SUBJECT: Ph.D. Proposal Presentation
   
BY: Lloyd Huang
   
TIME: Tuesday, January 12, 2016, 10:30 a.m.
   
PLACE: Boggs, 3-47
   
TITLE: Analysis and Optimization of the Liquid Salt Cooled Reactor Fuel Design Using Surrogate Models and Global Heuristic Optimization
   
COMMITTEE: Bojan Petrovic, Chair (NRE)
Farzan Rahnema (NRE)
Dingkang Zhang (NRE)
Weston Stacey (NRE)
Ivan Maldonado (UT NE)
 

SUMMARY

The Liquid Salt Cooled Reactor (LSCR) is a graphite moderated and liquid salt cooled reactor. The LSCR is designed to provide superior economics and safety compared to the current commercial light water reactors. Due to estimated similar capital cost and a higher thermal efficiency the LSCR is expected to have a lower levelized unit electricity cost despite a higher fuel cycle cost. The objective of this thesis is to develop a fuel design that minimizes the fuel cycle cost and facilitates that the LSCR is more economical than a typical LWR over a typical reactor lifetime. The focus is to identify advanced sampling, modeling, and optimization techniques that are ideal for minimizing the fuel cycle costs. Once the limiting factor is determined further changes in the design can be intelligently recommended. The fuel design features double heterogeneous geometry configured with two layers of TRISO fuel in a carbon matrix pressed on both sides of a carbon slab. The reactor neutronics is analyzed using CSAS6 and TRITON sequences of SCALE6.1 with the KENO Monte Carlo neutron transport code. Since the continuous energy depletion calculations are prohibitively expensive, one objective of this study is to implement a methodology to replace them with significantly faster multigroup calculations while preserving adequate accuracy. Impact of the double-heterogeneous geometry is accounted for by calculating MCDancoff factors to be used for the multi-group approximation. Automated calculations are performed in order to generate functional representations of average temperatures, MCDancoff Factors, cycle lengths, and burnups as functions of the parameters over the design space. These surrogate models are used to quickly sample the fuel design phase space thus avoiding major computational requirements and facilitating the use of optimization algorithms. The surrogate models are created using Latin Hypercube Sampling (LHS) to maximize space-filling while minimizing computational resources needed. The LHS design is generated with a quasi-optimized periodic design developed for speed and practicality. Due to the concave non-linear multivariable design space, a global heuristic optimization algorithm is necessary. The Differential Evolution algorithm has been selected for its fast and reliably deep convergence for concave optimization problems.