Abstract
In this paper, present solution of one-dimensional linear parabolic differential equation by using Forward difference, backward difference, and Crank Nicholson method. First, the solution domain is discretized using the uniform mesh for step length and time step. Then applying the proposed method, we discretize the linear parabolic equation at each grid point and then rearranging the obtained discretization scheme we obtain the system of equation generated with tri-diagonal coefficient matrix. Now applying inverse matrixes method and writing MATLAB code for this inverse matrixes method we obtain the solution of one-dimensional linear parabolic differential equation. The stability of each scheme analyses by using Von-Neumann stability analysis technique. To validate the applicability of the proposed method, two model example are considered and solved for different values of mesh sizes in both directions. The convergence has been shown in the sense of maximum absolute error (E∞) and Root mean error (E2). Also, condition number (K(A)) and Order of convergence are calculated. The stability of this Three class of numerical method is also guaranteed and also, the comparability of the stability of these three methods is presented by using the graphical and tabular form. The proposed method is validated via the same numerical test example. The present method approximate exact solution very well.
Highlights
Numerical analysis is a subject that involves computational methods for studying and solving mathematical problems
The aim of this paper is to develop the accurate and stable three methods forward difference, Backward difference and Crank Nicholson numerical method that is capable of producing a solution of linear type Partial Differential Equations (PDEs) equation and approximate the exact solution
Forward difference, Backward difference, and Crank Nicholson is used to obtaining the scheme to solve one-dimensional linear parabolic differential equation
Summary
Numerical analysis is a subject that involves computational methods for studying and solving mathematical problems. It is a branch of mathematics and computer the science that creates, analyzes, and implements algorithms for solving mathematical problems numerically [2, 13]. PDEs have a wide range of applications to realworld problems in science and engineering, the majority of PDEs do not have analytical solutions. It is, important to be able to obtain an accurate solution numerically.
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