Machine learning for nuclear fission systems : preliminary investigation of an autonomous control system for the MGEP
Name
1134768491-MIT.pdf
Size
18.36 MB
Format
Adobe PDF
Checksum (MD5)
c09b4000437fb3b7d520e5cecd5bdd0e
Author(s)
Wilson, Jarod(Jarod C.)
Advisor(s)
Benoit Forget, Kaichao Sun, and Akshay Dave.
Alternative Title
Preliminary investigation of an autonomous control system for the MIT Graphite Exponential Pile
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
Commercial nuclear technology today is facing challenges due to both economic viability and concerns over safety. Next-generation reactors could potentially improve with respect to both concerns through recent advancements in computation and machine learning, through autonomous control systems which minimize human error. The MIT Graphite Exponential Pile (MGEP) has been selected as the basis of a realworld demonstration of such a system, because of its simple properties and inherent safety. This study evaluated the preliminary feasibility of an autonomous control system for the MGEP through two parallel avenues; a practical investigation of various machine learning algorithms applied to fission systems, as well as the design and fabrication of a control rod for the pile. It was found that Convolutional Neural Networks (CNNs) outperform Support Vector Regression (SVR) in predicting the MITR power-shape. Additionally, acceptable results were achieved when applying the CNN algorithm to the MGEP to predict the flux distribution of its fuel elements. Finally, it was verified that neutron detectors in the pile respond predictably to control rod insertions. Taken together, the groundwork for the further development of an autonomous control system has been laid, and the path forward is promising.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: S.B., Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 69-70).
Subjects
Nuclear Science and Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
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