Abstract
The High Temperature Gas-cooled Reactor (HTGR) is provided with good safety, high quality of thermal source and low cost of power generation in full life cycle. Furthermore, when the helium turbine is used for heat-work conversion, the efficiency of the HTGR is high and up to a magnitude of 50%. One of the key technologies of helium turbine is the helium compressor design. According to the conventional design rule of the air-compressor, the stage number of the helium compressor was too much excessive. Therefore, this thesis has analyzed and optimized a new cascade of helium compressor with enhanced pressure ratio in order to increase the pressure ratio and decrease the stage number. The Artificial Neural Network is used to build the approximate function which is based on database sample space. The Genetic Algorithm is used to search a new design, and the Artificial Neural Network is reused to predict the aerodynamic performance of the new design. The mean camber line and thickness distribution are optimized respectively, and the optimization results show that the total pressure loss coefficient can be reduced by 14.48% than that of the primary.
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