Computational nuclear engineering and radiological science using python / Ryan G. Mcclarren.
Material type: TextPublisher: London, England : Academic Press, 2018Description: 1 online resource (462 pages) : illustrationsContent type:- text
- computer
- online resource
- 0128123710
- 9780128123713
- 621.48 23
- TK9145
Item type | Current library | Shelving location | Call number | Status | Date due | Barcode | Item holds | |
---|---|---|---|---|---|---|---|---|
Electronic Book | Kuakarun Nursing Library | Processing unit | Online Access | Eb35480 |
Getting Started in Python -- Digging Deeper Into Python -- Functions, Scoping, Recursion, and Other Miscellany -- NumPy and Matplotlib -- Dictionaries and Functions as Arguments -- Testing and Debugging -- Gaussian Elimination -- LU Factorization and Banded Matrices -- Iterative Methods for Linear Systems -- Interpolation -- Curve Fitting -- Closed Root Finding Methods -- Open Root Finding Methods -- Finite Difference Derivative Approximations -- Numerical Integration With Newton–Cotes Formulas -- Gauss Quadrature and Multi-dimensional Integrals -- Initial Value Problems -- One-Group Diffusion Equation -- One-Group k-Eigenvalue Problems -- Two-Group k-Eigenvalue Problems -- Introduction to Monte Carlo Methods -- Monte Carlo Eigenvalue Calculations
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.
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