摘要
在级环境下采用人工神经网络和遗传算法在对设计工况下的离心压气机扩压器叶片型线进行了优化,并采用数值方法对优化前、后离心压气机级的气动性能进行了对比分析.结果表明:在设计工况下,优化后的叶片扩压器静压恢复系数提高了11.7%,总压损失系数减少了21.12%,离心压气机级绝热等熵效率提高1.64%,达到了86.01%;非设计工况下离心压气机的气动性能也有显著改善;优化后离心压气机级在设计转速下喘振裕度有所提高,阻塞裕度略有降低.
The vaned diffuser profile designed preliminarily has been optimized using artificial neural network and genetic algorithm under stage environment to improve the aerodynamic performance at the design operation condition. The aerodynamic performance of the stage composed of the vaned diffuser optimized and the original impeller at the design and off-design operation conditions is predicted using the numerical method. The results show that the static pressure recovery coefficient of the vaned diffuser and the isentropic efficiency of the stage was increased by 11.7% and 1.64%, respectively. The total pressure loss coefficient of the vaned diffuser was decreased by 21.12%, and the isentropic efficiency of the stage was 86.1 % at the design operation condition. Moreover, the aerodynamic performances of the stage at the off-design operation conditions was improved significantly. In addition, the surge margin of the stage was increased, and the choke margin was decreased slightly.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2009年第11期32-36,共5页
Journal of Xi'an Jiaotong University
关键词
离心压气机
叶片扩压器
气动优化
人工神经网络
遗传算法
centrifugal compressor
vaned diffuser
aerodynamic optimization
artificial neural network
genetic algorithm
