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FUNDAMENTALS OF MONTE CARLO<br />
PARTICLE TRANSPORT<br />
<br />
FORREST B. BROWN<br />
<br />
Lecture notes for Monte Carlo course<br />
<br />
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Fundamentals of Monte Carlo Particle Transport<br />
Lecture 1<br />
<br />
Fundamentals<br />
of Monte Carlo<br />
Particle Transport<br />
<br />
Forrest B. Brown<br />
Monte Carlo Group (X-3)<br />
Los Alamos National Laboratory<br />
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Abstract<br />
Fundamentals of Monte Carlo Particle Transport<br />
Solving particle transport problems with the Monte Carlo method is simple just simulate the particle behavior. The devil is in the details, however. This<br />
course provides a balanced approach to the theory and practice of Monte<br />
Carlo simulation codes, with lectures on transport, random number<br />
generation, random sampling, computational geometry, collision physics,<br />
tallies, statistics, eigenvalue calculations, variance reduction, and parallel<br />
algorithms. This is not a course in how to use MCNP or any other code, but<br />
rather provides in-depth coverage of the fundamental methods used in all<br />
modern Monte Carlo particle transport codes. The course content is suitable<br />
for beginners and code users, and includes much advanced material of<br />
interest to code developers. (10 lectures, 2 hrs each)<br />
The instructor is Forrest B. Brown from the X-5 Monte Carlo team. He has 25 years experience in<br />
developing production Monte Carlo codes at DOE laboratories and over 200 technical publications on<br />
Monte Carlo methods and high-performance computing. He is the author of the RACER code used by the<br />
DOE Naval Reactors labs for reactor design, developed a modern parallel version of VIM at ANL, and is a<br />
lead developer for MCNP5, MCNP6, and other Monte Carlo codes at LANL.<br />
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Topics<br />
1. Introduction<br />
– Monte Carlo & the Transport Equation<br />
– Monte Carlo & Simulation<br />
<br />
2. Random Number Generation<br />
3. Random Sampling<br />
4. Computational Geometry<br />
5. Collision Physics<br />
6. Tallies & Statistics<br />
7. Eigenvalue Calculations – Part I<br />
8. Eigenvalue Calculations – Part II<br />
9. Variance Reduction<br />
10. Parallel Monte Carlo<br />
11. References<br />
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Introduction<br />
<br />
•<br />
<br />
Von Neumann invented scientific computing in the 1940s<br />
–<br />
–<br />
–<br />
–<br />
<br />
•<br />
<br />
Stored programs, "software"<br />
Algorithms & flowcharts<br />
Assisted with hardware design as well<br />
"Ordinary" computers today are called "Von Neumann machines"<br />
<br />
Von Neumann invented Monte Carlo methods for particle transport in<br />
the 1940s (with Ulam, Fermi, Metropolis, & others at LANL)<br />
– Highly accurate – no essential approximations<br />
– Expensive – typically the "method of last resort"<br />
– Monte Carlo codes for particle transport have been proven to work<br />
effectively on all types of computer architectures:<br />
SIMD, MIMD, vector, parallel, supercomputers,<br />
workstations, PCs, Linux clusters, clusters of anything,…<br />
<br />
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