空气动力学学报2018，Vol.36Issue(2)：163-179,17.DOI:10.7638/kqdlxxb-2017.0134
动力学模态分解及其在流体力学中的应用
Dynamic mode decomposition and its applications in fluid dynamics
摘要
Abstract
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.关键词
动力学模态分解/降阶模型/非定常流/Koopman算子/数据驱动Key words
dynamic mode decomposition/reduced-order model/unsteady flow/Koopman operator/data-driven分类
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寇家庆,张伟伟..动力学模态分解及其在流体力学中的应用[J].空气动力学学报,2018,36(2):163-179,17.基金项目
国家自然科学基金优秀青年科学基金(11622220)；国家自然科学基金面上项目(11572252)；高等学校创新引智计划资助(B17037)；CFD前沿技术(2015一F-016) （11622220）
