Developing optimization design of subsonic wing with losse type of agent model

Aim: The introduction of the full paper discusses relevant matters and then proposes developing what we believe to be a better optimal design, which is explained in sections 1 through 4. The core of section 1 is that, with the particle swarm optimization and simulated annealing algorithm, the moving grid method and the improved Hicks-Henne function deformation technology, we parameterize the 18 design variables of the tip of a wing and its root respectively. Section 2 briefs the sampling method with the loose type of surrogate model. To train the samples, sections 3 and 4 use the Latin Hypercube method to evaluate the prediction ability of the Kriging agent model, whose prediction precision is shown in Fig.4. The core of section 4 is that we use the agent model to perform the optimal design of the subsonic wing of a certain typical aircraft; the optimal design results, given in Figs. 5 through 8, and their analysis show preliminarily that our design method is effective for improving the aerodynamic characteristics of subsonic wing.