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.
翻译:现代民用飞机的空气动力设计需要一种真正的智能感,因为它需要很好地了解气动转心动力学和足够的经验。强化学习是一种人工一般智能,能够通过试验和感官学习尖端技能,而不是简单地从数据中提取特征或作出预测。本文件使用一种深层强化学习算法,学习减少超临界气动油的空气动力阻力的政策。该政策旨在根据墙马赫号分布的特性采取行动,使所学政策更加笼统。强化学习的初步政策通过模仿学习预先培训,其结果与随机产生的初始政策进行比较。然后,该政策在以代孕模型为基础的环境中接受培训,其中,通过强化学习,可以有效地减少200个空中油土的拖力。该政策还由不同流量条件下的多个气流油用计算液动力计算进行测试。结果显示,该政策在培训条件和其他类似条件下都是有效的,而且该政策可以反复应用,以进一步减少拖力。