Computational Nuclear Engineering and Radiological Science Using Python

Ryan McClarren

• Publisher: Academic Press
• Year: 2017
• Print ISBN: 9780128122532
• eBook ISBN: 9780128123713
• Topic: Technology & Engineering
• Subtopic: Mechanical Engineering
• Index: Technology & Engineering

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

Part I

Introduction to Python for Scientific Computing

Outline

Chapter 1. Getting Started in Python

Chapter 2. Digging Deeper Into Python

Chapter 3. Functions, Scoping, Recursion, and Other Miscellany

Chapter 4. NumPy and Matplotlib

Chapter 5. Dictionaries and Functions as Arguments

Chapter 6. Testing and Debugging

Chapter 1

Getting Started in Python

You can be shaped, or you can be broken. There is not much in between. Try to learn. Be coachable. Try to learn from everybody, especially those who fail. This is hard. ... How promising you are as a Student of the Game is a function of what you can pay attention to without running away.

David Foster Wallace, Infinite Jest

Abstract

This chapter introduces the reader to the basics of programming in Python version 3. Starting from a Hello World example, the concepts of comments, errors, and code indentation in Python are introduced. The manipulation of numerical values in Python is then demonstrated, including complex arithmetic. The concept of strings is used to introduce the notion of operator overloading. The reader is then shown how to accept keyboard input into a Python program. The chapter concludes with a discussion of branching and iteration in Python with examples.

Keywords

Python; Floating point numbers; Complex arithmetic; Hello World; Operator overloading; Strings; Keyboard input; Branching; If statement; Iteration; While loop

Chapter Outline

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)

1.6 Iteration

The Great Beyond

Further Reading

Problems

Short Exercises

Programming Projects

1. Harriot's Method for Solving Cubics

Chapter Points

• Python is a computer programming language that we can use to solve engineering problems.

• One stores information in variables and can make computations and comparisons with those variables.

• Branching executes different parts of a code depending on conditions the programmer defines.

• Iteration execute the same block of code repeatedly under...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Dedication
• About the Author
• Preface
• Acknowledgment
• Part I: Introduction to Python for Scientific Computing
• Chapter 1: Getting Started in Python
• Chapter 2: Digging Deeper Into Python
• Chapter 3: Functions, Scoping, Recursion, and Other Miscellany
• Chapter 4: NumPy and Matplotlib
• Chapter 5: Dictionaries and Functions as Arguments
• Chapter 6: Testing and Debugging
• Part II: Numerical Methods
• Chapter 7: Gaussian Elimination
• Chapter 8: LU Factorization and Banded Matrices
• Chapter 9: Iterative Methods for Linear Systems
• Chapter 10: Interpolation
• Chapter 11: Curve Fitting
• Chapter 12: Closed Root Finding Methods
• Chapter 13: Open Root Finding Methods
• Chapter 14: Finite Difference Derivative Approximations
• Chapter 15: Numerical Integration With Newton–Cotes Formulas
• Chapter 16: Gauss Quadrature and Multi-dimensional Integrals
• Chapter 17: Initial Value Problems
• Chapter 18: One-Group Diffusion Equation
• Chapter 19: One-Group k-Eigenvalue Problems
• Chapter 20: Two-Group k-Eigenvalue Problems
• Part III: Monte Carlo Methods
• Chapter 21: Introduction to Monte Carlo Methods
• Chapter 22: Monte Carlo Variance Reduction and Scalar Flux Estimation
• Chapter 23: Monte Carlo Eigenvalue Calculations
• Bibliography
• Index