Computational Nuclear Engineering and Radiological Science Using Python

Computational Nuclear Engineering and Radiological Science Using Python

1st Edition - October 19, 2017

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  • Author: Ryan McClarren
  • Paperback ISBN: 9780128122532
  • eBook ISBN: 9780128123713

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Description

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.

Key Features

  • 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

Readership

Nuclear engineers, scientists and engineers working in radiological sciences, graduate students in nuclear sciences

Table of Contents

  • Part I Introduction to Python
    1. Getting Started in Python
    2. Digging Deeper into Python
    3. Functions, Scoping, and Other Fun Stuff
    4. NumPy and Matplotlib
    5. Dictionaries and Functions as Arguments
    6. Testing and Debugging

    Part II Numerical Methods
    7. Gaussian Elimination
    8. LU Factorization and Banded Matrices
    9. Iterative Methods for Linear Systems
    10. Interpolation
    11. Curve Fitting
    12. Closed Root Finding Methods
    13. Newton’s Methods and Related Root-Finding Techniques
    14. Finite Difference Derivative Approximations
    15. Numerical Integration with Newton-Cotes Formulas
    16. Gauss Quadrature and Multi-dimensional Integrals
    17. Initial Value Problems
    18. One-Group Diffusion Equation
    19. One-Group k-Eigenvalue Problems
    20. Two-Group k-Eigenvalue Problems

    Part III Monte Carlo Methods
    21. Introduction to Monte Carlo Methods
    22. Non-analog and Other Monte Carlo Variance Reduction Techniques
    23. Monte Carlo Eigenvalue Calculations

    Part IV Appendices
    Appendix A. Installing and Running Python
    Appendix B. Jupyter Notebooks

Product details

  • No. of pages: 460
  • Language: English
  • Copyright: © Academic Press 2017
  • Published: October 19, 2017
  • Imprint: Academic Press
  • Paperback ISBN: 9780128122532
  • eBook ISBN: 9780128123713

About the Author

Ryan McClarren

Ryan McClarren is Associate Professor in the Department of Aerospace and Mechanical Engineering at the University of Notre Dame. He has spent his professional career educating students in the mathematics and computation required for modern engineering. His research centers around the study of uncertainties in large-scale simulation, and numerical methods for radiation transport problems. Additionally, he is the author of 44 publications in refereed journals and has been the editor of a special issue of the journal Transport Theory and Statistical Physics. He is well known in the computational nuclear engineering community and has research awards and grants from the NSF, DOE, and three national labs.

Affiliations and Expertise

Associate Professor, Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN, USA

Ratings and Reviews

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  • RyanMcClarren Mon Mar 30 2020

    A great way to learn

    A great way to learn numerical techniques and Python with practical nuclear engineering examples.

  • Jose R. Sat Jan 18 2020

    Computational nuclear engineering and radiological science

    Good practical book to learn the basics of computational maths and it's implementation. It is like a practical seminar session with hands on. This is the differential value of the book versus more theoretical ones.

  • GERMANGARCIA Sat Dec 08 2018

    Great book. Highly recommended

    Great book. Highly recommended