论文标题
通过深度加固学习学习超临界机翼的空气动力学设计
Learning the aerodynamic design of supercritical airfoils through deep reinforcement learning
论文作者
论文摘要
现代民用飞机的空气动力学设计需要真正的智力感，因为它需要对跨性别空气动力学和足够的经验有很好的了解。强化学习是一种人工通用情报，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功能或进行预测。本文利用深厚的增强学习算法来学习减少超临界机翼的空气动力学阻力的政策。该策略旨在根据壁马克号分布的功能采取行动，以便学到的策略可以更加笼统。通过模仿学习预测了增强学习的初始政策，并将结果与随机生成的初始政策进行了比较。然后，基于替代模型对环境进行培训，其中平均减少200个机翼的阻力可以通过增强学习有效地改善。该策略还通过计算流体动力学计算在不同流动条件下的多个机翼进行测试。结果表明，该政策在训练条件和其他类似条件下都是有效的，并且可以反复应用该政策以减少阻力。
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.
