Computational Nuclear Engineering and Radiological Science Using Python

Häftad, Engelska, 2017

1 489 kr

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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

Produktinformation

Utgivningsdatum2017-10-19
Mått191 x 235 x 27 mm
Vikt950 g
FormatHäftad
SpråkEngelska
Antal sidor460
FörlagElsevier Science
ISBN9780128122532

Tillhör följande kategorier

Tillämpad datateknik inom Data och IT
Energiteknik inom Naturvetenskap och teknik

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.

Part I Introduction to Python1. Getting Started in Python2. Digging Deeper into Python3. Functions, Scoping, and Other Fun Stuff4. NumPy and Matplotlib5. Dictionaries and Functions as Arguments6. Testing and DebuggingPart II Numerical Methods7. Gaussian Elimination8. LU Factorization and Banded Matrices9. Iterative Methods for Linear Systems10. Interpolation11. Curve Fitting12. Closed Root Finding Methods13. Newton’s Methods and Related Root-Finding Techniques14. Finite Difference Derivative Approximations15. Numerical Integration with Newton-Cotes Formulas16. Gauss Quadrature and Multi-dimensional Integrals17. Initial Value Problems18. One-Group Diffusion Equation19. One-Group k-Eigenvalue Problems20. Two-Group k-Eigenvalue ProblemsPart III Monte Carlo Methods21. Introduction to Monte Carlo Methods22. Non-analog and Other Monte Carlo Variance Reduction Techniques23. Monte Carlo Eigenvalue CalculationsPart IV AppendicesAppendix A. Installing and Running PythonAppendix B. Jupyter Notebooks