论文标题
机器学习以预测空气动力学摊位
Machine Learning to Predict Aerodynamic Stall
论文作者
论文摘要
使用机翼空气动力学模拟数据库对卷积自动编码器进行培训，并根据整体准确性和解释性进行评估。目的是预测摊位并研究自动编码器区分翼型压力分布的线性和非线性响应的能力，以与攻击角度变化。经过对学习基础架构的敏感性分析后，我们研究了针对极端压缩率的自动编码器确定的潜在空间，即非常低维的重建。我们还提出了一种使用解码器来生成新的合成翼型几何形状和空气动力学解决方案的策略，该策略是通过自动编码器学到的潜在表示中的插值和推断。
A convolutional autoencoder is trained using a database of airfoil aerodynamic simulations and assessed in terms of overall accuracy and interpretability. The goal is to predict the stall and to investigate the ability of the autoencoder to distinguish between the linear and non-linear response of the airfoil pressure distribution to changes in the angle of attack. After a sensitivity analysis on the learning infrastructure, we investigate the latent space identified by the autoencoder targeting extreme compression rates, i.e. very low-dimensional reconstructions. We also propose a strategy to use the decoder to generate new synthetic airfoil geometries and aerodynamic solutions by interpolation and extrapolation in the latent representation learned by the autoencoder.
