采用多层次优化方法的超紧凑GTF增压级气动设计

黄松; 赵胜丰; 阳诚武; 李子良; 卢新根

doi:10.13224/j.cnki.jasp.20210286

采用多层次优化方法的超紧凑GTF增压级气动设计

doi: 10.13224/j.cnki.jasp.20210286

黄松^{1, 2, 3},
赵胜丰^{1, 2, 3,*, ,},
阳诚武^{1, 2, 3},
李子良^{1, 2, 3},
卢新根^{1, 2, 3}

1.
中国科学院工程热物理研究所轻型动力重点实验室，北京 100190
2.
中国科学院中国科学院大学，北京 100049
3.
中国科学院轻型动力创新研究院，北京 100190

基金项目: 国家自然科学基金（52106065）；国家两机科技重大专项 (2019-Ⅱ-0004-0024)

详细信息

作者简介:
黄松（1995-），男，博士生，主要从事叶轮机械气动热力学及计算流体力学研究

通讯作者:
赵胜丰（1982-），男，研究员，博士，主要从事叶轮机械气动热力学研究。E-mail：zhaoshengfeng@iet.cn

中图分类号: V231.1
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- 被引次数: 0
出版历程
- 收稿日期: 2021-06-06
- 网络出版日期: 2023-03-17

Aggressive GTF booster aerodynamic design with multi-level optimization method

HUANG Song^{1, 2, 3},
ZHAO Shengfeng^{1, 2, 3,*
, ,},
YANG Chengwu^{1, 2, 3},
LI Ziliang^{1, 2, 3},
LU Xingen^{1, 2, 3}

1.
Key Laboratory of Light-duty Gas-turbine，Institute of Engineering Thermophysics， Chinese Academy of Sciences，Beijing 100190，China
2.
University of Chinese Academy of Sciences， Chinese Academy of Sciences，Beijing 100049，China
3.
Innovation Academy for Light-duty Gas Turbine， Chinese Academy of Sciences，Beijing 100190，China

摘要

摘要:
为克服超紧凑齿轮传动涡扇（GTF）发动机增压级优化设计面临的高维、耗时、黑箱三大难题，发展了一套精准高效可靠的优化设计方法，运用多目标粒子群算法结合下降单纯形算法的多层次优化策略，具有优化变量少、求解速度快、寻优能力强的优势。优化设计后，GTF增压级在100%设计转速下的峰值效率和失速裕度分别提升0.27%和1.31%。相比于复合弯掠高负荷静子原型，高负荷静子优化构型通过改变整个展向的叶型特征和三维积叠规律，使得自20%叶展至叶尖的流动分离向下游移动，减少由叶尖向轮毂的径向迁移和吸力面尾缘附近低速回流区域的范围，延缓了GTF增压级失速的发生，提升了GTF增压级的效率。
- 齿轮传动涡扇（GTF） /
- 增压级 /
- 多目标粒子群算法 /
- 多层次优化 /
- 峰值效率
Abstract:
To overcome three major problems of high-dimensional, time-consuming, and black box in the optimization design of aggressive geared turbofan (GTF) engine booster, a set of accurate, efficient and reliable optimization design methods were developed. Using multi-objective particle swarm optimization combined with descending simplex algorithm, it had the advantages of fewer optimization variables, fast solving speed, and strong optimization ability. After optimized design, the peak efficiency and stall margin of the GTF booster were increased by 0.27% and 1.31% at 100% design rotational speed, respectively. Compared with the prototype of the highly loaded stator using composite sweep and lean, the optimized configuration of the highly loaded stator further changed the blade profile characteristics and three-dimensional stacking rules of the entire span. The flow separation from 20% of the span to the tip of the blade moved downstream, reducing the radial migration from the tip to the hub. It reduced the range of the low-speed recirculation region near the trailing edge of the suction surface, delayed the stall of the GTF booster, and improved the efficiency of the GTF booster.
- geared turbofan (GTF) /
- booster /
- multi-objective particle swarm algorithm /
- multi-level optimization /
- peak efficiency

HTML

图 1 GTF增压级的子午示意图

Figure 1. Meridian schematic diagram of the GTF booster

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图 2 某型2.5级高负荷压气机示意图

Figure 2. Schematic diagram of the 2.5-stage highly loaded compressor

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图 3 三维数值计算特性线

Figure 3. Three-dimensional numerical calculation characteristic line

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图 4 4种不同增压级静子积叠方案

Figure 4. Four different booster stator stacking schemes

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图 5 4种不同积叠方案下GTF增压级的气动性能

Figure 5. Aerodynamic performance of GTF booster under four different stacking schemes

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图 6 4种不同积叠方案下高负荷静子的极限流线

Figure 6. Limiting streamline of the highly loaded stator under four different stacking schemes

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图 7 4种不同积叠方案下高负荷静子95%叶展的绝对马赫数

Figure 7. Absolute Mach number of 95% span of the highly loaded stator under four different stacking schemes

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图 8 DFFD方法的流程

Figure 8. Process description of DFFD method

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图 9 高负荷静子的DFFD控制体

Figure 9. DFFD control body of the highly loaded stator

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图 10 高负荷静子优化流程

Figure 10. Optimization process of the highly loaded stator

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图 11 高负荷静子优化前后增压级气动性能对比

Figure 11. Comparison of aerodynamic performance of booster before and after the optimization of highly loaded stator

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图 12 高负荷静子优化前后的几何特征对比

Figure 12. Comparison of geometric characteristics before and after optimization of highly loaded stator

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图 13 高负荷静子优化前后展向的总压损失对比

Figure 13. Comparison of total pressure loss in span-wise direction before and after optimization of highly loaded stator

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图 14 高负荷静子优化前后轴向的熵増对比

Figure 14. Comparison of entropy rise of axial direction before and after optimization of the highly loaded stator

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图 15 高负荷静子优化前后35%叶展的绝对马赫数

Figure 15. Absolute Mach number in 35% of span height before and after optimization of the highly loaded stator

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图 16 高负荷静子优化前后35%叶展的等熵马赫数

Figure 16. Isentropic Mach number in 35% of span height before and after optimization of the highly loaded stator

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图 17 高负荷静子优化前后95%叶展的绝对马赫数

Figure 17. Absolute Mach number in 95% of span height before and after optimization of the highly loaded stator

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图 18 高负荷静子优化前后的极限流线

Figure 18. Limiting streamlines before and after optimization of the highly loaded stator

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表 1 GTF增压级关键几何和气动参数

Table 1. Key geometric and aerodynamic parameters of the GTF booster

参数	数值
设计转速/（r/min）	8614.2
设计流量/（kg/s）	53.7
设计总压比	2.5
设计效率	0.88
叶片数目	51, 45, 67, 55, 96, 8
进口机匣半径/mm	482
进口轮毂比	0.76
转子叶尖间隙/mm	0.4
转子叶尖速度/（m/s）	423.4

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表 2 增压级各叶片排的展弦比和稠度

Table 2. Aspect ratio and solidity of each blade row of booster

参数	数值
进口导叶平均展弦比	2.19
第一级转子平均展弦比	1.09
第一级静子平均展弦比	1.49
第二级转子平均展弦比	1.00
第二级静子平均展弦比	1.36
出口支板平均展弦比	1.14
进口导叶平均稠度	1.00
第一级转子平均稠度	1.54
第一级静子平均稠度	1.47
第二级转子平均稠度	1.61
第二级静子平均稠度	1.89
出口支板平均稠度	0.36

下载: 导出CSV

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