Monte Carlo Methods for Particle Transport

Dr. Alireza Haghighat is professor and Director of Nuclear Engineering Program, Virginia Tech. He is a fellow of the American Nuclear Society. He has made pioneering contributions to the development of accurate and efficient deterministic, stochastic, and hybrid particle transport theory methods and their application to complex problems in nuclear reactors, nuclear security and radiation diagnosis and therapy.

When we decided to offer a new senior undergraduate level course on Monte Carlo in the nuclear engineering department at North Carolina State University, we easily decided to choose the Monte Carlo book by Dr. Haghighat since it came so highly recommended. One of things we liked most about this book is the way the topics are structured. The first seven chapters mainly cover the fundamental theories of Monte Carlo methods for particle transport. While the last five chapters present more advanced theories, they can be seamlessly coupled with hands-on training with industry standard Monte Carlo codes (in our course we selected MCNP6.2). We easily designed our course following the general outline of this book. Furthermore, the course materials have been refined and improved many times based on Dr. Haghighat's many years of teaching experience, as well as comments from students, practitioners, and reviewers. They are well explained and cover almost all the essential concepts in Monte Carlo. For the first half of the semester that focused on Monte Carlo methods, the course work assignments were mainly programming-based homework problems (mostly taken from the exercises in the book). Dr. Haghighat designed these exercises so well that they can not only strengthen the students' understanding, but also inspire their critical thinking. We highly recommend this textbook for senior undergraduate students, graduate students and researchers who are interested in Monte Carlo applications on radiation transport.- Professors Xu Wu and John Zino, North Carolina State University, USA.

• 1 Introduction. 2 Random Variables and Sampling. 3 Random Number Generator (RNG). 4 Fundamentals of Probability and Statistics. 5 Integrals and Associated Variance Reduction Techniques. 6 Fixed-Source Monte Carlo Particle Transport. 7 Variance Reducation Techniques or Fixed-Source Particles. 8 Scoring/ Tallying. 9 Geometry and Particle Tracking. 10 Eigenvalue (Criticality) Monte Carlo Method for Particle Transport. 11 Fission Matrix Methods for Eigenvalue Monte Carlo Simulation. 12 Vector and Parallel Processing of Monte Carlo Particle Transport