Particle swarm-based aerodynamic and aeroacoustic optimization of exhaust volute
-
摘要:
以某燃气轮机排气蜗壳为研究对象，开展了基于粒子群和帕累托前沿的气动与声学优化研究。以给定外部尺寸为约束，建立了排气蜗壳核心部件的参数化模型，确立了总压损失、静压恢复和多角度平均总声压级等气动和声学指标。优化过程气动性能预测采用RANS（Reynolds-averaged Navier-Stokes）方法，同时根据排气蜗壳直接排气时的流动发声特性，将其排气类比为亚声速喷流，声学性能预测采用Tam-Auriault模型。结果表明：①粒子群优化有较高效率和鲁棒性，经过6轮优化，排气蜗壳出口总压损失系数降低35%，静压恢复系数增加79%；②排气蜗壳气动和声学性能存在竞争关系，其气动和声学性能综合最优的总压损失系数降低20%，静压恢复系数增加48%，综合噪声增量1 dB，若优先考虑声学优化，噪声控制量可达3 dB。
Abstract:Optimization was conducted to improve the aerodynamic and aeroacoustic performances of a given exhaust volute by particle swarm optimization and Pareto front. By taking the given external dimensions as constraints, a parameterized model of the exhaust volute was established, and aerodynamic parameters such as the total pressure loss, the static pressure recovery and multi-angle-averaged overall sound pressure level were set as the performance indicators. The RANS (Reynolds-averaged Navier-Stokes) method was employed for the prediction of aerodynamic performances. At the same time, based on the sound generation characteristics of the exhaust volute, the exhaust process was analogized to a subsonic jet, and its acoustic performance was predicted using the Tam-Auriault model. The results showed that: ① the particle swarm algorithm was efficient and robust for parameterized optimization; after six rounds of optimization, the total pressure loss coefficient at the outlet of the exhaust volute was reduced by 35%, and the static pressure recovery coefficient was increased by 79%; ② there existed a conflict between the aerodynamic and aeroacoustic performances, the comprehensive aerodynamic and aeroacoustic optimization managed to reduce the total pressure loss coefficient by 20%, and the static pressure recovery coefficient increased by 48%, with a comprehensive noise increment about 1 dB; if the aeroacoustic performance optimization was considered, the noise reduction can be up to 3 dB.
-
图 10 1∶2缩比JT15D发动机喷口剖面（单位：mm）
Figure 10. 1∶2 scaled nozzle of the JT15D aero engine （unit：mm）
图 14 90°方向的喷流噪声预测与实验对比
Figure 14. Comparison between predicted and measured noise spectrum by observer at 90°
图 15 气流总压染色的优化前后模型对比
Figure 15. Comparison of total pressure distributions between the original and the optimized volute models
图 16 气流总压染色的优化前后流线对比
Figure 16. Comparison of streamlined colored with total pressure between the original and the optimized volute models
图 17 总压损失系数随优化代数的变化曲线
Figure 17. Evolution of total pressure loss coefficient with respect to optimization generations
图 18 静压恢复系数随优化代数的变化曲线
Figure 18. Evolution of static pressure recovery coefficient with respect to optimization generations
图 19 XOY平面90°观测点频谱和A计权频谱
Figure 19. Spectra and A-weighted spectra of the 90° observer at XOY plane
图 20 XOY平面45°、90°、135°观测点总声压级和A计权总声压级
Figure 20. OASPL and A-weighted OASPL of the 45°, 90°, 135° observers at the XOY plane
图 21 XOZ平面90°观测点频谱和A计权频谱
Figure 21. Spectra and A-weighted spectra of the 90° observer at XOZ plane
图 22 XOZ平面45°、90°、135°观测点总声压级和A计权总声压级
Figure 22. OASPL and A-weighted OASPL of the 45°, 90°, 135° observers at the XOZ plane
图 23 帕累托前沿曲线（$\left\langle {{S_{{\text{OASPL}}}}} \right\rangle - \sigma $）
Figure 23. Plot of Pareto front （$\left\langle {{S_{{\text{OASPL}}}}} \right\rangle - \sigma $）
图 24 帕累托前沿曲线（$\langle {{S_{{\text{OASPL}}_{\text{A}}}}} \rangle - \sigma $）
Figure 24. Plot of Pareto front （$\langle {{S_{{\text{OASPL}}_{\text{A}}}}} \rangle - \sigma $）
图 25 帕累托前沿曲线（$\left\langle {{S_{{\text{OASPL}}}}} \right\rangle - \varepsilon $）
Figure 25. Plot of Pareto front （$\left\langle {{S_{{\text{OASPL}}}}} \right\rangle - \varepsilon $）
图 26 帕累托前沿曲线（$\langle {{S_{{\text{OASPL}}_{\text{A}}}}} \rangle - \varepsilon $）
Figure 26. Plot of Pareto front （$\langle {{S_{{\text{OASPL}}_{\text{A}}}}} \rangle - \varepsilon $）
表 1 排气蜗壳参数约束关系
Table 1. Exhaust volute parameter constraints
控制点 坐标（柱坐标系） A $ {Z}_{A}=0, {R}_{A}={R}_{A} $ B $ {Z}_{B}\text{=}{L}_{1}, {R}_{B}\text{=}{R}_{A}+{L}_{1}\mathrm{tan}\;\alpha $ O1 $ {Z}_{{O}_{1}}\text{=}{L}_{1}-{R}_{1}\mathrm{sin}\;\alpha , {R}_{{O}_{1}}\text{=}{R}_{B}+{R}_{1}\mathrm{cos}\;\alpha $ C $ {Z}_{C}={Z}_{{O}_{1}}+{R}_{1}\mathrm{sin}\;\beta , {R}_{C}={R}_{{O}_{1}}+{R}_{1}\mathrm{cos}\;\beta $ D $ {Z}_{D}={L}_{1}+{R}_{2}-{L}_{2}, {R}_{D}={R}_{C}-\mathrm{tan}\;\beta （{Z}_{D}-{Z}_{C}） $ E $ {Z}_{E}={L}_{1}+{R}_{2}, {R}_{E}={R}_{C}-\mathrm{tan}\;\beta （{Z}_{D}-{Z}_{C}） $ F $ {Z}_{F}={L}_{1}+{R}_{2}, {R}_{F}={R}_{2}-{R}_{H} $ O2 $ {Z}_{{O}_{2}}={L}_{1}, {R}_{{O}_{2}}={R}_{2}+{R}_{H} $ G $ {Z}_{G}={L}_{1}, {R}_{G}={R}_{H} $ H $ {Z}_{H}=0, {R}_{H}={R}_{H} $ I $ {Z}_{I}={Z}_{E}-{L}_{4}, {R}_{I}={R}_{A}+\mathrm{tan}\;\alpha {Z}_{I} $ 
下载: 导出CSV
表 2 排气蜗壳设计工况
Table 2. Design operating condition of exhaust volute
参数 变量 单位 数值 质量流率 W/Sout kg/（m2·s） 52.1 进出口总温比 $ {{T_{{\text{inlet}}}^{\text{*}}} \mathord{\left/ {\vphantom {{T_{{\text{inlet}}}^{\text{*}}} {{T_{{\text{atm}}}}}}} \right. } {{T_{{\text{atm}}}}}} $ 2.1 进出口总压比 $ {{p_{{\text{inlet}}}^{\text{*}}} \mathord{\left/ {\vphantom {{p_{{\text{inlet}}}^{\text{*}}} {{p_{{\text{atm}}}}}}} \right. } {{p_{{\text{atm}}}}}} $ 1.145
下载: 导出CSV
表 3 网格无关性验证使用网格
Table 3. Mesh applied for analysis of grid independence
网格类型 网格
单元数/106网格
节点数/106第1层网格
高度/m网格
增长率稀疏网格 2.6 0.98 2.1×10−4 1.2 较稀疏网格 3.6 1.5 9.2×10−5 1.2 中等网格 4.5 1.9 7.1×10−5 1.2 精细网格 5.5 2.6 5.2×10−5 1.2
下载: 导出CSV
表 4 初始模型无量纲化几何参数
Table 4. Dimensionless parameters of initial models
编号 L1 L2 R1 R2 α/（°） β/（°） DS1 1.00 1.00 1.00 1.00 10.0 130.0 DS2 1.00 1.00 1.00 1.00 6.0 120.0 DS3 0.90 1.25 0.60 1.17 10.0 140.0 DS4 1.00 1.25 0.60 1.00 8.0 130.0 DS5 1.20 0.75 0.40 0.67 4.0 110.0 DS6 0.93 1.25 1.20 1.12 13.4 140.0 DS7 0.85 1.20 1.80 1.25 14.0 130.0 DS8 1.10 1.00 1.00 0.83 10.0 150.0
下载: 导出CSV
表 5 第六代模型无量纲化几何参数
Table 5. Dimensionless parameters of the 6th generation
编号 L1 L2 R1 R2 α/（°） β/（°） DS1 0.88 1.21 1.15 1.19 11.7 123.5 DS2 0.88 1.19 1.28 1.18 11.3 120.2 DS3 0.88 1.23 1.27 1.19 11.9 119.9 DS4 0.90 1.25 1.02 1.14 11.1 118.5 DS5 0.88 1.16 1.19 1.16 10.1 121.4 DS6 0.89 1.22 1.20 1.22 12.4 125.9 DS7 0.87 1.23 1.29 1.22 12.0 121.2 DS8 0.87 1.22 1.19 1.21 11.4 123.3
下载: 导出CSV
-
[1] FAROKHI S. A trade-off study of rotor tip clearance flow in a turbine/exhaust diffuser system[C]. Anaheim, US: ASME 1987 International Gas Turbine Conference and Exhibition, 1987. [2] 刘美伊, 孙涛, 刘家兴, 等. 燃气轮机排气系统整机环境部件特性修正方法[J]. 航空发动机, 2023, 49(5): 100-107. LIU Meiyi, SUN Tao, LIU Jiaxing, et al. Component characteristic correction of gas turbine exhaust system in marine application environment[J]. Aeroengine, 2023, 49(5): 100-107. (in ChineseLIU Meiyi, SUN Tao, LIU Jiaxing, et al. Component characteristic correction of gas turbine exhaust system in marine application environment[J]. Aeroengine, 2023, 49(5): 100-107. (in Chinese) [3] 黄恩德, 楚武利, 董玮. 一种涡轮排气蜗壳的优化设计[J]. 航空动力学报, 2017, 32(2): 455-462. HUANG Ende, CHU Wuli, DONG Wei. An optimal design of a turbine exhaust volute[J]. Journal of Aerospace Power, 2017, 32(2): 455-462. (in ChineseHUANG Ende, CHU Wuli, DONG Wei. An optimal design of a turbine exhaust volute[J]. Journal of Aerospace Power, 2017, 32(2): 455-462. (in Chinese) [4] UBERTINI S, DESIDERI U. Experimental performance analysis of an annular diffuser with and without struts[J]. Experimental Thermal and Fluid Science, 2000, 22(3/4): 183-195. [5] 霍文举. 田口方法优化设计透平排汽缸的应用[J]. 汽轮机技术, 1993, 35(1): 48-55. HUO Wenju. Application of Taguchi method in optimal design of turbine exhaust hood[J]. Turbine Technology, 1993, 35(1): 48-55. (in ChineseHUO Wenju. Application of Taguchi method in optimal design of turbine exhaust hood[J]. Turbine Technology, 1993, 35(1): 48-55. (in Chinese) [6] MOORE M J, SIEVERDING C H. Aerothermodynamics of low pressure steam turbines and condensers[M]. New York, US: Hemisphere Publishing, 1986. [7] 徐鑫, 刘常青, 孙勇, 等. 某型燃气轮机进气系统内流场的数值分析[J]. 航空发动机, 2014, 40(2): 51-55, 75. XU Xin, LIU Changqing, SUN Yong, et al. Numerical simulation on interior flow field for a gas turbine inlet system[J]. Aeroengine, 2014, 40(2): 51-55, 75. (in ChineseXU Xin, LIU Changqing, SUN Yong, et al. Numerical simulation on interior flow field for a gas turbine inlet system[J]. Aeroengine, 2014, 40(2): 51-55, 75. (in Chinese) [8] FERREIRA C. Gene expression programming: a new adaptive algorithm for solving problems[J]. Complex Systems, 2001, 13(2): 87-129. [9] LOPES H, WEINERT W. EGIPSYS: an enhanced gene expression programming approach for symbolic regression problems[J]. International Journal of Applied Mathematics and Computer Science, 2004, 14: 375-384. [10] ZHAO Yaomin, AKOLEKAR H D, WEATHERITT J, et al. RANS turbulence model development using CFD-driven machine learning[J]. Journal of Computational Physics, 2020, 411: 109413. doi: 10.1016/j.jcp.2020.109413 [11] KENNEDY J, EBERHART R. Swarm intelligence[M]. San Francisco, US: Morgan Kaufmann, 2001. [12] KENNEDY J, EBERHART R. Particle swarm optimization[R]. Perth, Australia: International Conference on Neural Networks, 1995. [13] SHI Y, EBERHART R. A modified particle swarm optimizer[R]. Anchorage, US: IEEE World Congress on Computational Intelligence, 1998. [14] TU Zhenguo, LU Yong. A robust stochastic genetic algorithm (StGA) for global numerical optimization[J]. IEEE Transactions on Evolutionary Computation, 2004, 8(5): 456-470. doi: 10.1109/TEVC.2004.831258 [15] NOEL M M. A new gradient based particle swarm optimization algorithm for accurate computation of global minimum[J]. Applied Soft Computing, 2012, 12(1): 353-359. doi: 10.1016/j.asoc.2011.08.037 [16] 刘超, 张茂伟, 吕玉红, 等. 基于离散粒子群算法的航空发动机总装工艺优化方法[J]. 航空发动机, 2020, 46(2): 81-86. LIU Chao, ZHANG Maowei, LYU Yuhong, et al. Optimization method of aeroengine final assembly process based on discrete particle swarm algorithm[J]. Aeroengine, 2020, 46(2): 81-86. (in ChineseLIU Chao, ZHANG Maowei, LYU Yuhong, et al. Optimization method of aeroengine final assembly process based on discrete particle swarm algorithm[J]. Aeroengine, 2020, 46(2): 81-86. (in Chinese) [17] 董桢, 周文祥, 潘慕绚, 等. 涡轴发动机部件特性修正及更新方法[J]. 航空发动机, 2018, 44(6): 11-16. DONG Zhen, ZHOU Wenxiang, PAN Muxuan, et al. Modification and updating method in component characteristics of turboshaft engine[J]. Aeroengine, 2018, 44(6): 11-16. (in ChineseDONG Zhen, ZHOU Wenxiang, PAN Muxuan, et al. Modification and updating method in component characteristics of turboshaft engine[J]. Aeroengine, 2018, 44(6): 11-16. (in Chinese) [18] TAM C K W, AURIAULT L. Jet mixing noise from fine-scale turbulence[J]. AIAA Journal, 1999, 37(2): 145-153. doi: 10.2514/2.691 [19] 郑克扬, 桂辛民, 岑拯. 喷流噪声特性与控制研究[J]. 北京航空航天大学学报, 1991, 17(2): 72-78. ZHENG Keyang, GUI Xinmin, CEN Zheng. Jet noise characteristic and control[J]. Journal of Beijing University of Aeronautics and Astronautics, 1991, 17(2): 72-78. (in ChineseZHENG Keyang, GUI Xinmin, CEN Zheng. Jet noise characteristic and control[J]. Journal of Beijing University of Aeronautics and Astronautics, 1991, 17(2): 72-78. (in Chinese) [20] 赵山. 纳什均衡与一般均衡的关系研究[J]. 数量经济技术经济研究, 2005, 22(1): 138-143. ZHAO Shan. Research on the relationship between Nash equilibrium and general equilibrium[J]. Quantitative & Technica Economics, 2005, 22(1): 138-143. (in Chinese doi: 10.3969/j.issn.1000-3894.2005.01.013ZHAO Shan. Research on the relationship between Nash equilibrium and general equilibrium[J]. Quantitative & Technica Economics, 2005, 22(1): 138-143. (in Chinese) doi: 10.3969/j.issn.1000-3894.2005.01.013 [21] 郭鹏, 杨晓琴. 博弈论与纳什均衡[J]. 哈尔滨师范大学自然科学学报, 2006, 22(4): 25-28. GUO Peng, YANG Xiaoqin. On the game theory and the Nash equilibrium[J]. Natural Science Journal of Harbin Normal University, 2006, 22(4): 25-28. (in ChineseGUO Peng, YANG Xiaoqin. On the game theory and the Nash equilibrium[J]. Natural Science Journal of Harbin Normal University, 2006, 22(4): 25-28. (in Chinese) [22] TUAN T A, HOANG L P, LE D D, et al. A framework for controllable Pareto front learning with completed scalarization functions and its applications[J]. Neural Networks, 2024, 169: 257-273. doi: 10.1016/j.neunet.2023.10.029 [23] 吴亚东, 曹安国, 刘鹏寅, 等. Pareto多目标算法在离心压缩机蜗壳优化中的应用[J]. 航空动力学报, 2016, 31(1): 92-99. WU Yadong, CAO Anguo, LIU Pengyin, et al. Application of Pareto multi-objective algorithm in centrifugal compressor volute optimization[J]. Journal of Aerospace Power, 2016, 31(1): 92-99. (in ChineseWU Yadong, CAO Anguo, LIU Pengyin, et al. Application of Pareto multi-objective algorithm in centrifugal compressor volute optimization[J]. Journal of Aerospace Power, 2016, 31(1): 92-99. (in Chinese) -
点击查看大图
计量
- 文章访问数: 392
- HTML浏览量: 251
- PDF量: 41
- 被引次数: 0