摘要
以65kW燃料电池动力系统的高速无油润滑离心空压机为优化对象,采用Bezier曲线对其叶片型线进行参数化解析,依据超拉丁抽样方法获得遗传算法优化所需的样本空间,在此基础上建立Kigring近似模型进行多工况优化.寻优及CFD(计算流体动力学)数值计算结果显示,常用工况点和额定工况点等熵效率及压比均得到提高,且常用工况点改善更为显著.这表明传统内燃机车用离心增压器设计及优化时不能兼顾多工况性能结论不适用燃料电池汽车,叶轮性能空气动力学解析同样证实该结论具有理论基础.与基于叶轮几何参数的优化结果对比显示,基于叶片型线参数化的优化可以更加显著地改善离心空压机性能,是一种更加全面和有效的离心叶轮优化方法.
In this paper, a high speed oil-free centrifugal compressor for the 65kW fuel cell pewertrain system application is selected as optimization objective. The impeller profile curve is parameterized with Bezier curve, and the latin hypercube sampling method is adopted to build the sample space for genetic algorithm optimization. Based on these pretreatments, the Kigring model is built for multi-operating condition optimization. The optimization and CFD calculation results show that both isentropic efficiency and pressure ratio are improved, especially under common operating condition.For the centrifugal impeller of traditional internal combustion powertrain, it is always believed that the isentropic efficiency at both rated and common operating condition cannot be improved at the same time. However, impeller aerodynamic analysis and multi-operating condition optimization indicate that this view is not suitable to the fuel cell powertrain system. The optimization based on parameterized impeller profile achieves a higher isentropic efficiency compared to the optimization based on impeller geometrical parameters, which shows that the optimization based on parameterized impeller profile is a more comprehensive and efficient method for centrifugal impeller optimization.
出处
《同济大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2017年第1期98-108,共11页
Journal of Tongji University:Natural Science
基金
"十二五"国家科技支撑计划(2015BAG06B00)
关键词
燃料电池汽车
参数化
离心空压机
多工况优化
遗传算法
fuel cell vehicle
parameterization
centrifugal compressor
multi-operating condition optimization
genetic algorithm
