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Model order reduction using DMD modes and adjoint DMD modes

机译:使用DMD模式和伴随DMD模式的模型降阶

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Dynamic mode decomposition (DMD) is known for its strength to capture frequency features of dynamic systems. However, without intrinsic orthogonality, it is not convenient to apply DMD in model order reduction. This work introduces adjoint DMD modes to combine with the original DMD modes for bi-orthogonal bases, and allows to construct a simple DMD-based Galerkin low-order model from a full-order Navier-Stokes equation system. The approach was applied on a benchmark case of the flow passing a fixed cylinder. Both the DMD modes and the adjoint DMD modes show well-organized paired structures. In a comparison of model results with the low-dimensional projection of the original simulation data, the DMD-based Galerkin model requires only 8 pairs of modes to represent all basic dynamics.
机译:动态模式分解(DMD)以其捕获动态系统频率特征的优势而闻名。但是,如果没有固有的正交性,将DMD应用于模型阶数减少将不方便。这项工作介绍了与双正交基的原始DMD模式相结合的伴随DMD模式,并允许从一个全阶Navier-Stokes方程系统构建一个简单的基于DMD的Galerkin低阶模型。该方法应用于流过固定气缸的基准情况。 DMD模式和伴随的DMD模式都显示了组织良好的配对结构。在将模型结果与原始模拟数据的低维投影进行比较时,基于DMD的Galerkin模型仅需要8对模式即可表示所有基本动力学。

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