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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.
First, the airfoil structure of the flexible wing was modeled, and the structure of the airfoil was analyzed and optimized. Secondly, the orthogonal test was used to determine the influence of the flexibility The main influencing factors of wing bearing capacity are tested by establishing the physical model and the main influencing factors with the optimized results as variables. Finally, a large number of experimental data are used as training samples to build an improved neural network model and predict the bearing capacity. The comparison of prediction results shows that: at the initial stage, the load and deflection of the flexible wing are approximately linear when the pressure is constant; when the load increases to a certain value under the same pressure, the relation curve between load and deflection is approximately linear, But the slope suddenly decreased. The maximum relative error between the neural network test value and the experimental test value was only 12% and 0.39% of the standard deviation, and the artificial neural network analysis method could be used to analyze the flexural rigidity of the inflatable wing.