The dynamic mode decomposition (DMD) is one of the post-processing methods used in analyzing dynamic or transient flow field data from experiments or CFD analyses. This method extracts resonant frequencies, damping coefficients, and spatial structures of mode shapes without a priori physical knowledge of the specific form of structure or power spectrum density analysis. The optimized DMD method is applied to improve the accuracy of DMD results in finding the eigen-components by fitting the nonlinear dynamic system from initial eigenvalues and modes determined by applying the exact DMD. A comparison is made between the optimized DMD and the exact DMD method. Two DMD methods are applied to the numerical data calculated with a cryogenic swirl injector to show validity of their algorithms in identifying acoustic modes of a dynamic system. Compared with the exact DMD, the optimized DMD can extract feasibly the acoustic modes of the dynamic system with higher accuracy. Furthermore, the optimized DMD can find the resonant modes of the system more completely.

动态模式分解（DMD）是一种后处理方法，用于分析来自实验或CFD分析的动态或瞬态流场数据。此方法无需模态结构或功率谱密度分析的特定形式的先验物理知识，即可提取谐振频率，阻尼系数和模式形状的空间结构。应用优化的DMD方法来提高DMD结果的准确性，方法是根据初始特征值和通过应用精确DMD确定的模式来拟合非线性动态系统，从而找到特征分量。在优化的DMD和精确的DMD方法之间进行了比较。两种DMD方法应用于低温旋流喷油器计算的数值数据，以显示其算法在识别动态系统的声模中的有效性。与精确的DMD相比，优化的DMD可以以较高的精度切实可行地提取动态系统的声学模式。此外，优化的DMD可以更完整地找到系统的谐振模式。