Impact Analysis of Helium Cooled Solid Blanket Structures on the Tritium Breeding Performance in CFETR

Abstract

Chinese Fusion Engineering Test Reactor (CFETR) is a test tokamak reactor to bridge the gap between ITER and future fusion power plant. As its objectives are to demonstrate generation of fusion power and to realize tritium self-sufficiency, the tritium breeding ratio (TBR) is a key design parameter. In the blanket design and optimization, the structures such as the first wall (FW), cooling plate (CP), stiffening plate (SP), cap and some other design parameters in detailed 3-D model have significant impacts on the tritium breeding performance. Based on a helium cooled solid breeder blanket option for CFETR, the impact analysis of the helium cooled solid blanket structures on tritium breeding performance was performed in this paper. Firstly, the detailed 3D neutronics model was built by using of a CAD to Monte Carlo Geometry conversion tool McCad. Then based on the detailed 3D neutronics model, the impact analyses of the blanket structures on tritium breeding performance were carried out, which include the FW, CP, SP, cap and side wall. By the sensitivity study of the blanket structures on the TBR, it gave the TBR variation trend and references for the blanket design and optimization.

Research on the efficient process-oriented structural optimization method of the large-scale vacuum cryostat for fusion reactors

“…Additionally, the design may involve various requirements, such as structural size, deformation, strength, or mass, rendering it a typical 'multi-variable and multi-objective' process. Currently, engineers mainly use three typical design methods in the optimized design of the components, such as trial-and-error, the control variables, and an orthogonal experimental approach [5][6][7][8][9][10][11][12][13][14][15][16]. In the trial-and-error method, structural parameters are manually adjusted to obtain the desirable results based on the designer's experience and intuition.…”

“…Nevertheless, the manual adjustment is both time-consuming and inefficient, with subjective results. The variable-control method is applied to the design and optimization of simple structures, such as magnet coils [7,8], supports [9][10][11], plasma-facing units for divertors [12], and the first wall of the blanket [13][14][15]. However, for large and complex structural components, multiple variables should be involved in the design, resulting in a significant increase in experimental points, and further costs in design and time.…”