scholarly journals Low cost 3D global instability analysis and flow sensitivity based on dynamic mode decomposition and high-order numerical tools

2014 ◽  
Vol 76 (3) ◽  
pp. 169-184 ◽  
Author(s):  
Esteban Ferrer ◽  
Javier de Vicente ◽  
Eusebio Valero
Keyword(s):  
Low Cost ◽  
High Order ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Global Instability ◽  
Instability Analysis ◽  
Flow Sensitivity ◽  
Mode Decomposition ◽  
Numerical Tools

scholarly journals Randomized Projection Learning Method for Dynamic Mode Decomposition

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2803
Author(s):  
Sudam Surasinghe ◽  
Erik Bollt
Keyword(s):  
Dimensional Space ◽  
Low Cost ◽  
Random Projection ◽  
Dynamic Mode Decomposition ◽  
Projection Algorithm ◽  
Dynamic Mode ◽  
Operator Matrix ◽  
Full Spectrum ◽  
Practical Applications ◽  
Mode Decomposition

A data-driven analysis method known as dynamic mode decomposition (DMD) approximates the linear Koopman operator on a projected space. In the spirit of Johnson–Lindenstrauss lemma, we will use a random projection to estimate the DMD modes in a reduced dimensional space. In practical applications, snapshots are in a high-dimensional observable space and the DMD operator matrix is massive. Hence, computing DMD with the full spectrum is expensive, so our main computational goal is to estimate the eigenvalue and eigenvectors of the DMD operator in a projected domain. We generalize the current algorithm to estimate a projected DMD operator. We focus on a powerful and simple random projection algorithm that will reduce the computational and storage costs. While, clearly, a random projection simplifies the algorithmic complexity of a detailed optimal projection, as we will show, the results can generally be excellent, nonetheless, and the quality could be understood through a well-developed theory of random projections. We will demonstrate that modes could be calculated for a low cost by the projected data with sufficient dimension.

scholarly journals A transition prediction method for flow over airfoils based on high-order dynamic mode decomposition

2019 ◽  
Vol 32 (11) ◽  
pp. 2408-2421 ◽  
Author(s):  
Mengmeng WU ◽  
Zhonghua HAN ◽  
Han NIE ◽  
Wenping SONG ◽  
Soledad Le CLAINCHE ◽  
...  
Keyword(s):  
Prediction Method ◽  
High Order ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Transition Prediction ◽  
Mode Decomposition

Combustion Instability Analysis of a Model Gas Turbine by Application of Dynamic Mode Decomposition

2019 ◽  
Vol 24 (1) ◽  
pp. 51-56 ◽  
Author(s):  
Yuangang Wang ◽  
Yuangang Wang ◽  
Chae Hoon Sohn ◽  
Jisu Yoon ◽  
Jinhyun Bae ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Combustion Instability ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Instability Analysis ◽  
Mode Decomposition

Experimental Measurement of In-Plane Rolling Nonpneumatic Tire Vibrations Using High-Speed Imaging

2019 ◽  
Vol 47 (3) ◽  
pp. 196-210
Author(s):  
Meghashyam Panyam ◽  
Beshah Ayalew ◽  
Timothy Rhyne ◽  
Steve Cron ◽  
John Adcox
Keyword(s):  
High Speed ◽  
Image Correlation ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
High Speed Imaging ◽  
Correlation Algorithm ◽  
Mode Decomposition ◽  
Series Of Experiments ◽  
Plane Deformations ◽  
Dominant Frequencies

ABSTRACT This article presents a novel experimental technique for measuring in-plane deformations and vibration modes of a rotating nonpneumatic tire subjected to obstacle impacts. The tire was mounted on a modified quarter-car test rig, which was built around one of the drums of a 500-horse power chassis dynamometer at Clemson University's International Center for Automotive Research. A series of experiments were conducted using a high-speed camera to capture the event of the rotating tire coming into contact with a cleat attached to the surface of the drum. The resulting video was processed using a two-dimensional digital image correlation algorithm to obtain in-plane radial and tangential deformation fields of the tire. The dynamic mode decomposition algorithm was implemented on the deformation fields to extract the dominant frequencies that were excited in the tire upon contact with the cleat. It was observed that the deformations and the modal frequencies estimated using this method were within a reasonable range of expected values. In general, the results indicate that the method used in this study can be a useful tool in measuring in-plane deformations of rolling tires without the need for additional sensors and wiring.

Oscillatory flow around a vertical wall-mounted cylinder: Dynamic mode decomposition

2021 ◽  
Vol 33 (2) ◽  
pp. 025113
Author(s):  
H. K. Jang ◽  
C. E. Ozdemir ◽  
J.-H. Liang ◽  
M. Tyagi
Keyword(s):  
Oscillatory Flow ◽  
Vertical Wall ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Mode Decomposition

Modeling the Turbulent Wake Behind a Wall-Mounted Square Cylinder

2020 ◽  
Author(s):  
Christian Amor ◽  
José M Pérez ◽  
Philipp Schlatter ◽  
Ricardo Vinuesa ◽  
Soledad Le Clainche
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Turbulent Wake ◽  
Orthogonal Decomposition ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Square Cylinder ◽  
Complex Flow ◽  
Reduced Order ◽  
Mode Decomposition ◽  
Proper Orthogonal

Abstract This article introduces some soft computing methods generally used for data analysis and flow pattern detection in fluid dynamics. These techniques decompose the original flow field as an expansion of modes, which can be either orthogonal in time (variants of dynamic mode decomposition), or in space (variants of proper orthogonal decomposition) or in time and space (spectral proper orthogonal decomposition), or they can simply be selected using some sophisticated statistical techniques (empirical mode decomposition). The performance of these methods is tested in the turbulent wake of a wall-mounted square cylinder. This highly complex flow is suitable to show the ability of the aforementioned methods to reduce the degrees of freedom of the original data by only retaining the large scales in the flow. The main result is a reduced-order model of the original flow case, based on a low number of modes. A deep discussion is carried out about how to choose the most computationally efficient method to obtain suitable reduced-order models of the flow. The techniques introduced in this article are data-driven methods that could be applied to model any type of non-linear dynamical system, including numerical and experimental databases.

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