Higher-order dynamic mode decomposition on-the-fly: A low-order algorithm for complex fluid flows

被引:17
|
作者
Amor, Christian [1 ]
Schlatter, Philipp [2 ]
Vinuesa, Ricardo [2 ]
Le Clainche, Soledad [3 ]
机构
[1] Technol Grad Univ, Okinawa Inst Sci, Complex Fluids & Flows Unit, 1919-1 Tancha, Onna, Okinawa 9040495, Japan
[2] KTH Royal Inst Technol, Flow Engn Mech, S-10044 Stockholm, Sweden
[3] Univ Politecn Madrid, ETSI Aeronaut & Espacio, Plaza Cardenal Cisneros 3, Madrid 28040, Spain
基金
瑞典研究理事会;
关键词
Data-driven methods; Machine learning; Higher-order dynamic mode decomposition; Turbulent flows; Synthetic jets; Three-dimensional cylinder; TURBULENCE; WAKE;
D O I
10.1016/j.jcp.2022.111849
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This article presents a new method to identify the main patterns describing the flow motion in complex flows. The algorithm is an extension of the higher-order dynamic mode decomposition (HODMD), which compresses the snapshots from the analysed database and progressively updates new compressed snapshots on-the-fly, so it is denoted as HODMD on -the-fly (HODMD-of). This algorithm can be applied in parallel to the numerical simulations (or experiments), and it exhibits two main advantages over offline algorithms: (i) it automatically selects on-the-fly the number of necessary snapshots from the database to identify the relevant dynamics; and (ii) it can be used from the beginning of a numerical simulation (or experiment), since it uses a sliding-window to automatically select, also on-the-fly, the suitable interval to perform the data analysis, i.e. it automatically identifies and discards the transient dynamics. The HODMD-of algorithm is suitable to build reduced order models, which have a much lower computational cost than the original simulation. The performance of the method has been tested in three different cases: the axi-symmetric synthetic jet, the three-dimensional wake of a circular cylinder and the turbulent wake behind a wall-mounted square cylinder. The obtained speed-up factors are around 7 with respect to HODMD; this value depends on the simulation and the configuration of the hyperparameters. HODMD-of also provides a significant reduction of the memory requirements, between 40 - 80% amongst the two-and three-dimensional cases studied in this paper.(c) 2022 Elsevier Inc. All rights reserved.
引用
收藏
页数:25
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