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ویرایش: [2 ed.]
نویسندگان: Duc Thai Nguyen
سری:
ISBN (شابک) : 3031487877, 9783031487873
ناشر: Springer
سال نشر: 2024
تعداد صفحات: 827
[813]
زبان: English
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 24 Mb
در صورت تبدیل فایل کتاب Finite Element Methods: Parallel-Sparse Statics and Eigen-Solutions به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب روشهای اجزای محدود: استاتیک موازی پراکنده و راهحلهای ویژه نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
این ویرایش جدید شامل سه فصل جدید، 7 تا 9 است که کاربردهای بسیار گسترده و عملی در مهندسی و علوم دارند. علاوه بر این، آخرین نتایج تحقیقات نویسنده که در کتاب درسی جدید گنجانده شده است، عملکرد بهتری نسبت به نرمافزار محبوب METIS برای پارتیشنبندی نمودارها، پارتیشنبندی شبکههای اجزا محدود، و تولید نظمهای کاهش پرکننده برای ماتریسهای پراکنده نشان میدهد. فصل 8 جدید و پیش نیاز آن، فصل 7، الگوریتمی پیشرفته برای محاسبه کوتاه ترین مسیرها برای شبکه های حمل و نقل واقعی (در مقیاس بزرگ) با حداقل زمان محاسباتی ارائه می کند. این رویکرد هنوز در هیچ کتاب درسی موجود ظاهر نشده است و می تواند درها را برای سایر کاربردهای مهندسی حمل و نقل باز کند. فصل 9 دامنه نسخه قبلی را با گنجاندن محاسبات حساسیت (gradient) و تابع داخلی MATLAB \"fmincon\" برای به دست آوردن راه حل بهینه (یا بهترین) برای مسائل مهندسی عمومی، به شدت گسترش می دهد.
This new edition includes three new chapters, 7 through 9, that have very broad, practical applications in engineering and science. In addition, the author’s latest research results incorporated into the new textbook demonstrates better performance than the popular METIS software for partitioning graphs, partitioning finite element meshes, and producing fill-reducing orderings for sparse matrices. The new Chapter 8, and its pre-requisite, Chapter 7, present a state-of-the-art algorithm for computing the shortest paths for real-life (large-scale) transportation networks with minimum computational time. This approach has not yet appeared in any existing textbooks and it could open the doors for other transportation engineering applications. Chapter 9 vastly expands the scope of the previous edition by including sensitivity (gradient) computation and MATLAB’s built-in function “fmincon” for obtaining the optimum (or best) solution for general engineering problems.
Preface Disclaimer of Warranty Acknowledgments Contents Chapter 1: A Review of Basic Finite Element Procedures 1.1 Introduction 1.2 Numerical Techniques for Solving Ordinary Differential Equations (ODE) 1.3 Identifying the ``Geometric´´ Versus ``Natural´´ Boundary Conditions 1.4 The Weak Formulations 1.5 Flowcharts for Statics Finite Element Analysis 1.6 Flowcharts for Dynamics Finite Element Analysis 1.7 Uncoupling the Dynamical Equilibrium Equations 1.8 One-Dimensional Rod Finite Element Procedures 1.8.1 One-Dimensional Rod Element Stiffness Matrix 1.8.2 Distributed Loads and Equivalent Joint Loads 1.8.3 Finite Element Assembly Procedures 1.8.4 Imposing the Boundary Conditions 1.8.5 Alternative Derivations of System of Equations from Finite Element Equations 1.9 Truss Finite Element Equations 1.10 Beam (or Frame) Finite Element Equations 1.11 Tetrahedral Finite Element Shape Functions 1.12 Finite Element Weak Formulations for General 2-D Field Equations 1.13 The Isoparametric Formulation 1.14 Gauss Quadrature 1.15 Summary 1.16 Exercises Chapter 2: Simple MPI/FORTRAN/MATLAB Application 2.1 Introduction 2.2 Computing Value of π by Integration 2.3 Matrix-Matrix Multiplication 2.4 MPI Parallel I/O 2.5 Unrolling Techniques 2.6 Parallel Dense Equation Solvers 2.6.1 Basic Symmetrical Equation Solver 2.6.2 Parallel Data Storage Scheme 2.6.3 Data Generating Subroutine 2.6.4 Parallel Choleski Factorization 2.6.5 A Blocked and Cache-Based Optimized Matrix-Matrix Multiplication 2.6.5.1 Loop Indexes and Temporary Array Usage 2.6.5.2 Blocking and Strip Mining 2.6.5.3 Unrolling of Loops 2.6.6 Parallel ``Block´´ Factorization 2.6.7 ``Block´´ Forward Elimination Subroutine 2.6.8 ``Block´´ Backward Elimination Subroutine 2.6.9 ``Block´´ Error Checking Subroutine 2.6.10 Numerical Evaluation 2.6.11 Conclusions 2.7 A Brief Review of MATLAB Syntax, Sequential, and Parallel Programming 2.8 Summary 2.9 Exercises Chapter 3: Direct Sparse Equation Solvers 3.1 Introduction 3.2 Sparse Storage Schemes 3.3 Three Basic Steps and Reordering Algorithms 3.3.1 Choleski Algorithm 3.3.2 LDLT Algorithm 3.3.3 LDU Algorithm 3.3.4 Reordering Algorithm 3.4 Symbolic Factorization with Reordering Column Numbers 3.4.1 Sparse Symbolic Factorization 3.4.2 Reordering Column Numbers 3.5 Sparse Numerical Factorization 3.6 Super (Master) Nodes (Degrees of Freedom) 3.7 Numerical Factorization with Unrolling Strategies 3.8 Forward/Backward Solutions with Unrolling Strategies 3.9 Alternative Approach for Handling an Indefinite Matrix 3.10 Conclusions 3.11 Unsymmetrical Matrix Equation Solver 3.12 Summary 3.13 Exercises Chapter 4: Sparse Assembly Process 4.1 Introduction 4.2 A Simple Finite Element Model (Symmetrical Matrices) 4.3 Finite Element Sparse Assembly Algorithms for Symmetrical Matrices 4.4 Symbolic Sparse Assembly of Symmetrical Matrices 4.5 Numerical Sparse Assembly of Symmetrical Matrices 4.5.1 Key Ideas for Sparse Numerical Assembly Algorithms 4.6 Step-by-Step Algorithms for Symmetrical Sparse Assembly 4.7 A Simple Finite Element Model (Unsymmetrical Matrices) 4.8 Reordering Algorithms 4.9 Imposing Dirichlet Boundary Conditions 4.10 Unsymmetrical Sparse Equations Data Formats 4.11 Symbolic Sparse Assembly of Unsymmetrical Matrices 4.12 Numerical Sparse Assembly of Unsymmetrical Matrices 4.13 Step-by-Step Algorithms for Unsymmetrical Sparse Assembly and Unsymmetrical Sparse Equation Solver 4.14 A Numerical Example 4.15 Summary 4.16 Exercises Chapter 5: Generalized Eigen-Solvers 5.1 Introduction 5.2 A Simple Generalized Eigen-Example 5.2.1 Inverse Iteration Procedure 5.2.2 Inverse Iterations with Orthonormality Conditions 5.3 Shifted Eigenproblems 5.4 Transformation Methods 5.4.1 Transformation Methods 5.5 Subspace Iteration Method [5.5] 5.6 Lanczos Eigensolution Algorithms 5.6.1 Derivation of Lanczos Algorithms 5.6.2 Basic Lanczos Algorithms for Eigensolution of Generalized Eigenequation K훟 = λM훟 5.6.3 Lanczos Eigensolution Error Analysis 5.6.4 Sturm Sequence Check 5.6.5 Proving the Lanczos Vectors Are M-Orthogonal 5.6.6 ``Classical´´ Gram-Schmidt Re-orthogonalization 5.6.7 Classical Gram-Schmidt Orthogonalization 5.6.8 Detailed Step-by-Step Lanczos Algorithms 5.6.9 Detailed Lanczos Algorithms for Eigensolution of Generalized Eigenequation K훟 = λM훟 5.6.10 Educational Software for Lanczos Algorithms 5.6.11 Unsymmetrical Eigensolvers 5.7 Balanced Matrix 5.8 Reduction to Hessenberg Form 5.9 QR Factorization 5.10 Householder QR Transformation 5.11 ``Modified´´ Gram-Schmidt Re-orthogonalization 5.11.1 ``Modified´´ Gram-Schmidt Algorithms 5.12 QR Iteration for Unsymmetrical Eigensolutions 5.13 QR Iteration with Shifts for Unsymmetrical Eigensolutions 5.14 Panel Flutter Analysis 5.14.1 Step-by-Step Procedures for Unsymmetrical Eigenequations 5.15 Block Lanczos Algorithms 5.15.1 Details of ``Block Lanczos´´ Algorithms 5.15.2 Step-by-Step ``Block Lanczos´´ Algorithms 5.15.3 A Numerical Example for ``Block Lanczos´´ Algorithms 5.16 Summary 5.17 Exercises Chapter 6: Finite Element Domain Decomposition Procedures 6.1 Introduction 6.2 A Simple Numerical Example Using Domain Decomposition (DD) Procedures (Classical Substructuring Formulation) 6.3 Imposing Boundary Conditions on ``Rectangular´´ Matrices 6.4 How to Construct a Sparse Assembly of ``Rectangular´´ Matrix 6.5 Mixed Direct-Iterative Solvers for Domain Decomposition 6.6 Preconditioned Matrix for PCG Algorithm with DD Formulation 6.6.1 Preconditioned Conjugate Gradient DD Algorithm for Solving 6.7 Generalized Inverse 6.8 FETI-D Domain Decomposition Formulation 6.9 Preconditioned Conjugate Projected Gradient (PCPG) of the Dual Interface Problem 6.10 Automated Procedures for Computing Generalized Inverse and Rigid Body Motions 6.11 Numerical Example of a 2D Determinate Truss (21-Bar and 10-Node) by FETI-D Formulation 6.12 A Preconditioning Technique for Indefinite Linear System 6.13 FETI-DP Domain Decomposition Formulation 6.13.1 FETI-DP Step-by-Step Procedures 6.14 Multilevel Subdomains and Multifrontal Solver 6.15 Iterative Solution with Successive Right-Hand Sides 6.15.1 Step-by-Step Iterative Optimization Procedures 6.15.2 GCR Step-by-Step Algorithms 6.15.3 Step-by-Step Algorithms to Generate a ``Good´´ Initial Guess for RHS Vectors 6.16 Summary Exercises Chapter 7: Heuristic Partitioning Algorithm for General Purpose Transportation Networks and Finite Element Meshes 7.1 Introduction 7.1.1 Shortest Distance Decomposition Algorithm (SDDA) 7.1.2 Algorithm Refinements 7.2 First Proposed SDDA Modification: Link Distance 7.3 Second Proposed SDDA Modification: Domain Swaps 7.4 Third Proposed SDDA Modification: The Friend-of-Foe (FoF) Count 7.5 Numerical Examples 7.5.1 Anaheim, Real-Life Transportation Network 7.5.2 Chicago, Real-Life Transportation Network 7.5.3 Philadelphia, Real-Life Transportation Network 7.5.4 Three-Dimensional Plus Sign Shape with Tetrahedron Elements 7.6 Summary Exercises Chapter 8: Parallel Domain Partitioning Shortest Path Algorithms 8.1 Introduction 8.2 A Brief Summary of the ``Conventional/Classical´´ Dijkstra Shortest Path Algorithm 8.3 Development of ``New Sequential and Parallel´´ Dijkstra SP Algorithm with Subdomains 8.4 A Simple Illustrative (10-Node and 19-Link) Example for Dijkstra SP Algorithm with Three Subdomains 8.5 Numerical Comparisons Among Sequential and Parallel Dijkstra´s Subdomain Algorithms 8.6 Summary 8.7 Acknowledgements 8.8 Exercises Chapter 9: Sensitivity Analysis and Optimization with Partitioned Subdomains 9.1 Introduction 9.2 Prerequisite Backgrounds 9.3 Finite Element System Equilibrium Equations with Subdomains 9.4 Finite Element Sensitivity Analysis with Subdomains 9.4.1 How to Express δZi in Terms of δb? 9.4.2 How to Express δZb in Terms of δb? 9.4.3 How to Express δξ in Terms of δb? 9.5 Direct Method for Sensitivity (Derivative) Computation 9.6 Finite Difference Method for Sensitivity (Derivative) Computation 9.7 Numerical Examples for Sensitivity (Derivative) Computation 9.8 Sensitivity (Derivative Computation) of Truss Axial Stress Constraints 9.9 Nonlinear Constraint Optimizer with FMINCON 9.10 Summary 9.11 Exercises Appendix 6.1: Singular Value Decomposition (SVD) and MATLAB Code for Image Application Appendix 6.2: MATLAB Source Code for Implementing FETI-DP Formulation Appendix 7.1: Real-Life Philadelphia Network Data Appendix 7.2: Partitioning Real-Life Philadelphia Network Data into Four Subdomains Appendix 7.3: MATLAB Code for SDDA, Including FoF, Domain Swapping, and Link Distance Appendix 8.1: Sequential Computation, Without Using Subdomains (Algorithm #1); for Table 8.7 Appendix 8.2: Embarrassingly Parallel Computation Without Subdomains (Algorithm #2); for Table 8.7 Appendix 8.3: Sequential Computation with Subdomains (Algorithm #3) for Table 8.7 Appendix 8.4: Parallel Computation with Subdomains (Algorithm #4) for Table 8.7 Appendix 8.5: User Manual for Input Data Descriptions Appendix 8.6: Example Input Data for the 10-node Network, for the Last Algorithm #4 (Parallel with Subdomains) Appendix 8.7: Sample of Output Data File for the 10-Node Transportation Network References Index