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翼型升力系数预测的知识嵌入元学习模型

Knowledge-embedded meta-learning model for lift coefficient prediction of airfoils

Shanghai Aircraft Design and Research Institute(上海飞机设计研究院);Shanghai Jiao Tong University(上海交通大学)

Hairun Xie, Jing Wang, Miao Zhang

摘要:

气动性能评估是飞机气动设计优化过程的重要组成部分,但传统的气动性能评估方法成本高、耗时长。尽管各种机器学习方法都能达到很高的精度,但由于泛化性能差和“黑箱”性质,其在工程中的应用仍然困难。本文提出了一种知识嵌入的Meta学习模型,该模型将数据与升力曲线的理论知识充分融合,用于求解任意超临界翼型在各种迎角下的升力系数。在该模型中,主网络负责表示升力和迎角之间的关系,而几何信息被编码到超网络中,用于预测主网络中涉及的未知参数。具体来说,三个具有不同架构的模型被训练以提供各种解释。与普通的神经网络相比,我们提出的模型可以表现出更好的泛化能力和竞争力的预测精度。然后,基于集成的相似性和显著性方法进行可解释性分析。结果表明,所提出的模型可以倾向于评估翼型几何形状的物理特性的影响。此外,还对该模型的例外情况和不足之处进行了详细的分析和讨论.

英文摘要:

Aerodynamic performance evaluation is an important part of the aircraft aerodynamic design optimization process; however, traditional methods are costly and time-consuming. Despite the fact that various machine learning methods can achieve high accuracy, their application in engineering is still difficult due to their poor generalization performance and "black box" nature. In this paper, a knowledge-embedded meta learning model, which fully integrates data with the theoretical knowledge of the lift curve, is developed to obtain the lift coefficients of an arbitrary supercritical airfoil under various angle of attacks. In the proposed model, a primary network is responsible for representing the relationship between the lift and angle of attack, while the geometry information is encoded into a hyper network to predict the unknown parameters involved in the primary network. Specifically, three models with different architectures are trained to provide various interpretations. Compared to the ordinary neural network, our proposed model can exhibit better generalization capability with competitive prediction accuracy. Afterward, interpretable analysis is performed based on the Integrated Gradients and Saliency methods. Results show that the proposed model can tend to assess the influence of airfoil geometry to the physical characteristics. Furthermore, the exceptions and shortcomings caused by the proposed model are analysed and discussed in detail.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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