OPTIMIZATION OF CORE LOADING PATTERN IN AHTR USING A NOVEL HYBRID ADAPTIVE GENETIC ALGORITHM AND TABU SEARCH (HAGATS)

Advanced High Temperature Reactor (AHTR) is a type of the innovative Molten Salt Reactor (MSR) types which shows several promising characteristics such as high efficiency of thermal-electric conversion, high discharge burn-up and atmospheric pressure operation. However, due to the utilization of fuel assemblies with single enrichment, the power peaking factor (PPF) reaches to 2.09 with a radial PPF is more than 1.56, the high PPF is detrimental to the safety and economy of the reactor. We used a home-made AHTR-GATS code to minimize the PPF by radially varying the enrichment of fuel assemblies while keeping the FA average U-235 enrichment equal to the pre-design. To prevent the early convergence and obtain a better response, a novel hybrid algorithm which combines genetic algorithm (GA) with tabu search (TS) was developed to search for the best configuration corresponding to the desired patterns. The results indicated that compared with the pre-design the effective multiplication factor, k-inf of the optimized reactors were slightly improved and corresponding PPF were greatly reduced, and the temperature coefficient of reactivity were still negative throughout its lifespan. Moreover, the performance evaluation results of HAGATS show that it has strong robustness, high search efficiency and is suitable for loading pattern optimization.