Description - Computational Nuclear Engineering and Radiological Science Using Python by Ryan McClarren
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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Format: Paperback / softback
(235mm x 191mm x mm)
Academic Press Inc
Publisher: Elsevier Science Publishing Co Inc
Country of Publication:
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Author Biography - Ryan McClarren
Ryan McClarren is an Assistant Professor in the Dept. of Nuclear Engineering at Texas A&M University where he has been teaching a course in computation and numerical methods for juniors and seniors in nuclear engineering and radiological health engineering. Texas A&M is well-respected in the nuclear engineering community for its computational research and education. Additionally, he is the author of 38 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 won research awards from the NSF, DOE, and three national labs.
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