One-dimensional finite element grids based on a localized truncation error analysis

✍ Scribed by S. C. Hagen; J. J. Westerink; R. L. Kolar

• Publisher: John Wiley and Sons
• Year: 2000
• Tongue: English
• Weight: 375 KB
• Volume: 32
• Category: Article
• ISSN: 0271-2091
• DOI: 10.1002/(sici)1097-0363(20000130)32:2<241::aid-fld947>3.0.co;2-#

✦ Synopsis

With the exponential increase in computing power, modelers of coastal and oceanic regions are capable of simulating larger domains with increased resolution. Typically, these models use graded meshes wherein the size of the elements can vary by orders of magnitude. However, with notably few exceptions, the graded meshes are generated using criteria that neither optimize placement of the node points nor properly incorporate the physics, as represented by discrete equations, underlying tidal flow and circulation to the mesh generation process. Consequently, the user of the model must heuristically adjust such meshes based on knowledge of local flow and topographical features -a rough and time consuming proposition at best. Herein, a localized truncation error analysis (LTEA) is proposed as a means to efficiently generate meshes that incorporate estimates of flow variables and their derivatives. In a one-dimensional (1D) setting, three different LTEA-based finite element grid generation methodologies are examined and compared with two common algorithms: the wavelength to Dx ratio criterion and the topographical length scale criterion. Errors are compared on a per node basis. It is shown that solutions based on LTEA meshes are, in general, more accurate (both locally and globally) and more efficient. In addition, the study shows that the first four terms of the ordered truncation error series are in direct competition and, subsequently, that the leading order term of the truncation error series is not necessarily the dominant term. Analyses and results from this 1D study lay the groundwork for developing an efficient mesh generating algorithm suitable for two-dimensional (2D) models.