目的 针对电热防除冰验证试验及系统优化设计的关键指标——表面温度,开展多区域电热表面温度预测方法研究。方法 以多区域电热分布翼形部件前缘为对象,在其加热区域中心和交接处设置表面温度测点,进行电加热防除冰结冰风洞试验,分析温度变化与多区域加热控制律的关系。结合试验内容及参数数据,考虑温度参数的长时间依赖特征,提出采用长短期记忆网络（Long Short-Term Memory,LSTM）,建立表面温度预测模型,并围绕加热区域表面温度测点位置特征,讨论模型输入项包含测点位置不同的单一温度数据,以及无测点温度数据的预测效果。结果 输入项不同,预测效果有一定差异,但预测值与测量值变化趋势基本一致且差值小,相关系数均达到89%以上,差值均方根误差小于2,而选用中间区域的单测点,整体预测效果最好,相关系数高于96%,均方根误差小于1。结论 仅对某一翼形部件多区域电加热防除冰试验表面温度进行了建模及预测验证,对于飞行器其他部件的电热防除冰的表面温度还有待进一步的验证。
Abstract
Focusing on the key indicator of surface temperature in the verification test and system optimization design of electric heating anti-icing, the work aims to study the multi-zone electric heating surface temperature prediction method. With the leading edge of the multi-zone electric heating distribution wing component as the object, surface temperature measurement points were set at the center and junction of its heating zone to conduct icing wind tunnel tests on electric heating for preventing ice and freezing, and analyze the relationship between temperature changes and multi-zone heating control laws. Based on the experimental content and parameter data, considering the long-term dependence characteristics of temperature parameters, a long short-term memory (LSTM) network was proposed to establish a surface temperature prediction model. Based on the characteristics of the surface temperature measurement point locations in the heating zone, the prediction performance of models incorporating single temperature data from different measurement point locations, as well as models without temperature data from measurement points, was discussed. There were some differences in the prediction performance among different input items, but the trend of the predicted values and the measured values was basically consistent and the difference was small, with a correlation coefficient of over 89% and a root mean square error of less than 2. However, selecting a single measurement point in the middle region had the best overall prediction performance, with a correlation coefficient of over 96% and a root mean square error of less than 1. Modeling and predictive validation have been conducted only for the surface temperature of a multi-zone electric heating anti-icing test on a certain wing component. The surface temperature prediction for electric heating anti-icing on other aircraft components still requires further verification.
关键词
多区域电热防除冰 /
表面温度 /
结冰风洞试验 /
长时间依赖 /
长短期记忆网络 /
预测模型
Key words
multi-zone electric heating anti-icing /
surface temperature /
icing wind tunnel tests /
long-term dependence characteristics /
long short-term memory network /
prediction model
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参考文献
[1] 肖春华, 桂业伟, 林贵平. 飞机电热除冰的研究进展与展望[J]. 科技导报, 2011, 29(18): 67-73.
XIAO C H, GUI Y W, LIN G P.A review of Studies of Aircraft Electrothermalde-Icing[J]. Science & Technology Review, 2011, 29(18): 67-73.
[2] 林贵平, 卜雪琴, 申晓斌, 等. 飞机结冰与防冰技术[M]. 北京: 北京航空航天大学出版社, 2016: 1-20.
LIN G P, BU X Q, SHEN X B, et al.Aircraft Icing and Anti-Icing Technology[M]. Beijing: Beijing University of Aeronautics & Astronautics Press, 2016: 1-20.
[3] 熊建军, 倪章松, 赵照, 等. 飞机模型电加热防/除冰控制系统设计与应用[C]//第十六届中国航空测控技术年会论文集. 北京: 《测控技术》杂志社, 2019.
XIONG J J, NI Z S, ZHAO Z, et al.Design and Application of Anti/De-Icing Control System by Electric Heating for Aircraft Model[J]. Measurement & Control Technology. Beijing: Measurement and Control Technology Magazine, 2019.
[4] 常士楠. 大型飞机的防/除冰问题[C]//大型飞机关键技术高层论坛暨中国航空学会2007年学术年会论文集. 深圳: 中国航空学会, 20077.
CHANG S L, Anti-Icing/Deicing Problems for Large Aircraft[C]//Proceedings of the 2007 Annual Conference of the Chinese Aeronautical Society. Shenzhen: Chinese Society of Aeronautics and Astronautics, 2007: 469-475.
[5] POURBAGIAN M, HABASHI W G.Aero-Thermal Optimization of In-Flight Electro-Thermal Ice Protection Systems in Transient De-Icing Mode[J]. International Journal of Heat and Fluid Flow, 2015, 54: 167-182.
[6] 易贤, 王斌, 李伟斌, 等.飞机结冰冰形测量方法研究进展[J]. 航空学报, 2017, 38(2): 1-12.
YI X, WANG B, LI W B.Research Progress on Ice Shape Measurement Approaches for Aircraft Icing[J]. Acta Aeronauticaet Astronautica Sinica, 2017, 38(2): 1-12.
[7] 倪章松, 刘森云, 王桥, 等. 3 m×2 m结冰风洞试验技术研究进展[J]. 实验流体力学, 2019, 33(6): 46-53.
NI Z S, LIU S Y, WANG Q, et al.Research Progress of Test Technologies for 3 m×2 m Icing Wind Tunnel[J]. Journal of Experiments in Fluid Mechanics, 2019, 33(8): 23-26.
[8] 刘森云, 王桥, 易贤, 等. 3m×2m结冰风洞试验技术新进展(2020—2022年)[J]. 空气动力学学报, 2023(1): 57-65.
LIU S Y, WANG Q, YI X, et al. New Progress of 3m×2m Icing Wind Tunnel Test Technology (2020-2022)[J]. Acta Aerodynamica Sinica, 2023(1): 57-65.
[9] 赖庆仁, 柳庆林, 郭龙, 等. 基于大型结冰风洞的航空发动机结冰与防冰试验技术[J]. 实验流体力学, 2021, 35(3): 1-8.
LAI Q R, LIU Q L, GUO L, et al.Icing and Anti-Icing Test Technology of Aero-Engine Based on Large-Scale Icing Wind Tunnel[J]. Journal of Experiments in Fluid Mechanics, 2021, 35(3): 1-8.
[10] 郭向东, 柳庆林, 赖庆仁, 等. 大型结冰风洞气流场适航符合性验证[J]. 空气动力学学报, 2021(2): 184-195.
GUO X D, LIU Q L, LAI Q R, et al.Airworthiness Compliance Verification of Airflow Field in Large Icing Wind Tunnel[J]. Acta Aerodynamica Sinica, 2021(2): 184-195.
[11] 朱光亚. 电加热防冰部件加热功率的分布特性研究[D]. 南京: 南京航空航天大学, 2014.
ZHU G Y.Study on Distribution Characteristics of Heating Power of Electric Heating Anti-Icing Parts[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2014.
[12] 常士楠, 候雅琴, 袁修干. 周期电加热控制律对除冰表面温度的影响[J]. 航空动力学报, 2007, 22(8): 1247-1251.
CHANG S L, HOU Y Q, YUAN X G.Influence of Periodic Electro Heating Pulse on Deicing Surface Temperature[J]. Journal of Aerospace Power, 2007, 22(8): 1247-1251.
[13] 邹小玲. 直升机旋翼防除冰设计与分析[J]. 直升机技术, 2009(3): 39-46.
ZOU X L.The Design and Analysis of Helicopter Anti-Icing/Deicing System[J]. Helicopter Technique, 2009(3): 39-46.
[14] 程鹏宇, 赵嘉, 韩龙哲, 等. 双向多尺度LSTM的短时温度预测[J]. 江西师范大学学报(自然科学版), 2022, 46(2): 134-139.
CHENG P Y, ZHAO J, HAN L Z, et al.The short-Term Temperature Prediction Based on Bidirectional Multi-Scale LSTM[J]. Journal of Jiangxi Normal University(Natural Science), 2022, 46(2): 134-139.
[15] 常帅. 基于LSTM网络的高温电涡流传感器测量系统的研究[D]. 西安: 西安理工大学, 2021.
CHANG S.Research on Measurement System of High Temperature Eddy Current Sensor Based on LSTM Network[D]. Xi'an: Xi'an University of Technology, 2021.
[16] 柴聪聪, 易贤, 郭磊, 等. 基于BP神经网络的冰形特征参数预测[J]. 实验流体力学, 2021, 35(3): 16-21.
CHAI C C, YI X, GUO L, et al.Prediction of Ice Shape Characteristic Parameters Based on BP Neural Network[J]. Journal of Experiments in Fluid Mechanics, 2021, 35(3): 16-21.
[17] 柴聪聪, 王强, 易贤, 等. 基于卷积神经网络的结冰翼型气动参数预测[J]. 飞行力学, 2021(5): 13-18.
CHAI C C, WANG Q, YI X, et al.Prediction of Aerodynamic Parameters of Iced Airfoil Based on Convolutional Neural Network[J]. Flight Dynamics, 2021(5): 13-18.
[18] 武美先, 张东利. BP神经网络改进及其在无损检测中的应用综述[J]. 中国测试, 2023, 49(S2): 1-6.
WU M X, ZHANG D L.Overview of BP Neural Network Improvement and Its Application in Non-Destructive Testing[J]. China Measurement & Test, 2023, 49(S2): 1-6.
[19] 李淑, 覃娴萍, 翟晓童, 等. 神经网络结构自适应研究综述[J]. 模式识别与人工智能, 2023, 36(12): 1087-1103.
LI S, QIN X P, ZHAI X T, et al.A Review on Architecture Adaptation of Neural Networks[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(12): 1087-1103.
[20] 庞浩. 基于Attention-LSTM模型的表面温度逐时预报研究[D]. 南京: 南京信息工程大学, 2024.
PANG H.Study on Hourly Prediction of Surface Temperature Based on Attention-LSTM Model[D]. Nanjing: Nanjing University of Information Science & Technology, 2024.
[21] 王鹏, 方凯, 汪晨, 等. 基于多模态数据融合的室温预测算法研究[J]. 传感技术学报, 2023, 36(7): 1048-1054.
WANG P, FANG K, WANG C, et al.Research on room Temperature Prediction Algorithm Based on Multimodal Data Fusion[J]. Chinese Journal of Sensors and actuators, 2023, 36(7): 1048-1054.
[22] 彭曙蓉, 陈慧霞, 孙万通, 等. 基于改进LSTM的光伏发电功率预测方法研究[J]. 太阳能学报, 2024, 45(11): 296-302.
PENG S R, CHEN H X, SUN W T, et al.Research on Photovoitaic Power Prediction Method Based on Improved LSTM[J]. ACTA Energiae Solaris Sinica, 2024, 45(11): 296-302.
[23] 杜家豪, 聂婕, 叶敏, 等.基于全局跨尺度时空注意力的深度神经网络海表面温度预测模型[J]. 海洋环境科学, 2023, 42(6): 944-954.
DU J H, NIE J, YE M, et al.A Deep Neural Networks Prediction Model for sea Surface Temperature Based on Global Cross-Scale Spatial-Temporal Attention[J]. Marine Environment Science, 2023, 42(6): 944-954.
[24] 何永勃, 曹祝兵, 于洁. 基于BAM和CNN-GRU混合模型的辅助动力装置排气温度预测方法[J]. 推进技术, 2022, 43(11): 372-380.
HE Y B, CAO Z B, YU J.Prediction Method of Auxiliary Power Unit Exhaust Temperature Based on BAM and CNN-GRU Mixed Model[J]. Journal of Propulsion Technology, 2022, 43(11): 372-380.
[25] 张观山, 丁小明, 何芬, 等. 基于LSTM-AT的温室空气温度预测模型构建[J]. 农业工程学报, 2024, 40(18): 194-201.
ZHANG G S, DING X M, HE F, et al.Predicting Greenhouse Air Temperature Using LSTM-AT[J]. Transactions of the Chinese Society of Agricultural Engineering, 2024, 40(18): 194-201.
[26] 张兆礼, 张建秋, 汪鑫, 等. 基于CNN-LSTM的液压自动抓梁健康状态预测[J]. 机床与液压, 2024, 52(23): 215-220.
ZHANG Z L, ZHANG J Q, WANG X, et al.Health Status Prediction of Hydraulic Automatic Grab Beam Based on CNN-LSTM[J]. Machine Tool & Hydraulics, 2024, 52(23): 215-220.
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