| Description |
1 online resource (462 pages) : illustrations |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
Front Cover -- Computational Nuclear Engineering and Radiological Science Using PythonTM -- Copyright -- Contents -- About the Author -- Preface -- Acknowledgment -- Part I Introduction to Python for Scienti c Computing -- 1 Getting Started in Python -- 1.1 Why Python? -- 1.1.1 Comments -- 1.1.2 Errors -- 1.1.3 Indentation -- 1.2 Numeric Variables -- 1.2.1 Integers -- 1.2.2 Floating Point Numbers -- 1.2.2.1 Built-in Mathematical Functions -- 1.2.3 Complex Numbers -- 1.3 Strings and Overloading -- 1.4 Input -- 1.5 Branching (If Statements) |
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1.6 Iteration The Great Beyond -- Further Reading -- Problems -- Short Exercises -- Programming Projects -- 1. Harriot's Method for Solving Cubics -- 2 Digging Deeper Into Python -- 2.1 A First Numerical Program -- 2.2 For Loops -- 2.3 Lists and Tuples -- 2.3.1 Lists -- 2.3.2 Tuples -- 2.4 Floats and Numerical Precision -- Further Reading -- Problems -- Short Exercises -- Programming Projects -- 1. Nuclear Reaction Q Values -- 2. Calculating e, the Base of the Natural Logarithm -- 3 Functions, Scoping, Recursion, and Other Miscellany |
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3.1 Functions3.1.1 Calling Functions and Default Arguments -- 3.1.2 Return Values -- 3.2 Docstrings and Help -- 3.3 Scope -- 3.4 Recursion -- 3.5 Modules -- 3.6 Files -- Problems -- Short Exercises -- Programming Projects -- 1. Monte Carlo Integration -- 4 NumPy and Matplotlib -- 4.1 NumPy Arrays -- 4.1.1 Creating Arrays in Neat Ways -- 4.1.2 Operations on Arrays -- 4.1.3 Universal Functions -- 4.1.4 Copying Arrays and Scope -- 4.1.5 Indexing, Slicing, and Iterating -- 4.1.6 NumPy and Complex Numbers -- 4.2 Matplotlib Basics |
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4.2.1 Customizing Plots Further Reading -- Problems -- Short Exercises -- Programming Projects -- 1. Inhour Equation -- 2. Fractal Growth -- 3. Charges in a Plane -- 5 Dictionaries and Functions as Arguments -- 5.1 Dictionaries -- 5.2 Functions Passed to Functions -- 5.3 Lambda Functions -- Problems -- Short Exercises -- Programming Projects -- 1. Plutonium Decay Chain -- 2. Simple Cryptographic Cipher -- 6 Testing and Debugging -- 6.1 Testing Your Code -- 6.2 Debugging -- 6.3 Assertions -- 6.4 Error Handling -- Further Reading |
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Problems Short Exercises -- Programming Projects -- 1. Test Function for k-Eigenvalue -- Part II Numerical Methods -- 7 Gaussian Elimination -- 7.1 A Motivating Example -- 7.2 A Function for Solving 3x3 Systems -- 7.3 Gaussian Elimination for a General System -- 7.4 Round off and Pivoting -- 7.5 Time to Solution for Gaussian Elimination -- Further Reading -- Problems -- Short Exercises -- Programming Projects -- 1. Xenon Poisoning -- 2. Flux Capacitor Waste -- 3. Four-Group Reactor Theory -- 4. Matrix Inverse |
| Summary |
Computational Nuclear Engineering and Radiological Science Using Python provides the necessary knowledge users need to embed more modern computing techniques into current practices, while also helping practitioners replace Fortran-based implementations with higher level languages. The book is especially unique in the market with its implementation of Python into nuclear engineering methods, seeking to do so by first teaching the basics of Python, then going through different techniques to solve systems of equations, and finally applying that knowledge to solve problems specific to nuclear engineering. Along with examples of code and end-of-chapter problems, the book is an asset to novice programmers in nuclear engineering and radiological sciences, teaching them how to analyze complex systems using modern computational techniques. For decades, the paradigm in engineering education, in particular, nuclear engineering, has been to teach Fortran along with numerical methods for solving engineering problems. This has been slowly changing as new codes have been written utilizing modern languages, such as Python, thus resulting in a greater need for the development of more modern computational skills and techniques in nuclear engineering. Offers numerical methods as a tool to solve specific problems in nuclear engineering Provides examples on how to simulate different problems and produce graphs using Python Supplies accompanying codes and data on a companion website, along with solutions to end-of-chapter problems Publisher |
| Subject |
Nuclear engineering.
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Radiology.
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Python (Computer program language)
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Radiology |
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Génie nucléaire.
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Radiologie.
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Python (Langage de programmation)
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radiology.
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TECHNOLOGY & ENGINEERING -- Mechanical.
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Nuclear engineering
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Python (Computer program language)
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Radiology
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| Other Form: |
Print version: McClarren, Ryan G. Computational nuclear engineering and radiological science using python. London, England : Academic Press, 2018 9780128122532 (DLC) 2017957003 (OCoLC)979562306 |
| ISBN |
0128123710 (ebk) |
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9780128123713 |
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9780128122532 |
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0128122536 |
| Standard No. |
AU@ 000061015417 |
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AU@ 000061338604 |
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CHNEW 001014665 |
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GBVCP 1002873428 |
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