Computational Nuclear Engineering And Radiological Science Using Python

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

This book addresses the fundamental technologies, architectures, application domains, and future research directions of the Internet of Things (IoT). It also discusses how to create your own IoT system according to applications requirements, and it presents a broader view of recent trends in the IoT domain and open research issues. This book encompasses various research areas such as wireless networking, advanced signal processing, IoT, and ubiquitous computing. Internet of Things: Theory to Practice discusses the basics and fundamentals of IoT and real-time applications, as well as the associated challenges and open research issues. The book includes several case studies about the use of IoT in day-to-day life. The authors review various advanced computing technologies—such as cloud computing, fog computing, edge computing, and Big Data analytics—that will play crucial roles in future IoT-based services. The book provides a detailed role of blockchain technology, Narrowband IoT (NB-IoT), wireless body area network (WBAN), LoRa (a longrange low power platform), and Industrial IoT (IIoT) in the 5G world. This book is intended for university/college students, as well as amateur electronic hobbyists and industry professionals who are looking to stay current in the IoT domain.

All engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally “analog” disciplines—mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates their specific value to engineers by presenting concrete examples based on physical systems. The book proceeds from basic learning models to deep neural networks, gradually increasing readers’ ability to apply modern machine learning techniques to their current work and to prepare them for future, as yet unknown, problems. Rather than taking a black box approach, the author teaches a broad range of techniques while conveying the kinds of problems best addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow, demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally benefits undergraduate engineering students who wish to acquire the skills required by future employers, and practicing engineers who wish to expand and update their problem-solving toolkit.

This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences. Constructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sources of uncertainty in its inputs. Part III demonstrates techniques for quantifying the impact of parametric uncertainties on a problem, specifically how input uncertainties affect outputs. The final section covers techniques for applying uncertainty quantification to make predictions under uncertainty, including treatment of epistemic uncertainties. It presents the theory and practice of predicting the behavior of a system based on the aggregation of data from simulation, theory, and experiment. The text focuses on simulations based on the solution of systems of partial differential equations and includes in-depth coverage of Monte Carlo methods, basic design of computer experiments, as well as regularized statistical techniques. Code references, in python, appear throughout the text and online as executable code, enabling readers to perform the analysis under discussion. Worked examples from realistic, model problems help readers understand the mechanics of applying the methods. Each chapter ends with several assignable problems. Uncertainty Quantification and Predictive Computational Science fills the growing need for a classroom text for senior undergraduate and early-career graduate students in the engineering and physical sciences and supports independent study by researchers and professionals who must include uncertainty quantification and predictive science in the simulations they develop and/or perform.

Nuclear Engineering Mathematical Modeling and Simulation presents the mathematical modeling of neutron diffusion and transport. Aimed at students and early career engineers, this highly practical and visual resource guides the reader through computer simulations using the Monte Carlo Method which can be applied to a variety of applications, including power generation, criticality assemblies, nuclear detection systems, and nuclear medicine to name a few. The book covers optimization in both the traditional deterministic framework of variational methods and the stochastic framework of Monte Carlo methods. Specific sections cover the fundamentals of nuclear physics, computer codes used for neutron and photon radiation transport simulations, applications of analyses and simulations, optimization techniques for both fixed-source and multiplying systems, and various simulations in the medical area where radioisotopes are used in cancer treatment. Provides a highly visual and practical reference that includes mathematical modeling, formulations, models and methods throughout Includes all current major computer codes, such as ANISN, MCNP and MATLAB for user coding and analysis Guides the reader through simulations for the design optimization of both present-day and future nuclear systems

Monte Carlo Methods and Codes for Nuclear Engineering Analysis provides a comprehensive survey of the state-of-the-art in radiation transport methods used by Monte Carlo (MC) codes. It then goes on to explore the real-world implementation of these methods in codes used by nuclear and scientists engineers, considering the advantages and disadvantages of the various techniques, design philosophies, and algorithm implementations. After a foreword and introduction giving a brief history of Monte Carlo methods, code systems, and their applications in nuclear science and engineering, subsequent chapters describe the fundamentals of Monte Carlo radiation transport methods by dividing the field into a number of topics or focus areas. The subjects selected include potential geometry and particle tracking, nuclear data, variance reduction, time-dependent analysis and parallel computing. Each chapter presents a comprehensive survey of the state-of-the-art implementations, algorithms, and methodologies used by production-level Monte Carlo codes for the area. A concluding chapter provides a handy summary by briefly listing the methods used by key Monte Carlo codes for each focus area in several tables. This book is an essential guide to Monte Carlo methods and codes for nuclear scientists, engineers and code developers in academia and industry and students studying this topic. discusses and compares the radiation transport methods in real-life Monte Carlo (MC) codes used by nuclear scientists and engineers presents in one convenient volume information previously scattered between conference papers, journal articles, and code manuals, thus allowing MC code users to compare the features and make and educated selections of the codes best meeting their needs chapters begin at a level that is appropriate for readers who are unfamiliar with the field, then go on to address the state-of-the-art

Python Programming and Numerical Methods: A Guide for Engineers and Scientists introduces programming tools and numerical methods to engineering and science students, with the goal of helping the students to develop good computational problem-solving techniques through the use of numerical methods and the Python programming language. Part One introduces fundamental programming concepts, using simple examples to put new concepts quickly into practice. Part Two covers the fundamentals of algorithms and numerical analysis at a level that allows students to quickly apply results in practical settings. Includes tips, warnings and "try this" features within each chapter to help the reader develop good programming practice Summaries at the end of each chapter allow for quick access to important information Includes code in Jupyter notebook format that can be directly run online

These papers are concerned with new advances and novel solutions in the areas of biofluids, image-guided surgery, tissue engineering and cardovascular mechanics, implant analysis, soft tissue mechanics, bone remodeling and motion analysis. The contents also feature a special section on dental materials, dental adhesives and orthodontic mechanics. This edition contains many examples, tables and figures, and together with the many references, provides the reader with invaluable information on the latest theoretical developments and applications.