The invention discloses an aircraft
high lift device two-dimensional to three-dimensional optimization method based on deep
reinforcement learning, and belongs to the technical field of aircrafts. According to the method, in an environment based on two-dimensional computational fluid
mechanics, an efficient and universal aircraft
high lift device optimization strategy suitable for a target three-dimensional
optimization problem is trained, the training cost of the three-dimensional
optimization problem is remarkably reduced, and high calculation overhead of traditional three-dimensional flow
field simulation is avoided; the flow field locality and strategy
translation invariance are utilized to decompose a three-dimensional problem into a plurality of two-dimensional profiles for independent optimization, so that an
intelligent agent only needs to process a low-dimensional action space, and the problem of
curse of dimensionality caused by a high-dimensional action space is effectively solved; when the trained
intelligent agent is used for solving the similar
optimization problem, the optimization efficiency is high, the convergence speed is high, and the method has high universality for variable design working conditions and the appearance of the
high lift device by means of the previous experience for solving the similar problem.