基于神经网络的气弹性系统识别,用于预测高灵活性翅膀的飞动
Qing Guo1,2, Xiaoqiang Li3, Zhijie Zhou4
1School of Aeronautics, Northwestern Polytechnical University, Xi'an, 710072, China. gq@nwpu.edu.cn.
Scientific reports
|January 3, 2025
概括
这项研究引入了一种新的神经网络 (NN) 方法,用于预测灵活飞机机翼的飞速度. 这种方法简化了复杂的气弹性建模,并提高了飞机的安全性.
科学领域:
- 空气动力学 在空气动力学.
- 飞机设计 飞机设计
- 航空弹性 航空弹性
背景情况:
- 飞是飞机中一个关键和不可预测的不稳定现象.
- 在高度灵活的翅膀中模拟空气弹性是复杂的.
- 现有的方法难以应对波动的不可预测性.
研究的目的:
- 提出一种基于神经网络 (NN) 的新方法,用于气弹性系统的识别.
- 为了简化模拟高灵活性翼的空气弹性.
- 为了准确预测灵活的翅膀的飞速度.
主要方法:
- 开发了一个神经网络 (NN) 框架用于飞建模.
- 建造了NN模型,用于具有不同材料和尺寸的高灵活性翅膀.
- 利用系统识别技术进行飞速度预测.
主要成果:
- 基于NN的方法有效地预测了灵活的翅膀的飞速度.
- 这种方法简化了复杂的空气弹性的建模.
- 对模拟结果的准确性进行了验证.
结论:
- 拟议的基于NN的系统识别是预测飞速度的可行方法.
- 这种技术提供了一个简化的方法来建模复杂的气弹性行为.
- 该方法提高了飞机设计更安全的潜力.
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