柔性机翼承载能力的试验与预测
Experimental and prediction study for bearing capacity of inflatable wing
-
摘要: 对柔性机翼进行了承载能力的试验及预测研究,首先对柔性机翼的翼型结构进行建模,并对充气机翼的结构进行了分析和优化;其次应用正交试法确定出影响柔性机翼承载能力的主要影响因素,以优化后的结果建立实物模型和主要影响因素为变量进行试验;最后以大量的试验数据为训练样本建立改进的神经网络模型,并进行承载能力预测.试验与预测结果对比研究表明:在初始阶段,柔性机翼在压强一定时,载荷与挠度近似呈线性关系;在同一气压值下,载荷增加到一定值时,载荷与挠度的关系曲线呈近似线性关系,而是斜率突然减小;神经网络测试值和试验实测值最大相对误差与标准方差只有12%和0.39%,人工神经网络解析方法可以用于对充气机翼抗弯刚度的分析.
-
关键词:
- 柔性机翼 /
- 弯曲变形 /
- 正交试验 /
- BP(back propagation)神经网络 /
- 挠度预测
Abstract: Experimental and prediction study for bearing capacity of inflatable wing have been done and described in this paper.Firstly,model of aerofoil was established,structure of inflatable wing was analyzed and optimized,then method of orthogonal experiment was used to ascertain the main impact of influence bearing capacity and experimental of inflatable wing was finished for bearing capacity.Artificial neural network was finally adopted to predict bearing capacity.The results show that : (1) In the initial experiment,at the same pressure,the relationship between loading and deflection approximately abides by linearity relations.When reached the fixed pressure,the relationship between loading and deflection is also linearity,but the slope abruptly increases.(2)The relative error between the predicted result and the experiment result is 12%,and standard deviation 0.39%,and show that the bearing capacity prediction model of neural net work can predict the bearing capacity of inflatable wing accurately and rapidly. -
[1] 李占科,徐顶国,牛文.不同柔度的柔性翼气动特性试验[J] .航空动力学报,2011,26(1):136-140. LI Zhanke,XU Dinguo,NIU Wen.Investigation on aerodynamic characteristics of flexible wing with different flexibilities[J].Journal of Aerospace Power,2011,26(1):136-140.(in Chinese) [2] Usui M,Simpson A,Smith S,et al.Development and flight testing of a UAV with inflatable/rigidizable wings .AIAA 2004-1373,2004. [3] Simpson A,Rawashdeh O,Jacob J,et al.Flying on air:UAV flight testing with inflatable wing technology .AIAA 2004-6570,2004. [4] Cadogan D,Smith T,Uhelsky F,et al.Morphing inflatable wing development for compact package unmanned aerial vehicles .AIAA 2004-1807,2004. [5] Allred R E,Hoyt A E,Harrah L A,et al.Light curing rigidizable inflatable wing .AAIA 2004-1809,2004. [6] Allred R E,Hoyt A E,Harrah L A,et al.Light rigidizable inflatable wings for UAVS:resin and manufacturing development .AIAA 2005-1882,2005. [7] Simpson A,Coulombe N,Jacob J,et al.Morphing of inflatable wings .AIAA 2004-1503,2004. [8] Kearns J D,Usui M,Smith S W,et al.Development of U V-curable inflatable wings for low-density flight applications .AIAA 2004-1503,2004. [9] 吕强,叶正寅,李栋.充气结构机翼的设计和试验研究[J].飞行力学,2007,25(4):77-80. LV Qiang,YE Zhengyin,LI Dong.Design and capability analysis of an aircraft with inflatable wing[J].Flight Dynamics,2007,25(4):77-80.(in Chinese) [10] 王伟,王华,贾青萍.充气机翼承载能力和气动特性分析[J] .航空动力学报,2010, 25(10):2296-2301. WANG Wei,WANG Hua,JIA Qinping.Analysis on bearing capacity and aerodynamic performance of an inflatable wing[J].Journal of Aerospace Power,2010, 25(10):2296-2301.(in Chinese) [11] 王志飞,王华,贾青萍,等.基于人工神经网络的柔性机翼挠度预测[J] .北京航空航天大学学报,2011,37(4):405-408. WANG Zhifei,WANG Hua,JIA Qinping,et al.Flexural rigidity prediction for inflatable wing based on BP neural network[J].Journal of Beijing University of Aeronautics and Astronautics,2011,37(4):405-408.(in Chinese) [12] 贾青萍.充气机翼的结构设计与性能分析 .北京:北京航空航天大学,2008. JIA Qingping.Structure design and performance analysis of inflatable wings .Beijing:Beijing University of Aeronautics and Astronautics,2008.(in Chinese) [13] 王越,曹长修.BP网络局部极小产生的原因分析及避免[J].计算机工程,2002,28(6):35-37. WANG Yue,CAO Changxiu.Analysis of local minimization for BP algorithm and its avoidance methodes[J].Computer Engineering,2002,28(6):35-37.(in Chinese) [14] 张丹,张卫红.基于铸件热应力及变形的人工神经网络和遗传算法优化方法[J].航空学报,2006,27(4):697-702. ZHANG Dan,ZHANG Weihong.Optimization of thermal stress by neural and deformation of the casting during solidification network and genetic algorithm[J].Acta Aeronautica et Astronautica Sinica,2006,27(4):697-702.(in Chinese) [15] LIU Bin,PAN Fusheng,ZHANG Jing,et al.Improvement and application of neural network models in development of wrought magnesium alloys[J].Transaction of Nonferrous Metals Society of China,2011,34(3):44-48.
点击查看大图
计量
- 文章访问数: 1457
- HTML浏览量: 5
- PDF量: 535
- 被引次数: 0