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Warianty tytułu
Konferencja
Solid Mechanics Conference (SolMech 2018) (41 ; 27–31.08. 2018 ; Warsaw, Poland)
Języki publikacji
Abstrakty
The combined stochastic-deterministic approach, which may be applied to the numerical analysis of a wide range of scalar elliptic problems of civil engineering, is presented in this paper. It is based on the well-known Monte Carlo concept with a random walk procedure, in which series of random paths are constructed. Additionally, it incorporates selected features of the meshless finite difference method, especially star selection criteria and a local weighted function approximation. The approach leads to the explicit stochastic formula relating one unknown function value with all a-priori known data parameters. Therefore, it allows for a fast and effective estimation of the solution value at the selected point(s), without the necessity of generation of large systems of equations, combining all unknown values. In such a manner, the proposed approach develops and extends the original standard Monte Carlo one toward analysis of boundary value problems with more complex shape geometry, natural boundary conditions, non-homogeneous right-hand sides as well as anisotropic and non-linear material models. The paper is illustrated with numerical results of selected elliptic problems, including a torsion problem of a prismatic bar, a stationary heat flow analysis with anisotropic and non-linear material functions, as well as an inverse heat problem. Moreover, the appropriate coupling with other deterministic methods (e.g., the finite element method) is considered.
Czasopismo
Rocznik
Tom
Strony
337–--375
Opis fizyczny
Bibliogr. 56 poz., rys. kolor.
Twórcy
autor
- Cracow University of Technology, Department of Civil Engineering, Institute for Computational Civil Engineering, Warszawska 24, 31-155 Cracow, Poland
Bibliografia
- 1. J. Orkisz, Finite Difference Method (part III), Handbook of Computational Solid Mechanics, pp. 336–431, Springer, Berlin, 1998.
- 2. W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, Numerical Recipes, The Art of Parallel Scientific Computing, Cambridge University Press, Cambridge, 1999.
- 3. O.C. Zienkiewicz, R.L. Taylor, Finite Element Method Its Basis and Fundamentals, Elsevier, London, 2005.
- 4. Ch. Constanda, D. Doty, W. Hamill, Boundary Integral Equation Methods and Numerical Solutions: Thin Plates on an Elastic Foundation, Springer, New York, 2016.
- 5. W.K. Liu, S. Jun, Y.F. Zhang, Reproducing kernel particle methods, International Journal for Numerical Methods in Engineering, 20, 1081–1106, 1995.
- 6. S. Li, W.K. Liu, Meshfree and particle methods and their applications, AppliedMechanics Review, 55, 1–34, 2002.
- 7. G.R. Liu, Mesh Free Methods: Moving Beyond The Finite Element Method, CRC Press, Boca Raton, 2003.
- 8. S. Milewski, Meshless finite difference method with higher order approximation – applications in mechanics, Archives of Computational Methods in Engineering, 19, 1, 1–49, 2012.
- 9. N. Metropolis, S. Ulam, The Monte Carlo method, Journal of the American Statistical Association, American Statistical Association, 44, 247, 335–341, 1949.
- 10. R. Eckhardt, S. Ulam, John von Neumann, and the Monte Carlo method, Los Alamos Science, Special Issue, 15, 131–137, 1987.
- 11. S. Milewski, Combination of the meshless finite difference approach with the Monte Carlo random walk technique for solution of elliptic problems, Computers & Mathematics with Applications, 76, 4, 854–876, 2018.
- 12. B. Moller, M. Beer, Fuzzy Randomness, Uncertainty in Civil Engineering and Computational Mechanics, Springer, Berlin, 2004.
- 13. B.D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge, 2007.
- 14. D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Reading, Addison-Wesley Professional, Boston, 1989.
- 15. R. Cazacu, L. Grama, Steel truss optimization using Genetic Algorithms and FEA, Procedia Technology, 12, C, 339–346, 2014.
- 16. G. Wang, F. Guo, A stochastic boundary element method for piezoelectric problems, Engineering Analysis with Boundary Elements, 95, 248–254, 2018.
- 17. S. Milewski, Determination of the truss static state by means of the combined FE/GA approach, on the basis of strain and displacement measurements, Inverse Problems in Science and Engineering, 27, 11, 1537–1558, 2019.
- 18. G.C. Luh, C.Y. Wu, Non-linear system identification using genetic algorithms, Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 213, 2, 105–118, 1999.
- 19. N. Perrone, R. Kao, A general finite difference method for arbitrary meshes, Computers and Structures, 5, 45–58, 1975.
- 20. M.J. Wyatt, G. Davies, C. Snell, A new difference based finite element method, Instn Engineers, 59, 2, 395–409, 1975.
- 21. T. Liszka, J. Orkisz, The finite difference method at arbitrary irregular grids and its application in applied mechanics, Computers and Structures, 11, 83–95, 1980.
- 22. S. Milewski, Selected computational aspects of the meshless finite difference method, Numerical Algorithms, 63, 1, 107–126, 2013.
- 23. M.D. Donsker, M. Kac, A sampling method for determining the lowest eigenvalue and the principle eigenfunction of Schrödinger’s equation, Journal of Research of the National Bureau of Standards, 44, 551–557, 1951.
- 24. G.E. Forsythe, R.A. Leibler, Matrix inversion by a Monte Carlo method, Mathematical Tables and Other Aids to Computation, 4, 127–129, 1950.
- 25. J.H. Curtiss, Monte Carlo methods for the iteration of linear operators, Journal of Mathematics and Physics, 32, 209–232, 1953.
- 26. J.H. Curtiss, A theoretical comparison of the efficiencies of two classical methods and a Monte Carlo method for computing one component of the solution of a set of linear algebraic equations, Symposium on Monte Carlo Methods, H.A. Meyer [Ed.], Wiley, New York, pp. 191–233, 1956.
- 27. H. Niederreiter, Quasi-Monte Carlo methods and pseudo-random numbers, Bulletin of American Mathematical Society, 84, 957–1041, 1978.
- 28. M. Mascagni, High dimensional numerical integration and massively parallel computing, Contemporary Mathematics, 1, 115, 53–73, 1991.
- 29. M.E. Muller, Some continuous Monte Carlo methods for the Dirichlet problem, Annals of Mathematical Statistics, 27, 569–589, 1956.
- 30. J.F. Reynolds, A Proof of the Random-Walk Method for Solving Laplace’s Equation in 2-D, The Mathematical Gazette, Mathematical Association, 49, 370, 416–420, 1965.
- 31. S. Hoshino, K. Ichida, Solution of partial differential equations by a modified random walk, Numerical Mathematics, 18, 61–72, 1971.
- 32. A.S. Sipin, Solving first boundary value problem for elliptic equations by Monte Carlo method, Monte Carlo Methods, Computational Mathematics and Mathematical Physics, 2, 113–119, 1979.
- 33. T.E. Booth, Exact Monte Carlo solutions of elliptic partial differential equations, Journal of Computational Physics, 39, 396–404, 1981.
- 34. A.F. Ghoniem, F. S. Sherman, Grid-free simulation of diffusion using random walk methods, Journal of Computational Physics, 61, 1–37, 1985.
- 35. S.M. Ermakov, V.V. Nekrutkin, A.S. Sipin, Random Processes for Classical Equations of Mathematical Physics, Kluwer Academic Publishers, Dordrecht, 1989.
- 36. K.K. Sabelfeld, Monte Carlo Methods in Boundary Value Problems, Springer, Berlin, Heidelberg, New York, 1991.
- 37. A. Haji-Sheikh, E.M. Sparrow, The floating random walk and its application to Monte Carlo solutions of heat equations, SIAM Journal on Applied Mathematics, 14, 2, 370–389, 1966.
- 38. I. Dimov, O. Tonev, Random walk on distant mesh points Monte Carlo Methods, Journal of Statistical Psysics, 70, 1993.
- 39. G.A. Mikhailov, New Monte Carlo Methods With Estimating Derivatives, V. S. P. Publishers, Boston, 1995.
- 40. M.N. Sadiku, Monte Carlo Methods for Electromagnetics, Taylor, London, 2009.
- 41. W. Yu, X. Wang, Advanced Field-Solver Techniques for RC Extraction of Integrated Circuits, Springer, Berlin, 2014.
- 42. S. Talebi, K. Gharehbash, H.R. Jalali, Study on random walk and its application to solution of heat conduction equation by Monte Carlo method, Progress in Nuclear Energy, 96, 18–35, 2017.
- 43. A. Jurlewicz, P. Kern, M.M. Meerschaert, H.-P. Scheffler, Fractional governing equations for coupled random walks, Computers and Mathematics with Applications, 64, 10, 3021–3036, 2012.
- 44. C.N. Angstmann, B.I. Henry, I. Ortega-Piwonka, Generalized master equations and fractional Fokker–Planck equations from continuous time random walks with arbitrary initial conditions, Computers and Mathematics with Applications, 73, 6, 1315–1324, 2017.
- 45. P. Ramachandran, M. Ramakrishna, S.C. Rajan, Efficient random walks in the presence of complex two-dimensional geometries, Computers and Mathematics with Applications, 53, 2, 329–344, 2007.
- 46. K. Sabelfeld, N. Mozartova, Sparsified randomization algorithms for large systems of linear equations and a new version of the random walk on boundary method, Monte Carlo Methods and Applications, 15, 3, 257–284, 2009.
- 47. G. Milstein, M.V. Tretyakov, Stochastic Numerics for Mathematical Physics, Springer, Berlin, 2010.
- 48. F. Bernal, J.A. Acebron, A comparison of higher-order weak numerical schemes for stopped stochastic differential equations, Communications in Computational Physics, 20, 703–732, 2016.
- 49. K. Baclawski, M.D. Donsker, M. Kac, Probability, Number Theory, and Statistical Physics, Selected Papers, The MIT Press, Cambridge, Massachusetts, pp. 268–280, 1979.
- 50. C.-O. Hwang, M. Mascagni, J.A. Given, A Feynman–Kac path-integral implementation for Poisson’s equation using an h-conditioned Green’s function, Mathematics and Computers in Simulation, 62, 3–6, 347–355, 2003.
- 51. P. Lancaster, K. Salkauskas, Surfaces generated by moving least-squares method, Mathematics of Computation, 155, 37, 141–158, 1981.
- 52. P. Lancaster, K. Salkauskas, Curve and Surface Fitting, Academic Press Inc., London, 1990.
- 53. J. Jaśkowiec, S. Milewski, The effective interface approach for coupling of the FE and meshless FD methods and applying essential boundary conditions, Computers and Mathematics with Applications, 70, 5, 962–979, 2015.
- 54. S. Milewski, R. Putanowicz, Higher order meshless schemes applied to the finite element method in elliptic problems, Computers & Mathematics with Applications, 77, 3, 779–802, 2019.
- 55. J. Jaśkowiec, S. Milewski, Coupling finite element method with meshless finite difference method in thermomechanical problems, Computers and Mathematics with Applications. 72, 9, 2259–2279, 2016.
- 56. P.C. Hansen, The L-Curve and its Use in the Numerical Treatment of Inverse Problems, Computational Inverse Problems, [in:] Electrocardiology, Advances in Computational Bioengineering, WIT Press, Boston, pp. 119–142, 2000.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-feffc478-f5b1-4cc5-94d4-2cddd53e10af