Please help with 2-variable partial differential equation

In summary, a 2-variable partial differential equation is a mathematical equation that involves two independent variables and their partial derivatives. It is used to model and describe physical phenomena in various fields of science. The process of solving such an equation involves finding a function that satisfies it and any given boundary conditions. 2-variable partial differential equations have many real-world applications in fields such as physics, engineering, and economics. They differ from ordinary differential equations in terms of the number of independent variables and their use in modeling systems that vary in space and time. However, solving a 2-variable partial differential equation can be challenging due to the need for advanced mathematical techniques, accurate boundary conditions, and the possibility of using numerical methods.
  • #1
Langam
1
0
As a part of my research work, I need to find the number of charged particles at a given time 't', at a distance 'x' from anode. I derived a set of PDEs as per my requirement and assumptions which needs to be solved analytically.

\begin{equation}
\frac{\partial{N_e}}{\partial{t}} = \alpha N_e |W_e| - \beta N_eN_p -\eta N_e |W_e|-W_e\frac{\partial{N_e}}{\partial{x}} + D_e\frac{\partial^2{N_e}}{\partial{x}^2}
\end{equation}
\begin{equation}
\frac{\partial{N_p}}{\partial{t}} = \alpha N_e |W_e| - \beta N_eN_p -W_p\frac{\partial{N_p}}{\partial{x}}
\end{equation}
\begin{equation}
\frac{\partial{N_n}}{\partial{t}} = \eta N_e |W_e| - W_n\frac{\partial{N_n}}{\partial{x}}
\end{equation}
where,
N – no of particles, W- drift velocity, \alpha - ionization coefficient, \beta - recombination coefficient, \eta - attachment coefficient, e - electrons, p - positive ions, n - negative ions.
The initial electron and positive ion distributions are equal and given by:
\begin{equation}
\begin{aligned}
N_e(t=0,x) & = N_p(t=0,x)\\
& = 10^8 \exp(-[\frac{(x-d/2)^2}{(4.883*10^-3)^2}])
\end{aligned}
\end{equation}
Assuming, the anode is perfect absorber of electrons, i.e. derivative is zero, while cathode is a non-emitter of negative ions, as boundary conditions:
\begin{equation}
\begin{aligned}
\frac{\partial{N_e}}{\partial{t}}\vert_{x=0,t} & = 0\\
N_n(x=d,t)&= 0
\end{aligned}
\end{equation}
Here, W- drift velocity, $\alpha$ - ionization coefficient, $\eta$ - attachment coefficient depend on field(E) given by:
\begin{equation}
-\epsilon _r* \frac{\partial{E}}{\partial{x}} + \frac{e(N_p-N_e-N_n)}{\epsilon _0} =0
\end{equation}
Boundary conditions for field:
\begin{equation}
E(x=0) = E_{app}
\end{equation}
Since, the time step I have to consider is very small (less than nanoseconds), I assume that the \alpha, W, \eta and field (E) is constant for each time step. Similarly, N_p is taken to be constant while computing $N_e$ and likewise.

I looked up some textbooks and materials on net and have tried solving these equations. I am not confident about the solution and approach though.

My solution:
For N_e

While solving for $N_e$, since I expect an exponential increase, I assume the solution of the form:
\begin{equation}
N_e(x,t)= e^{(Xx+Yt)}u(x,t)
\end{equation}
Then I try to simplify it to get it into a heat equation of the form,
\begin{equation}
\frac{\partial{u}}{\partial{t}}= D_e\frac{\partial^2{u}}{\partial{x^2}}
\end{equation}

I used separable variables form on this equation and applied initial and boundary conditions to arrive at the solution:
\begin{equation}
\begin{aligned}
N_e(x,t)&= e^{[\frac{W_e}{2D_e}]x+[A - \frac{W_e^2}{4D_e}]t}e^{\lambda t} [10^8 e^{-d^2(10.48*10^3)}] \sinh{\sqrt\frac{\lambda}{D_e}x}\\
\text{where,}\ \lambda &= \frac{D_e}{x^2}[ln(e^{-ax^2+bx} + \sqrt(e^{-2ax^2+2bx}+1))]^2\\
a & = \frac{1}{M},\ b = \frac{d}{M}-\alpha, \ M =(4.883*10^{-3})^2
\end{aligned}
\end{equation}

For N_p
I define new variables
\begin{equation}
\begin{aligned}
\zeta & = Ax+Bt\\
\eta & = Cx+Dt\\
\text{such that}, N_p & = f(\zeta , \eta)
\end{aligned}
\end{equation}
I try to get it into the linear homogeneous equation of form,
\begin{equation}
\frac{\partial{N_p}}{\partial{\zeta}}+N_p N_e \beta = \alpha N_e |W_e|
\end{equation}

I solve this using integrating factors and arrive at solution :
\begin{equation}
N_p(x,t)=\frac{\alpha W_p |W_e|}{\beta x}+e^{N_e\beta t}[10^8 e^{-\frac{(x-W_pt-d/2)^2}{(4.883*10^{-3})^2}}-\frac{\alpha W_p |W_e|}{\beta (x-W_pt)}]\\
\end{equation}

For N_n:
I define new variables such that,
\begin{equation}
(x,t)=(\zeta=x-W_nt, \eta= x+W_nt)
\end{equation}
Then on simplification and applying boundary conditions, I arrive at
\begin{equation}
N_n = \frac{N_e\eta |W_e| (x-d)}{2W_n}
\end{equation}

For E:
\begin{equation}
E(x) = E_{app} + \frac{e(N_p-N_e-N_n)}{\epsilon _0 * \epsilon _r}
\end{equation}

I tried in Mathematica. It could not list the answer to these equations. Please let me know whether the approach and the solution is correct for my boundary and initial conditions.

Thanks in advance,
Langam
 
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  • #2


Dear Langam,

Thank you for sharing your research work with us. Your approach seems to be correct and your solution is well thought out. However, without knowing the details of your assumptions and the physical system you are studying, it is difficult for me to verify the correctness of your solution. In order to validate your results, I would suggest performing numerical simulations using a software such as COMSOL or ANSYS, which can handle complex PDEs and boundary conditions. This will not only help you verify your solution but also give you a better understanding of the system you are studying.

Additionally, you can also try to compare your results with existing literature on similar systems and see if they match. If not, you can further refine your assumptions and equations to improve the accuracy of your solution.

I wish you all the best in your research work.
 

Related to Please help with 2-variable partial differential equation

1. What is a 2-variable partial differential equation?

A 2-variable partial differential equation is a mathematical equation that involves two independent variables and their partial derivatives. It is used to model and describe physical phenomena in various fields of science, such as physics, engineering, and economics.

2. How is a 2-variable partial differential equation solved?

The process of solving a 2-variable partial differential equation involves finding a function that satisfies the equation and any given boundary conditions. This can be done analytically or numerically using various methods such as separation of variables, integral transforms, and finite difference methods.

3. What are some real-world applications of 2-variable partial differential equations?

2-variable partial differential equations have many applications in the fields of physics, engineering, and economics. They are used to model and analyze various physical phenomena, such as heat transfer, fluid dynamics, and electromagnetic fields. They are also used in economic models to describe the relationship between two variables, such as supply and demand.

4. What are the differences between a 2-variable partial differential equation and an ordinary differential equation?

The main difference between these two types of equations is the number of independent variables. A 2-variable partial differential equation involves two independent variables and their partial derivatives, while an ordinary differential equation involves only one independent variable and its derivatives. Additionally, 2-variable partial differential equations are used to model systems that vary in space and time, while ordinary differential equations are used for systems that vary only in time.

5. What are the challenges in solving a 2-variable partial differential equation?

Solving a 2-variable partial differential equation can be a challenging task, as it often requires advanced mathematical techniques and computational methods. It is also important to accurately define the boundary conditions and initial conditions for the problem. In some cases, the equation may not have an analytical solution, and numerical methods must be used, which can also be computationally intensive.

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