Improving the optimization algorithm of NTCOC for application in the HCSB blanket for CFETR Phase II

Abstract Blanket is the key component of Chinese Fusion Engineering Test Reactor (CFETR). In our previous work, considering that the radial arrangement of the internal functional zones has the greatest influence on the blanket performances and is also the most important basis of the 3D design, NTCOC, a Neutronics/Thermal-hydraulic Coupling Optimization Code which adopts 1D neutronics and 2D thermal-hydraulic simplified calculation models, has been independently developed for facilitating the radial build design and optimization of the CFETR helium cooled solid breeder (HCSB) blanket. Through setting three leading design requirements as the optimization criteria, this code has been applied on optimizing the radial build arrangement of a conceptual design of HCSB blanket for CFETR Phase I, which preliminarily verifies the reasonability of the code. However, recently, the design phase of CFETR has gradually changed from I to II, the fusion power of which will be quintuple to the previous I. This means that the internal nuclear power deposition in each blanket component under II operation condition will be much larger than the previous I. Therefore, much larger coolant inlet mass flowrate will be needed and more breeder units should to be inserted, both of which will make the total pressure drop of blanket for Phase II much larger than the previous I and even exceed the limit of CFETR HCSB blanket greatly. This can’t be sustained in CFETR, a power plant that should produce electricity. To solve this problem, in this paper, the previous integrated neutronics/thermal-hydraulic optimization algorithm is improved by adding the new pressure drop limit into the optimization criteria. Then the improved code has been applied on optimizing the radial build arrangement of a conceptual design of HCSB blanket for CFETR Phase II through constant and variable step length two methods. The calculation results verifies the feasibility and reasonability of the improved optimization algorithm preliminarily.

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