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Nuclear Science and Engineering
Fusion Science and Technology
ITER reaches major construction milestone
The 1,250-ton cryostat base is positioned over the ITER tokamak pit for installation. The base is the heaviest lift of the tokamak assembly. Photo: ITER
ITER, the world’s largest international scientific collaboration, is beginning the assembly of the fusion reactor tokamak that will include 12 essential hardware systems provided by US ITER, which is managed by Oak Ridge National Laboratory. The first major machine element to be installed is the 1,250-ton base of the cryostat, which was placed into the tokamak assembly pit on May 26. ITER is located in southeastern France.
Amit Thakur, Umasankari Kannan
Nuclear Science and Engineering | Volume 193 | Number 10 | October 2019 | Pages 1160-1171
Technical Paper | dx.doi.org/10.1080/00295639.2019.1599607
Articles are hosted by Taylor and Francis Online.
Evolutionary algorithms play an important role for solving various optimization problems related to fuel management in reactor physics like core loading pattern optimization (LPO) or refueling. In general, all algorithms make a sample of solution candidates and evaluate the fitness of all candidates, and then the candidates with better fitness value are used to generate the next sample of solution candidates. In optimization algorithms, internal parameters [like population size, weighting factor in estimation of distribution algorithm (EDA) and population size, cross-over rate, etc., in Genetic Algorithm (GA)] have a stiffness problem as the value of these parameters is fixed at the first generation and is not being changed subsequently. However, the flexibility of changing the value of even one internal parameter during the generations will make the algorithm more efficient. In this paper we propose that fuzzy logics can be used in an innovative way to eliminate the stiffness problem related to internal parameters in evolutionary algorithms. As a test case, EDA for initial core LPO of the advanced heavy water reactor is chosen, and the use of fuzzy logics has shown a significant improvement in the algorithm’s performance. The appropriate value of weighting factor α in EDA has been predicted using fuzzy logics in each generation, and this has resulted in efficiency improvement of the algorithm. The improved methodology is expected to give better performance with other optimization algorithms, such as the GA or Ant Colony Optimization Algorithm, etc.