滑翔制导炮弹鸭舵的气动外形快速优化研究

Research on the Rapid Aerodynamic Shape Optimization of Canards of Gliding Guided Projectile

为提高炮弹气动力计算的精度,常需要通过计算流体力学数值仿真反复迭代计算炮弹的气动参数,优化效率较低。本文提出一种基于多可信度代理模型的气动外形快速优化方法,在保证计算精度的条件下大幅提高计算效率。以某滑翔制导炮弹的鸭舵外形优化为例,采用两种可信度样本训练出多可信度代理模型代替耗时的计算流体力学仿真获得气动参数,依据滑翔升阻比最大、稳定性与机动性相匹配等设计要求,利用遗传算法搜寻鸭舵的最优外形参数。与初始方案相比,优化方案在升阻比方面提升显著。通过与数值模拟结果对比,该方法对升阻比的预测平均误差为1.94%,精度较高,同时计算量大大降低,验证了其可行性和有效性。

In order to improve the accuracy of calculation,it is often necessary to calculate the aerodynamic parameters of the projectile iteratively through computational fluid dynamics simulation,so the optimization efficiency is low.In this paper,a rapid aerodynamic shape ptimization method based on multi-fidelity surrogate model is proposed,which improves the calculation efficiency greatly under the condition of ensuring the calculation accuracy.Taking the canard shape optimization of gliding guided projectile as an example,the multi-fidelity surrogate is trained by two kinds of reliability samples to replace the computational fluid dynamics simulation process to calculate the aerodynamic parameters.According to the design requirements of maximum gliding lift-to-drag ratio,matching stability and maneuverability,the genetic algorithm is used to search for the optimal design of canard aerodynamic shape.Compared with the initial scheme,the lift-to-drag ratio of the optimized scheme are improved significantly.Compared with the results of numerical simulation,the average error of the method for the lift-to-drag ratio prediction is 1.94%,the accuracy is higher,and the amount of calculation is greatly reduced,which verifies the feasibility and effectiveness of the method.