采用Kriging模型的离心压缩机叶轮多目标参数优化  

Multi-objective parameter optimization of centrifugal compressor impeller with Kriging model

作　　者：左曙光[1] 韦开君[1] 吴旭东[1] 聂玉洁[1] 许思传[1] 

机构地区：[1]同济大学新能源汽车工程中心,上海201804

出　　处：《农业工程学报》2016年第2期77-83,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家重大科学仪器设备开发专项(2012YQ15025605);国家863项目(2012AA110501)

摘　　要：为进一步探索高性能、低噪声的离心压缩机优化设计方法,该文选用某燃料电池车用小型高转速离心压缩机为研究对象,通过三维内流场非定常分析对其气动性能和气动噪声进行计算,仿真求得的压升曲线与试验基本一致。基于该数值模型,采用最优拉丁方试验设计分析了叶片进口角、叶片出口角、尾缘倾角、叶顶间隙和叶片厚度对压缩比、等熵效率和整机声功率级的影响,结果表明叶片厚度和叶顶间隙最为关键,与压缩比和等熵效率负相关,与声功率级正相关,前倾叶片较后倾叶片噪声更低。采用Kriging模型对数值计算结果进行拟合,利用多目标遗传算法对Kriging模型进行循环优化设计。优化结果表明,Kriging模型精度满足需求,优化方案在设计工况点的压缩比提高3.56%,等熵效率提高1.02%,整机声功率级下降3.79 d B,在非设计工况点的压缩比和等熵效率也有提高,综合性能得到明显改善。该研究可为高性能、低噪声离心压缩机的优化设计提供参考。The high-speed centrifugal compressor used in the air supply system is the major noise source of the fuel cell vehicle. Therefore, it is important for the compressor to achieve low noise level as well as high compression ratio and efficiency. This paper presents an optimal design method for the centrifugal compressors using numerical simulation, Kriging model and genetic algorithm at the operating point. The rotational speed at the operation point is 80 000 r/min, the mass flow rate is 0.08 kg/s, and the compression ratio is 1.65. The steady RANS simulations are preliminarily used to provide the performance maps as well as the consistent initial conditions for the subsequent unsteady simulations. Performance maps are compared between numerical and experimental results at 40 000 and 50 000 r/min, which show a good agreement. Next, the unsteady simulations are performed to calculate the sound power level of the compressor. In order to analyze the influences of the blade inlet angle, blade outlet angle, trailing edge angle, tip clearance and blade thickness on the compression ratio, isentropic efficiency and sound power level, the optimal Latin square design is adopted to create the sample space. Each one of the sample points is simulated with the presented numerical method. The results show that the tip clearance and blade thickness are 2 primary factors. The compression ratio and efficiency decline when the tip clearance and blade thickness decrease, while the sound power level rises. The front incline is found to be better than the back incline. The Kriging model is built to reflect the functional relationship between the impeller design parameters and the performance parameters. Then, the multi-objective optimization is conducted with the genetic algorithm based on the Kriging model instead of the numerical model. The errors of the compression ratio, isentropic efficiency and sound power level between the Kriging model and the numerical model at optimized point are 0.11%, 0.46% and 0.01%, respectively. The blade

关 键 词：模型 优化 叶轮 离心压缩机 非定常分析 气动噪声 KRIGING模型 

分 类 号：TH452[机械工程—机械制造及自动化]