基于壁面静压的发动机进口总压畸变重构

刘旭

doi:10.13224/j.cnki.jasp.20220702

基于壁面静压的发动机进口总压畸变重构

doi: 10.13224/j.cnki.jasp.20220702

刘旭

中国航空工业集团有限公司中国飞行试验研究院，西安 710089

详细信息

作者简介:
刘旭（1983-），男，高级工程师，硕士，主要从事航空发动机飞行试验和技术研究工作

中图分类号: V235.1
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- 被引次数: 0
出版历程
- 收稿日期: 2022-09-18
- 网络出版日期: 2023-04-25

Reconstruction of engine inlet total pressure distortion based on wall static pressure

LIU Xu

Chinese Flight Test Establishment，Aviation Industry Corporation of China，Limited，Xi’an 710089，China

摘要

摘要:
为了满足航空发动机畸变容限控制技术对发动机进气畸变解算的要求，根据插板扰流畸变模拟试验测得的稳、动态总压和静压数据，采用神经网络方法开展了基于壁面静压的发动机进口稳态总压流场和稳态周向畸变指数重构以及动态总压紊流度重构研究。结果表明：采用神经网络方法，可以较好地建立壁面稳态静压与流场稳态总压分布的相关关系，实现通过有限壁面静压测量数据重构稳态总压流场，重构流场高、低压区范围、总压数值以及稳态周向畸变指数与测量流场吻合良好；通过在神经网络输入参数中增加中心总压以及增加壁面稳态静压测点数量可以提高稳态总压流场的重构精度；根据壁面动态静压的紊流度和气流马赫数采用神经网络方法可以直接重构获得动态总压的紊流度，重构误差在±0.25%以内。
- 神经网络 /
- 壁面静压 /
- 总压畸变 /
- 紊流度 /
- 流场畸变重构
Abstract:
In order to establish an aero-engine inlet total pressure distortion estimation method for the application of engine distortion tolerance control, using the measured steady and dynamic pressure data of inlet distortion flow field simulating test, a study of reconstructing steady total pressure distribution and steady-state circumferential distortion index and dynamic total pressure turbulence based on steady and dynamic wall static pressures by using neural network method was conducted. The result showed that the steady total pressure distribution could be well related to the wall steady static pressure by neural network model, so that the steady total pressure flow field could be reconstructed from wall steady static pressure, the high and low pressure extent and steady-state circumferential distortion index of reconstructed total pressure field were very close to those of measured flow field. Adding center total pressure and more wall steady static pressure probes data into the neural network inputs could improve the reconstructing accuracy. The dynamic total pressure turbulence level can be reconstructed by dynamic static pressure turbulence and airflow Mach number, with the reconstruction error within ±0.25%.
- neural network /
- wall static pressure /
- total pressure distortion /
- turbulence level /
- flow flied distortion reconstruction

HTML

图 1 畸变模拟试验装置

Figure 1. Distortion simulation test device

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图 2 测量截面测点布局示意图

Figure 2. Probes layout on the measuring plane

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图 3 M1模型流场重构结果（H/D=0.3，Ma=0.43）

Figure 3. Reconstructed flow field of M1 model （H/D=0.3，Ma=0.43）

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图 4 M1模型流场重构结果（H/D=0.4，Ma=0.35）

Figure 4. Reconstructed flow field of M1 model （H/D=0.4，Ma=0.35）

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图 5 网络输入参数中增加中心压力对流场重构精度的影响

Figure 5. Influence of adding central pressure into the network inputs on the flow field reconstructing accuracy

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图 6 网络输入参数中增加中心压力对稳态周向畸变指数重构精度的影响

Figure 6. Influence of adding central total pressure into the network inputs on the steady-state circumferential distortion index reconstructing accuracy reconstructing accuracy

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图 7 网络输入参数中壁面静压测点数量对流场重构精度的影响

Figure 7. Number of wall static pressure probes in the network inputs on the flow field reconstructing accuracy

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图 8 壁面动态静压紊流度与动态总压紊流度的对比

Figure 8. Comparison of dynamic wall static pressure turbulence and dynamic total pressure turbulence

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图 9 壁面动态静压紊流度与动态总压紊流度的相关性

Figure 9. Correlation between dynamic wall static pressure turbulence and dynamic total pressure turbulence

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图 10 不同神经网络模型动态总压紊流度重构结果对比

Figure 10. Dynamic total pressure turbulence reconstructing error of different neutral network models

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图 11 其他测点动态总压紊流度重构结果

Figure 11. Dynamic total pressure turbulence reconstructed results of other probes

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表 1 神经网络结构

Table 1. Neural network architectures

神经网络结构元素	设置
输入参数	见表2
隐含层1	Tansig（s）函数，节点数：30
隐含层2	Tansig（s）函数，节点数：30
输出层	Purelin（s）函数
输出目标	24路稳态总压

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表 2 神经网络输入参数类型

Table 2. Input parameters of neural networks

模型编号	输入参数
M1	12路壁面静压（p_s1～p_s12）
M2	12路壁面静压（p_s1～p_s12）+1路中心体前端总压（p_tc）
M3	12路壁面静压（p_s1～p_s12）+3路中心体壁面静压（p_sc1、p_sc2、p_sc3）平均值
M4	6路壁面静压（p_s1、p_s3、···、p_s11）+1路中心体前端总压（p_tc）
M5	4路壁面静压（p_s1、p_s4、p_s7、p_s10）+1路中心体前端总压（p_tc）
注：参数符号同图2。

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表 3 重构流场与测量流场的稳态周向畸变指数和低压区范围对比

Table 3. Steady-state circumferential distortion index and low pressure extent comparison between reconstructed flow filed and measured ones

工况	$ \Delta {\stackrel{-}{\sigma }}_{0} $/%		$ {\theta }^{-} $/（°）
工况	测量值	M1重构值		测量值	M1重构值
H/D=0.3, Ma=0.43	2.22	2.27		185.3	183.8
H/D=0.4, Ma=0.35	2.26	2.13		183.8	180.8

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表 4 不同输入参数的动态总压紊流度神经网络重构模型

Table 4. Dynamic total pressure turbulence reconstructing neutral networks with different inputs

模型	输入参数	输出参数
WLD1	${\varepsilon }_{ {p}_{\rm sd} }$	${\varepsilon }_{ {p}_{\rm td} }$
WLD2	${\varepsilon }_{ {p}_{\rm sd} }$+Ma	${\varepsilon }_{ {p}_{\rm td} }$
WLD3	${\varepsilon }_{ {p}_{\rm sd} }$+$ \Delta {\stackrel{-}{\mathrm{\sigma }}}_{0} $	${\varepsilon }_{ {p}_{\rm td} }$
WLD4	${\varepsilon }_{ {p}_{\rm sd} }$+Ma+$ \Delta {\stackrel{-}{\mathrm{\sigma }}}_{0} $	${\varepsilon }_{ {p}_{\rm td} }$

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