DOCSLIB.ORG

Domain Decomposition Methods for Uncertainty Quantification

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Domain Decomposition Methods for Uncertainty Quantification Domain Decomposition Methods for Uncertainty Quantification A thesis submitted to the Faculty of Graduate and Postdoctoral Affairs in partial fulfillment of the requirements for the degree Doctor of Philosophy by Waad Subber Department of Civil and Environmental Engineering Carleton University Ottawa-Carleton Institute of Civil and Environmental Engineering November 2012 ©2012 - Waad Subber Library and Archives Bibliotheque et Canada Archives Canada Published Heritage Direction du 1+1 Branch Patrimoine de I'edition 395 Wellington Street 395, rue Wellington Ottawa ON K1A0N4 Ottawa ON K1A 0N4 Canada Canada Your file Votre reference ISBN: 978-0-494-94227-7 Our file Notre reference ISBN: 978-0-494-94227-7 NOTICE: AVIS: The author has granted a non­ L'auteur a accorde une licence non exclusive exclusive license allowing Library and permettant a la Bibliotheque et Archives Archives Canada to reproduce, Canada de reproduire, publier, archiver, publish, archive, preserve, conserve, sauvegarder, conserver, transmettre au public communicate to the public by par telecommunication ou par I'lnternet, preter, telecommunication or on the Internet, distribuer et vendre des theses partout dans le loan, distrbute and sell theses monde, a des fins commerciales ou autres, sur worldwide, for commercial or non­ support microforme, papier, electronique et/ou commercial purposes, in microform, autres formats. paper, electronic and/or any other formats. The author retains copyright L'auteur conserve la propriete du droit d'auteur ownership and moral rights in this et des droits moraux qui protege cette these. Ni thesis. Neither the thesis nor la these ni des extraits substantiels de celle-ci substantial extracts from it may be ne doivent etre imprimes ou autrement printed or otherwise reproduced reproduits sans son autorisation. without the author's permission. In compliance with the Canadian Conformement a la loi canadienne sur la Privacy Act some supporting forms protection de la vie privee, quelques may have been removed from this formulaires secondaires ont ete enleves de thesis. cette these. While these forms may be included Bien que ces formulaires aient inclus dans in the document page count, their la pagination, il n'y aura aucun contenu removal does not represent any loss manquant. of content from the thesis. Canada Abstract In conjunction with modern high performance computing platforms, domain decom­ position methods permit computational simulations of physical and engineering systems using extremely high resolution numerical models. Such numerical models substantially reduce discretization error. In realistic simulations of numerous physical and engineer­ ing systems, it is however necessary to quantify confidence in numerical simulations for their acceptability as reliable alternatives to field experiments. The spectral stochastic fi­ nite element method is a popular computational tool for uncertainty quantification in many engineering systems of practical interest. For large-scale engineering systems, the spectral stochastic finite element method becomes computationally challenging as the size of the resulting algebraic linear system grows rapidly with the spatial mesh resolution and the order of the stochastic dimension. To address this issue, domain decomposition methods are described for uncertainty quantification of large- scale engineering systems. The math­ ematical framework is based on non-overlapping domain decomposition in the geometric space and functional expansion along the stochastic dimension. For numerical illustrations, two-dimensional and three-dimensional flow through porous media and elasticity problems with spatially varying non-Gaussian material properties are considered. The parallel per­ formance of the algorithms are investigated with respect to the mesh size, subdomain size, fixed problem size per subdomain, level of uncertainty and the order of stochastic dimen­ sion. For a fixed mesh resolution, the probabilistic domain decomposition techniques can­ not reduce the total solution time beyond a certain number of processors due to hardware related issues such as latency and bandwidth of the communication network. To fully ex­ ploit the massively parallel computers, parallelization is exploited in temporal dimension for uncertain dynamical systems. In particular, a probabilistic version of a parallel time integration scheme is developed to simulate the trajectories of a noisy non-linear dynam­ ical system described by stochastic differential equations. The numerical experiments are performed on a Linux cluster using MPI and PETSc parallel libraries. Dedicated to the living memory o f my beloved Mother. “Finding a good preconditioner to solve a given sparse linear system is often viewed as a combination of art and science.” Yousef Saad, Iterative methods for sparse linear systems, 2003. v Acknowledgments I would like to express my sincere gratitude and appreciation to my thesis advisor Pro­ fessor Abhijit Sarkar for his continued guidance and support throughout this research. Without his assistance and inspiration, I could not reach this far. I am very grateful to him for introducing me to the world of Uncertainty Quantification and High Performance Com­ puting. I would like to deeply thank my friend and colleague Mohammad Khalil for the invaluable discussions, support and encouragement during the course of this study. I sin­ cerely thank Professor John Goldak who has profoundly contributed to my knowledge and understanding of the finite element method. I am grateful to Professor Dominique Poirel for given me the opportunity to learn the fundamentals of computational fluid dynamics. Furthermore, I would like to thank Professor Heng Khoo and Professor Edward Sherwood for their helpful discussions and support throughout my graduate studies. Thanks and grati­ tude are also due to my fellow graduate students and friends especially Anton Matachniouk and Amin Fereidooni with whom I had wonderful times. Very special thanks to our Gradu­ ate Administrator Mrs. Payal Chadha for continuous and kind support during my graduate career. Finally, I would like to extend my gratitude and appreciation to my family who I am indebted forever for their support and encouragement in all of my accomplishments. Citations Chapter 3 closely follows the following references: 1. A. Toselli and O. Widlund. Domain Decomposition Methods - Algorithms and The­ ory, volume 34 of Springer Series in Computational Mathematics. Springer, Berlin, 2005. 2. B. Smith, P. Bjorstad, and W. Gropp. Domain Decomposition: Parallel Multilevel Methods for Elliptic Partial Differential Equations. Cambridge University Press, New York, 1996. 3. T. Mathew. Domain Decomposition Methods for the Numerical Solution of Partial Differential Equations. Number 61 in Lecture Notes in Computational Science and Engineering. Springer, Berlin, 2008. 4. A. Quarteroni and A. Valli. Domain Decomposition Methods for Partial Differential Equations. Numerical Mathematics and Scientific Computation. Oxford University Press, USA, 1999. Chapters 4 and 5 are closely based on the following references: 1. W. Subber and A. Sarkar. Domain decomposition method of stochastic PDEs: An iterative solution technique using one-level parallel preconditioner. 2012. Submitted to Journal of Probabilistic Engineering Mechanics. 2. W. Subber and A. Sarkar. Domain decomposition of stochastic PDEs: A novel pre­ conditioner and its parallel performance. In High Performance Computing Sympo­ sium, volume 5976 of Lecture Notes in Computer Science, pages 251268. Springer, 2010. 3. W. Subber, H. Monajemi, M. Khalil, and A. Sarkar. A scalable parallel uncertainty analysis and data assimilation framework applied to some hydraulic problems. In International Symposium on Uncertainties in Hydrologic and Hydraulic. Montreal, Canada, 2008. Chapter 6 is closely based on the following references: 1. W. Subber and A. Sarkar. A domain decomposition method of stochastic PDEs: An iterative solution techniques using a two-level scalable preconditioner. 2012. Submitted to Journal of Computational Physics. 2. W. Subber and A. Sarkar. Domain decomposition methods of stochastic PDEs: A two-level scalable preconditioner. Journal of Physics: Conference Series, 341(1), 2012. vii viii Chapter 7 is closely based on the following references: 1. W. Subber and A. Sarkar. Primal and dual-primal iterative substructuring methods of stochastic PDEs. Journal o f Physics: Conference Series, 256(1), 2010. 2. W. Subber and A. Sarkar. Domain decomposition methods of stochastic PDEs. In The Twentieth International Conference on Domain Decomposition Methods (DD20). California, USA, 2011. Accepted. Chapter 8 is closely based on the following references: 1. W. Subber and A. Sarkar. Dual-primal domain decomposition method for uncertainty quantification. 2012. To be Submitted to Journal of Computer Methods in Applied Mechanics and Engineering. 2. W. Subber and A. Sarkar. Primal and dual-primal iterative substructuring methods of stochastic PDEs. Journal of Physics: Conference Series, 256(1), 2010. 3. W. Subber and A. Sarkar. Domain decomposition methods of stochastic PDEs. In The Twentieth International Conference on Domain Decomposition Methods (DD20). California, USA, 2011. Accepted. Chapter 9 is closely based on the following references: 1. W. Subber and A. Sarkar. Domain decomposition method of stochastic PDEs: An iterative solution technique using one-level parallel preconditioner. 2012. Submitted to Journal of Probabilistic Engineering Mechanics.
Load more

Recommended publications
  • A High-Order Boris Integrator
    A high-order Boris integrator Mathias Winkela,∗, Robert Speckb,a, Daniel Ruprechta aInstitute of Computational Science, University of Lugano, Switzerland. bJ¨ulichSupercomputing Centre, Forschungszentrum J¨ulich,Germany. Abstract This work introduces the high-order Boris-SDC method for integrating the equations of motion for electrically charged particles in an electric and magnetic field. Boris-SDC relies on a combination of the Boris-integrator with spectral deferred corrections (SDC). SDC can be considered as preconditioned Picard iteration to com- pute the stages of a collocation method. In this interpretation, inverting the preconditioner corresponds to a sweep with a low-order method. In Boris-SDC, the Boris method, a second-order Lorentz force integrator based on velocity-Verlet, is used as a sweeper/preconditioner. The presented method provides a generic way to extend the classical Boris integrator, which is widely used in essentially all particle-based plasma physics simulations involving magnetic fields, to a high-order method. Stability, convergence order and conserva- tion properties of the method are demonstrated for different simulation setups. Boris-SDC reproduces the expected high order of convergence for a single particle and for the center-of-mass of a particle cloud in a Penning trap and shows good long-term energy stability. Keywords: Boris integrator, time integration, magnetic field, high-order, spectral deferred corrections (SDC), collocation method 1. Introduction Often when modeling phenomena in plasma physics, for example particle dynamics in fusion vessels or particle accelerators, an externally applied magnetic field is vital to confine the particles in the physical device [1, 2]. In many cases, such as instabilities [3] and high-intensity laser plasma interaction [4], the magnetic field even governs the microscopic evolution and drives the phenomena to be studied.
    [Show full text]
  • Strong Form Meshfree Collocation Method for Higher Order and Nonlinear Pdes in Engineering Applications
    University of Colorado, Boulder CU Scholar Civil Engineering Graduate Theses & Dissertations Civil, Environmental, and Architectural Engineering Spring 1-1-2019 Strong Form Meshfree Collocation Method for Higher Order and Nonlinear Pdes in Engineering Applications Andrew Christian Beel University of Colorado at Boulder, [email protected] Follow this and additional works at: https://scholar.colorado.edu/cven_gradetds Part of the Civil Engineering Commons, and the Partial Differential Equations Commons Recommended Citation Beel, Andrew Christian, "Strong Form Meshfree Collocation Method for Higher Order and Nonlinear Pdes in Engineering Applications" (2019). Civil Engineering Graduate Theses & Dissertations. 471. https://scholar.colorado.edu/cven_gradetds/471 This Thesis is brought to you for free and open access by Civil, Environmental, and Architectural Engineering at CU Scholar. It has been accepted for inclusion in Civil Engineering Graduate Theses & Dissertations by an authorized administrator of CU Scholar. For more information, please contact [email protected]. Strong Form Meshfree Collocation Method for Higher Order and Nonlinear PDEs in Engineering Applications Andrew Christian Beel B.S., University of Colorado Boulder, 2019 A thesis submitted to the Faculty of the Graduate School of the University of Colorado Boulder in partial fulfillment of the requirement for the degree of Master of Science Department of Civil, Environmental and Architectural Engineering 2019 This thesis entitled: Strong Form Meshfree Collocation Method for Higher Order and Nonlinear PDEs in Engineering Applications written by Andrew Christian Beel has been approved for the Department of Civil, Environmental and Architectural Engineering Dr. Jeong-Hoon Song (Committee Chair) Dr. Ronald Pak Dr. Victor Saouma Dr. Richard Regueiro Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline.
    [Show full text]
  • Workshop High-Order Numerical Approximation for Partial Differential Equations
    Workshop High-Order Numerical Approximation for Partial Differential Equations Hausdorff Center for Mathematics University of Bonn February 6-10, 2012 Organizers Alexey Chernov (University of Bonn) Christoph Schwab (ETH Zurich) Program overview Time Monday Tuesday Wednesday Thursday Friday 8:00 Registration 8:40 Opening 9:00 Stephan Sloan Ainsworth Demkowicz Buffa 10:00 Canuto Nobile Sherwin Houston Beir~ao da Veiga 10:30 Reali 11:00 Coffee break 11:30 Quarteroni Gittelson Sch¨oberl Sch¨otzau Sangalli 12:00 Bespalov 12:30 Lunch break 14:30 Dauge Xiu Wihler D¨uster Excursions: 15:00 Yosibash Arithmeum 15:30 Melenk Litvinenko Hesthaven or Coffee break 16:00 Maischak Rank Botanic 16:30 Coffee break Coffee break Gardens 17:00 Maday Hackbusch Hiptmair {18:00 Free Time 20:00 Dinner 2 Detailed program Monday, 9:00{9:55 Ernst P. Stephan hp-adaptive DG-FEM for Parabolic Obstacle Problems Monday, 10:00{10:55 Claudio Canuto Adaptive Fourier-Galerkin Methods Monday, 11:30{12:25 Alfio Quarteroni Discontinuous approximation of elastodynamics equations Monday, 14:30{15:25 Monique Dauge Weighted analytic regularity in polyhedra Monday, 15:30{16:25 Jens Markus Melenk Stability and convergence of Galerkin discretizations of the Helmholtz equation Monday, 17:00{17:55 Yvon Maday A generalized empirical interpolation method based on moments and application Tuesday, 9:00{9:55 Ian H. Sloan PDE with random coefficients as a problem in high dimensional integration Tuesday, 10:00{10:55 Fabio Nobile Stochastic Polynomial approximation of PDEs with random coefficients Tuesday,
    [Show full text]
  • ICES REPORT 14-04 Collocation on Hp Finite Element Meshes
    ICES REPORT 14-04 February 2014 Collocation On Hp Finite Element Meshes: Reduced Quadrature Perspective, Cost Comparison With Standard Finite Elements, And Explicit Structural Dynamics by Dominik Schillinger, John a. Evans, Felix Frischmann, Rene R. Hiemstra, Ming-Chen Hsu, Thomas J.R. Hughes The Institute for Computational Engineering and Sciences The University of Texas at Austin Austin, Texas 78712 Reference: Dominik Schillinger, John a. Evans, Felix Frischmann, Rene R. Hiemstra, Ming-Chen Hsu, Thomas J.R. Hughes, "Collocation On Hp Finite Element Meshes: Reduced Quadrature Perspective, Cost Comparison With Standard Finite Elements, And Explicit Structural Dynamics," ICES REPORT 14-04, The Institute for Computational Engineering and Sciences, The University of Texas at Austin, February 2014. Collocation on hp finite element meshes: Reduced quadrature perspective, cost comparison with standard finite elements, and explicit structural dynamics Dominik Schillingera,b,∗, John A. Evansc, Felix Frischmannd, Ren´eR. Hiemstrab, Ming-Chen Hsue, Thomas J.R. Hughesb aDepartment of Civil Engineering, University of Minnesota, Twin Cities, USA bInstitute for Computational Engineering and Sciences, The University of Texas at Austin, USA cAerospace Engineering Sciences, University of Colorado Boulder, USA dLehrstuhl f¨ur Computation in Engineering, Technische Universit¨at M¨unchen, Germany eDepartment of Mechanical Engineering, Iowa State University, Ames, USA Abstract We demonstrate the potential of collocation methods for efficient higher-order analysis on standard nodal finite element meshes. We focus on a collocation method that is variation- ally consistent and geometrically flexible, converges optimally, embraces concepts of reduced quadrature, and leads to symmetric stiffness and diagonal consistent mass matrices. At the same time, it minimizes the evaluation cost per quadrature point, thus reducing formation and assembly effort significantly with respect to standard Galerkin finite element methods.
    [Show full text]
  • The Numerical Solution of Helmholtz Equation Using Modified Wavelet Multigrid Method
    International Journal of Modern Mathematical Sciences, 2020, 18(1): 76-91 International Journal of Modern Mathematical Sciences ISSN:2166-286X Journal homepage:www.ModernScientificPress.com/Journals/ijmms.aspx Florida, USA Article The Numerical Solution of Helmholtz Equation Using Modified Wavelet Multigrid Method S. C. Shiralashetti1, A. B. Deshi2,*, M. H. Kantli3 1Department of Mathematics, Karnatak University, Dharwad, India-580003 2Department of Mathematics, KLECET, Chikodi, India-591201 3Department of Mathematics, BVV’s BGMIT, Mudhol, India-587313 *Author to whom correspondence should be addressed; E-Mail: [email protected] Article history: Received 11 June 2020, Revised 23 July 2020, Accepted 1 September 2020, Published 14 September 2020. Abstract: This paper presents a modified wavelet multigrid technique for solving elliptic type partial differential equations namely Helmholtz equation. The solution is first obtained on the coarser grid points, and then it is refined by obtaining higher accuracy by increasing the level of resolution. The implementation of the classical numerical methods has been found to involve some difficulty to observe fast convergence in low computational time. To overcome this, we have proposed modified wavelet multigrid method using wavelet intergrid operators similar to classical intergrid operators. Some of the numerical test problems are presented to demonstrate the applicability and attractiveness of the present implementation. Keywords: Wavelet multigrid; Helmholtz equation; Intergrid operators; Numerical methods. Mathematics Subject Classification: 65T60, 97N40, 35J25 1. Introduction The mathematical modelling of engineering problems usually leads to sets of partial differential equations and their boundary conditions. There are several applications of elliptic partial differential equations (EPDEs) in science and engineering. Many physical processes can be modeled using EPDEs.
    [Show full text]
  • Partial Differential Equations
    Next: Using Matlab Up: Numerical Analysis for Chemical Previous: Ordinary Differential Equations Subsections z Finite Difference: Elliptic Equations { The Laplace Equations { Solution Techniques { Boundary Conditions { The Control Volume Approach z Finite Difference: Parabolic Equations { The Heat Conduction Equation { Explicit Methods { A Simple Implicit Method { The Crank-Nicholson Method z Finite Element Method { Calculus of variation { Example: The shortest distance between two points { The Rayleigh-Ritz Method { The Collocation and Galerkin Method { Finite elements for ordinary-differential equations z Engineering Applications: Partial Differential Equations Partial Differential Equations An equation involving partial derivatives of an unknown function of two or more independent variables is called a partial differential equation, PDE. The order of a PDE is that of the highest-order partial derivative appearing in the equation. A general linear second-order differential equation is (8.1) Depending on the values of the coefficients of the second-derivative terms eq. (8.1) can be classified int one of three categories. z : Elliptic Laplace equation(steady state with two spatial dimensions) z : Parabolic Heat conduction equation(time variable with one spatial dimension) z : Hyperbolic Wave equation(time variable with one spatial dimension) Finite Difference: Elliptic Equations Elliptic equations in engineering are typically used to characterize steady-state, boundary-value problems. The Laplace Equations z The Laplace equation z The Poisson equation Solution Techniques z The Laplacian Difference Equation : use central difference based on the grid scheme and Substituting these equations into the Laplace equation gives For the square grid, , and by collecting terms This relationship, which holds for all interior point on the plate, is referred to as the Laplacian difference equation.
    [Show full text]
  • 65 Numerical Analysis
    e Q (e t o 5 M SectionsSet 1Q (Section 65)MR September 2012 65 NUMERICAL ANALYSIS MR2918625 65-06 FRecent advances in scientific computing and matrix analysis. Proceedings of the International Workshop held at the University of Macau, Macau, December 28{30, 2009. Edited by Xiao-Qing Jin, Hai-Wei Sun and Seak-Weng Vong. International Press, Somerville, MA; Higher Education Press, Beijing, 2011. xii+126 pp. ISBN 978-1-57146-202-2 Contents: Zheng-jian Bai and Xiao-qing Jin [Xiao Qing Jin1], A note on the Ulm-like method for inverse eigenvalue problems (1{7) MR2908437; Che-man Cheng [Che-Man Cheng], Kin-sio Fong [Kin-Sio Fong] and Io-kei Lok [Io-Kei Lok], Another proof for commutators with maximal Frobenius norm (9{14) MR2908438; Wai-ki Ching [Wai-Ki Ching] and Dong-mei Zhu [Dong Mei Zhu1], On high-dimensional Markov chain models for categorical data sequences with applications (15{34) MR2908439; Yan-nei Law [Yan Nei Law], Hwee-kuan Lee [Hwee Kuan Lee], Chao-qiang Liu [Chaoqiang Liu] and Andy M. Yip, An additive variational model for image segmentation (35{48) MR2908440; Hai-yong Liao [Haiyong Liao] and Michael K. Ng, Total variation image restoration with automatic selection of regularization parameters (49{59) MR2908441; Franklin T. Luk and San-zheng Qiao [San Zheng Qiao], Matrices and the LLL algorithm (61{69) MR2908442; Mila Nikolova, Michael K. Ng and Chi-pan Tam [Chi-Pan Tam], A fast nonconvex nonsmooth minimization method for image restoration and reconstruction (71{83) MR2908443; Gang Wu [Gang Wu1], Eigenvalues of certain augmented complex stochastic matrices with applications to PageRank (85{92) MR2908444; Yan Xuan and Fu-rong Lin, Clenshaw-Curtis-rational quadrature rule for Wiener-Hopf equations of the second kind (93{110) MR2908445; Man-chung Yeung [Man-Chung Yeung], On the solution of singular systems by Krylov subspace methods (111{116) MR2908446; Qi- fang Yu, San-zheng Qiao [San Zheng Qiao] and Yi-min Wei, A comparative study of the LLL algorithm (117{126) MR2908447.
    [Show full text]
  • ANALYSIS of a TIME MULTIGRID ALGORITHM for DG-DISCRETIZATIONS in TIME 1. Introduction. the Parallelization of Algorithms For
    ANALYSIS OF A TIME MULTIGRID ALGORITHM FOR DG-DISCRETIZATIONS IN TIME MARTIN J. GANDER ∗ AND MARTIN NEUMULLER¨ † Abstract. We present and analyze for a scalar linear evolution model problem a time multigrid algorithm for DG-discretizations in time. We derive asymptotically optimized parameters for the smoother, and also an asymptotically sharp convergence estimate for the two grid cycle. Our results hold for any A-stable time stepping scheme and represent the core component for space-time multigrid methods for parabolic partial differential equations. Our time multigrid method has excellent strong and weak scaling properties for parallelization in time, which we show with numerical experiments. Key words. Time parallel methods, multigrid in time, DG-discretizations, RADAU IA AMS subject classifications. 65N55, 65L60, 65F10 1. Introduction. The parallelization of algorithms for evolution problems in the time direction is currently an active area of research, because today’s supercomputers with their millions of cores can not be effectively used any more when only paralleliz- ing the spatial directions. In addition to multiple shooting and parareal [21, 15, 10], domain decomposition and waveform relaxation [14, 13, 11], and direct time parallel methods [23, 9, 12], multigrid methods in time are the fourth main approach that can be used to this effect, see the overview [8] and references therein. The parabolic multigrid method proposed by Hackbusch in [16] was the first multigrid method in space-time. It uses a smoothing iteration (e.g. Gauss-Seidel) over many time levels, but coarsening is in general only possible in space, since time coarsening might lead to divergence of the algorithm.
    [Show full text]
  • Comparative Study of Collocation Method-Spline, Finite Difference and Finite Element Method for Solving Flow of Electricity Ina Cable of Transmission Lines
    Dr.A.K.Pathak et. al. /International Journal of Modern Sciences and Engineering Technology (IJMSET) ISSN 2349-3755; Available at https://www.ijmset.com Volume 1, Issue 3, 2014, pp.36-43 COMPARATIVE STUDY OF COLLOCATION METHOD-SPLINE, FINITE DIFFERENCE AND FINITE ELEMENT METHOD FOR SOLVING FLOW OF ELECTRICITY INA CABLE OF TRANSMISSION LINES 2 Dr A.K.Pathak1 Dr. H.D.Doctor Lecturer, Prof & Head Of H&S.S. Department Mathematics Department S.T.B.S.College Of Diploma Engg. Veer Narmad South Gujarat Uni. Surat, India Surat, India [email protected] [email protected] Abstract The present work describes spline collocation ,finite difference and finite element approximation for solving flow of electricity in a cable of transmission lines problem. The mathematical problem gives rise to solve a linear partial differential equation which is of parabolic type. The method involves the solution of algebraic linear equation which can be written in the matrix form is main advantage. The solution are obtained by these two methods and compared with analytic solution to demonstrate the justification and simplicity of the spline approximation converge to finite difference approximation as well as analytic solution. Keywords: Spline collocation, Finite difference, finite element method, Partial differential equation. 1. INTRODUCTION: The present work describes spline collocation, finite difference and finite element method for solving flow of electricity in a cable of transmission lines problem. The mathematical problem gives rise to solve a linear partial differential equation which is of parabolic type. The method involves the solution of algebraic linear equation which can be written in the matrix form is main advantage.
    [Show full text]
  • A Review of Vortex Methods and Their Applications: from Creation to Recent Advances
    fluids Review A Review of Vortex Methods and Their Applications: From Creation to Recent Advances Chloé Mimeau * and Iraj Mortazavi Laboratory M2N, CNAM, 75003 Paris, France; [email protected] * Correspondence: [email protected]; Tel.: +33-0140-272-283 Abstract: This review paper presents an overview of Vortex Methods for flow simulation and their different sub-approaches, from their creation to the present. Particle methods distinguish themselves by their intuitive and natural description of the fluid flow as well as their low numerical dissipation and their stability. Vortex methods belong to Lagrangian approaches and allow us to solve the incompressible Navier-Stokes equations in their velocity-vorticity formulation. In the last three decades, the wide range of research works performed on these methods allowed us to highlight their robustness and accuracy while providing efficient computational algorithms and a solid mathematical framework. On the other hand, many efforts have been devoted to overcoming their main intrinsic difficulties, mostly relying on the treatment of the boundary conditions and the distortion of particle distribution. The present review aims to describe the Vortex methods by following their chronological evolution and provides for each step of their development the mathematical framework, the strengths and limits as well as references to applications and numerical simulations. The paper ends with a presentation of some challenging and very recent works based on Vortex methods and successfully applied to problems such as hydrodynamics, turbulent wake dynamics, sediment or porous flows. Keywords: hydrodynamics; incompressible flows; particle methods; remeshing; semi-Lagrangian vortex methods; vortex methods; Vortex-Particle-Mesh methods; vorticity; wake dynamics Citation: Mimeau, C.; Mortazavi, I.
    [Show full text]
  • Adaptivity for Meshfree Point Collocation Methods in Linear Elastic Solid Mechanics Joshua Wayne Derrick University of South Carolina
    University of South Carolina Scholar Commons Theses and Dissertations 2015 Adaptivity For Meshfree Point Collocation Methods In Linear Elastic Solid Mechanics Joshua Wayne Derrick University of South Carolina Follow this and additional works at: https://scholarcommons.sc.edu/etd Part of the Civil Engineering Commons Recommended Citation Derrick, J. W.(2015). Adaptivity For Meshfree Point Collocation Methods In Linear Elastic Solid Mechanics. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/3715 This Open Access Thesis is brought to you by Scholar Commons. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. ADAPTIVITY FOR MESHFREE POINT COLLOCATION METHODS IN LINEAR ELASTIC SOLID MECHANICS by Joshua Wayne Derrick Bachelor of Science in Engineering University of South Carolina, 2013 Submitted in Partial Fulfillment of the Requirements For the Degree of Master of Science in Civil Engineering College of Engineering and Computing University of South Carolina 2015 Accepted by: Robert L. Mullen, Director of Thesis Juan Caicedo, Reader Joseph Flora, Reader Shamia Hoque, Reader Lacy Ford, Senior Vice Provost and Dean of Graduate Studies © Copyright by Joshua Wayne Derrick, 2015 All Rights Reserved ii ACKNOWLEDGEMENTS I would like to express my sincere thanks and gratitude to my academic advisor, Dr. Robert L. Mullen, for accepting the task of advising me the remainder of my graduate studies after the departure of my research advisor. Dr. Mullen’s invaluable guidance and help was more than I could have asked for and I appreciate all of the energy he spent helping me to complete this research.
    [Show full text]
  • Appendix A. Basic Mathematical Concepts
    Appendix A. Basic Mathematical Concepts A.1 Hilbert and Banach Spaces (a) Hilbert Spaces Let X be a real vector space. An inner product on X is a function X×X → R, denoted by (u, v), that satisfies the following properties: (i) (u, v)=(v, u) for all u, v ∈ X; (ii) (αu + βv,w)=α(u, w)+β(v, w) for all α, β ∈ R and all u, v, w ∈ X; (iii) (u, u) ≥ 0 for all u ∈ X; (iv) (u, u) = 0 implies u =0. Two elements u, v ∈ X are said to be orthogonal in X if (u, v) = 0. The inner product (u, v) defines a norm on X by the relation u =(u, u)1/2 for all u ∈ X. The distance between two elements u, v ∈ X is the positive number u − v . A Cauchy sequence in X is a sequence {uk | k =0, 1,...} of elements of X that satisfies the following property: for each positive number ε>0, there exists an integer N = N(ε) > 0 such that the distance uk − um between any two elements of the sequence is smaller than ε provided both k and m are larger than N(ε). A sequence in X is said to converge to an element u ∈ X if the distance uk − u tends to 0 as k tends to ∞. A Hilbert space is a vector space equipped with an inner product for which all the Cauchy sequences are convergent. Examples n n (i) R endowed with the Euclidean product (u, v)= uivi is a finite- i=1 dimensional Hilbert space.
    [Show full text]