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为降低机翼翼型气动优化的仿真代价,提出了一种基于本征正交分解(POD)的翼型自适应快速优化方法。建立气动流场数据的代理模型以预测流场。为简化求解规模和难度,采用POD代理模型优化。为进一步提高精度,提出了基于序贯POD代理模型自适应优化,在优化过程中实时更新POD基,即在优化中间结果的基础上重新采样,以更新POD空间,基于动态数据库构建代理模型。给出了不同优化方法的求解流程。以NACA0012翼型为例分别对直接翼型优化、基于代理模型的翼型优化和基于POD代理模型的翼型优化方法进行了对比分析。结果表明:与直接优化方法相比,基于POD代理模型的翼型优化能显著提升优化效率,且拟合精度更高。
In order to reduce the simulation cost of airfoil aerofoil optimization, an airfoil adaptive fast optimization method based on intrinsic orthogonal decomposition (POD) is proposed. A proxy model of aerodynamic flow data is constructed to predict the flow field. To simplify the scale and difficulty of the solution, POD agent model optimization is adopted. In order to further improve the accuracy, a POD based on sequential POD proxy model is proposed. The POD base is updated in real time during the optimization process. That is, the POD base is re-sampled based on the optimized intermediate results to update the POD space and build the proxy model based on the dynamic database. The solution flow of different optimization methods is given. Taking the NACA0012 airfoil as an example, the direct airfoil optimization, the airfoil optimization based on the proxy model and the airfoil optimization method based on the POD proxy model are compared and analyzed respectively. The results show that, compared with the direct optimization method, the airfoil optimization based on the POD agent model can significantly improve the optimization efficiency and the fitting accuracy is higher.