Iterative differential quadrature algorithms for modified Burgers equation. Issue 1 (5th March 2018)

• Record Type: Journal Article
• Authors: V.S., Aswin; Awasthi, Ashish
• Is Part Of: Engineering computations. Volume 35:Issue 1(2018)
• Page Start: 235
• Page End: 250
• Publication Date: 2018-03-05
• Subjects: Differential quadrature method -- Finite difference methods -- Modified Burgers equation -- Vectorized MATLAB programs; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-04-2017-0140
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: This paper aims to investigate the features of three vectorized iterative numerical schemes used to simulate the behavior of modified Burgers equation (MBE). Design/methodology/approach: Two of the schemes comprise differential quadrature and finite difference methods, while the third scheme consists of only differential quadrature for the derivative approximations. Proposed schemes are simulated for well-posed problems of MBE having known the analytic solution. The computational complexity of the schemes is examined through monitoring the time taken to complete the simulation. The results are compared with the analytic solution with the help of discrete error norms. Also, the accuracy of the proposed schemes is compared with that of the existing schemes in the literature. Vectorized MATLAB programs of the schemes are used for all investigations. Findings: It is observed that all the three schemes succeeded in producing a good replication of the exact solution. The results are closer to the analytical solution than the results in the literature. Among the three schemes, the scheme labeled as FDTDQS is found highly accurate and computationally cheaper using fewer grid points. From the vectorized MATLAB programs provided, it is evident that the implementation of the schemes is simple. Originality/value: This study gives an idea about three numerical schemes for a highly nonlinear problem. This mathematical framework can be adopted to any one-dimensional partial differential equation as well, and the provided program will be helpful to generate more fast and accurate vectorized code in MATLAB.