The aerodynamic design of modern civil aircraft requires a true sense of intelligence since it requires a good understanding of transonic aerodynamics and sufficient experience. Reinforcement learning is an artificial general intelligence that can learn sophisticated skills by trial-and-error, rather than simply extracting features or making predictions from data. The present paper utilizes a deep reinforcement learning algorithm to learn the policy for reducing the aerodynamic drag of supercritical airfoils. The policy is designed to take actions based on features of the wall Mach number distribution so that the learned policy can be more general. The initial policy for reinforcement learning is pretrained through imitation learning, and the result is compared with randomly generated initial policies. The policy is then trained in environments based on surrogate models, of which the mean drag reduction of 200 airfoils can be effectively improved by reinforcement learning. The policy is also tested by multiple airfoils in different flow conditions using computational fluid dynamics calculations. The results show that the policy is effective in both the training condition and other similar conditions, and the policy can be applied repeatedly to achieve greater drag reduction.

中文翻译：

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通过深度强化学习学习超临界翼型的空气动力学设计

现代民用飞机的空气动力学设计需要真正意义上的智能，因为它需要对跨音速空气动力学有很好的理解和足够的经验。强化学习是一种通用人工智能，它可以通过反复试验来学习复杂的技能，而不是简单地从数据中提取特征或进行预测。本文利用深度强化学习算法来学习减少超临界翼型气动阻力的策略。该策略旨在根据墙马赫数分布的特征采取行动，以便学习到的策略可以更通用。强化学习的初始策略通过模仿学习进行预训练，并将结果与​​随机生成的初始策略进行比较。然后在基于代理模型的环境中训练该策略，其中 200 个翼型的平均减阻可以通过强化学习得到有效改善。该策略还通过使用计算流体动力学计算在不同流动条件下的多个翼型件进行测试。结果表明，该策略在训练条件和其他类似条件下均有效，并且可以重复应用该策略以实现更大的减阻。