Parametric studies of aerodynamic properties of wings using various forms of machine learning

C. Farhat, N. Alhazmi, P. Avery, R. Tezaur, Y. Ghazi
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Abstract

In aerodynamic studies, models are essential tools for understanding complex fluid flow phenomena. However, their use can be expensive in terms of computer power and calculation time. Therefore, machine learning algorithms have become essential when it comes to analyzing uncertainty in modelling and predicting the values for new input parameters with sensitivity quantifications and in a reasonably short time. The aim of this paper is to predict the key factors in aircraft design by finding the best estimation of the dependent variable in the form of the lift to drag ratio, for any new input-dependent values in the form of Mach numbers and angle of attack. Therefore, different regressions of classical supervised learning algorithms have been applied. The statistical errors have been calculated for these regressions in order to choose the best fit for an unknown model. In addition, artificial neural networks (ANN) have been used to train the data, and to predict the ratio of lift to drag in a practical time compared to the use of experimental tests and the computational fluid dynamics (CFD) technique.
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利用各种形式的机器学习对机翼气动特性进行参数化研究
在空气动力学研究中,模型是理解复杂流体流动现象的重要工具。然而,就计算能力和计算时间而言,它们的使用可能是昂贵的。因此,当涉及到分析建模中的不确定性和预测新的输入参数的值时,机器学习算法在相当短的时间内变得至关重要。本文的目的是通过寻找以升阻比形式出现的因变量的最佳估计来预测飞机设计中的关键因素,对于任何新的以马赫数和迎角形式出现的输入依赖值。因此,对经典监督学习算法的不同回归进行了应用。为了选择最适合未知模型的回归,我们计算了这些回归的统计误差。此外,与使用实验测试和计算流体力学(CFD)技术相比,使用人工神经网络(ANN)对数据进行训练,并在实际时间内预测升阻比。
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