Application of genetic algorithm and deep reinforcement learning for in-core fuel management

The nuclear reactor core is composed of few hundred assemblies. The loading of these assemblies is done with the goal of reducing its overall cost while maintaining safety limits. Typically, the core designers choose a unique position and fuel enrichment for each assembly through use of expert judgement. In this thesis, alternatives to the current core reload design process are explored. Genetic algorithm and deep Q-learning are applied in an attempt to reduce core design time and improve the final core layout. The reference core represents a 4-loop pressurized water reactor where fixed number of fuel enrichments and burnable poison distributions are assumed. The algorithms automatically shuffles the assembly positions to find the optimum loading pattern. It is determined that both algorithms are able to successfully start with a poorly performing core loading pattern and discover a well performing one, by the metrics of boron concentration, cycle exposure, enthalpy-rise factor, and pin power peaking. This shows potential for further applications of these algorithms for core design with a more expanded search space.

Name thesis:degree_name

Bachelor

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Nuclear Science and Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Reed, Jane C.,S.B.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Koroush Shirvan.

• Nuclear Science and Engineering.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127308

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127308