航空动力学分析和基于ANN的NACA气面的优化,以提高无人机性能
Sanan H Khan1, Mohd Danish2, Md Ayaz2
1Department of Mechanical and Aerospace Engineering, UAE University, Al-Ain, Abu Dhabi, 15551, United Arab Emirates. shkhan@uaeu.ac.ae.
Scientific reports
|April 8, 2025
概括
在无人机 (UAV) 应用中,NACA 4415的气形突出,提供卓越的空气动力学性能. 使用人工神经网络和遗传算法进行优化,进一步提高了其在苛刻的飞行条件中的效率.
科学领域:
- 航空航天工程 航空航天工程
- 计算流体动力学的流体动力学.
- 机器学习 机器学习
背景情况:
- 无人驾驶飞行器 (UAV) 的性能严重依赖于机翼设计的机动性,稳定性和效率.
- 选择最佳的气形状是提高无人机在各种应用中的能力的关键.
研究的目的:
- 评估和优化NACA 2412,NACA 4415和NACA 0012无人机翼的空气动力学性能.
- 确定最适合UAV操作环境的气形状.
- 利用计算和机器学习方法来优化气翼设计.
主要方法:
- 计算流体动力学 (CFD) 模拟被用来分析气翼特征.
- 使用XFOIL模拟来评估各种飞行条件下的升力,阻力和停机行为.
- 为了优化,开发了一个混合人工智能 (AI) 模型,将人工神经网络 (ANN) 和遗传算法 (GA) 结合起来.
主要成果:
- 纳卡4415表现出优越的空气动力学性能,实现了最高的起重-拖拉比率和有利的停机特性.
- 对NACA 4415的CFD和XFOIL分析证实了更顺的空气流和延迟的流量分离,这有助于提高其效率.
- 该ANN-GA模型确定了最大气翼效率的最佳参数 (攻击角度和雷诺兹数),ANN准确地预测了性能.
结论:
- NACA 4415非常适用于需要高效率和稳定性的无人机,特别是在苛刻的条件下.
- 结合CFD,XFOIL和AI模型,为优化无人机机翼设计提供了一种强大的方法.
- 这些发现为提高无人机在精密农业和基础设施监测等领域的效率和灵活性提供了宝贵的见解.
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