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Benoit Forget, Kaichao Sun, and Akshay Dave. Wilson, Jarod(Jarod C.) Massachusetts Institute of Technology. Department of Nuclear Science and Engineering. Massachusetts Institute of Technology. Department of Nuclear Science and Engineering 2020-01-08T19:34:00Z 2020-01-08T19:34:00Z 2019 2019 https://hdl.handle.net/1721.1/123363 1134768491 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). 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. by Jarod Wilson. S.B. S.B. Massachusetts Institute of Technology, Department of Nuclear Science and Engineering 70 pages eng Massachusetts Institute of Technology 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. http://dspace.mit.edu/handle/1721.1/7582 Nuclear Science and Engineering. Machine learning for nuclear fission systems : preliminary investigation of an autonomous control system for the MGEP Preliminary investigation of an autonomous control system for the MIT Graphite Exponential Pile Thesis application/pdf 2020-01-08T19:33:56Z Publication Bachelor NucEng unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="8780a3e1-089e-483b-b509-508b58d0245f"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Machine learning for nuclear fission systems : preliminary investigation of an autonomous control system for the MGEP</Title> <Subtitle>Preliminary investigation of an autonomous control system for the MIT Graphite Exponential Pile</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <Authors> <Author> <DisplayName>Wilson, Jarod(Jarod C.)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Nuclear Science and Engineering.</Keyword> <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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>