| Citation: | YAO Guangyu, QIU Lu, ZHU Jianqin. Convective heat transfer coefficient prediction of pin-fin channel based on neural network[J]. Journal of Aerospace Power, 2023, 38（7）：1668-1674 doi: 10.13224/j.cnki.jasp.20220713
|
The heat transfer process of pin-fin channel was studied by simulation, and a prediction model of the internal convective heat transfer coefficient was constructed. Firstly, several flow resistance elements were constructed to predict the cooling air flow rate, and then the Reynolds number in the channel was calculated according to the flow rate. Secondly, the Reynolds number and geometric parameters were combined and fed into a genetic algorithm and back propagation neural network to predict the average convective heat transfer coefficient of elements in the pin-fin channel respectively. Finally, a conversion method of equivalent heat transfer coefficient of pin-fin heat conduction was established based on ribbed heat transfer model, so as to apply the model to the cooling effect prediction of actual double-wall turbine blades. Numerical simulation results showed that the model can predict the flow rate of cool air and the convective heat transfer coefficient in the channel, and the relative error was controlled within 5%.
| [1] |
卫海洋,徐敏,刘晓曦. 涡轮叶片冷却技术的发展及关键技术[J]. 飞航导弹,2012(2): 61-64.
WEI Haiyang,XU Min,LIU Xiaoxi. Development and key technology of turbine blade cooling technology[J]. Flying Missiles,2012(2): 61-64. (in Chinese)
|
| [2] |
倪萌,朱惠人,裘云,等. 航空发动机涡轮叶片冷却技术综述[J]. 燃气轮机技术,2005(4): 25-33, 38.
NI Meng,ZHU Huiren,QIU Yun,et al. Review of aero engine turbine blade cooling technology[J]. Gas Turbine Technology,2005(4): 25-33, 38. (in Chinese)
|
| [3] |
郭文,王鹏飞. 涡轮叶片冷却技术分析[J]. 航空动力,2020(6): 55-58.
GUO Wen,WANG Pengfei. Analysis of turbine blade cooling technology[J]. Aerospace Power,2020(6): 55-58. (in Chinese)
|
| [4] |
CHYU M K, SIW S C, MOON H K. Effects of height-to-diameter ratio of pin element on heat transfer from staggered pin-fin arrays[R]. Orlando, US: ASME Turbo Expo: Power for Land, Sea, and Air, 2009.
|
| [5] |
WON S Y,MAHMOOD G I,LIGRANI P M. Spatially-resolved heat transfer and flow structure in a rectangular channel with pin fins[J]. International Journal of Heat and Mass Transfer,2004,47(8/9): 1731-1743.
|
| [6] |
LEE H L,CHOU H M,YANG Y C. The function estimation in predicting heat flux of pin fins with variable heat transfer coefficients[J]. Energy Conversion and Management,2004,45(11/12): 1749-1758.
|
| [7] |
MONTELPARE S,RICCI R. An experimental method for evaluating the heat transfer coefficient of liquid-cooled short pin fins using infrared thermography[J]. Experimental Thermal and Fluid Science,2004,28(8): 815-824. doi: 10.1016/j.expthermflusci.2003.10.007
|
| [8] |
LAWSON S A,THRIFT A A,THOLE K A,et al. Heat transfer from multiple row arrays of low aspect ratio pin fins[J]. International Journal of Heat and Mass Transfer,2011,54(17/18): 4099-4109.
|
| [9] |
BAILEY J C, BUNKER R S. Heat transfer and friction in channels with very high blockage 45° staggered turbulators[R]. Atlanta, US: ASME Turbo Expo 2003, Collocated with the 2003 International Joint Power Generation Conference, 2003.
|
| [10] |
人工智能已嵌入航空发动机产业全流程[J]. 计测技术, 2019, 39(1): 33.
|
| [11] |
ZHU Linyang,ZHANG Weiwei,KOU Jiaqing,et al. Machine learning methods for turbulence modeling in subsonic flows around airfoils[J]. Physics of Flids,2019,31: 015105.1-015105.14.
|
| [12] |
LI Zuobiao, SU Liangjun, WEN Fengbo. Deep learning method for fast prediction of film cooling performance[J]. Physics of Fluids, 34: 047111.1-047111.21.
|
| [13] |
YANG Li,MIN Zheng,YUE Tianwei,et al. High resolution cooling effectiveness reconstruction of transpiration cooling using convolution modeling method[J]. International Journal of Heat and Mass Transfer,2019,133: 1134-1144. doi: 10.1016/j.ijheatmasstransfer.2019.01.010
|
| [14] |
丁水汀,魏霞,陶智,等. 基于神经网络的带肋回转通道换热性能的预测[J]. 北京航空航天大学学报,2007,33(4): 401-404.
DING Shuiting,WEI Xia,TAO Zhi,et al. Prediction of heat transfer performance of ribbed rotary channel based on neural network[J]. Journal of Beijing University of Aeronautics and Astronautics,2007,33(4): 401-404. (in Chinese)
|
| [15] |
吴丁杰,温立书. 一种基于哈里斯鹰算法改进的BP神经网络[J]. 网络安全技术与应用,2022(1): 38-40.
WU Dingjie,WEN Lishu. An improved BP neural network based on Harris eagle algorithm[J]. Network Security Technology and Application,2022(1): 38-40. (in Chinese)
|
| [16] |
惠阳,王永岗,彭辉,等. 基于优化PSO-BP算法的耦合时空特征下地铁客流预测[J]. 交通运输工程学报,2021,21(4): 210-222.
HUI Yang,WANG Yonggang,PENG Hui,et al. Subway passenger flow prediction based on optimized PSO-BP algorithm[J]. Journal of Transportation Engineering,2021,21(4): 210-222. (in Chinese)
|
| [17] |
李松,刘力军,解永乐. 遗传算法优化BP神经网络的短时交通流混沌预测[J]. 控制与决策,2011,26(10): 1581-1585.
LI Song,LIU Lijun,XIE Yongle. Short-term traffic flow chaotic prediction based on BP neural network optimized by genetic algorithm[J]. Control and Decision,2011,26(10): 1581-1585. (in Chinese)
|
| [18] |
陈磊. F119发动机的设计特点与关键技术[J]. 航空发动机,2011,37(3): 65.
CHEN Lei. Design characteristics and key technologies of F119 engine[J]. Aero Engine,2011,37(3): 65. (in Chinese)
|
DownLoad:
