- PII
- 10.31857/S2686954323600726-1
- DOI
- 10.31857/S2686954323600726
- Publication type
- Status
- Published
- Authors
- Volume/ Edition
- Volume 514 / Issue number 1
- Pages
- 44-51
- Abstract
- Based on the geometric characteristics of the tetrahedron, quantitative estimates of its degeneracy are proposed and their relationship with the condition number of local bases generated by the edges emerging from the same vertex is established. The concept of the tetrahedron degeneracy index is introduced in several versions and their practical equivalence to each other is established. To assess the quality of a particular tetrahedral partition, it is proposed to calculate the empirical distribution function of the degeneracy index on its tetrahedral elements. A model irregular triangulation (tetrahedralization or tetrahedral partition) of three-dimensional space is proposed, depending on the control parameter that determines the quality of its elements. The coordinates of the tetrahedra vertices of the model triangulation tetrahedrons are the sums of the corresponding coordinates of the nodes of some given regular grid and random increments to them. For various values of the control parameter, the empirical distribution function of the tetrahedron degeneration index is calculated, which is considered as a quantitative characteristic of the quality of tetrahedra in the triangulation of a three-dimensional region.
- Keywords
- индекс вырождения тетраэдр триангуляция регулярная сетка псевдослучайный вектор эмпирическая функция распределения
- Date of publication
- 01.01.2023
- Year of publication
- 2023
- Number of purchasers
- 0
- Views
- 43
References
- 1. Gallagher R.H. Finite Element Analysis: Fundamentals. Berlin, Heidelberg: Springer-Verlag 1976. 396 c.
- 2. Fletcher C.A.J. Computational Galerkin methods. NY, Berlin, Heidelberg, Tokio: Springer-Verlag, 1984. 309 c.
- 3. Cockburn B., Shu C.-W. The Runge-Kutta discontinuous Galerkin method for conservation laws V: multidimensional systems // Journal of Computational Physics, 1998, V. 141, C. 199–224. https://doi.org/10.1006/jcph.1998.5892
- 4. Sugihara K. Degeneracy and Instability in Geometric Computation. In: Kimura, F. (eds) Geometric Modelling. GEO 1998. IFIP V. 75. Boston: Springer, 2001, C. 3–17. https://doi.org/10.1007/978-0-387-35490-3_1
- 5. Василевский Ю.В., Данилов А.А., Липников К.Н., Чугунов В.Н. Автоматизированные технологии построения неструктурированных расчетных сеток. “Нелинейная вычислительная механика прочности.” Т. IV. Под общ. ред. В.А. Левина. Москва: Физматлит, 2016. 216 с.
- 6. Preparata F.P., Shamos M.I. Computational Geometry: An introduction. NY, Berlin, Heidelberg, Tokio: Springer-Verlag, 1985. 400 c.
- 7. Hjelle Ø., Dæhlen M. Triangulations and Applications. Berlin, Heidelberg: Springer, 2006, 240 c.
- 8. De Loera J.A., Rambau J., Santos F. Triangulations. Structures for Algorithms and Applications. (Algorithms and Computation in Mathematics, V. 25) First Edition. Berlin Heidelberg: Springer-Verlag, 2010. 548 c.
- 9. Криксин Ю.А., Тишкин В.Ф. Об одном подходе к оценке вырождения треугольного элемента в триангуляции // Доклады Российской академии наук. Математика, информатика, процессы управления. 2023. Т. 510. С. 52–56. https://doi.org/10.31857/S2686954323600088
- 10. Barbu A., Zhu S.-Ch. Monte Carlo Methods. Los Angeles: Springer, 2020. 433 c.
- 11. Соболь И.М. Численные методы Монте-Карло. Москва: Наука, 1973. 312 с.
- 12. Wackerly D.D., Mendenhall III W., Scheaffer R.L. Mathematical Statistics with Applications. 7th edition. Belmont: Thomson Higher Education, 2008. 939 c.