基于降阶模型的翼型结冰冰形预测方法

刘藤; 李栋; 黄冉冉; 张振辉

doi:10.13700/j.bh.1001-5965.2018.0474

基于降阶模型的翼型结冰冰形预测方法

doi: 10.13700/j.bh.1001-5965.2018.0474

西北工业大学航空学院, 西安 710072

详细信息

作者简介:
刘藤男, 硕士研究生。主要研究方向:计算流体力学

李栋男, 博士, 教授, 博士生导师。主要研究方向:设计空气动力学、计算流体力学

通讯作者:
李栋.E-mail:ldgh@nwpu.edu.cn

中图分类号: V211⁺.3
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- 被引次数: 0
出版历程
- 收稿日期: 2018-08-14
- 录用日期: 2018-12-28
- 网络出版日期: 2019-05-20

Ice shape prediction method of aero-icing based on reduced order model

School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China

More Information

Corresponding author: LI Dong.E-mail:ldgh@nwpu.edu.cn

摘要

摘要:
翼型结冰冰形的数值模拟预测通常比较复杂耗时，为了更加快速准确地预测冰形以减少计算资源消耗，建立了基于本征正交分解（POD）和Kriging模型的冰形快速预测方法。利用CFD数值模拟结果来构建样本空间，以飞行迎角为例详述了降阶模型的冰形预测的实现手段，并结合试验设计方法，完成了多参数的结冰冰形快速预测，同时研究了先进的Blind-Kriging模型的相关方法以及对于预测结果的改进。结果表明，降阶模型预测翼型结冰冰形与CFD数值模拟结果吻合较好，表明降阶模型可以快速、精确地应用于翼型结冰冰形预测。
- 冰形预测 /
- 降阶模型 /
- 本征正交分解(POD) /
- Kriging模型 /
- Blind-Kriging模型
Abstract:
The numerical simulation prediction of aero-icing ice shape is usually complicated and time-consuming. In order to predict ice shape more quickly and accurately and to reduce computational resource consumption, a quick ice shape prediction method based on proper orthogonal decomposition and Kriging model is proposed in this paper. Using a database of high-fidelity CFD numerical simulations to build sample space, in view of the change of attack angle, the procedure for predicting the ice shape by reduced order model is introduced. With the data sampling method of parameter space, multiparameter prediction of the ice shape is achieved. Meanwhile, the related methods of the advanced Blind-Kriging model and the improvement of the prediction results are studied. The result shows that the prediction results of the airfoil icing shape using the reduced order model agree well with the CFD numerical simulation results. The conclusion is made that the reduced order model is a quick and accurate approach for predicting the ice shapes of airfoil icing.
- ice-shape prediction /
- reduced order model /
- proper orthogonal decomposition (POD) /
- Kriging model /
- Blind-Kriging model

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图 1 降阶模型建立流程

Figure 1. Reduced order model building process

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图 2 FENSAP-ICE软件模块相互关系

Figure 2. Relationship among FENSAP-ICE software modules

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图 3 FENSAP-ICE冰形模拟结果

Figure 3. FENSAP-ICE ice shape simulation results

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图 4 冰形模拟与实验对比验证

Figure 4. Comparison and validation of ice shape simulation and experiment

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图 5 不同飞行迎角的冰形

Figure 5. Ice shapes of different flying attack angles

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图 6 单参数POD与CFD冰形对比

Figure 6. Ice shape comparison between single-parameter POD and CFD

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图 7 连续最大结冰条件^[21]

Figure 7. Continuous maximum icing condition^[21]

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图 8 样本空间分布

Figure 8. Sample space distribution

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图 9 多参数POD与CFD冰形对比

Figure 9. Ice shape comparison of multiparameter' POD and CFD

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图 10 冰形预测结果对比

Figure 10. Comparison of ice shape prediction results

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表 1 算例计算条件

Table 1. Calculation conditions of example

计算状态	数值
来流速度/(m·s^-1)	100
液态水含量(LWC)/(g·m^-3)	1
平均水滴直径(MVD)/μm	20
环境压力/Pa	101 325
结冰温度/K	263.15
结冰时间/s	360

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表 2 快照矩阵特征值

Table 2. Eigenvalue of snapshot matrix

特征值序号	λ_x/10^-6	λ_y/10^-6
1	174 832 6	10 328.48
2	8.937 12	20.761 14
3	3.189 29	5.440 25
4	1.121 569	1.452 005
5	0.613 951	0.556 798

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表 3 多参数样本选择范围

Table 3. Selection range for multiparameter sample

参数	最小值	最大值
飞行迎角/(°)	0	5
飞行速度/(m·s^-1)	90	130
结冰温度/°F	-22	+32
MVD/μm	15	40
高度/ft	0	22 000

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表 4 验证算例计算条件

Table 4. Calculation conditions of verification example

算例状态	数值
飞行迎角/(°)	1.325
飞行速度/(m·s^-1)	91.875
MVD/μm	25.5
高度/ft	12 031
结冰温度/°F	16
结冰时间/s	360

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表 5 计算状态

Table 5. Calculation conditions

计算状态	数值
MVD/μm	20
环境压力/Pa	101 325
结冰温度/K	263.15
结冰时间/s	360

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