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  • 1
    Monograph available for loan
    Monograph available for loan
    Berlin [u.a.] : Springer
    Associated volumes
    Call number: PIK M 032-10-0183
    In: Texts in computational science and engineering
    Description / Table of Contents: Contents: Computing with Formulas ; Basic Constructions ; Input Data and Error Handling ; Array Computing and Curve Plotting ; Sequences and Difference Equations ; Files, Strings, and Dictionaries ; Introduction to Classes ; Random Numbers and Simple Games ; Object-Oriented Programming
    Type of Medium: Monograph available for loan
    Pages: XXVII, 693 S. : graph. Darst.
    ISBN: 9783642024740
    Series Statement: Texts in computational science and engineering 6
    Location: A 18 - must be ordered
    Branch Library: PIK Library
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  • 2
    Monograph available for loan
    Monograph available for loan
    Berlin [u.a.] : Springer
    Associated volumes
    Call number: 18/M 13.0018
    In: Texts in computational science and engineering
    Type of Medium: Monograph available for loan
    Pages: XXXII, 792 S. : graph. Darst.
    Edition: 3rd ed.
    ISBN: 9783642302923
    Series Statement: Texts in computational science and engineering 6
    Classification:
    Informatics
    Location: Reading room
    Branch Library: GFZ Library
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  • 3
    Monograph available for loan
    Monograph available for loan
    Berlin : Springer
    Associated volumes
    Call number: AWI S4-19-91819
    In: Texts in computational science and engineering, 3
    Type of Medium: Monograph available for loan
    Pages: XXIV, 750 Seiten , Illustrationen
    Edition: third edition, corrected 2nd printing 2009
    ISBN: 3540739157 , 9783540739159 , 9783540739166 (electronic)
    Series Statement: Texts in computational science and engineering 3
    Language: English
    Note: Table of Contents 1 Introduction 1.1 Scripting versus Traditional Programming 1.1.1 Why Scripting is Useful in Computational Science 1.1.2 Classification of Programming Languages 1.1.3 Productive Pairs of Programming Languages 1.1.4 Gluing Existing Applications 1.1.5 Scripting Yields Shorter Code 1.1.6 Efficiency 1.1.7 Type-Specification (Declaration) of Variables 1.1.8 Flexible Function Interfaces 1.1.9 Interactive Computing 1.1.10 Creating Code at Run Time 1.1.11 Nested Heterogeneous Data Structures 1.1.12 GUI Programming 1.1.13 Mixed Language Programming 1.1.14 When to Choose a Dynamically Typed Language 1.1.15 Why Python? 1.1.16 Script or Program? 1.2 Preparations for Working with This Book 2 Getting Started with Python Scripting 2.1 A Scientific Hello World Script 2.1.1 Executing Python Scripts 2.1.2 Dissection of the Scientific Hello World Script 2.2 Working with Files and Data 2.2.1 Problem Specification 2.2.2 The Complete Code 2.2.3 Dissection 2.2.4 Working with Files in Memory 2.2.5 Array Computing 2.2.6 Interactive Computing and Debugging 2. 2.7 Efficiency Measurements 2.2.8 Exercises 2.3 Gluing Stand-Alone Applications 2.3.1 The Simulation Code 2.3.2 Using Gnuplot to Visualize Curves 2.3.3 Functionality of the Script 2.3.4 The Complete Code 2.3.5 Dissection 2.3.6 Exercises 2.4 Conducting Numerical Experiments 2.4.1 Wrapping a Loop Around Another Script 2.4.2 Generating an HTML Report 2.4.3 Making Animations 2.4.4 Varying Any Parameter 2.5 File Format Conversion 2.5.1 A Simple Read/Write Script 2.5.2 Storing Data in Dictionaries and Lists 2.5.3 Making a Module with Functions 2.5.4 Exercises 3 Basic Python 3.1 Introductory Topics 3.1.1 Recommended Python Documentation 3.1.2 Control Statements 3.1.3 Running Applications 3.1.4 File Reading and Writing 3.1.5 Output Formatting 3.2 Variables of Different Types 3.2.1 Boolean Types 3.2.2 The None Variable 3.2.3 Numbers and Numerical Expressions 3.2.4 Lists and Tuples 3.2.5 Dictionaries 3.2.6 Splitting and Joining Text 3.2.7 String Operations 3.2.8 Text Processing 3.2.9 The Basics of a Python Class 3.2.10 Copy and Assignment 3.2.11 Determining a Variable's Type 3.2.12 Exercises 3.3 Functions 3.3.1 Keyword Arguments 3.3.2 Doc Strings 3.3.3 Variable Number of Arguments 3.3.4 Call by Reference 3.3.5 Treatment of Input and Output Arguments 3.3.6 Function Objects 3.4 Working with Files and Directories 3.4.1 Listing Files in a Directory 3.4.2 Testing File Types 3.4.3 Removing Files and Directories 3.4.4 Copying and Renaming Files 3.4.5 Splitting Pathnames 3.4.6 Creating and Moving to Directories 3.4.7 Traversing Directory Trees 3.4.8 Exercises 4 Numerical Computing in Python 4.1 A Quick NumPy Primer 4.1.1 Creating Arrays 4.1.2 Array Indexing 4.1.3 Loops over Arrays 4.1.4 Array Computations 4.1.5 More Array Functionality 4.1.6 Type Testing 4.1.7 Matrix Objects 4.1.8 Exercises 4.2 Vectorized Algorithms 4.2.1 From Scalar to Array in Function Arguments 4.2.2 Slicing 4.2.3 Exercises 4.3 More Advanced Array Computing 4.3.1 Random Numbers 4.3.2 Linear Algebra 4.3.3 Plotting 4.3.4 Example: Curve Fitting 4.3.5 Arrays on Structured Grids 4.3.6 File I/O with NumPy Arrays 4.3.7 Functionality in the Numpyutils Module 4.3.8 Exercises 4.4 Other Tools for Numerical Computations 4.4.1 The ScientificPython Package 4.4.2 The SciPy Package 4.4.3 The Python- Matlab Interface 3 4.4.4 Symbolic Computing in Python 4.4.5 Some Useful Python Modules 5 Combining Python with Fortran, C, and C++ 5.1 About Mixed Language Programming 5.1.1 Applications of Mixed Language Programming 5.1.2 Calling C from Python 5.1.3 Automatic Generation of Wrapper Code 5.2 Scientific Hello World Examples 5.2.1 Combining Python and Fortran 5.2.2 Combining Python and C 5.2.3 Combining Python and C++ Functions 5.2.4 Combining Python and C++ Classes 5.2.5 Exercises 5.3 A Simple Computational Steering Example 5.3.1 Modified Time Loop for Repeated Simulations 5.3.2 Creating a P ython Interface 5.3.3 The Steering Python Script 5.3.4 Equipping the Steering Script with a GUI 5.4 Scripting Interfaces to Large Libraries 6 Introduction to GUI Programming 6.1 Scientific Hello World GUI 6.1.1 Introductory Topics 6.1.2 The First Python/Tkinter Encounter 6.1.3 Binding Events 6.1.4 Changing the Layout 6.1.5 The Final Scientific Hello World GUI 6.1.6 An Alternative to Tkinter Variables 6.1.7 About the Pack Command 6.1.8 An Introduction to the Grid Geometry Manager 6.1.9 Implementing a GUI as a Class 6.1.10 A Simple Graphical Function Evaluator 6.1.11 Exercises 6.2 Adding GUis to Scripts 6.2.1 A Simulation and Visualization Script with a GUI 6.2.2 Improving the Layout 6.2.3 Exercises 6.3 A List of Common Widget Operations 6.3.1 Frame 6.3.2 Label 6.3.3 Button 6.3.4 Text Entry 6.3.5 Balloon Help 6.3.6 Option Menu 6.3.7 Slider 6.3.8 Check Button 6.3.9 Making a Simple Megawidget 6.3.10 Menu Bar 6.3.11 List Data 6.3.12 Listbox 6.3.13 Radio Button 6.3.14 Combo Box 6.3.15 Message Box 6.3.16 User-Defined Dialogs 6.3.17 Color-Picker Dialogs 6.3.18 File Selection Dialogs 6.3.19 Toplevel 6.3.20 Some Other Types of Widgets 6.3.21 Adapting Widgets to the User's Resize Actions 6.3.22 Customizing Fonts and Colors 6.3.23 Widget Overview 6.3.24 Exercises 7 Web Interfaces and CGI Programming 7.1 Introductory CGI Scripts 7.1.1 Web Forms and CGI Scripts 7.1.2 Generating Forms in CGI Scripts 7.1.3 Debugging CGI Scripts 7.1.4 A General Shell Script Wrapper for CGI Scripts 7.1.5 Security Issues 7.2 Adding Web Interfaces to Scripts 7.2.1 A Class for Form Parameters 7.2.2 Calling Other Programs 7.2.3 Running Simulations 7.2.4 Getting a CGI Script to Work 7.2.5 Using Web Applications from Scripts 7.2.6 Exercises 8 Advanced Python 8.1 Miscellaneous Topics 8.1.1 Parsing Command-Line Arguments 8.1.2 Platform-Dependent Operations 8.1.3 Run-Time Generation of Code 8.1.4 Exercises 8.2 Regular Expressions and Text Processing 8.2.1 Motivation 8.2.2 Special Characters 8.2.3 Regular Expressions for Real Numbers 8.2.4 Using Groups to Extract Parts of a Text 8.2.5 Extracting Interval Limits 8.2.6 Extracting Multiple Matches 8.2.7 Splitting Text 8.2.8 Pattern-Matching Modifiers 8.2.9 Substitution and Backreferences 8.2.10 Example: Swapping Arguments in Function Calls 8.2.11 A General Substitution Script 8.2.12 Debugging Regular Expressions 8.2.13 Exercises 8.3 Tools for Handling Data in Files 8.3.1 Writing and Reading Python Data Structures 8.3.2 Pickling Objects 8.3.3 Shelving Objects 8.3.4 Writing and Reading Zip and Tar Archive Files 8.3.5 Downloading Internet Files 8.3.6 Binary Input/Output 8.3.7 Exercises 8.4 A Database for NumPy Arrays 8.4.1 The Structure of the Database 8.4.2 Pickling 8.4.3 Formatted ASCII Storage 8.4.4 Shelving 8.4.5 Comparing the Various Techniques 8.5 Scripts Involving Local and Remote Hosts 8.5.1 Secure Shell Commands 8.5.2 Distributed Simulation and Visualization 8.5.3 Client/Server Programming 8.5.4 Threads 8.6 Classes 8.6.1 Class Programming 8.6.2 Checking the Class Type 8.6.3 Private Data 8.6.4 Static Data 8.6.5 Special Attributes 8.6.6 Special Methods 8.6.7 Multiple Inheritance 8.6.8 Using a Class as a C-like Structure 8.6.9 Attribute Access via String Names 8.6.10 New-Style Classes 8.6.11 Implementing Get/Set Functions via Properties 8.6.12 Subclassing Built-in Types 8.6.13 Building Class Interfaces at Run Time 8.6.14 Building Flexible Class Interfaces 8.6.15 Exercises 8.7 Scope of Variables 8.7.1 Global, Local, and Class Variables 8.7.2 Nested Functions 8.7.3 Dictionaries of Variables in Namespaces 8.8 Exceptions 8.8.1 Handling Exceptions 8.8.2 Raising Exceptions 8.9 Iterators 8.9.1 Constructing an Iterator 8.9.2 A Pointwise Grid Iterator 8.9.3 A Vectorized Grid Iterator 8.9.4 Generators 8.
    Location: AWI Reading room
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  • 4
    Monograph available for loan
    Monograph available for loan
    Berlin [u.a.] : Springer
    Associated volumes
    Call number: PIK M 032-08-0172
    In: Texts in computational science and engineering
    Description / Table of Contents: Contents: 1 Introduction. 2 Getting Started with Python Scripting. 3 BasicPython. 4 Numerical Computing in Python. 5 Combining Python with Fortran, C, and C++. 6 Introduction to GUI Programming. 7 Web Interfaces and CGI Programming. 8 Advanced Python. 9 Fortran Programming with NumPy Arrays. 10 C and C++ Programming with NumPy Arrays. 11 More Advanced GUI Programming. 12 Tools and Examples. A Setting up the Required Software Environment. B Elements of Software Engineering.
    Type of Medium: Monograph available for loan
    Pages: xxiv, 750 S. : Ill., graph. Darst.
    Edition: 3. ed.
    ISBN: 9783540739159
    Series Statement: Texts in computational science and engineering 3
    Location: A 18 - must be ordered
    Branch Library: PIK Library
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  • 5
    Monograph available for loan
    Monograph available for loan
    Berlin : Springer
    Associated volumes
    Call number: AWI S4-18-91822
    In: Texts in computational science and engineering, 6
    Type of Medium: Monograph available for loan
    Pages: XXXI, 922Seiten , Illustrationen, graphische Darstellungen
    Edition: Fifth edition
    ISBN: 9783662498873 , 9783662498866
    Series Statement: Texts in computational science and engineering 6
    Language: English
    Note: Contents: 1 Computing with Formulas. - 1.1 The First Programming Encounter: a Formula. - 1.1.1 Using a Program as a Calculator. - 1.1.2 About Programs and Programming. - 1.1.3 Tools for Writing Programs. - 1.1.4 Writing and Running Your First Python Program. - 1.1.5 Warning About Typing Program Text. - 1.1.6 Verifying the Result. - 1.1.7 Using Variables. - 1.1.8 Names of Variables. - 1.1.9 Reserved Words in Python. - 1.1.10 Comments. - 1.1.11 Formatting Text and Numbers. - 1.2 Computer Science Glossary. - 1.3 Another Formula: Celsius-Fahrenheit Conversion. - 1.3.1 Potential Error: Integer Division. - 1.3.2 Objects in Python. - 1.3.3 Avoiding Integer Division. - 1.3.4 Arithmetic Operators and Precedence. - 1.4 Evaluating Standard Mathematical Functions. - 1.4.1 Example: Using the Square Root Function. - 1.4.2 Example: Computing with sinh x. - 1.4.3 A First Glimpse of Rounding Errors. - 1.5 Interactive Computing. - 1.5.1 Using the Python Shell. - 1.5.2 Type Conversion. - 1.5.3 IPython. - 1.6 Complex Numbers. - 1.6.1 Complex Arithmetics in Python. - 1.6.2 Complex Functions in Python. - 1.6.3 Unified Treatment of Complex and Real Functions. - 1.7 Symbolic Computing. - 1.7.1 Basic Differentiation and Integration. - 1.7.2 Equation Solving. - 1.7.3 Taylor Series and More. - 1.8 Summary. - 1.8.1 Chapter Topics. - 1.8.2 Example: Trajectory of a Ball. - 1.8.3 About Typesetting Conventions in This Book. - 1.9 Exercises. - 2 Loops and Lists. - 2.1 While Loops. - 2.1.1 A Naive Solution. - 2.1.2 While Loops. - 2.1.3 Boolean Expressions. - 2.1.4 Loop Implementation of a Sum. - 2.2 Lists. - 2.2.1 Basic List Operations. - 2.2.2 For Loops. - 2.3 Alternative Implementations with Lists and Loops. - 2.3.1 While Loop Implementation of a for Loop. - 2.3.2 The Range Construction. - 2.3.3 For Loops with List Indices. - 2.3.4 Changing List Elements. - 2.3.5 List Comprehension. - 2.3.6 Traversing Multiple Lists Simultaneously. - 2.4 Nested Lists. - 2.4.1 A table as a List of Rows or Columns. - 2.4.2 Printing Objects. - 2.4.3 Extracting Sublists. - 2.4.4 Traversing Nested Lists. - 2.5 Tuples. - 2.6 Summary. - 2.6.1 Chapter Topics. - 2.6.2 Example: Analyzing List Data. - 2.6.3 How to Find More Python Information. - 2.7 Exercises. - 3 Functions and Branching. - 3.1 Functions. - 3.1.1 Mathematical Functions as Python Functions. - 3.1.2 Understanding the Program Flow. - 3.1.3 Local and Global Variables. - 3.1.4 Multiple Arguments. - 3.1.5 Function Argument or Global Variable?. - 3.1.6 Beyond Mathematical Functions. - 3.1.7 Multiple Return Values. - 3.1.8 Computing Sums. - 3.1.9 Functions with No Return Values. - 3.1.10 Keyword Arguments. - 3.1.11 Doc Strings. - 3.1.12 Functions as Arguments to Functions. - 3.1.13 The Main Program. - 3.1.14 Lambda Functions. - 3.2 Branching. - 3.2.1 If-else Blocks. - 3.2.2 Inline if Tests. - 3.3 Mixing Loops, Branching, and Functions in Bioinformatics Examples. - 3.3.1 Counting Letters in DNA Strings. - 3.3.2 Efficiency Assessment. - 3.3.3 Verifying the Implementations. - 3.4 Summary. - 3.4.1 Chapter Topics. - 3.4.2 Example: Numerical Integration. - 3.5 Exercises. - 4 User Input and Error Handling. - 4.1 Asking Questions and Reading Answers. - 4.1.1 Reading Keyboard Input. - 4.2 Reading from the Command Line. - 4.2.1 Providing Input on the Command Line. - 4.2.2 A Variable Number of Command-Line Arguments. - 4.2.3 More on Command-Line Arguments. - 4.3 Turning User Text into Live Objects. - 4.3.1 The Magic Eval Function. - 4.3.2 The Magic Exec Function. - 4.3.3 Turning String Expressions into Functions. - 4.4 Option-Value Pairs on the Command Line. - 4.4.1 Basic Usage of the Argparse Module. - 4.4.2 Mathematical Expressions as Values. - 4.5 Reading Data from File. - 4.5.1 Reading a File Line by Line. - 4.5.2 Alternative Ways of Reading a File. - 4.5.3 Reading a Mixture of Text and Numbers. - 4.6 Writing Data to File. - 4.6.1 Example: Writing a Table to File. - 4.6.2 Standard Input and Output as File Objects. - 4.6.3 What is a File, Really?. - 4.7 Handling Errors. - 4.7.1 Exception Handling. - 4.7.2 Raising Exceptions. - 4.8 A Glimpse of Graphical User Interfaces. - 4.9 Making Modules. - 4.9.1 Example: Interest on Bank Deposits. - 4.9.2 Collecting Functions in a Module File. - 4.9.3 Test Block. - 4.9.4 Verification of the Module Code. - 4.9.5 Getting Input Data. - 4.9.6 Doc Strings in Modules. - 4.9.7 Using Modules. - 4.9.8 Distributing Modules. - 4.9.9 Making Software Available on the Internet. - 4.10 Making Code for Python 2 and 3. - 4.10.1 Basic Differences Between Python 2 and 3. - 4.10.2 Turning Python 2 Code into Python 3 Code. - 4.11 Summary. - 4.11.1 Chapter Topics. - 4.11.2 Example: Bisection Root Finding. - 4.12 Exercises. - 5 Array Computing and Curve Plotting. - 5.1 Vectors. - 5.1.1 The Vector Concept. - 5.1.2 Mathematical Operations on Vectors. - 5.1.3 Vector Arithmetics and Vector Functions. - 5.2 Arrays in Python Programs. - 5.2.1 Using Lists for Collecting Function Data. - 5.2.2 Basics of Numerical Python Arrays. - 5.2.3 Computing Coordinates and Function Values. - 5.2.4 Vectorization. - 5.3 Curve Plotting. - 5.3.1 MATLAB-Style Plotting with Matplotlib. - 5.3.2 Matplotlib; Pyplot Prefix. - 5.3.3 SciTools and Easyviz. - 5.3.4 Making Animations. - 5.3.5 Making Videos. - 5.3.6 Curve Plots in Pure Text. - 5.4 Plotting Difficulties. - 5.4.1 Piecewisely Defined Functions. - 5.4.2 Rapidly Varying Functions. - 5.5 More Advanced Vectorization of Functions. - 5.5.1 Vectorization of StringFunction Objects. - 5.5.2 Vectorization of the Heaviside Function. - 5.5.3 Vectorization of a Hat Function. - 5.6 More on Numerical Python Arrays. - 5.6.1 Copying Arrays. - 5.6.2 In-Place Arithmetics. - 5.6.3 Allocating Arrays. - 5.6.4 Generalized Indexing. - 5.6.5 Testing for the Array Type. - 5.6.6 Compact Syntax for Array Generation. - 5.6.7 Shape Manipulation. - 5.7 High-Performance Computing with Arrays. - 5.7.1 Scalar Implementation. - 5.7.2 Vectorized Implementation. - 5.7.3 Memory-Saving Implementation. - 5.7.4 Analysis of Memory Usage. - 5.7.5 Analysis of the CPU Time. - 5.8 Higher-Dimensional Arrays. - 5.8.1 Matrices and Arrays. - 5.8.2 Two-Dimensional Numerical Python Arrays. - 5.8.3 Array Computing. - 5.8.4 Matrix Objects. - 5.9 Some Common Linear Algebra Operations. - 5.9.1 Inverse, Determinant, and Eigenvalues. - 5.9.2 Products. - 5.9.3 Norms. - 5.9.4 Sum and Extreme Values. - 5.9.5 Indexing. - 5.9.6 Transpose and Upper/Lower Triangular Parts. - 5.9.7 Solving Linear Systems. - 5.9.8 Matrix Row and Column Operations. - 5.9.9 Computing the Rank of a Matrix. - 5.9.10 Symbolic Linear Algebra. - 5.10 Plotting of Scalar and Vector Fields. - 5.10.1 Installation. - 5.10.2 Surface Plots. - 5.10.3 Parameterized Curve. - 5.10.4 Contour Lines. - 5.10.5 The Gradient Vector Field. - 5.11 Matplotlib. - 5.11.1 Surface Plots. - 5.11.2 Contour Plots. - 5.11.3 Vector Field Plots. - 5.12 Mayavi. - 5.12.1 Surface Plots. - 5.12.2 Contour Plots. - 5.12.3 Vector Field Plots. - 5.12.4 A 3D Scalar Field and Its Gradient Field. - 5.12.5 Animations. - 5.13 Summary. - 5.13.1 Chapter Topics. - 5.13.2 Example: Animating a Function. - 5.14 Exercises. - 6 Dictionaries and Strings. - 6.1 Dictionaries. - 6.1.1 Making Dictionaries. - 6.1.2 Dictionary Operations. - 6.1.3 Example: Polynomials as Dictionaries. - 6.1.4 Dictionaries with Default Values and Ordering. - 6.1.5 Example: Storing File Data in Dictionaries. - 6.1.6 Example: Storing File Data in Nested Dictionaries. - 6.1.7 Example: Reading and Plotting Data Recorded at Specific Dates. - 6.2 Strings. - 6.2.1 Common Operations on Strings. - 6.2.2 Example: Reading Pairs of Numbers. - 6.2.3 Example: Reading Coordinates. - 6.3 Reading Data fromWeb Pages. - 6.3.1 About Web Pages. - 6.3.2 How to Access Web Pages
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  • 6
    Keywords: Mathematics ; Computer programming ; Software engineering ; Computer mathematics ; Mathematics ; Computational Science and Engineering ; Programming Techniques ; Software Engineering ; Numerical and Computational Physics, Simulation ; Mathematical and Computational Engineering
    Description / Table of Contents: Preface --- Algorithms and implementations --- Analysis --- Generalizations --- Models --- Scientific Software Engineering --- References --- Index.
    Pages: Online-Ressource (XIV, 200 pages) , 29 illustrations
    ISBN: 9783319294391
    Language: English
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  • 7
    Unknown
    Cham : Birkhäuser
    Keywords: Topological Groups ; Harmonic analysis ; Functional analysis ; Topological Groups, Lie Groups ; Abstract Harmonic Analysis ; Functional Analysis ; Mathematical Physics
    Description / Table of Contents: Preface --- Introduction --- Notation and conventions --- 1 Preliminaries on Lie groups --- 2 Quantization on compact Lie groups --- 3 Homogeneous Lie groups --- 4 Rockland operators and Sobolev spaces --- 5 Quantization on graded Lie groups --- 6 Pseudo-differential operators on the Heisenberg group --- A Miscellaneous --- B Group C* and von Neumann algebras --- Schrödinger representations and Weyl quantization --- Explicit symbolic calculus on the Heisenberg group --- List of quantizations --- Bibliography --- Index
    Pages: Online-Ressource (XIII, 557 pages) , 1 illustrations in color
    ISBN: 9783319295589
    Language: English
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  • 8
    Electronic Resource
    Electronic Resource
    Springer
    Transport in porous media 9 (1992), S. 165-185 
    ISSN: 1573-1634
    Keywords: oil recovery ; instability ; heterogeneous medium ; shock waves
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Technology
    Notes: Abstract We study the simultaneous one-dimensional flow of water and oil in a heterogeneous medium modelled by the Buckley-Leverett equation. It is shown both by analytical solutions and by numerical experiments that this hyperbolic model is unstable in the following sense: Perturbations in physical parameters in a tiny region of the reservoir may lead to a totally different picture of the flow. This means that simulation results obtained by solving the hyperbolic Buckley-Leverett equation may be unreliable.
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  • 9
    Electronic Resource
    Electronic Resource
    Chichester : Wiley-Blackwell
    International Journal for Numerical Methods in Fluids 9 (1989), S. 213-233 
    ISSN: 0271-2091
    Keywords: Preconditioning ; Conjugate gradients ; Non-symmetric matrices ; Finite elements ; Convective transport ; Engineering ; Engineering General
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Preconditioning techniques based on incomplete Gaussian elimination for large, sparse, non-symmetric matrix systems are described. A certain level of fill-in may be specified in the incomplete factorizations. All methods considered may be applied to matrices with arbitrary sparsity patterns, for instance those associated with the general preprocessor algorithms or adaptive mesh techniques. The preconditioners have been combined with five conjugate gradient-like methods and tested on finite element discretized scalar convection-diffusion equations in 2D and 3D. It is found from numerical experiments that an amount of fill-in corresponding to about 50% of the number of original non-zero matrix entries is the optimal choice for this class of preconditioners. The preconditioners show almost no sensitivity to grid distortion. In problems with significantly variable coefficients or anisotropy the preconditioners stabilize the basic iterative schemes in addition to reducing the computational work substantially, mostly by more than 90%. The modified preconditioning technique, where fill-in is added on the main diagonal, performs in general better than the standard incomplete LU factorization, but is inferior to the latter in 3D problems and for matrix systems with complicated sparsity patterns.
    Additional Material: 16 Ill.
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  • 10
    Electronic Resource
    Electronic Resource
    Chichester : Wiley-Blackwell
    International Journal for Numerical Methods in Fluids 10 (1990), S. 651-681 
    ISSN: 0271-2091
    Keywords: Porous media ; Two-phase flow ; Oil recovery ; Finite elements ; Preconditioning ; Conjugate gradients ; Engineering ; Engineering General
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: The equations governing immiscible, incompressible, two-phase, porous media flow are discretized by generalized streamline diffusion Petrov-Galerkin methods in space and by implicit differences in time. Systems of non-linear algebraic equations are solved by Newton-Raphson iteration employing ILU-preconditioned conjugate-gradient-like methods to the non-symmetric matrix system in each iteration. The resulting solution methods are robust, enable complex grids with irregular nodal orderings and allow capillary effects.Several numerical formulations are tested and compared for one-, two- and three-dimensional flow cases, with emphasis on problems involving saturation shocks, heterogeneous media and curved boundaries. For reservoirs consisting of multiple rock types with differing capillary pressure properties, it is shown that traditional Bubnov-Galerkin methods give poor results and the new Petrov-Galerkin formulations are required. Investigations regarding the behaviour of several preconditioned conjugate-gradient-like methods in these type of problems are also reported.
    Additional Material: 14 Ill.
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