EI、Scopus 收录
中文核心期刊

融入压力分布信息的气动力建模方法

AERODYNAMIC MODELING METHOD INCORPORATING PRESSURE DISTRIBUTION INFORMATION

  • 摘要: 气动外形优化设计与飞行器性能分析中, 直接运用数值模拟或风洞实验获取气动力的成本高, 构建代理模型是提高外形优化和性能分析效率的重要途径. 然而, 构建模型的过程中, 研究者只关注积分后的气动力和力矩信息. 本文通过充分利用采样过程中所产生的压力分布信息, 来提高建模的精度和泛化性, 进而降低样本获取的成本. 提出了一种小样本框架下融入压力分布信息的气动力建模方法, 首先通过数值模拟或风洞试验获得不同流动参数状态下翼型表面的压力分布信息和气动系数, 其次通过本征正交分解技术对压力分布信息进行特征提取, 获取不同输入参数状态下压力分布信息对应的POD系数, 之后结合输入参数通过Kriging算法对压力分布信息进行建模, 将压力分布信息积分得到低精度气动系数的预测模型, 最后低精度气动系数结合输入参数通过Kriging算法构造高精度的气动系数预测模型. 通过同状态变翼型算例以及CAS350翼型变状态算例进行验证, 该方法相比于传统的克里金模型直接预测气动力, 有效提高了气动力的预测精度和模型的鲁棒性, 同时缩小了学习样本的数据量.

     

    Abstract: In aerodynamic shape optimization design and aircraft performance analysis, the cost of directly using numerical simulation or wind tunnel experiments to obtain aerodynamic forces is high. Building surrogate model is an important way to improve the efficiency of shape optimization and performance analysis. However, in the process of building the model, researchers only focus on the aerodynamic force and moment information after integration. In this paper, the accuracy and generalization of modeling are improved by making full use of the pressure distribution information generated in the sampling process, thereby reducing the cost of sample acquisition. In this paper, an aerodynamic modeling method integrating pressure distribution information under the framework of small sample is proposed. Firstly, the pressure distribution information and aerodynamic coefficients of airfoil surface under different flow parameters are obtained by numerical simulation or wind tunnel experiments. Secondly, the pressure distribution information is extracted by proper orthogonal decomposition technology to obtain the POD coefficients corresponding to the distribution information under different input parameters. Then, the pressure distribution information is modeled by Kriging algorithm combined with input parameters. The pressure distribution information is integrated to obtain the prediction model of low precision aerodynamic coefficients. Finally, the low precision aerodynamic coefficients are combined with the input parameters to construct a high precision aerodynamic prediction model by Kriging algorithm. The method is verified by the same-state variable airfoil example and the CAS350 airfoil variable-state example. Compared with the traditional Kriging model, this method can effectively improve the prediction accuracy of aerodynamic force and the robustness of the model, and reduce the data amount of learning samples.

     

/

返回文章
返回