合页式可调总压畸变发生器效果预测及调节方法研究

王铭祖; 王志强

doi:10.13224/j.cnki.jasp.20220201

合页式可调总压畸变发生器效果预测及调节方法研究

doi: 10.13224/j.cnki.jasp.20220201

王铭祖,
王志强^*, ,

南京航空航天大学能源与动力学院，南京 210016

基金项目: 国家科技重大专项（J2019-Ⅴ-0017-0112）

详细信息

作者简介:
王铭祖（1997-），男，硕士生，研究领域为叶轮机气动力学。E-mail：435060026@qq.com

通讯作者:
王志强（1981-），男，副教授、硕士生导师，博士，研究领域为叶轮机气动力学。E-mail：wangzq1981@126.com

中图分类号: V231.3
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出版历程
- 收稿日期: 2022-04-11
- 网络出版日期: 2024-06-29

Research on effect prediction and adjustment method of hinge-type adjustable total pressure distortion generator

WANG Mingzu,
WANG Zhiqiang^{*
, ,}

College of Energy and Power Engineering，Nanjing University of Aeronautics and Astronautics，Nanjing 210016，China

摘要

摘要:
为研究一种新型合页式可调总压畸变发生器的畸变特性，并实现针对特定目标图谱，指导此型畸变发生器调节的目的。采用风洞实验方法对其畸变流场进行研究，利用遗传算法优化的反向传播人工神经网络进行建模与分析，得到了可通过来流马赫数及畸变发生器合页开合角度预测下游稳态总压畸变图谱的预测模型，并以此为依据开发了调节方案快速设计程序，为此类总压畸变发生器的实际使用建立了一套有效的方法。结果表明，预测模型可以快速有效预测畸变发生器下游测量截面处各测点的总压恢复系数，而调节方案快速设计程序可有效简化此型畸变发生器的使用流程。
- 合页式可调畸变发生器 /
- 总压畸变 /
- 人工神经网络 /
- 稳态畸变指数 /
- 风洞实验
Abstract:
In order to study the characteristics of a new hinge-type adjustable distortion generator and realize the purpose of guiding the adjustment of this type of distortion generator for specific target map, wind tunnel test was conducted to analyze its distortion flow field, and the GA-BPANN was used for modeling, finally a prediction model that can predict the downstream steady-state total pressure distortion map through the Mach number of the incoming flow and the angle of the distortion generator hinge was obtained; based on this model, a rapid design program was designed. The results showed that the prediction model can quickly and effectively predict the total pressure recovery coefficient of each measuring point at the downstream measurement section of the distortion generator, and the rapid design program can effectively simplify the use process of this type of distortion generator.
- hinge adjustable distortion generator /
- total-pressure distortion /
- artificial neural network /
- steady-state distortion index /
- wind tunnel test

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图 1 实验台示意图和照片

Figure 1. Diagram and photo of the test system

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图 2 实验段示意图

Figure 2. Diagram of test section

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图 3 合页示意图

Figure 3. Schematic drawing of hinge

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图 4 合页安装位置编号示意图

Figure 4. Diagram of hinge’s installation position number

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图 5 测点位置示意图

Figure 5. Distribution of measuring point position

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图 6 实验组1合页位置示意图

Figure 6. Location of hinge in test1

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图 7 实验组2合页位置示意图

Figure 7. Location of hinge in test2

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图 8 实验组3合页位置示意图

Figure 8. Location of hinge in test3

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图 9 人工神经网络示意图

Figure 9. Schematic diagram of artificial neural network

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图 10 测试集样本各测点总压恢复系数分布曲线

Figure 10. Distribution curve of the total pressure recovery coefficient of each measurement point of the test set sample

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图 11 测试集样本各测点相对误差分布曲线

Figure 11. Relative error distribution curve of each measurement point of the test set sample

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图 12 总压恢复系数误差概率分布图象

Figure 12. Total pressure recovery coefficient error probability distribution image

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图 13 不同来流马赫数下的误差概率分布

Figure 13. Error probability distribution of different incoming Mach number

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图 14 模型1实验和预测模型总压恢复系数图

Figure 14. Total pressure recovery coefficient maps of tests and predict models in model_1

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图 15 模型2实验和预测模型总压恢复系数图

Figure 15. Total pressure recovery coefficient maps of tests and predict models in model_2

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图 16 模型3实验和预测模型总压恢复系数云图

Figure 16. Total pressure recovery coefficient maps of tests and predict models in model_3

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图 17 稳态畸变指数误差概率分布图

Figure 17. Error probability distribution of distortion index

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图 18 稳态畸变指数直接预测与间接预测均方误差

Figure 18. Distortion index reconstructing mean square error comparison of the indirect and direct methods

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图 19 case 1～case 6稳态畸变指数随马赫数变化曲线

Figure 19. Variation of distortion index with Ma in case 1—case 6

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图 20 调节方案快速设计程序流程图

Figure 20. Flow chart of rapid design procedure of regulation scheme

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图 21 目标1调节方案及总压恢复系数云图

Figure 21. Target 1 adjustment scheme and cloud map of total pressure recovery coefficient

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图 22 目标2调节方案及总压恢复系数云图

Figure 22. Target 2 adjustment scheme and cloud map of total pressure recovery coefficient

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表 1 合页尺寸参数表

Table 1. Hinge size specification

参数	数值
前缘半径R/mm	3
插槽宽度D₁/mm	3
翼面厚度H/mm	1.5
翼面长度D₂/mm	20
开合角度α/（°）	0，30，60，90，120
模型整体厚度δ/mm	20

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表 2 3种模型中人工神经网络输入层、隐藏层和输出层节点数量

Table 2. Number of nodes in the input layer,hidden layer and output layer of artificial neural network in three models

层级	model_1	model_2	model_3
输入层	6	7	18
隐藏层	4/5	5/6	12/13
输出层	1	1	1

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表 3 case 1～case 6稳态畸变指数相对误差表

Table 3. case 1—case 6 Relative error table of distortion index

方案	来流马赫数	相对误差/%
case 1	0.1	9.6
	0.2	6.2
	0.26	7.3
case 2	0.1	22.5
	0.2	5.2
	0.3	6.8
case 3	0.1	7.9
	0.2	9.9
	0.3	9.1
case 4	0.1	5
	0.2	9.3
	0.3	2.5
case 5	0.1	48
	0.2	0.4
	0.25	2.1
case 6	0.1	17
	0.2	1
	0.25	5.8

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表 4 目标1稳态畸变指数及畸变角

Table 4. Target 1 steady state distortion index and distortion angle

图谱	稳态畸变指数		畸变角/（°）
图谱	结果	偏差	结果	偏差
目标	0.0294		155.78
预测	0.0233	0.0061	152.78	3
实验	0.0229	0.0065	151.28	4.5

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表 5 目标2稳态畸变指数及畸变角

Table 5. Target 2 steady state distortion index and distortion angle

图谱	稳态畸变指数		畸变角/（°）
图谱	结果	偏差	结果	偏差
目标	0.0054		115.3
预测	0.0037	0.0017	100.3	15
实验	0.0039	0.0015	98.86	16.44

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