摘要：
随着计算流体力学和先进流动测试技术的发展,流动的刻画越来越精细,伴随而来的海量流场信息的模态提取与复杂动力学特征的模型化成为当前流体力学的研究热点.动力学模态分解(Dynamic Mode Decomposition,DMD)作为一个全新的时空耦合型动力学建模方法,得到迅速推广.DMD是一种数据驱动的非定常流场模态分析手段,可以准确捕捉各个流动模态的频率及增长特性,并建立流场演化的动力学降阶模型,以重构或预测流场动力学过程.本文针对DMD在流体力学研究的应用问题,重点综述了DMD算法自提出以来的一系列改进以及对不同流动现象的应用,并通过典型测试算例说明DMD的应用过程.在此基础上,讨论了DMD的研究现状及未来发展方向.%With the development of computational fluid dynamics,the revelation for the flow structure in unsteady flows becomes much increasingly delicate.This brings a mass of flow information and catalyzes the study of mode extraction to analyze complex dynamic behaviors.This review discusses a representative approach for flow mode extraction,called dynamic mode decomposition (DMD).DMD is a novel technique for modeling flow dynamics from both spatial and temporal data,which becomes popular recently.As a data-driven algorithm,DMD is capable of capturing the frequency and growth rate of flow modes,helping to construct efficient reducedorder models for flow analysis and control.The availability of DMD has been shown in many complex flow phenomena,like turbulence and transition.To improve its robustness,different methodologies have been introduced,including sparsity-promoting,compressive sensing,timedelayed embedding,etc.Moreover,DMD shows a close relationship with Koopman theory (describing the dynamics of a nonlinear system by an infinite-dimensional linear operator) and proper orthogonal decomposition (a well-known technique for analyzing fluid data).In the present paper,the efficacy of DMD has been shown by two test cases:1) identification of a lowdimensional system,2) analysis of transonic buffet phenomenon.Furthemore,the future development of DMD is discussed.
