Citation: | ZHANG X H,ZHANG P T,PENG B,et al. Prediction of icing wind tunnel temperature field with machine learning[J]. Journal of Experiments in Fluid Mechanics, 2022,36(5):8-15.. DOI: 10.11729/syltlx20210196 |
[1] |
林贵平, 卜雪琴, 申晓斌. 飞机结冰与防冰技术[M]. 北京: 北京航空航天大学出版社, 2016.
LIN G P, BU X Q, SHEN X B. Aircraft icing and anti-icing technology[M]. Beijing: Beijing University of Aeronautics & Astronautics Press, 2016.
|
[2] |
VAN-ZANTE J, IDE R, STEEN L C. NASA Glenn icing research tunnel: 2012 cloud calibration procedure and results[C]//Proc of the 4th AIAA Atmospheric and Space Environments Conference. 2012: 2933. doi: 10.2514/6.2012-2933 http://dx. doi.org/10.2514/6.2012-2933
|
[3] |
郭向东,张平涛,张珂,等. 3 m × 2 m结冰风洞热流场品质提高及评估[J]. 实验流体力学,2021,35(4):41-51. DOI: 10.11729/syltlx20200118
GUO X D,ZHANG P T,ZHANG K,et al. Improvement and evaluation of thermal flow-field quality in CARDC icing wind tunnel[J]. Journal of Experiments in Fluid Mechanics,2021,35(4):41-51. doi: 10.11729/syltlx20200118
|
[4] |
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,2021,39(5):13-18. DOI: 10.13645/j.cnki.f.d.20210811.001
CHAI C C,WANG Q,YI X,et al. Aerodynamic parameters prediction of airfoil ice accretion based on convolutional neural network[J]. Flight Dynamics,2021,39(5):13-18. doi: 10.13645/j.cnki.f.d.20210811.001
|
[5] |
陈海,钱炜祺,何磊. 基于深度学习的翼型气动系数预测[J]. 空气动力学学报,2018,36(2):294-299.
CHEN H,QIAN W Q,HE L. Aerodynamic coefficient prediction of airfoils based on deep learning[J]. Acta Aerodynamica Sinica,2018,36(2):294-299.
|
[6] |
何磊,钱炜祺,易贤,等. 基于转置卷积神经网络的翼型结冰冰形图像化预测方法[J]. 国防科技大学学报,2021,43(3):98-106. DOI: 10.11887/j.cn.202103013
HE L,QIAN W Q,YI X,et al. Graphical prediction method of airfoil ice shape based on transposed convolution neural networks[J]. Journal of National University of Defense Technology,2021,43(3):98-106. doi: 10.11887/j.cn.202103013
|
[7] |
OGRETIM E,HUEBSCH W,SHINN A. Aircraft ice accretion prediction based on neural networks[J]. Journal of Aircraft,2006,43(1):233-240. doi: 10.2514/1.16241
|
[8] |
CHANG S N,LENG M Y,WU H W,et al. Aircraft ice accretion prediction using neural network and wavelet packet transform[J]. Aircraft Engineering and Aerospace Technology,2016,88(1):128-136. doi: 10.1108/aeat-05-2014-0057
|
[9] |
SURESH S,OMKAR S N,MANI V,et al. Lift coefficient prediction at high angle of attack using recurrent neural network[J]. Aerospace Science and Technology,2003,7(8):595-602. doi: 10.1016/S1270-9638(03)00053-1
|
[10] |
兰其龙. 基于神经网络预测控制的低温风洞多变量控制策略研究[D]. 绵阳: 中国空气动力研究与发展中心, 2016.
LAN Q L. Research on multivariable control strategy based on neural network predictive control in cryogenic wind tunnel[D]. Mianyang: China Aerodynamics Research and Development Center, 2016.
|
[11] |
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,2021,39(2):184-195. DOI: 10.7638/kqdlxxb-2019.0086
GUO X D,LIU Q L,LAI Q R,et al. Airworthiness compliance verification of aerodynamic flowfield of a large-scale icing wind tunnel[J]. Acta Aerodynamica Sinica,2021,39(2):184-195. doi: 10.7638/kqdlxxb-2019.0086
|
[12] |
郭向东,张平涛,赵献礼,等. 大型结冰风洞热流场符合性验证[J]. 实验流体力学,2020,34(5):79-88. DOI: 10.11729/syltlx20190113
GUO X D,ZHANG P T,ZHAO X L,et al. The compliance verification of thermodynamic flowfield in the large icing wind tunnel[J]. Journal of Experiments in Fluid Mechanics,2020,34(5):79-88. doi: 10.11729/syltlx20190113
|
[13] |
KENNEDY J, EBERHART R C. Particle swarm optimization[C]//Proc of the Proceedings of ICNN'95 - International Conference on Neural Networks. 1995. doi: 10.1109/ICNN. 1995.488968 http://dx. doi.org/10.1109/ICNN.1995.488968
|
[14] |
EBERHART R C, SHI Y H. Particle swarm optimization: developments, applications and resources[C]//Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546). 2001. doi: 10.1109/CEC. 2001.934374 http://dx. doi.org/10.1109/CEC.2001.934374
|
[15] |
SHI Y H, EBERHART R C. A modified particle swarm optimizer[C]//Proc of IEEE Icec Conference. 2009
|
[16] |
CORTES C,VAPNIK V. Support-vector networks[J]. Machine Learning,1995,20(3):273-297. doi: 10.1007/BF00994018
|
[17] |
HAYKIN S. 神经网络与机器学习[M]. 北京: 机械工业出版社, 2011.
HAYKIN S. Neural networks and machine learning[M]. Beingjing: Neural Networks and Machine Learning, 2011.
|
[18] |
BURGES C. A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Discovery,1998,2(2):121-167. doi: 10.1023/A:1009715923555
|
[19] |
ABOUHAWWASH M,SEADA H,DEB K. Towards faster convergence of evolutionary multi-criterion optimization algorithms using Karush Kuhn Tucker optimality based local search[J]. Computers & Operations Research,2017,79:331-346. doi: 10.1016/j.cor.2016.04.026
|
[20] |
韩玲. 基于人工神经网络: 多层感知器(MLP)的遥感影像分类模型[J]. 测绘通报,2004(9):29-30,42. DOI: 10.3969/j.issn.0494-0911.2004.09.010
HAN L. The classification model of RS images based on artificial neural network—MLP[J]. Bulletin of Surveying and Mapping,2004(9):29-30,42. doi: 10.3969/j.issn.0494-0911.2004.09.010
|
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