| 基于随机森林的大迎角非线性非定常气动建模方法 |
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| 引用本文： | 赵清杰,刘若宇.基于随机森林的大迎角非线性非定常气动建模方法[J].北京理工大学学报,2017,37(11):1171-1177. |
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| 作者姓名： | 赵清杰 刘若宇 |
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| 作者单位： | 北京理工大学计算机学院,智能信息技术北京市重点实验室,北京 100081;北京理工大学计算机学院,智能信息技术北京市重点实验室,北京 100081 |
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| 基金项目： | 国家自然科学基金资助项目（61175096） |
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| 摘 要： | 提出了一种新的大迎角非线性非定常气动力和气动力矩建模方法.传统的依据物理机理分析、实验观测等来建立飞机气动系数与飞行状态之间的建模方法在大迎角非线性非定常气动力和气动力矩建模中存在着局限性，导致模型精度不高，针对这个问题，提出了随机森林建模方法.根据风洞中飞机大迎角俯仰机动的特点，结合随机森林模型的原理，确定了与大迎角随机森林模型相关的输入特征，通过误差分析实验确定了随机森林模型中决策树个数和内部节点随机选择属性个数等关键参数的取值，利用F-18缩比模型在低速风洞中实验数据进行实验，结果表明，与经典的多项式模型相比所建立的随机森林模型得到的预测结果与真实数据之间的误差更小.
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| 关 键 词： | 大迎角 非线性 非定常 随机森林模型 系统建模 |
| 收稿时间： | 2015/6/27 0:00:00 |
Modeling of Aircraft Nonlinear Unsteady Aerodynamics at High Angle Attack Based on Random Forest |
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| ZHAO Qing-jie and LIU Ruo-yu.Modeling of Aircraft Nonlinear Unsteady Aerodynamics at High Angle Attack Based on Random Forest[J].Journal of Beijing Institute of Technology(Natural Science Edition),2017,37(11):1171-1177. |
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| Authors: | ZHAO Qing-jie and LIU Ruo-yu |
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| Affiliation: | Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China |
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| Abstract: | A new modeling technique for aircraft nonlinear unsteady aerodynamics and aerodynamic moments at high angle attack was proposed. Traditional modeling methods based on the physical mechanism analysis and experimental observation possess some limitations leading to lower model accuracy. Aiming at the problem, a random forest method was proposed for modeling. Combining with the principle of random forest, the input features of the random forest model were determined according to the characteristics of large amplitude pitch oscillations in the wind tunnel. Through error analysis experiment, some key parameter values of the random forest model were determined, including the number of the decision trees, the number of variables randomly sampled as candidates at each split, and so on. Finally, experiments were carried out with the data obtained in the low speed tunnel with F-18 shrinkage ratio model. The results show that, the prediction of random forest model is closer to the wind tunnel data compared to polynomial model, which verifies random forest can be more effective for modeling the nonlinear unsteady aerodynamics and aerodynamic moments at high angle of attack. |
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| Keywords: | high incidence nonlinear unsteady random forest model system modeling |
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