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使用机器学习和替代模型的机翼空气动力学性能预测
Amir Teimourian1, Daniel Rohacs2, Kamil Dimililer3
1Department of Aeronautical Engineering, University of Kyrenia, Northern Cyprus, Via Mersin 10, Turkey.
Heliyon
|April 19, 2024
概括
机器学习模型准确地预测了气翼升降-拖拉比率. 随机森林在预测准确度方面表现出色,而线性回归则为空气动力学性能分析提供更快的训练和评估时间.
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.
- 机器学习应用 机器学习应用
背景情况:
- 在航空航天设计中,空气动力学性能预测至关重要.
- 机器学习 (ML) 为分析复杂的空气动力学数据集提供了强大的工具.
- 预测提升-拖拉比率对于优化气面效率至关重要.
研究的目的:
- 评估5个ML算法,用于预测气翼升空-拖拉比率.
- 为了评估各种列车/测试比率的算法性能.
- 为了比较预测准确度和计算效率.
主要方法:
- 探索随机森林,梯度增强回归,决策树回归器,AdaBoost和线性回归.
- 将算法应用于气形数据集,以预测提升-拖拉比率.
- 使用R平方,平均平方误差,训练时间和评估时间指标进行评估.
主要成果:
- 随机森林显示出优异的预测性能,特别是在0.2列车/测试比率.
- 在测试的算法中,线性回归实现了最快的训练和评估时间.
- 性能在各个算法和训练/测试分割之间差异很大.
结论:
- 机器学习模型对于空气动力学性能预测是有效的.
- 算法选择涉及预测准确性和计算速度之间的权衡.
- 随机森林推用于高精度的提升-拖拉比率预测,而线性回归则适用于快速分析.
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