收稿日期: 2022-05-20
修回日期: 2022-06-17
录用日期: 2022-07-13
网络出版日期: 2023-03-28
基金资助
国家自然科学基金(12172165);江苏省自然科学基金(BK20211259)
Optimization of Wind Turbine Vortex Generator Based on Back Propagation Neural Network
Received date: 2022-05-20
Revised date: 2022-06-17
Accepted date: 2022-07-13
Online published: 2023-03-28
采用最优拉丁超立方试验设计法细化涡流发生器参数,确定试验方案,仿真计算风力机的推力和转矩,获得试验数据.基于反向传播(BP)神经网络,构建遗传算法优化BP神经网络的风力机涡流发生器气动性能模型,通过计算气动性能模型预测值与仿真值的误差与均方根,验证气动性能模型的可靠性;耦合鱼群算法和风力机涡流发生器气动性能模型,建立风力机涡流发生器优化方法,对涡流发生器高度、长度和安装角度进行迭代求解,实现涡流发生器优化.结果表明:相比原涡流发生器方案,涡流发生器优化后的风力机叶片截面流动分离得到有效抑制和延迟,表面流体分离现象得到改善,风力机功率提升1.711%,推力下降0.875%.
夏云松, 谭剑锋, 韩水, 高金娥 . 基于反向传播神经网络的风力机涡流发生器优化[J]. 上海交通大学学报, 2023 , 57(11) : 1492 -1500 . DOI: 10.16183/j.cnki.jsjtu.2022.169
The optimal Latin hypercube experimental design method is used to refine the vortex generator parameters, determine the test scheme, simulate and calculate the thrust and torque of the wind turbine, and obtain the experimental data. Based on the back propagation (BP) neural network, the aerodynamic performance model of the wind turbine vortex generator optimized by genetic algorithm is constructed. The reliability of the aerodynamic performance model is verified by calculating the error and root mean square of the predicted and simulated values of the aerodynamic performance model. Coupling the fish swarm algorithm and the aerodynamic performance model of the wind turbine vortex generator, an optimization method of the wind turbine vortex generator is established, and the height, length, and installation angle of the vortex generator are solved iteratively to realize the optimization of the vortex generator. The results show that compared with the original vortex generator scheme, the flow separation of the wind turbine blade section optimized by the vortex generator is effectively restrained and delayed, the surface fluid separation phenomenon is improved, the power of the wind turbine is increased by 1.711%, and the thrust of the wind turbine is decreased by 0.875%.
| [1] | ZHAO Z Z, JIANG R F, FENG J X, et al. Researches on vortex generators applied to wind turbines: A review[J]. Ocean Engineering, 2022, 253: 111266. |
| [2] | 李爽. 风力机翼型动态失速的模型及流动控制机制研究[D]. 北京: 中国科学院大学(中国科学院工程热物理研究所), 2021. |
| [2] | LI Shuang. Study of dynamic stall model and flow control mechanism of wind turbine airfoil[D]. Beijing: Institute of Physics, Chinese Academy of Sciences, 2021. |
| [3] | MANOLESOS M, VOUTSINAS S G. Experimental investigation of the flow past passive vortex generators on an airfoil experiencing three-dimensional separation[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2015, 142: 130-148. |
| [4] | GAO L Y, ZHANG H, LIU Y, et al. Effects of vortex generators on a blunt trailing-edge airfoil for wind turbines[J]. Renewable Energy, 2015, 76: 303-311. |
| [5] | TIMMER W A, VAN ROOIJ R P J O M. Summary of the delft university wind turbine dedicated airfoils[J]. Journal of Solar Energy Engineering, 2003, 125(4): 488-496. |
| [6] | MUELLER-VAHL H, PECHLIVANOGLOU G, NAYERI C N, et al. Vortex generators for wind turbine blades: A combined wind tunnel and wind turbine parametric study[C]// Proceedings of ASME Turbo Expo 2012:Turbine Technical Conference and Exposition. Copenhagen, Denmark: ASME, 2012: 899-914. |
| [7] | DE TAVERNIER D, FERREIRA C, VIRé A, et al. Controlling dynamic stall using vortex generators on a wind turbine airfoil[J]. Renewable Energy, 2021, 172: 1194-1211. |
| [8] | 张惠, 赵宗德, 周广鑫, 等. 涡流发生器对风力机翼型气动性能影响的实验研究[J]. 太阳能学报, 2017, 38(4): 951-958. |
| [8] | ZHANG Hui, ZHAO Zongde, ZHOU Guangxin, et al. Experimental investigation of effect of vortex generator on aerodynamic performance of wind turbine airfoil[J]. Acta Energiae Solaris Sinica, 2017, 38(4): 951-958. |
| [9] | 张惠, 赵宗德, 周广鑫, 等. 涡发生器参数对风力机翼型性能影响实验研究[J]. 太阳能学报, 2017, 38(12): 3399-3405. |
| [9] | ZHANG Hui, ZHAO Zongde, ZHOU Guangxin, et al. Experimental investigation of effect of vortex generator’s parameter on performance of wind turbine aerofoil[J]. Acta Energiae Solaris Sinica, 2017, 38(12): 3399-3405. |
| [10] | 杨瑞, 马超善, 方亮, 等. 加装叶片涡流发生器对变桨距风力机功率的影响[J]. 兰州理工大学学报, 2019, 45(6): 64-68. |
| [10] | YANG Rui, MA Chaoshan, FANG Liang, et al. Effect of installing vortex generator onto blade on power of variable-pitch wind turbine[J]. Journal of Lanzhou University of Technology, 2019, 45(6): 64-68. |
| [11] | FATEHI M, NILI-AHMADABADI M, NEMATOLLAHI O, et al. Aerodynamic performance improvement of wind turbine blade by cavity shape optimization[J]. Renewable Energy, 2019, 132: 773-785. |
| [12] | 赵清鑫, 张兰挺. 基于径向基神经网络的风力机叶片铺层优化[J]. 太阳能学报, 2020, 41(4): 229-234. |
| [12] | ZHAO Qingxin, ZHANG Lanting. Ply parameter optimization of wind turbine blade based on radial basis function netural network[J]. Acta Energiae Solaris Sinica, 2020, 41(4): 229-234. |
| [13] | BI J X, FAN W Z, WANG Y, et al. A fault diagnosis algorithm for wind turbine blades based on BP neural network[J]. IOP Conference Series: Materials Science and Engineering, 2021, 1043(2): 022032. |
| [14] | HAND M M, SIMMS D A, FINGERSH L J, et al. Unsteady aerodynamics experiment phase VI: Wind tunnel test configurations and available data campaigns[R]. USA: Office of Scientific and Technical Information, 2001. |
| [15] | 季宁, 张卫星, 于洋洋, 等. 基于Kriging代理模型和MOPSO算法的注塑成型质量多目标优化[J]. 塑料工业, 2020, 48(5): 67-71. |
| [15] | JI Ning, ZHANG Weixing, YU Yangyang, et al. Multi-objective optimization of injection molding quality based on kriging agent model and MOPSO algorithm[J]. China Plastics Industry, 2020, 48(5): 67-71. |
| [16] | 李晓磊. 一种新型的智能优化方法-人工鱼群算法[D]. 杭州: 浙江大学, 2003. |
| [16] | LI Xiaolei. A new intelligent optimization method-artificial fish school algorithm[D]. Hangzhou: Zhejiang University, 2003. |
/
| 〈 |
|
〉 |
