Spatial-temporal features of the coherent structure of sheet/cloud cavitation flows using a frequency-weighted dynamic mode decomposition approach

2021 ◽  
Vol 33 (5) ◽  
pp. 053317
Author(s):  
Hao Zhang ◽  
Yunqiao Liu ◽  
Benlong Wang
Keyword(s):  
Coherent Structure ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Cloud Cavitation ◽  
Decomposition Approach ◽  
Mode Decomposition ◽  
Temporal Features

scholarly journals A dynamic mode decomposition approach for large and arbitrarily sampled systems

2015 ◽  
Vol 27 (2) ◽  
pp. 025113 ◽  
Author(s):  
Florimond Guéniat ◽  
Lionel Mathelin ◽  
Luc R. Pastur
Keyword(s):  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Decomposition Approach ◽  
Mode Decomposition ◽  
Sampled Systems

scholarly journals On the Potential of Reduced Order Models for Wind Farm Control: A Koopman Dynamic Mode Decomposition Approach

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6513
Author(s):  
Nassir Cassamo ◽  
Jan-Willem van Wingerden
Keyword(s):  
Wind Farm ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Reduced Order Models ◽  
Decomposition Approach ◽  
Reduced Order ◽  
Mode Decomposition ◽  
Non Linear ◽  
Farm Systems ◽  
Turbine Wake

The high dimensions and governing non-linear dynamics in wind farm systems make the design of numerical optimal controllers computationally expensive. A possible pathway to circumvent this challenge lies in finding reduced order models which can accurately embed the existing non-linearities. The work presented here applies the ideas motivated by non-linear dynamical systems theory—the Koopman Operator—to an innovative algorithm in the context of wind farm systems—Input Output Dynamic Mode Decomposition (IODMD)—to improve on the ability to model the aerodynamic interaction between wind turbines in a wind farm and uncover insights into the existing dynamics. It is shown that a reduced order linear state space model can reproduce the downstream turbine generator power dynamics and reconstruct the upstream turbine wake. It is further shown that the fit can be improved by judiciously choosing the Koopman observables used in the IODMD algorithm without jeopardizing the models ability to rebuild the turbine wake. The extensions to the IODMD algorithm provide an important step towards the design of linear reduced order models which can accurately reproduce the dynamics in a wind farm.

scholarly journals On the potential of reduced order models for wind farm control: a Koopman dynamic mode decomposition approach

2020 ◽  
Author(s):  
Nassir Cassamo ◽  
Jan-Willem van Wingerden
Keyword(s):  
Wind Farm ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Reduced Order Models ◽  
Decomposition Approach ◽  
Reduced Order ◽  
Mode Decomposition ◽  
Non Linear ◽  
Farm Systems ◽  
Turbine Wake

The high dimensions and governing non linear dynamics in wind farm systems make the design of numerical optimal controllers computationally expensive. A possible pathway to circumvent this challenge lies in finding reduced order models which can accurately embed the existing non linearities. The work here presented applies the ideas motivated by non linear dynamical systems theory - the Koopman Operator - to an innovative algorithm in the context of wind farm systems - Input Output Dynamic Mode Decomposition - to improve on the ability to model the aerodynamic interaction between wind turbines in a wind farm and uncover insights into the existing dynamics. It is shown that a reduced order linear state space model can reproduce the downstream turbine generator power dynamics and reconstruct the upstream turbine wake. It is further shown that the fit can be improved by judiciously choosing the Koopman observables used in the IODMD algorithm without jeopardizing the models ability to rebuild the turbine wake. The extensions to the IODMD algorithm provide an important step towards the design of linear reduced order models which can accurately reproduce the dynamics in a wind farm.

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.

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