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22.921
Nuclear Power Plant Dynamics and Control Nuclear Power Plant Dynamics and Control John Bernard Tue Jan 12, Thu Jan 14, Tue Jan 19, Thu Jan 21, Tue Jan 26, 01-02:30pm, NW12-222 Pre-register on WebSIS and attend first class. Listeners allowed, space permitting Prereq: Level: G 3 units Standard A - F Grading Introduction to reactor dynamics, including subcritical multiplication, critical operation in absence of thermal feedback effects and effects of xenon, fuel and moderator temperature, etc. Derivation of point kinetics and dynamic period equations. Techniques for reactor control including signal validation, supervisory algorithms, model-based trajectory tracking, and rule-based control. Overview of light-water reactor start-up. Lectures and demonstrations with use of the MIT Research Reactor. Open to undergraduates with permission of instructor. Co-sponsor: Nuclear Reactor Laboratory Undergraduates welcome. Contact: Kathleen O'Connell, NW12-208, 253-4220, katieo@mit.edu |
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22.S903
Special Subject in Nuclear Science and Engineering Nuclear Data: Evaluation Process for Experiment to Radiation Transport Calculations Benoit Forget, Vladimir Sobes Mon Jan 25 thru Fri Jan 29, 09am-12:00pm, 24-307 Pre-register on WebSIS and attend first class. Limited to 10 participants. Listeners allowed, space permitting Prereq: Permission of instructor or 22.11 Level: G 6 units Standard A - F Grading Seminar or lecture on a topic in nuclear science and engineering that is not covered in the regular curriculum. 22.S905 is graded P/D/F. Nuclear data is the foundation of all nuclear transport calculations. Nuclear data research is where raw experimental data meets state-of-the-art physical models and gets folded into a single nuclear data evaluation that is released to the end-user community. Nuclear data users include fission and fusion research, medical research, nuclear astrophysics and many others. Course covers techniques currently applied in nuclear data evaluation. The R-Matrix theory of nuclear reactions is introduced as the founding model for the evaluation process. Practical implementation of the physics of the R-Matrix model is discussed while the student learns how multiple experimental data sets are combined in a single evaluation through Bayesian updating and experimental complications, such as non-zero temperatures, are taken into account in the evaluation process. Basic programming skills are required for "for credit" students. Recitation: MTWR 3:00-4:00pm, room 24-307 Contact: Benoit Forget, 24-214, 253-1655, bforget@mit.edu |