In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced order method is based on dynamic mode decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to enhance the future state prediction of the target function and reduce the parameter space dimensionality. The pipeline is based on high-fidelity simulations carried out by the application of finite volume method for turbulent flows, and automatic mesh morphing through radial basis functions interpolation technique. The proposed pipeline is able to save 1/3 of the overall computational resources thanks to the application of DMD. Moreover exploiting DyAS and performing the regression on a lower dimensional space results in the reduction of the relative error in the approximation of the time-varying lift coefficient by a factor 2 with respect to using only the DMD.

中文翻译：

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通过减少模型和参数空间来增强形状设计问题中的CFD预测

在这项工作中，我们提出了一种先进的计算管线，用于近似和预测参数化翼型轮廓的升力系数。非侵入式降阶方法基于动态模式分解（DMD），并与动态活动子空间（DyAS）耦合，以增强目标函数的未来状态预测并减小参数空间维数。管道是基于有限流方法对湍流进行的高保真模拟，并通过径向基函数插值技术自动进行网格变形。由于DMD的应用，建议的管道可以节省总计算资源的1/3。