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气动导数的英文

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"气动导数"怎么读用"气动导数"造句

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  • aerodynamic derivative

例句与用法

  • Identification of aerodynamic derivatives of bridge deckes in turbulence flow
    紊流风场中桥梁气动导数的识别
  • On the sensitivity of aircraft longitudinal flying qualities to aerodynamic derivatives at high angle of attack
    飞机纵向飞行品质对大迎角气动导数的敏感性研究
  • Static coefficients are defined to describe the static effect of wind on bridge , and the aerodynamic derivatives are used to describe the aerodynamic effect
    静力三分力系数用于描述风对桥梁的静力作用,气动导数是颤振和抖振分析方法的核心。
  • Then the study algorithm is improved to enhance the identification result of the aerodynamic derivatives , and different bp anns are built to identify the static coefficients
    然后改进了学习算法以识别桥梁模型的气动导数,并采用了不同的bp网络识别桥梁模型的静力三分力系数。
  • It is shown that this approach is feasible and can be applied in actual bridge engineering . some experiments are done in the wind tunnel to acquire enough samples including static coefficients and aerodynamic derivatives
    为了获得足够的样本数,在西南交通大学风工程试验中心进行了9个模型试验,得到了模型的静力三分力系数和一个气动导数值。
  • Several icing parameters were imported to calculate the change of drag coefficient and typical derivatives between clean aircraft and iced aircraft , and the aircraft flight envelope change of iced and un - iced situation was computed , and the small disturbance flight dynamics model was modified to study the effect of the ice accretion on the aircraft dynamics by importing the ice parameter to it , and the longitudinal and lateral stability and the elevator , aileron and rudder step response were also studied and simulated
    根据这些参数计算了飞机结冰后阻力系数以及典型气动导数的变化,并计算了结冰前后飞机的飞行包线;同时对结冰前后飞机的纵向、横侧动稳定性以及升降舵、副翼和方向舵阶跃操纵响应进行仿真计算。
  • The purpose of this thesis is to use artificial neural networks ( ann ) to identify the aerodynamic parameters . first introduced in this paper is the basic theory of ann and wind engineering in bridge , then ann is presented to identify the aerodynamic derivatives of ideal thin plate . a back - propagation ( bp ) ann is established and trained for many times to contrast the influences of some factors on the prediction results
    首先对人工神经网络和桥梁风工程的基本理论作了简单的介绍,接着采用人工神经网络识别了理想薄平板的气动导数,并分析了数据处理方式、隐层单元数、训练次数、随机赋值次数、样本数量对训练结果的影响,讨论了训练的稳定性。
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