摘要
无叶扩压器的旋转失速限制了离心压缩机的运行范围。已有的失速预测模型在应用于压缩机末级时会产生较大的偏差。对于高压场合下通常采用的叶轮,因其叶片出口宽度和半径比很小,偏差则更加明显。该文利用文献中的实验结果,建立了基于小波神经网络的失速和性能预测模型;并分析了几何尺寸对压缩机稳定性和设计点效率的影响。研究结果表明:叶轮出口无量纲宽度不同,扩压器尺寸的影响表现也不同。扩压器的收缩形状,对临界角和性能的影响较小。分析的结果对于高压压缩机末级的设计具有一定的指导意义。
Vaneless diffuser rotating stall in centrifugal compressor limits its operation range. Some correlations could be found for stall prediction in open literature, but they can＇t cover the case of last stage configuration, especially for very low blade-outlet-width to impeller-radius ratio impellers typically used in high pressure applications. Stall and performance prediction model based on wavelet neural network （WNN） was established using experimental data in literature. Resulted model was further used to analyze geometries＇ effects on stability and performance at design point. Results show that： for impellers with variable dimensionless outlet widths, diffusers of different scales exhibit distinct responses. Pinch shapes of diffusers have negligible influences on critical angle and performance. Analysis may guide diffuser design of last stage of centrifugal compressor.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2007年第32期83-86,共4页
Proceedings of the CSEE
基金
国家高技术研究发展计划资助项目(2006AA05Z250)
国家自然科学基金项目(50676054)~~
关键词
高压离心压缩机
末级
旋转失速
小波神经网络
high-pressure centrifugal compressor
last stage
rotating stall
wavelet neural network
作者简介
高闯（1982-），男，博士研究生，主要从事叶轮机械气动非稳定性的研究和控制，chuanggao@sjtu．edu．cn。
