基于POD和代理模型的热气防冰性能预测方法
收稿日期: 2022-01-25
修回日期: 2022-02-15
录用日期: 2022-03-25
网络出版日期: 2022-03-30
基金资助
国家科技重大专项(J2019-III-0010-0054)
Hot air anti-icing performance estimation method based on POD and surrogate model
Received date: 2022-01-25
Revised date: 2022-02-15
Accepted date: 2022-03-25
Online published: 2022-03-30
Supported by
National Science and Technology Major Project(J2019-III-0010-0054)
大型客机机翼、短舱多采用热气防冰作为主要防冰策略。为了缩短热气防冰系统优化设计周期,提出了基于本征正交分解和代理模型的防冰性能快速预测方法。采用本征正交分解对数值仿真积累的温度和溢流水快照进行特征分析,选取包含绝大部分样本特征的基模态线性拟合所有快照;基于支持向量机回归方法建立笛形管结构参数与线性拟合系数间的代理模型,实现对热气防冰蒙皮外表面温度分布和溢流水分布的快速预测。针对三维缝翼笛形管热气防冰系统开展的验证表明:该方法对防冰表面温度分布的预测效果较好,并能够较为准确地预测水滴撞击区域内的溢流水分布;建立的预测方法计算成本较数值计算方法大幅降低,对于热气防冰系统优化设计工作具有重要意义。
杨倩 , 郭晓峰 , 李芹 , 董威 . 基于POD和代理模型的热气防冰性能预测方法[J]. 航空学报, 2023 , 44(1) : 626992 -626992 . DOI: 10.7527/S1000-6893.2022.26992
Most large civil aircraft use hot air anti-icing systems as anti-icing strategies for airfoils and nacelles. A novel estimation method for hot air anti-icing system performance based on Proper Orthogonal Decomposition (POD) and surrogate models is proposed to shorten the design cycle of hot air anti-icing system. POD is adopted for data compression and characteristics extraction for the anti-icing performance snapshot matrix obtained by numerical calculation, and a lower-dimensional approximation for the snapshot matrix is derived from the projection subspace consisting of a set of basis modes. Support vector regression method is used to construct the surrogate models between the fitting coefficients of basis modes and the piccolo tube geometric parameters. The validation of the estimation method on a three-dimensional slat piccolo tube hot air anti-icing system shows that this method has a good surface temperature prediction and can accurately predict the runback water distribution within the droplet impingement area. The time consumption of the established estimation method reduces significantly compared to the numerical simulation method, which is of great significance for the hot air anti-icing system optimal design.
1 | 桂业伟, 周志宏, 李颖晖, 等. 关于飞机结冰的多重安全边界问题[J]. 航空学报, 2017, 38(2): 520734. |
GUI Y W, ZHOU Z H, LI Y H, et al. Multiple safety boundaries protection on aircraft icing[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(2): 520734 (in Chinese). | |
2 | 李浩然, 段玉宇, 张宇飞, 等. 结冰模拟软件AERO-ICE中的关键数值方法[J]. 航空学报, 2021, 42(S1): 726371. |
LI H R, DUAN Y Y, ZHANG Y F, et al. Numerical method of ice-accretion software AERO-ICE[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(S1): 726371 (in Chinese). | |
3 | APPIAH-KUBI P. U.S inflight icing accidents and incidents, 2006 to 2010[D]. Knoxville: University of Tennessee, 2011. |
4 | GREEN S. A study of U.S. inflight icing accidents and incidents, 1978 to 2002[C]∥ 44th AIAA Aerospace Sciences Meeting and Exhibit. Reston: AIAA, 2006. |
5 | 周志宏, 易贤, 桂业伟, 等. 过冷大水滴条件下结冰风洞试验中模型参数的计算方法: CN104268399A[P]. 2015-01-07. |
ZHOU Z H, YI X, GUI Y W, et al. Computing method of model parameters in icing wind tunnel experiment under supercooled large droplet condition: CN104268399A[P]. 2015-01-07 (in Chinese). | |
6 | 杨倩, 董威, 郭之强, 等. 涡扇发动机短舱结冰试验相似方法[J]. 航空动力学报, 2019, 34(9): 1988-2000. |
YANG Q, DONG W, GUO Z Q, et al. Scaling method of turbofan engine nacelle under icing test[J]. Journal of Aerospace Power, 2019, 34(9): 1988-2000 (in Chinese). | |
7 | 郁嘉, 卜雪琴, 林贵平, 等. 非结冰气象条件下机翼热气防冰系统数值模拟[J]. 空气动力学学报, 2016, 34(5): 562-567. |
YU J, BU X Q, LIN G P, et al. Numerical simulation of a wing hot air anti-icing system in dry air conditions[J]. Acta Aerodynamica Sinica, 2016, 34(5): 562-567 (in Chinese). | |
8 | 郭之强, 郑梅, 董威, 等. 表面凸起对机翼热气防冰腔内换热强化的影响[J]. 航空学报, 2017, 38(2): 520709. |
GUO Z Q, ZHENG M, DONG W, et al. Influence of surface convex on heat transfer enhancement of wing hot air anti-icing system[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(2): 520709 (in Chinese). | |
9 | 姜萍. 飞行器热气防冰系统数值模拟与设计[D]. 南京: 南京航空航天大学, 2017. |
JIANG P. Numerical simulation and design of airplane’s hot-air anti-icing system[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2017 (in Chinese). | |
10 | 郭辉, 徐浩军, 刘凌. 基于回归型支持向量机的空战目标威胁评估[J]. 北京航空航天大学学报, 2010, 36(1): 123-126. |
GUO H, XU H J, LIU L. Target threat assessment of air combat based on support vector machines for regression[J]. Journal of Beijing University of Aeronautics and Astronautics, 2010, 36(1): 123-126 (in Chinese). | |
11 | POURBAGIAN M, TALGORN B, HABASHI W G, et al. Constrained problem formulations for power optimization of aircraft electro-thermal anti-icing systems[J]. Optimization and Engineering, 2015, 16(4): 663-693. |
12 | YONDO R, ANDRéS E, VALERO E. A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses[J]. Progress in Aerospace Sciences, 2018, 96: 23-61. |
13 | 叶年辉, 龙腾, 武宇飞, 等. 基于Kriging代理模型的约束差分进化算法[J]. 航空学报, 2021, 42(6): 324580. |
YE N H, LONG T, WU Y F, et al. Kriging-assisted constrained differential evolution algorithm[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(6): 324580 (in Chinese). | |
14 | 张智超, 高太元, 张磊, 等. 基于径向基神经网络的气动热预测代理模型[J]. 航空学报, 2021, 42(4): 524167. |
ZHANG Z C, GAO T Y, ZHANG L, et al. Aeroheating agent model based on radial basis function neural network[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 524167 (in Chinese). | |
15 | ROWLEY C W. Model reduction for fluids, using balanced proper orthogonal decomposition[J]. International Journal of Bifurcation and Chaos, 2005, 15(3): 997-1013. |
16 | PINNAU R. Model reduction via proper orthogonal decomposition[M]∥Model Order Reduction: Theory, Research Aspects and Applications. Berlin, Heidelberg: Springer, 2008: 95-109. |
17 | VOLKWEIN S. Model reduction using proper orthogonal decomposition[R]. Graz: Institute of Mathematics and Scientific Computing, University of Graz, 2011. |
18 | 蒋耀林. 模型降阶方法[M]. 北京: 科学出版社, 2010. |
JIANG Y L. Model reduction method[M]. Beijing: Science Press, 2010 (in Chinese). | |
19 | STEIN M. Large sample properties of simulations using Latin hypercube sampling[J]. Technometrics, 1987, 29(2): 143-151. |
20 | SMOLA A J, SCH?LKOPF B. A tutorial on support vector regression[J].Statistics and Computing, 2004, 14(3): 199-222. |
21 | AWAD M, KHANNA R. Support vector regression[M]∥Efficient Learning Machines. Berkeley: Apress, 2015: 67-80. |
22 | SIROVICH L. Turbulence and the dynamics of coherent structures. I. Coherent structures[J]. Quarterly of Applied Mathematics, 1987, 45(3): 561-571. |
23 | SIROVICH L. Turbulence and the dynamics of coherent structures. II. Symmetries and transformations[J]. Quarterly of Applied Mathematics, 1987, 45(3): 573-582. |
24 | SIROVICH L. Turbulence and the dynamics of coherent structures. III. Dynamics and scaling[J]. Quarterly of Applied Mathematics, 1987, 45(3): 583-590. |
25 | 陈希, 招启军. 考虑遮蔽区影响的旋翼三维水滴撞击特性计算新方法[J]. 航空学报, 2017, 38(6): 120745. |
CHEN X, ZHAO Q J. New method for predicting 3-D water droplet impingement on rotor considering influence of shadow zone[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(6): 120745 (in Chinese). | |
26 | ZHU J, DONG W, ZHENG M, et al. Numerical investigation of heat and mass transfer on an anti-icing inlet cone[J]. Journal of Propulsion and Power, 2016, 32(3): 789-797. |
27 | DONG W, ZHU J, ZHOU Z X, et al. Heat transfer and temperature analysis of an aeroengine strut under icing conditions[J]. Journal of Aircraft, 2015, 52(1): 216-225. |
28 | SAN J Y, CHEN J J. Effects of jet-to-jet spacing and jet height on heat transfer characteristics of an impinging jet array[J]. International Journal of Heat and Mass Transfer, 2014, 71: 8-17. |
29 | GOLDSTEIN R J, BEHBAHANI A I, HEPPELMANN K K. Streamwise distribution of the recovery factor and the local heat transfer coefficient to an impinging circular air jet[J]. International Journal of Heat and Mass Transfer, 1986, 29(8): 1227-1235. |
30 | 邱亚松, 白俊强, 华俊. 基于本征正交分解和代理模型的流场预测方法[J]. 航空学报, 2013, 34(6): 1249-1260. |
QIU Y S, BAI J Q, HUA J. Flow field estimation method based on proper orthogonal decomposition and surrogate model[J]. Acta Aeronautica et Astronautica Sinica, 2013, 34(6): 1249-1260 (in Chinese). |
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