基于代理模型法的航空燃油泵空化性能优化

王晓晖; 张凯; 苗森春; 李柯剑; 申正精

doi:10.13224/j.cnki.jasp.20230406

基于代理模型法的航空燃油泵空化性能优化

doi: 10.13224/j.cnki.jasp.20230406

王晓晖^{1, 2, 3,},
张凯^1,,
苗森春^1,*, ,,
李柯剑¹,
申正精¹

1.
兰州理工大学能源与动力工程学院，兰州 730050
2.
兰州理工大学土木工程博士后流动站，兰州 730050
3.
新乡航空工业（集团）有限公司博士后工作站，河南新乡 453000

基金项目: 中国博士后科学基金（2022M712676）；甘肃省杰出青年基金（20JR10RA203）；河南省博士后科研启动项目（2021-181）

详细信息

作者简介:
王晓晖（1986-），男，副教授、硕士生导师，博士，主要从事航空燃油泵内流机理及工程应用研究

通讯作者:
苗森春（1988-），男，副教授、硕士生导师，博士，主要从事流体机械性能优化理论研究。E-mail：miaosc88@126.com

中图分类号: V233.2⁺2
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出版历程
- 收稿日期: 2023-06-21
- 网络出版日期: 2025-04-14

Optimization of aviation fuel pump cavitation performance based on surrogate model

WANG Xiaohui^{1, 2, 3
,},
ZHANG Kai^1
,,
MIAO Senchun^{1,*
, ,},
LI Kejian¹,
SHEN Zhengjing¹

1.
College of Energy and Power Engineering，Lanzhou University of Technology，Lanzhou 730050，China
2.
School of Civil Engineering Postdoctoral Station，Lanzhou University of Technology，Lanzhou 730050，China
3.
Henan Xinxiang Aviation Industry Group Corporation Postdoctoral Workstation，Xinxiang Henan 453000，China

摘要

摘要:
为提高航空燃油泵空化性能，以某型离心式航空燃油泵为例，对叶轮流道几何参数进行参数化优化设计，生成以流道几何参数为输入、以增压值和空化体积为输出的响应样本空间。基于最优预测元模型，拟合最优代理模型，并进行全局敏感性分析，发现叶轮进口边参数与中间型线参数对燃油泵增压值与空化体积影响较大。通过遗传算法获得最优流道几何参数，采用数值模拟方法，对获得的最优参数进行验证，结果表明：优化后燃油泵的压力系数提高0.035，空化性能提高22.28%。叶轮空化面积与空泡体积显著下降，不同叶片间压力载荷分布得到改善，叶轮内部压力脉动幅值有所下降。
- 燃油泵 /
- 空化性能 /
- 代理模型 /
- 遗传算法 /
- 数值模拟
Abstract:
In order to improve the cavitation performance of aviation fuel pump, a centrifugal aviation fuel pump was used as an example to optimize the impeller runner geometry parameters with full parameters, and generate a response sample space with design parameters as inputs and boost pressure value and cavitation volume as outputs. Based on the meta-model of optimal prognosis, the optimal surrogate model was fitted and the global sensitivity analysis was performed. It was found that the impeller inlet edge parameters and the middle profile parameters had a significant influence on the boost pressure value and cavitation volume of the fuel pump. The optimal design parameters were obtained by the genetic algorithm, and the numerical simulation method was used to verify the obtained optimal parameters. The results showed that the pressure coefficient of the optimized fuel pump was improved by 0.035 and the cavitation performance was improved by 22.28%. The impeller cavitation area and cavitation volume decreased significantly, the pressure load distribution between different vanes was improved, and the amplitude of pressure pulsation inside the impeller was reduced.
- fuel pump /
- cavitation performance /
- surrogate model /
- genetic algorithm /
- numerical simulation

HTML

图 1 全流道计算域与单流道计算域

Figure 1. Full-flow computational domain and single-flow computational domain

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图 2 网格无关性验证

Figure 2. Mesh independence verification

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图 3 网格划分

Figure 3. Result of mesh generation

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图 4 叶片近壁面y⁺分布

Figure 4. Blade y⁺ distribution

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图 5 燃油泵试验台示意图

Figure 5. Fuel pump test schematic

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图 6 燃油泵外特性曲线

Figure 6. Fuel pump external performance curve

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图 7 燃油泵空化特性曲线

Figure 7. Fuel pump cavitation performance curve

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图 8 叶片进口边与型线控制

Figure 8. Inlet side and impeller profile control

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图 9 全局敏感性分析框架

Figure 9. Global sensitivity analysis framework

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图 10 预测系数矩阵

Figure 10. Coefficient of prognosis matrix

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图 11 高度敏感的输入变量与输出变量之间的响应关系

Figure 11. Response relationship between highly sensitive input variables and output variables

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图 12 优化算法收敛性

Figure 12. Optimization convergence

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图 13 优化验证

Figure 13. Optimization verification

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图 14 叶片三维模型对比

Figure 14. Three-dimensional comparison of blade

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图 15 优化前后外特性曲线

Figure 15. External performance curves of numerical simulation before and after optimization

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图 16 优化前后不同空化数下的空泡分布

Figure 16. Cavitation bubble distribution under different cavitation numbers before and after optimization

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图 17 叶片压力载荷分布

Figure 17. Pressure load distribution of blade

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图 18 监测点位置

Figure 18. Location of monitoring points

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图 19 优化前后压力脉动频域图

Figure 19. Frequency domain diagram of pressure pulsation before and after optimization

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表 1 不同响应面模型决定系数与预测系数

Table 1. Determination coefficients and prognosis coefficients of different response surface models

输出变量	响应模型	C_d	C_pre
$ {C}_{V} $	2阶移动最小二乘法模型（指数权重）	0.973	0.969
$ {C}_{V} $	Kriging 模型（各向同性）	0.976	0.958
$ {C}_{V} $	1阶多项式回归模型（无混合项）	0.960	0.956
$ {C}_{V} $	1阶移动最小二乘法模型（指数权重）	0.961	0.956
$ {C}_{V} $	1阶多项式回归模型（有混合项）	0.956	0.936
Δp	1阶多项式回归模型（无混合项）	0.900	0.865
Δp	2阶多项式回归模型（无混合项）	0.877	0.840
Δp	Kriging 模型（各向同性）	0.921	0.832
Δp	1阶移动最小二乘法模型（指数权重）	0.841	0.830
Δp	2阶移动最小二乘法模型（指数权重）	0.863	0.819
Δp	1阶多项式回归模型（有混合项）	0.843	0.797

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