Conventional aerodynamic inverse design (AID) methods have major limitations in terms of optimality and actuality of target parameter distribution. In this research, the target pressure distribution (TPD) of the FX63–137 airfoil was manually corrected to enhance its lift-to-drag ratio from a design perspective, and the corresponding geometry was obtained by the elastic surface algorithm (ESA) inverse design method. An artificial neural network (ANN) was incorporated into the ESA to optimize the TPD and filter out pressure distributions that are not achievable or correspond to fishtail geometries. For this purpose, a genetic algorithm–deep learning (GA–DL) model was developed to optimize the TPD in which the DL model was used to approximate the cost function as a surrogate model. The data generated from CFD simulations during shape modifications in the inverse design were used to train the DL model to correlate PDs with aerodynamic coefficients. Using this approach, the TPD of the FX63–137 airfoil was optimized at a determined angle of attack to reach the maximum lift-to-drag ratio. The unrealistic PDs corresponding to fishtail geometries were eliminated automatically by the ANN during the online process of the inverse design. The results showed that the lift-to-drag ratio of the FX63–137 airfoil increased by more than 18 %. Contrary to traditional methods, the GA–DL model produced several greatly different improved geometries, which were more robust over a wide range of angles of attack. The proposed method addresses the greatest weakness of AID methods in determining the optimal target parameter distribution regardless of the user's input and initial design and without imposing further computational cost for acquiring an external dataset by exploiting the data obtained in the early stages of the design procedure.

中文翻译：

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通过压力分布优化进行空气动力学逆设计的深度学习遗传算法方法


传统的气动逆设计（AID）方法在目标参数分布的最优性和现实性方面存在很大的局限性。本研究从设计角度对FX63-137翼型的目标压力分布（TPD）进行手动修正，以提高其升阻比，并通过弹性表面算法（ESA）反设计获得相应的几何形状方法。 ESA 中纳入了人工神经网络 (ANN)，以优化 TPD 并过滤掉无法实现或与鱼尾几何形状相对应的压力分布。为此，开发了遗传算法-深度学习（GA-DL）模型来优化TPD，其中DL模型被用来近似成本函数作为替代模型。逆向设计中形状修改过程中 CFD 模拟生成的数据用于训练 DL 模型，将 PD 与空气动力系数相关联。使用这种方法，FX63-137 翼型的 TPD 在确定的迎角处进行了优化，以达到最大升阻比。在逆向设计的在线过程中，人工神经网络自动消除了与鱼尾几何形状相对应的不切实际的PD。结果表明，FX63-137翼型的升阻比增加了18%以上。与传统方法相反，GA-DL 模型产生了几种截然不同的改进几何形状，这些几何形状在较宽的攻角范围内更加稳健。 所提出的方法解决了 AID 方法的最大弱点，即无论用户的输入和初始设计如何，都可以确定最佳目标参数分布，并且无需通过利用在设计过程的早期阶段获得的数据来获取外部数据集而施加进一步的计算成本。