超声速涡轮叶型全局气动优化设计

推进技术 ›› 2019, Vol. 40 ›› Issue (5): 1051-1057.

超声速涡轮叶型全局气动优化设计

李志^1,2,刘艳²,杨金广²,徐乐²,张敏²

中国空气动力研究与发展中心空气动力学国家重点实验室，四川绵阳 621000;大连理工大学能源与动力学院，辽宁大连 116024,大连理工大学能源与动力学院，辽宁大连 116024,大连理工大学能源与动力学院，辽宁大连 116024,大连理工大学能源与动力学院，辽宁大连 116024,大连理工大学能源与动力学院，辽宁大连 116024

发布日期:2021-08-15
作者简介:李志，硕士生，研究领域为轴流涡轮气动优化。E-mail: tianren4561367@163.com 通讯作者:刘艳，博士，教授，研究领域为叶轮机械气动热力学。
基金资助:
空气动力学国家重点实验室研究基金(SKLA201701)。

Global Aerodynamic Optimization Design of Supersonic Turbine Blade Profiles

State Key Laboratory of Aerodynamics，China Aerodynamics Research and Development Center， Mianyang 621000，China;School of Energy and Power Engineering，Dalian University of Technology，Dalian 116024，China,School of Energy and Power Engineering，Dalian University of Technology，Dalian 116024，China,School of Energy and Power Engineering，Dalian University of Technology，Dalian 116024，China,School of Energy and Power Engineering，Dalian University of Technology，Dalian 116024，China and School of Energy and Power Engineering，Dalian University of Technology，Dalian 116024，China

Published:2021-08-15

摘要/Abstract

摘要： 针对涡轮叶型全局优化设计计算时间长、样本空间大等难点提出一种可行的优化设计方法，该方法将控制叶型的17个参数作为优化变量，采用第二代多目标遗传算法进行全局自动寻优。基于此方法，搭建了涡轮叶型全局优化设计平台。利用此平台，分别采用轴向稠度固定和自由优化两种方式对超声速涡轮叶型进行了优化设计。数值计算结果表明，两组优化设计叶型在设计工况下总压损失系数比参考叶型分别低19.5%和10.0%，流道中的激波强度更弱，且在变工况条件下都具有较好的气动性能。深入分析流场与激波结构后发现，外尾激波相比于内尾激波对总损失的影响更大，通过减小气流膨胀转折角或内尾激波气流转折角能够有效削弱外尾激波强度。

关键词: 全局优化；超声速涡轮叶片；激波弱化；气动性能

Abstract: In view of the design difficulty of global optimization design for turbine blades， such as the long computing time and large sample space， a feasible optimization design method is put forward. Seventeen parameters controlling the blade profile are used as optimization variables， and the second generation of multi-objective genetic algorithm is applied for global optimization automatically. Based on this method， a global optimization design platform for turbine blades is built. By using this design platform， the optimal design of a supersonic turbine blade is carried out using two modes: fixed axial solidity and free optimization. Numerical results show that at the design condition， the total pressure loss coefficient of the optimization blades is decreased by 19.5% and 10.0% compared to the reference blade， and the intensity of shock wave is reduced obviously for the two optimized profiles obtained from two methods respectively. And the aerodynamic performance at all operating conditions is also improved. The flow field and shock wave structure show that the outer trail shock wave has a greater influence on the total loss than the inner trail shock wave. The outer trail shock wave can be reduced by decreasing expansion turning angle or the inner trail shock wave turning angle.

Key words: Global optimization；Supersonic turbine blades；Shock-wave attenuation；Pneumatics performance

李志,刘艳,杨金广,徐乐,张敏. 超声速涡轮叶型全局气动优化设计[J]. 推进技术, 2019, 40(5): 1051-1057.

LI Zhi^1,2,LIU Yan²,YANG Jin-guang²,XU Le²,ZHANG Min². Global Aerodynamic Optimization Design of Supersonic Turbine Blade Profiles[J]. Journal of Propulsion Technology, 2019, 40(5): 1051-1057.

参考文献

[1] Dunham J. Improvements to the Ainley-Mathieson Method of Turbine Performance Prediction[J]. ASME Journal of Engineering for Power, 1970, 92(3): 252-256.
[2] Sonoda T, Arima T, Olhofer M, et al. A Study of Advanced High-Loaded Transonic Turbine Airfoils[J]. ASME Journal of Turbomachinery, 2006, 128(4): 650-657.
[3] 余佳, 马伟涛, 季路成. 弱化激波涡轮叶型研究[J]. 工程热物理学报, 2015, 36(12): 2584-2588.
[4] 赵洪雷, 王松涛, 韩万金, 等. 多级涡轮三维气动优化设计的可行性分析与实现[J]. 热能动力工程, 2008, 23(1): 11-15.
[5] 潘尚能, 罗建桥. 涡轮多学科优化中的气动设计技术探讨[J]. 航空动力学报, 2012, 27(3): 635-643.
[6] 韩永志, 高行山, 李立州, 等. 基于Kriging模型的涡轮叶片多学科设计优化[J]. 航空动力学报, 2007, 22(7): 1055-1059.
[7] 张立章, 尹泽勇, 米栋, 等. 基于自适应本征正交分解的涡轮级多学科设计优化[J]. 推进技术, 2017, 38(6): 1249-1258. (ZHANG Li-zhang, YIN Ze-yong, MI Dong, et al. Multidisciplinary Design Optimization for Turbine Stage Based on Self-Adaptive Proper Orthogonal Decomposition[J]. Journal of Propulsion Technology, 2017, 38(6): 1249-1258.)
[8] Pritchard L J. An Eleven Parameter Axial Turbine Airfoil Geometry Model[R]. ASME 85-GT-219.
[9] Youngren H H. Analysis and Design of Transonic Cascades with Splitter Vanes[R]. Cambridge: Massachusettes Institute of Technology, Report 203, 1991.
[10] Drela M, Giles M B. Viscous-Inviscid Analysis of Transonic and Low Reynolds Number Airfoils[J]. AIAA Journal, 1987, 25(10): 1347-1355.
[11] Drela M. Two-Dimensional Transonic Aerodynamic Design and Analysis Using the Euler Equation[R]. Cambridge: Massachusetts Institute of Technology, Report 187, 1986.
[12] Giles M. Newton Solution of Steady Two-Dimensional Transonic Flow[R]. Cambridge: Massachusetts Institute of Technology, Report 186, 1985.
[13] 公茂果, 焦李成, 杨咚咚, 等. 进化多目标优化算法研究[J]. 软件学报, 2009, 20(2): 271-289.
[14] 潘锦珊. 气体动力学基础[M]. 北京:国防工业出版社, 2012.
[15] 董明, 葛宁, 陈云. 跨声速涡轮叶栅激波损失控制方法[J]. 航空动力学报, 2018, 33(5): 1226-1235.　　　　　　　　　　　　　　收稿日期:2018-05-11；修订日期:2018-07-09。基金项目:空气动力学国家重点实验室研究基金(SKLA201701)。作者简介:李志,硕士生,研究领域为轴流涡轮气动优化。E-mail: tianren4561367@163.com通讯作者:刘艳,博士,教授,研究领域为叶轮机械气动热力学。E-mail: yanliu@dlut.edu.cn(编辑:史亚红)