考虑冷气掺混的跨声速透平叶型气动优化设计方法  

Aerodynamic Optimization Design Method for Transonic Turbine Blade with Coolant Ejection

作　　者：王广北 张晓东[2] WANG Guangbei;ZHANG Xiaodong(Research Center of Fluid Machinery Engineering and Technology,Jiangsu University,Zhenjiang,China,212000;Advanced Gas Turbine Laboratory,Institute of Engineering Thermophysics,Chinese Academy of Sciences,Beijing,China,100190)

机构地区：[1]江苏大学流体机械工程技术研究中心,江苏镇江212000 [2]中国科学院工程热物理研究所先进燃气轮机实验室,北京100190

出　　处：《热能动力工程》2024年第10期29-36,45,共9页Journal of Engineering for Thermal Energy and Power

基　　金：国家科技重大专项(J2019-Ⅱ-0010-0030)。

摘　　要：为拓展叶型设计空间、实现跨声速涡轮叶型设计中的精细化型面控制,并在叶型优化时考虑叶身气膜冷气出流的影响,提出了基于Bezier曲线构型的“12+7”参数化造型方法,其中叶型吸力面进口段、出口段、压力面和前缘均采用Bezier曲线。通过集成自主涡轮叶型造型程序、MATLAB人工神经网络工具箱及NUMECA商用软件,搭建了可考虑冷气的涡轮叶型数值优化平台,并在考虑冷气掺混的条件下对典型重燃透平一级导叶进行了气动优化研究。优化变量包括安装角、后弯角、前缘半楔角以及吸力面Bezier曲线控制系数,优化目标为总压损失系数最小。结果表明:经过优化后的叶型模拟与实验结果拟合良好;总压损失系数从0.065 19减小为0.060 48,减小了7.23%;能量损失系数从0.051 8减小为0.047 78,减小了7.76%,气动性能得到增强。In order to expand the blade design space,realize the precise control of blade surfaces in transonic blade profile design and consider the influence of film cooling outflow during blade profile opti-mization,a"12+7"parametric modeling method based on Bezier curves was proposed.Bezier curves were applied to the suction surface inlet,outlet,pressure surface and leading edge of the blade.A nu-merical optimization platform for turbine blade profile design considering film cooling was constructed by integrating an autonomous turbine blade modeling program,MATLAB artificial neural network toolbox,and the commercial software NUMECA.The aerodynamic optimization with coolant ejection was per-formed on the first-stage guide vane of a typical heavy-duty gas turbine.The optimization variables in-cluded the stagger angle,trailing edge angle,leading edge 1/2 wedge angle,suction surface Bezier curve control coefficients,and the optimization objective was to minimize the total pressure loss coefficient.The results show that the simulation of optimized blade profile fits well with experimental result,total pressure loss coefficient decreases by 7.23%from 0.06519 to 0.06048 and energy loss coefficient decreases by 7.76%from 0.0518 to 0.04778,indicating improved aerodynamic performance.

关 键 词：涡轮 气动优化 气膜冷却 人工神经网络 

分 类 号：TK261[动力工程及工程热物理—动力机械及工程]