2022
DOI: 10.1007/s00162-022-00609-y
|View full text |Cite
|
Sign up to set email alerts
|

Towards robust data-driven reduced-order modelling for turbulent flows: application to vortex-induced vibrations

Abstract: This work presents a robust method that minimises the impact of user-selected parameter on the identification of generic models to study the coherent dynamics in turbulent flows. The objective is to gain insight into the flow dynamics from a data-driven reduced order model (ROM) that is developed from measurement data of the respective flow. For an efficient separation of the coherent dynamics, spectral proper orthogonal decomposition (SPOD) is used, projecting the flow field onto a low-dimensional subspace, s… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
1
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 8 publications
(3 citation statements)
references
References 44 publications
0
1
0
Order By: Relevance
“…Recently, Schubert et al [195] developed a data-driven ROM based on SPOD and applied it to reconstruct the turbulent wake dynamics of a circular cylinder undergoing VIV at Re = 4000. These researchers applied a sparsification procedure to determine the temporal coefficients corresponding to the POD modes and enhanced the reliability and robustness of the ROM.…”
Section: Projection-based Rommentioning
confidence: 99%
“…Recently, Schubert et al [195] developed a data-driven ROM based on SPOD and applied it to reconstruct the turbulent wake dynamics of a circular cylinder undergoing VIV at Re = 4000. These researchers applied a sparsification procedure to determine the temporal coefficients corresponding to the POD modes and enhanced the reliability and robustness of the ROM.…”
Section: Projection-based Rommentioning
confidence: 99%
“…Some other works regarding ROM in VIV cylinders have been carried out using the variant spectral proper orthogonal decomposition (SPOD). Schubert et al [34] presented an improved method to gain insight into the flow dynamics from a data-driven reducedorder model. They used the SPOD to reduce the data obtained from a PIV of a 1 DoF oscillating cylinder at Re D = 4000.…”
Section: Introductionmentioning
confidence: 99%
“…A comparison study of various methods of calibration based on Tikhonov regularization, used to improve the accuracy of reduced order models can be found in [16]. Another approach to identify reduced order models from data uses a conservative and restrictive sparse identification with a quadratic polynomial combination selected from a library of functions [17]. Sparse identification of ROMs can also be achieved by sparse regression techniques from machine learning using the 1 regularization norm, see [18] for more details on Sparse identification of nonlinear dynamics (SINDy).…”
Section: Introductionmentioning
confidence: 99%