\partial x_3}v^*\Big]dAd\tau \\ & = -\int_0^t\int_{D(f)}\exp\{-\eta_1\tau\}\Big[k_1\frac{\partial u^*}{\partial x_3}\mathcal{H}_1+k_2\frac{\partial v^*}{\partial x_3}\mathcal{H}_2\Big]dAd\tau \\ & = \int_0^t\int_{R(f)}\exp\{-\eta_1\tau\}\Big[k_1\nabla\cdot\Big(\nabla u^*\mathcal{H}_1\Big) +k_2\nabla\cdot\Big(\nabla v^*\mathcal{H}_2\Big)dxd\tau \\ & = \int_0^t\int_{R(f)}\exp\{-\eta_1\tau\}\Big[k_1\nabla u^*\cdot\nabla\mathcal{H}_1+k_2\nabla v^*\cdot\nabla \mathcal{H}_2\Big]dxd\tau \\ &+\exp\{-\eta_1t\}\int_{R(f)}\Big[b_1u^*\mathcal{H}_1+b_2v^*\mathcal{H}_2\Big]dx \\ &+\eta_1\int_0^t\int_{R(f)}\exp\{-\eta_1\tau\}\Big[b_1u^*\mathcal{H}_{1,\tau}+b_2v^*\mathcal{H}_{2,\tau}\Big]dxd\tau \\ &+\gamma\int_0^t\int_{R(f)}\exp\{-\eta_1\tau\}(u^*-v^*)(\mathcal{H}_{1}-\mathcal{H}_{2})dxd\tau \\ &\doteq \mathcal{F}_1+\mathcal{F}_2+\mathcal{F}_3+\mathcal{F}_4. \end{align}$ $

(2.6)

An application of the Schwarz inequality leads to

$\begin{align} \mathcal{F}_1&\leq\frac{1}{2}\int_0^t\exp\{-\eta_1\tau\}\Big[k_1||\nabla u^*(\tau)||_{L^2(R(f))}^2+k_2||\nabla v^*(\tau)||_{L^2(R(f))}^2\Big]d\tau \\ &+ \frac{1}{2}\int_0^t\exp\{-\eta_1\tau\}\Big[k_1||\nabla \mathcal{H}_1||_{L^2(R(f))}^2+k_2||\nabla \mathcal{H}_2||_{L^2(R(f))}^2\Big]d\tau, \end{align}$ $

(2.7)

$\begin{align} \mathcal{F}_2&\leq\frac{1}{2}\exp\{-\eta_1t\}\Big[b_1||u^*(t)||_{L^2(R(f))}^2+b_2||v^*(t)||_{L^2(R(f))}^2\Big] \\ &+\frac{1}{2}\exp\{-\eta_1t\}\Big[b_1||\mathcal{H}_1(t)||_{L^2(R(f))}^2+b_2||\mathcal{H}_2(t)||_{L^2(R(f))}^2\Big], \end{align}$ $

(2.8)

$\begin{align} \mathcal{F}_3&\leq\int_0^t\exp\{-\eta_1\tau\}\Big[b_1\eta_1||u^*(\tau)||_{L^2(R(f))}^2+b_2\eta_1||v^*(\tau)||_{L^2(R(f))}^2\Big]d\tau \\ &+\frac{1}{4}\int_0^t\exp\{-\eta_1\tau\}\Big[b_1\eta_1||\mathcal{H}_{1,\tau}(\tau)||_{L^2(R(f))}^2 +b_2\eta_1||\mathcal{H}_{2,\tau}(\tau)||_{L^2(R(f))}^2\Big]d\tau, \end{align}$ $

(2.9)

$\begin{align} \mathcal{F}_4&\leq\gamma\int_0^t\exp\{-\eta_1\tau\}||(u^*-v^*)(\tau)||_{L^2(R(f))}^2d\tau \\ &+\frac{1}{4}\gamma\int_0^t\exp\{-\eta_1\tau\}||(\mathcal{H}_1-\mathcal{H}_2)(\tau)||_{L^2(R(f))}^2d\tau. \end{align}$ $

(2.10)

Inserting Eqs (2.7)–(2.10) into (2.6) and combining (2.5), it can be obtained

$\begin{align} \int_0^t\exp\{-\eta_1\tau\}&\Big[k_1||\nabla u^*(\tau)||_{L^2(R(f))}^2+k_2||\nabla v^*(\tau)||_{L^2(R(f))}^2\Big]d\tau \\ &\leq\int_0^t\exp\{-\eta_1\tau\}\Big[k_1||\nabla \mathcal{H}_1||_{L^2(R(f))}^2+k_2||\nabla \mathcal{H}_2||_{L^2(R(f))}^2\Big]d\tau \\ &+\exp\{-\eta_1t\}\Big[b_1||\mathcal{H}_1(t)||_{L^2(R(f))}^2+b_2||\mathcal{H}_2(t)||_{L^2(R(f))}^2\Big] \\ &+\frac{1}{2}\int_0^t\exp\{-\eta_1\tau\}\Big[b_1\eta_1||\mathcal{H}_{1,\tau}(\tau)||_{L^2(R(f))}^2 +b_2\eta_1||\mathcal{H}_{2,\tau}(\tau)||_{L^2(R(f))}^2\Big]d\tau \\ &+\frac{1}{2}\gamma\int_0^t\exp\{-\eta_1\tau\}||(\mathcal{H}_1-\mathcal{H}_2)(\tau)||_{L^2(R(f))}^2d\tau. \end{align}$ $

(2.11)

From (2.11), we can conclude that Lemma 2.1 holds.

We not only need a prior bounds for $ v $ and $ v^* $, but also for $ u $ and $ u^* $. Since $ u $ and $ u^* $ are undisturbed solutions of Eqs (1.1)–(1.4), in Lemma 2.1 we only need to set $ f = 0 $ and replace $ L_{21} $ and $ L_{22} $ with $ L_{11} $ and $ L_{12} $, respectively, and then we can obtain the a prior bounds for $ u $ and $ u^* $.

Lemma 2.2. If $ L_{11}, L_{12}\in H^1([0, \infty)\times D) $, then

$\begin{align} \int_0^t\exp\{-\eta_1\tau\}&\Big[k_1||\nabla u(\tau)||_{L^2(R)}+k_2||\nabla v(\tau)||_{L^2(R)}\Big]d\tau\leq d_2(t), \end{align}$ $

where

$\begin{align} d_2(t)& = \int_0^t\exp\{-\eta_1\tau\}\Big[k_1||\nabla \mathcal{H}_3||_{L^2(R)}^2+k_2||\nabla \mathcal{H}_4||_{L^2(R)}^2\Big]d\tau \\ &+\exp\{-\eta_1t\}\Big[b_1||\mathcal{H}_3(t)||_{L^2(R)}^2+b_2||\mathcal{H}_4(t)||_{L^2(R)}^2\Big] \\ &+\frac{1}{2}\int_0^t\exp\{-\eta_1\tau\}\Big[b_1\eta_1||\mathcal{H}_{3,\tau}(\tau)||_{L^2(R)}^2 +b_2\eta_1||\mathcal{H}_{4,\tau}(\tau)||_{L^2(R)}^2\Big]d\tau \\ &+\frac{1}{2}\gamma\int_0^t\exp\{-\eta_1\tau\}||(\mathcal{H}_3-\mathcal{H}_4)(\tau)||_{L^2(R)}^2d\tau \end{align}$ $

and

$ \mathcal{H}_3(t, {\boldsymbol{x}}) = L_{11}(t, x_1, x_2)\exp\{-\sigma x_3\},\ \mathcal{H}_4(t, {\boldsymbol{x}}) = L_{12}(t, x_1, x_2)\exp\{-\sigma x_3\}. $

Remark 2.1. Lemmas 2.1 and 2.2 will provide a priori estimates for the proof of the lemmas in the next section.

3. Auxiliary functions

Let $ w $ and $ s $ represent the difference between the perturbed solutions and the unperturbed solutions, i.e.,

$\begin{align} w = u-u^*,\ s = v-v^*, \end{align}$ $

(3.1)

then $ w $ and $ s $ satisfy

$\begin{align} b_1w_t = k_1\triangle w-\gamma(w-s),&\ \text{in}\ R(\epsilon)\times\{t > 0\}, \end{align}$ $

(3.2)

$\begin{align} b_2s_t = k_2\triangle s+\gamma(w-s),&\ \text{in} \ R(\epsilon)\times\{t > 0\}, \end{align}$ $

(3.3)

$\begin{align} w = s = 0, \ & \text{on}\ \partial D\times\{x_3 > \epsilon\}\times\{t > 0\}, \end{align}$ $

(3.4)

$\begin{align} w = s = 0, \ &\text{in}\ R(\epsilon)\times\{t = 0\}. \end{align}$ $

(3.5)

To obtain the continuous dependence of the solution on the perturbation parameter, we establish a new energy function

$\begin{align} V(t, x_3) = \int_0^t\Big[||w(\tau)||_{L^2(R(x_3))}^2+||s(\tau)||_{L^2(R(x_3))}^2\Big]d\tau,\ x_3\geq\epsilon. \end{align}$ $

(3.6)

Noting the definition of $ R(x_3) $, we can obtain the derivative of $ V(t, x_3) $ as follows:

$\begin{align*} -\frac{\partial}{\partial x_3}V(t, x_3) = \int_0^t\Big[||w(\tau)||_{L^2(D(x_3))}^2+||s(\tau)||_{L^2(D(x_3))}^2\Big]d\tau. \end{align*}$ $

We introduce two auxiliary functions $ \varphi $ and $ \psi $ such that

$\begin{align} b_1\varphi_\tau+k_1\triangle\varphi = -w,\ b_2\psi_\tau+k_2\triangle\psi = -s,\ &\text{in}\ R(x_3), 0 < \tau < t, \end{align}$ $

(3.7)

$\begin{align} \varphi(\tau, x_1, x_2, x_3) = \psi(\tau, x_1, x_2, x_3) = 0, \ & \text{on}\ \partial D\times\{x_3\}, 0 < \tau < t, \end{align}$ $

(3.8)

$\begin{align} \varphi(\tau, x_1, x_2, x_3) = \psi(\tau, x_1, x_2, x_3) = 0,\ &(x_1, x_2)\in D, 0 < \tau < t, \end{align}$ $

(3.9)

$\begin{align} \varphi(t, {\boldsymbol{x}}) = \psi(t, {\boldsymbol{x}}) = 0, \ & \text{in}\ R(x_3), \end{align}$ $

(3.10)

$\begin{align} \varphi, \nabla\varphi, \psi, \nabla\psi\rightarrow0(\text{uniformly}\ \text{in} \ x_1, x_2, \tau) \ & \text{as}\ x_3\rightarrow \infty, \end{align}$ $

(3.11)

where $ x_3 > \epsilon $.

Next, we will derive some necessary properties of the auxiliary functions, which will play a crucial role in proving the continuous dependence of the solutions.

Lemma 3.1. If $ \varphi, \psi\in H^1([0, t]\times R(x_3)) $, then

$\begin{align*} \int_0^t\Big[b_1||\varphi_\tau(\tau)||_{L^2(R(x_3))}^2+b_2||\psi_\tau(\tau)||_{L^2(R(x_3))}^2\Big]d\tau\leq a_1V(t, x_3), \ x_3\geq\epsilon, \end{align*}$ $

where $ a_1 = \max\{b_1^{-1}, b_2^{-1}\} $.

Proof. We begin with

$\begin{align*} \int_0^t\int_{R(x_3)}\varphi_\tau\Big[b_1\varphi_\tau+k_1\triangle\varphi+w\Big]dxd\tau& = 0, \\ \int_0^t\int_{R(x_3)}\psi_\tau\Big[b_2\psi_\tau+k_2\triangle\psi+s\Big]dxd\tau& = 0. \end{align*}$ $

Using the divergence theorem $ \oint_{\partial R(x_3)}Fds = \int_{R(x_3)}div F dx $ and (3.8)–(3.11), we have

$\begin{align} b_1\int_0^t||\varphi_\tau(\tau)||_{L^2(R(x_3))}^2d\tau& = -\frac{1}{2}k_1||\nabla\varphi(0)||_{L^2(R(x_3))}^2 +\int_0^t\int_{R(x_3)}w\varphi_\tau dxd\tau \\ &\leq\Big[\int_0^t||\varphi_\tau(\tau)||_{L^2(R(x_3))}^2d\tau\int_0^t||w(\tau)||_{L^2(R(x_3))}^2d\tau\Big]^\frac{1}{2}, \end{align}$ $

(3.12)

and

$\begin{align} b_2\int_0^t||\psi_\tau(\tau)||_{L^2(R(x_3))}^2d\tau\leq\Big[\int_0^t||\psi_\tau(\tau)||_{L^2(R(x_3))}^2d\tau \int_0^t||s(\tau)||_{L^2(R(x_3))}^2d\tau\Big]^\frac{1}{2}. \end{align}$ $

(3.13)

Using the Schwarz inequality, (3.12) and (3.13), Lemma 3.1 can be obtained.

Lemma 3.2. If $ \varphi, \psi\in H^1(R(x_3)) $, then

$\begin{align*} \int_0^t\Big[k_1||\nabla\varphi(\tau)||_{L^2(R(x_3))}^2+k_2||\nabla\psi(\tau)||_{L^2(R(x_3))}^2\Big]d\tau \leq a_2V(t, x_3), \end{align*}$ $

where $ a_2 = \frac{1}{\lambda}\max\{k_1^{-1}, k_2^{-1}\} $.

Proof. We begin with

$\begin{align*} \int_0^t\int_{R(x_3)}\varphi\Big[b_1\varphi_\tau+k_1\triangle\varphi+w\Big]dxd\tau& = 0, \\ \int_0^t\int_{R(x_3)}\varphi\Big[b_2\psi_\tau+k_2\triangle\psi+s\Big]dxd\tau& = 0. \end{align*}$ $

Using the divergence theorem and Lemma 2.2, we have

$\begin{align} k_1\int_0^t||\nabla\varphi(\tau)||_{L^2(R(x_3))}^2d\tau& = -\frac{1}{2}b_1||\varphi(0)||_{L^2(R(x_3))}^2 +\int_0^t\int_{R(x_3)}w\varphi dxd\tau \\ &\leq\Big[\int_0^t||\varphi(\tau)||_{L^2(R(x_3))}^2d\tau\int_0^t||w(\tau)||_{L^2(R(x_3))}^2d\tau\Big]^\frac{1}{2} \\ &\leq\frac{1}{\sqrt{\lambda}}\Big[\int_0^t||\nabla_2\varphi(\tau)||_{L^2(R(x_3))}^2d\tau\int_0^t||w(\tau)||_{L^2(R(x_3))}^2d\tau\Big]^\frac{1}{2} \end{align}$ $

(3.14)

and

$\begin{align} k_2\int_0^t||\nabla\psi(\tau)||^2_{L^2(R(x_3))}d\tau \leq\frac{1}{\sqrt{\lambda}}\Big[\int_0^t||\nabla_2\psi(\tau)||_{L^2(R(x_3))}^2d\tau\int_0^t||s(\tau)||^2_{L^2(R(x_3))}d\tau\Big]^\frac{1}{2}. \end{align}$ $

(3.15)

Using the following inequality

$\begin{align} \sqrt{ab}+\sqrt{cd}\leq\sqrt{(a+c)(b+d)},\ \text{for}\ \ a,b,c,d > 0, \end{align}$ $

(3.16)

the Young inequality and Lemma 3.1, we can have from (3.14) and (3.15)

$\begin{align} \int_0^t\Big[k_1||\nabla\varphi(\tau)||_{L^2(R(x_3))}^2&+k_2||\nabla\psi(\tau)||_{L^2(R(x_3))}^2\Big]d\tau \\ &\leq\frac{1}{\sqrt{\lambda}}\Big\{\int_0^t\Big[k_1||\nabla_2\varphi(\tau)||_{L^2(R(x_3))}^2+k_2||\nabla_2\psi(\tau)||_{L^2(R(x_3))}^2\Big]d\tau \\ &\cdot\int_0^t\Big[k_1^{-1}||w(\tau)||_{L^2(R(x_3))}^2+k_2^{-1}||s(\tau)||^2_{L^2(R(x_3))}\Big]d\tau\Big\}^\frac{1}{2}. \end{align}$ $

(3.17)

From (3.17) we can obtain Lemma 3.2.

Lemma 3.3. If $ \varphi, \psi\in H^1(R(x_3)) $, then

$\begin{align} k_1\int_0^t&||\frac{\partial\varphi}{\partial x_3}(\tau)||_{L^2(D(x_3))}^2d\tau +k_2\int_0^t||\frac{\partial\psi}{\partial x_3}(\tau)||_{L^2(D(x_3))}^2d\tau \leq a_3V(t, x_3), \end{align}$ $

where $ a_3 $ is a positive constant.

Proof. Letting $ \delta $ be a positive constant. We compute

$\begin{align} \int_0^t\int_{R(x_3)}\Big[\frac{\partial\varphi}{\partial x_3}-\delta\varphi_\tau\Big] \Big[b_1\varphi_\tau+k_1\triangle\varphi+w\Big]dxd\tau& = 0, \end{align}$ $

(3.18)

$\begin{align} \int_0^t\int_{R(x_3)}\Big[\frac{\partial\psi}{\partial x_3}-\delta\psi_\tau\Big] \Big[b_2\psi_\tau+k_2\triangle\psi+s\Big]dxd\tau& = 0. \end{align}$ $

(3.19)

Using the divergence theorem and (3.8)–(3.10) in (3.18) and (3.19), we obtain

$\begin{align} \frac{1}{2}k_1\delta||\nabla\varphi(0)||_{L^2(R(x_3))}^2d\tau&+b_1\delta\int_0^t||\varphi_\tau(\tau)||_{L^2(R(x_3))}^2d\tau +\frac{1}{2}k_1\int_0^t||\frac{\partial\varphi}{\partial x_3}(\tau)||_{L^2(D(x_3))}^2d\tau \\ & = \int_0^t\int_{R(x_3)}\frac{\partial\varphi}{\partial x_3}\varphi_\tau dxd\tau +\int_0^t\int_{R(x_3)}\Big[\frac{\partial\varphi}{\partial x_3}-\delta\varphi_\tau\Big] wdxd\tau. \end{align}$ $

(3.20)

Using the Schwarz inequality, we obtain

$\begin{align} \int_0^t\int_{R(x_3)}\frac{\partial\varphi}{\partial x_3}\varphi_\tau dxd\tau &\leq\Big[\int_0^t||\frac{\partial\varphi}{\partial x_3}(\tau)||_{L^2(R(x_3))}^2d\tau\int_0^t||\varphi_\tau(\tau)||_{L^2(R(x_3))}^2d\tau\Big]^\frac{1}{2}, \end{align}$ $

(3.21)

$\begin{align} \int_0^t\int_{R(x_3)}\frac{\partial\varphi}{\partial x_3}wdxd\tau& \leq\Big[\int_0^t||\frac{\partial\varphi}{\partial x_3}(\tau)||_{L^2(R(x_3))}^2d\tau\int_0^t||w(\tau)||_{L^2(R(x_3))}^2d\tau\Big]^\frac{1}{2}, \end{align}$ $

(3.22)

$\begin{align} -\delta\int_0^t\int_{R(x_3)}\varphi_\tau wdxd\tau& \leq\delta\Big[\int_0^t||\varphi_\tau(\tau)||_{L^2(R(x_3))}^2d\tau\int_0^t||w(\tau)||_{L^2(R(x_3))}^2d\tau\Big]^\frac{1}{2}. \end{align}$ $

(3.23)

Inserting (3.21)–(3.23) into (3.20) and dropping the first two terms in the left of (3.20), we have

$\begin{align} \frac{1}{2}k_1\int_0^t||\frac{\partial\varphi}{\partial x_3}(\tau)||_{L^2(D(x_3))}^2d\tau &\leq\Big[\int_0^t||\frac{\partial\varphi}{\partial x_3}(\tau)||_{L^2(R(x_3))}^2d\tau\int_0^t||\varphi_\tau(\tau)||_{L^2(R(x_3))}^2d\tau\Big]^\frac{1}{2} \\ &+\Big[\int_0^t||\frac{\partial\varphi}{\partial x_3}(\tau)||_{L^2(R(x_3))}^2d\tau\int_0^t||w(\tau)||_{L^2(R(x_3))}^2d\tau\Big]^\frac{1}{2} \\ &+\delta\Big[\int_0^t||\varphi_\tau(\tau)||_{L^2(R(x_3))}^2d\tau\int_0^t||w(\tau)||_{L^2(R(x_3))}^2d\tau\Big]^\frac{1}{2}. \end{align}$ $

(3.24)

Similar, we can also have from (3.19)

$\begin{align} \frac{1}{2}k_2\int_0^t||\frac{\partial\psi}{\partial x_3}(\tau)||_{L^2(D(x_3))}^2d\tau &\leq\Big[\int_0^t||\frac{\partial\psi}{\partial x_3}(\tau)||_{L^2(R(x_3))}^2d\tau\int_0^t||\psi_\tau(\tau)||_{L^2(R(x_3))}^2d\tau\Big]^\frac{1}{2} \\ &+\Big[\int_0^t||\frac{\partial\psi}{\partial x_3}(\tau)||_{L^2(R(x_3))}^2d\tau\int_0^t||s(\tau)||_{L^2(R(x_3))}^2d\tau\Big]^\frac{1}{2} \\ &+\delta\Big[\int_0^t||\psi_\tau(\tau)||_{L^2(R(x_3))}^2d\tau\int_0^t||s(\tau)||_{L^2(R(x_3))}^2d\tau\Big]^\frac{1}{2}. \end{align}$ $

(3.25)

Using (3.16) and Lemmas 3.1 and 3.2, we obtain

$\begin{align} k_1\int_0^t||\frac{\partial\varphi}{\partial x_3}(\tau)||_{L^2(D(x_3))}^2d\tau &+k_2\int_0^t||\frac{\partial\psi}{\partial x_3}(\tau)||_{L^2(D(x_3))}^2d\tau \\ &\leq 2a_1a_2\Big\{\int_0^t\Big[b_1||\frac{\partial\varphi}{\partial x_3}(\tau)||_{L^2(R(x_3))}^2+b_2||\frac{\partial\psi}{\partial x_3}(\tau)||_{L^2(R(x_3))}^2\Big]d\tau \\ &\cdot\int_0^t\Big[k_1||\varphi_\tau(\tau)||_{L^2(R(x_3))}^2+k_2||\psi_\tau(\tau)||_{L^2(R(x_3))}^2\Big]d\tau\Big\}^\frac{1}{2} \\ &+2a_2\Big\{\int_0^t\Big[b_1||\frac{\partial\varphi}{\partial x_3}(\tau)||_{L^2(R(x_3))}^2+b_2||\frac{\partial\psi}{\partial x_3}(\tau)||_{L^2(R(x_3))}^2\Big]d\tau \\ &\cdot\int_0^t\Big[||w(\tau)||_{L^2(R(x_3))}^2+||s(\tau)||_{L^2(R(x_3))}^2\Big]d\tau\Big\}^\frac{1}{2} \\ &+2a_1\delta\Big\{\int_0^t\Big[k_1||\varphi_\tau(\tau)||_{L^2(R(x_3))}^2+k_2||\psi_\tau(\tau)||_{L^2(R(x_3))}^2\Big]d\tau \\ &\cdot\int_0^t\Big[||w(\tau)||_{L^2(R(x_3))}^2+||s(\tau)||_{L^2(R(x_3))}^2\Big]d\tau\Big\}^\frac{1}{2} \\ &\leq a_3V(t, x_3), \end{align}$ $

(3.26)

where $ a_3 = 2a_1^2a_2^2+2a_2^2+2a_1^2 $.

In the next section, we will use Lemma 3.3 to derive the continuous dependence of the solutions.

4. Main results

In this section, we first derive a bound for $ V(t, \epsilon) $. To do this, we define

$\begin{align} u(t, \mathit{\boldsymbol{x}}) = L_{11}(t, x_1, x_2),\ v(t, \mathit{\boldsymbol{x}})& = L_{12}(t, x_1, x_2), \ -\epsilon\leq x_3\leq 0, (x_1, x_2)\in D, t\in[0, +\infty), \end{align}$ $

(4.1)

$\begin{align} u^*(t, \mathit{\boldsymbol{x}}) = L_{21}(t, x_1, x_2),\ v^*(t, \mathit{\boldsymbol{x}})& = L_{22}(t, x_1, x_2), \ -\epsilon\leq x_3\leq f(x_1, x_2), (x_1, x_2)\in D, t\in[0, +\infty). \end{align}$ $

(4.2)

When $ -\epsilon\leq x_3\leq\epsilon $, we let

$\begin{align} w(t, \mathit{\boldsymbol{x}}) = u(t, \mathit{\boldsymbol{x}})-u^*(t, \mathit{\boldsymbol{x}}),\ s(t, \mathit{\boldsymbol{x}}) = v(t, \mathit{\boldsymbol{x}})-v^*(t, \mathit{\boldsymbol{x}}), (x_1, x_2)\in D, t\in[0, +\infty). \end{align}$ $

(4.3)

In view of (3.1) and (4.3), using the triangle inequality, it can be obtained that

$\begin{align} k_1\int_0^t\int_{R(-\epsilon)}\Big(\frac{\partial w}{\partial x_3}\Big)^2dxd\tau& +k_2\int_0^t\int_{R(-\epsilon)}\Big(\frac{\partial s}{\partial x_3}\Big)^2dxd\tau \\ &\leq \int_0^t\int_{R(-\epsilon)}\Big[k_1\Big(\frac{\partial u}{\partial x_3}\Big)^2 +k_2\Big(\frac{\partial v}{\partial x_3}\Big)^2\Big]dxd\tau \\ & +\int_0^t\int_{R(-\epsilon)}\Big[k_1\Big(\frac{\partial u^*}{\partial x_3}\Big)^2 +k_2\Big(\frac{\partial v^*}{\partial x_3}\Big)^2\Big]dxd\tau. \end{align}$ $

(4.4)

Using Lemmas 2.1 and 2.2, (4.1) and (4.2), from (4.4), we obtain

$\begin{align} k_1\int_0^t\int_{R(-\epsilon)}\Big(\frac{\partial w}{\partial x_3}\Big)^2dxd\tau& +k_2\int_0^t\int_{R(-\epsilon)}\Big(\frac{\partial s}{\partial x_3}\Big)^2dxd\tau \\ &\leq \int_0^t\int_{R}\Big[k_1\Big(\frac{\partial u}{\partial x_3}\Big)^2 +k_2\Big(\frac{\partial v}{\partial x_3}\Big)^2\Big]dxd\tau \\ & +\int_0^t\int_{R(f)}\Big[k_1\Big(\frac{\partial u^*}{\partial x_3}\Big)^2 +k_2\Big(\frac{\partial v^*}{\partial x_3}\Big)^2\Big]dxd\tau. \\ &\leq e^{\eta_1t}[d_1(t)+d_2(t)]\doteq d_3(t). \end{align}$ $

(4.5)

Now, we write the main theorem as:

Theorem 4.1. If $ L_{11}, L_{12}\in H^1([0, \infty)\times R), L_{21}, L_{22}\in H^1([0, \infty)\times R(f)) $ and $ t < \frac{\pi}{4a_1\gamma} $, then

$\begin{align} V(t, x_3)&\leq\exp\{-d_4(x_3-\epsilon)\}\Big\{ \frac{32}{d_4\pi}\max\{\frac{1}{k_1}, \frac{1}{k_2}\}d_3(t)\epsilon \\ &+d_5\int_0^t\Big[||(L_{11}-L_{21})(\tau)||^2_{L^2(D)}+||(L_{12}-L_{22})(\tau)||^2_{L^2(D)}\Big]d\tau\Big\}, x_3\geq\epsilon \end{align}$ $

holds, where $ d_4 = a_3^{-1}\max\{k_1, k_2\}^{-1} $ and $ d_5 = \frac{d_4\pi}{2}+\frac{2}{d_4} $.

Proof. Let $ x_3\geq\epsilon $ be a fixed point on the coordinate axis $ x_ 3 $. Using (3.7)–(3.11) and the divergence theorem, we can have

$\begin{align} V(x_3, t)& = -\int_0^t\int_{R(x_3)}w\Big[b_1\varphi_\tau+k_1\triangle\varphi\Big]dxd\tau -\int_0^t\int_{R(x_3)}s\Big[b_2\psi_\tau+k_2\triangle\psi\Big]dxd\tau \\ & = -\int_0^t\int_{R(x_3)}\Big[b_1\varphi_\tau w+b_2\psi_\tau s\Big]dxd\tau +\int_0^t\int_{R(x_3)}\Big[k_1\nabla w\cdot\nabla \varphi+k_2\nabla s\cdot\nabla \psi\Big]dxd\tau \\ &+\int_0^t\int_{D(x_3)}\Big[k_1w\frac{\partial \varphi}{\partial x_3}+k_2s\frac{\partial \psi}{\partial x_3}\Big]dAd\tau \\ & = -\int_0^t\int_{R(x_3)}\Big[b_1\varphi_\tau w+b_2\psi_\tau s\Big]dxd\tau -\int_0^t\int_{R(x_3)}\Big[k_1\triangle w \varphi+k_2\triangle s\psi\Big]dxd\tau \\ &+\int_0^t\int_{D(x_3)}\Big[k_1w\frac{\partial \varphi}{\partial x_3}+k_2s\frac{\partial \psi}{\partial x_3}\Big]dAd\tau \\ & = -\int_0^t\int_{R(x_3)}\Big[b_1\varphi_\tau w+b_2\psi_\tau s\Big]dxd\tau-\int_0^t\int_{R(x_3)}\Big[b_1\varphi w_\tau+b_2\psi s_\tau\Big]dxd\tau \\ &+\int_0^t\int_{D(x_3)}\Big[k_1w\frac{\partial \varphi}{\partial x_3}+k_2s\frac{\partial \psi}{\partial x_3}\Big]dAd\tau -\gamma\int_0^t\int_{R(x_3)}\Big(\varphi-\psi\Big)\Big(w-s\Big)dxd\tau. \end{align}$ $

(4.6)

In light of (1.4) and (3.10), it is clear that

$\begin{align} \int_0^t\int_{R(x_3)}\Big[b_1\varphi_\tau w+b_1\varphi w_\tau\Big]dxd\tau = 0,\ \int_0^t\int_{R(x_3)}\Big[b_2\psi_\tau s+b_2\psi s_\tau\Big]dxd\tau = 0. \end{align}$ $

(4.7)

A combination of the Hölder inequality, (3.16) and Lemma 3.3 leads to

$\begin{align} \int_0^t\int_{D(x_3)}\Big[k_1w\frac{\partial \varphi}{\partial x_3}&+k_2s\frac{\partial \psi}{\partial x_3}\Big]dAd\tau \\ &\leq k_1\Big[\int_0^t||\frac{\partial \varphi}{\partial x_3}(\tau)||^2_{L^2(D(x_3))}d\tau\int_0^t||w(\tau)||^2_{L^2(D(x_3))}d\tau\Big]^\frac{1}{2} \\ &+k_2\Big[\int_0^t||\frac{\partial \psi}{\partial x_3}(\tau)||^2_{L^2(D(x_3))}d\tau\int_0^t||s(\tau)||^2_{L^2(D(x_3))}d\tau\Big]^\frac{1}{2} \\ &\leq \max\{\sqrt{k_1}, \sqrt{k_2}\}\Big[\int_0^t\Big(k_1||\frac{\partial \varphi}{\partial x_3}(\tau)||^2_{L^2(D(x_3))}+k_2||\frac{\partial \psi}{\partial x_3}(\tau)||^2_{L^2(D(x_3))}\Big) d\tau\Big]^\frac{1}{2} \\ &\cdot\Big[\int_0^t\Big(||w(\tau)||^2_{L^2(D(x_3))}+||s(\tau)||^2_{L^2(D(x_3))}\Big)d\tau\Big]^\frac{1}{2} \\ &\leq\sqrt{a_3}\max\{\sqrt{k_1}, \sqrt{k_2}\}\sqrt{V(t, x_3)}\Big[-\frac{\partial}{\partial x_3}V(t, x_3)\Big]^\frac{1}{2}. \end{align}$ $

(4.8)

For the fourth term in the right of (4.6), we compute

$\begin{align} -\gamma\int_0^t\int_{R(x_3)}&\Big(\varphi-\psi\Big)\Big(w-s\Big)dxd\tau \\ & \leq\gamma\Big[\int_0^t\Big(||\varphi(\tau)||^2_{L^2(R(x_3))}+||\psi(\tau)||^2_{L^2(R(x_3))}\Big)d\tau \\ &\cdot\int_0^t \Big(||w(\tau)||^2_{L^2(R(x_3))}+||s(\tau)||^2_{L^2(R(x_3))}\Big)d\tau\Big]^\frac{1}{2}. \end{align}$ $

(4.9)

Using the inequality (see p182 in ^[19])

$\begin{align} \int_0^1\phi^{2}dx\leq \frac{4}{\pi^2}\int_0^1(\phi')^{2}dx,\ \text{for}\ \phi(0) = 0, \end{align}$ $

(4.10)

we have from (4.9)

$\begin{align} -\gamma&\int_0^t\int_{R(x_3)}\Big(\varphi-\psi\Big)\Big(w-s\Big)dxd\tau \\ & \leq\gamma\frac{2t}{\pi}\Big[\int_0^t\Big(||\varphi_\tau(\tau)||^2_{L^2(R(x_3))}+||\psi_\tau(\tau)||^2_{L^2(R(x_3))}\Big)d\tau V(t, x_3)\Big]^\frac{1}{2} \\ &\leq\gamma\frac{2t}{\pi}a_1V(t, x_3), \end{align}$ $

(4.11)

where we have also used Lemma 3.1. Combining (4.6), (4.7), (4.8) and (4.11) and choosing $ t < \frac{\pi}{4a_1\gamma} $, we can have

$\begin{align} V(t, x_3)&\leq-\frac{1}{d_4}\frac{\partial}{\partial x_3}V(t, x_3), x_3 > \epsilon. \end{align}$ $

(4.12)

Integrating (4.12) from $ \epsilon $ to $ x_3 $, we have

$\begin{align} V(t, x_3)\leq V(t, \epsilon)\exp\{-d_4(x_3-\epsilon)\}, x_3\geq\epsilon. \end{align}$ $

(4.13)

Equation (4.13) only indicates that the solutions to (1.1)–(1.4) decay exponentially as $ x_3\rightarrow \infty $. This decay result is not rigorous because we do not yet know whether $ V(t, \epsilon) $ depends on the perturbation parameter $ \epsilon $. Therefore, we derive the explicit bound of $ V(t, \epsilon) $ in terms of $ \epsilon $ and $ L_{ij}(ij = 1, 2) $.

After letting $ x_3 = \epsilon $ in (4.12), we have

$\begin{align} V(\epsilon, t)&\leq \frac{1}{d_4}\int_0^t\Big[||w(\tau)||^2_{L^2(D(\epsilon))}+||s(\tau)||^2_{L^2(D(\epsilon))}\Big]dAd\tau \\ & = \frac{2}{d_4}\int_0^t\int_{-\epsilon}^\epsilon\int_{D(x_3)}\Big[w\frac{\partial w}{\partial x_3}+s\frac{\partial s}{\partial x_3}\Big]dxd\tau \\ &+\frac{2}{d_4}\int_0^t\Big[||(L_{11}-L_{21})(\tau)||^2_{L^2(D)}+||(L_{12}-L_{22})(\tau)||^2_{L^2(D)}\Big]d\tau \\ &\leq\frac{2}{d_4}\Big[\int_0^t||w(\tau)||_{L^2(D(x_3)\times[-\epsilon, \epsilon])}^2d\tau\int_0^t||\frac{\partial w}{\partial x_3}(\tau)||_{L^2(D(x_3)\times[-\epsilon, \epsilon])}^2d\tau\Big]^\frac{1}{2} \\ &+\frac{2}{d_4}\Big[\int_0^t||s(\tau)||_{L^2(D(x_3)\times[-\epsilon, \epsilon])}^2d\tau\int_0^t||\frac{\partial s}{\partial x_3}(\tau)||_{L^2(D(x_3)\times[-\epsilon, \epsilon])}^2d\tau\Big]^\frac{1}{2} \\ &+\frac{2}{d_4}\int_0^t\Big[||(L_{11}-L_{21})(\tau)||^2_{L^2(D)}+||(L_{12}-L_{22})(\tau)||^2_{L^2(D)}\Big]d\tau. \end{align}$ $

(4.14)

Using (4.10) again, we have

$\begin{align} \int_0^t||w(\tau)||_{L^2(D(x_3)\times[-\epsilon, \epsilon])}^2d\tau& \leq\frac{16\epsilon^2}{\pi^2}\int_0^t||\frac{\partial w}{\partial x_3}(\tau)||_{L^2(D(x_3)\times[-\epsilon, \epsilon])}^2d\tau \\ &+2\epsilon\int_0^t||(L_{11}-L_{21})(\tau)||^2_{L^2(D)}d\tau, \end{align}$ $

(4.15)

$\begin{align} \int_0^t||s(\tau)||_{L^2(D(x_3)\times[-\epsilon, \epsilon])}^2d\tau& \leq\frac{16\epsilon^2}{\pi^2}\int_0^t||\frac{\partial s}{\partial x_3}(\tau)||_{L^2(D(x_3)\times[-\epsilon, \epsilon])}^2d\tau \\ &+2\epsilon\int_0^t||(L_{12}-L_{22})(\tau)||^2_{L^2(D)}d\tau. \end{align}$ $

(4.16)

Inserting (4.15) into (4.16) and combining the Schwarz inequality, we obtain

$\begin{align} V(\epsilon, t) &\leq\frac{32}{d_4\pi}\epsilon\int_0^t\Big[||\frac{\partial w}{\partial x_3}(\tau)||_{L^2(D(x_3)\times[-\epsilon, \epsilon])}^2 +||\frac{\partial s}{\partial x_3}(\tau)||_{L^2(D(x_3)\times[-\epsilon, \epsilon])}^2\Big]d\tau \\ &+\Big[\frac{d_4\pi}{2}+\frac{2}{d_4}\Big]\int_0^t\Big[||(L_{11}-L_{21})(\tau)||^2_{L^2(D)}+||(L_{12}-L_{22})(\tau)||^2_{L^2(D)}\Big]d\tau \\ &\leq\frac{32}{d_4\pi}\max\{\frac{1}{k_1}, \frac{1}{k_2}\}\epsilon\int_0^t\Big[k_1||\frac{\partial w}{\partial x_3}(\tau)||_{L^2(R(-\epsilon))}^2 +k_2||\frac{\partial s}{\partial x_3}(\tau)||_{L^2(R(-\epsilon))}^2\Big]d\tau \\ &+\Big[\frac{d_4\pi}{2}+\frac{2}{d_4}\Big]\int_0^t\Big[||(L_{11}-L_{21})(\tau)||^2_{L^2(D)}+||(L_{12}-L_{22})(\tau)||^2_{L^2(D)}\Big]d\tau. \end{align}$ $

(4.17)

In view of (4.5) and (4.13), from (4.17) we have Theorem 4.1.

Remark 4.1. Theorem 4.1 indicates that $ V(t, x_3) $ continuously depends on $ \epsilon $ and the base data. That is, when $ \epsilon $ approaches 0, then $ u(t, x_3) $ and $ v(t, x_3) $ approach 0. If $ \epsilon = 0 $, Theorem 4.1 is the Saint-Venant's principle type decay result.

Remark 4.2. In any cross-section of $ R $, the continuous dependence result can still be obtained. We compute

$\begin{align} \int_0^t\Big[||w(\tau)||^2_{L^2(D(x_3))}&+||s(\tau)||^2_{L^2(D(x_3))}\Big]d\tau \\ & = -2\int_0^t\int_{R(x_3)}\Big[w\frac{\partial w}{\partial x_3}+s\frac{\partial s}{\partial x_3}\Big]dxd\tau \\ &\leq2 \sqrt{V(x_3)}\Big[\int_0^t\Big[k_1||\frac{\partial w}{\partial x_3}(\tau)||_{L^2(R(-\epsilon))}^2 +k_2||\frac{\partial s}{\partial x_3}(\tau)||_{L^2(R(-\epsilon))}^2\Big]d\tau\Big]^\frac{1}{2}. \end{align}$ $

(4.18)

Using (4.18) and Theorem 4.1, we can obtain the continuous dependence result.

5. Conclusions

This article adopts the methods of the a prior estimates and energy estimate to obtain the continuous dependence of the solution on the base. This method can be further extended to other linear partial differential equation systems, such as pseudo-parabolic equation

$ u_t = \Delta u+\delta \Delta u_t, $

where $ \delta $ is a positive constant. However, for nonlinear equations (e.g., the Darcy equations), due to the inability to control nonlinear terms and derive a prior bounds for nonlinear terms, Lemma 3.3 will be difficult to obtain. This is a difficult problem we need to solve next.

Use of AI tools declaration

The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

Acknowledgments

This work is supported by the Research Team Project of Guangzhou Huashang College (2021HSKT01).

Conflict of interest

The author declares there is no conflict of interest.

References

[1]	Edelstein AS, Cammaratra RC (1996) Nanomaterials: synthesis, properties and applications, Bristol: Institute of physics.
[2]	Metikoš-Huković M, Grubač Z, Radić N, et al. (2006) Sputter deposited nanocrystalline Ni and Ni-W films as catalysts for hydrogen evolution. J Mol Catal A Chem 249: 172–180. doi: 10.1016/j.molcata.2006.01.020
[3]	Brenner A (1963) Electrodeposition of Alloys: Principles and Practice, Vol. 2, Publ. Academic Press Inc., London.
[4]	Panagopoulos CN, Plainakis GD, Tsoutsouva MG (2015) Corrosion of nanocrystalline Ni-W coated copper. J Surf Eng Mater Adv Technol 5: 65–72.
[5]	Sriraman KR, Raman SGS, Seshadri SK (2006) Synthesis and evaluation of hardness and sliding wear resistance of electrodeposited nanocrystalline Ni–W alloys. Mater Sci Eng A 418: 303–311. doi: 10.1016/j.msea.2005.11.046
[6]	Quiroga Arganaraz MP, Ribotta SB, Folquer ME, et al. (2011) Ni–W coatings electrodeposited on carbon steel: Chemical composition, mechanical properties and corrosion resistance. Electrochim Acta 56: 5898–5903. doi: 10.1016/j.electacta.2011.04.119
[7]	Iwasaki H, Higashi K, Nieh TG (2004) Tensile deformation and microstructure of a nanocrystalline Ni–W alloy produced by electrodeposition. Scripta Mater 50: 395–399.
[8]	Matsui I, Takigawa Y, Uesugi T, et al. (2013) Effect of orientation on tensile ductility of electrodeposited bulk nanocrystalline Ni–W alloys. Mater Sci Eng A 578: 318–322. doi: 10.1016/j.msea.2013.04.114
[9]	Panagopoulos CN, Plainakis GD, Lagaris DA (2011) Nanocrystalline Ni–W coatings on copper. Mater Sci Eng B 176: 477–479. doi: 10.1016/j.mseb.2010.03.058
[10]	Wu Y, Chang D, Kim D, et al. (2003) Influence of boric acid on the electrodepositing process and structures of Ni–W alloy coating. Surf Coat Technol 173: 259–264.
[11]	Hou KH, Chang Y-F, Chang S-M, et al. (2010) The heat treatment effect on the structure and mechanical properties of electrodeposited nano grain size Ni–W alloy coatings. Thin Solid Films 518: 7535–7540.
[12]	Mizushima I, Tang PT, Hansen HN, et al. (2006) Residual stress in Ni–W electrodeposits. Electrochim Acta 51: 6128–6134. doi: 10.1016/j.electacta.2005.11.053
[13]	Panagopoulos CN, Georgiou EP (2007) The effect of hydrogen charging on the mechanical behaviour of 5083 wrought aluminum alloy. Corros Sci 49: 4443–4451. doi: 10.1016/j.corsci.2007.03.047
[14]	Metals Handbook. 9th Ed., Alloy Phase Diagrams, ASM, USA, 1992.
[15]	Cullity BD (1959) Elements of X-ray Diffraction, 1st Edition, Addison-Wesley Publishing Company, Massachusetts.
[16]	Auerswald J, Fecht H-J (2010) Nanocrystalline Ni-W for wear-resistant coatings and electroforming. J Electrochem Soc 157: 199–205.
[17]	Mizushima I, Tang PT, Hansen HN, et al. (2006) Residual stress in Ni–W electrodeposits. Electrochim Acta 51: 6128–6134. doi: 10.1016/j.electacta.2005.11.053
[18]	Dieter GE (1988) Mechanical Metallurgy, SI Metric Edition, Publ. McGraw-Hill, London.
[19]	Kumar KS, Van Swygenhoven H, Suresh S (2003) Mechanical behavior of nanocrystalline metals and alloys. Acta Mater 51: 5743–5774. doi: 10.1016/j.actamat.2003.08.032
[20]	Hahn H, Padmanabhan KA (1995) Mechanical response of nanostructured materials. Nanostruct Mater 6: 191–200. doi: 10.1016/0965-9773(95)00042-9
[21]	Wang N, Wang Z, Aust KT, et al. (1995) Effect of grain size on mechanical properties of nanocrystalline materials. Acta Metall Mater 43: 519–528.
[22]	Flinn RA, Trojan PK (1994) Engineering materials and their applications, 4^thed., Wiley-VCH, Boston.
[23]	Meyers MA, Mishra A, Benson DJ (2006) Mechanical properties on nanocrystalline materials. Progr Mater Sci 51: 427–556. doi: 10.1016/j.pmatsci.2005.08.003
[24]	Panagopoulos CN, Pelegri AA (1993) Tensile properties of zinc coated aluminium. Surf Coat Technol 57: 203–206. doi: 10.1016/0257-8972(93)90041-L
[25]	Armstrong DEJ, Haseeb ASMA, Roberts SG, et al. (2012) Nanoindentation and micro-mechanical fracture toughness of electrodeposited nanocrystalline Ni–W alloy films. Thin Solid Films 520: 4369–4372.
[26]	Panagopoulos CN, Papachristos VD, Sigalas G (1999) Tensile behaviour of as deposited and heat-treated electroless Ni–P deposits. J Mater Sci 34: 2587–2600.

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