ne.gif (2791 bytes)      NE582 Monte Carlo

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Course Syllabus

Instructor:       Ronald E. Pevey 

Office:             201A ESA Building 

Office Hours:   Posted on office door 

Telephone:       974-7573 

Course Description:  This course covers the theory and techniques of the Monte Carlo method with a special emphasis on the Boltzmann Transport equation.  The course will give the student a grounding in the basic principles of the method and in the variance reduction techniques that are used in modern Monte Carlo transport computer codes.  In addition, the student will gain experience writing Monte Carlo computer solutions to a variety of problem types.

Recommended Text: Mostly, we use the course notes, available in the Public area. Formally, I assign: Lewis, E.E., and Miller, W.F., Jr.; Computational Methods of Neutron Transport,  American Nuclear Society, La Grange Park, IL, 1993.  (But we only use one chapter of this book, which is also the text for NE583.)  We will also use Carter, L.L., and Cashwell, E. D., Particle-Transport Simulation with the Monte Carlo Method, TID-26607, which is in the public domain, and the MCNP manual. Also, I recently became aware of a new edition of the 60s classic by Spanier and Gelbard, so I will refer to pages from it; you can borrow it if you are interested . (My wife found it at McKay's for $8!)

Course Objectives:  By taking this course, the student should gain a basic understanding of: 

  • Basic concepts of random statistical processes
  • Theory and practice of MC solution of event-based problems
  • Analog particle transport and the heuristic variance reduction techniques
  • Non-analog particle transport and the variance reduction techniques
  • Variance reduction techniques specific to MCNP
  • Theory and practice of MC for functional  and matrix representation
  • The basic principles of Monte Carlo integration
  • General application of MC to solution of integral and differential equations
  • Detailed application to the Boltzmann Transport Equation

Computational requirements:  Since the course covers computer methodology, the student is required to perform the computational homework and final exam using a computer language of your choice (not EXCEL). The class examples (and code snippets provided) will be in Java, which is available free from SUN. (You can use other lanuages to graph your results, although I usually just use EXCEL.) But you will get no credit if the language does the statistical processing for you.

In the course Public area there is a tutorial JavaLite.pdf, which covers the minimal subset of Java that you will need to know.

Grading: The final grade is assigned based on the following formula:

  • Two tests and a takehome final, each worth. 30%
  • Homework: 10%


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