Skip to main content
Log in

Reduced-order representation of stratified wakes by proper orthogonal decomposition utilizing translational symmetry

  • Regular Paper
  • Published:
Journal of Visualization Aims and scope Submit manuscript

Abstract

Visualizations of reduced-order representations of stratified wakes of Reynolds number \(Re \in \{5,25,100\}\times 10^3\) are presented at a fixed internal Froude number. The reduced-order representations are constructed by applying proper orthogonal decomposition (POD) to numerical datasets that are high-resolution, three-dimensional and time-dependent. Due to the transient nature of the flow, the dynamics to be represented are highly non-stationary, posing a challenge for the effectiveness of POD. The translational symmetry inherent in the computational configuration is utilized for the POD analysis. This technique turns out to be effective in terms of improving the convergence of energy content represented by the POD modes and enhancing the interpretability of the temporal dynamics. Individual POD modes representing distinct dynamics of various scales are visualized. In the turbulent region, visualizations of the reconstructed vertical vorticity fields suggest that the dominant length scale of flow structures decreases with the modal index. For internal wave motions, visualizations of the reconstructed vertical velocity fields show the opposite trend, as the wavelength of internal waves observed in the wake’s ambient increases with the modal index. The temporal coefficients for a given mode are observed to vary minimally between \(Re = 2.5\times 10^4\) and \(10^5\), suggesting a potential asymptote of the large-scale temporal dynamics in terms of Reynolds number.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. In a discrete numerical dataset, the optimal value of \(\lambda \) does not necessarily match an integer multiple of the grid spacing \(\varDelta x\). In order to increase accuracy, \(\lambda \) is computed as an integer multiple of \(0.25\varDelta x\) by interpolating v onto points between the grid points. The reader is referred to Halawa (2020) for details on how the shifts are implemented and how the interpolation is applied.

  2. Four representative modes are chosen here to cover a wider range of modes; in-between modes that are not included show the same general trend.

References

  • Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2(4):433–459

    Article  Google Scholar 

  • Abdilghanie AM, Diamessis PJ (2013) The internal gravity wave field emitted by a stably stratified turbulent wake. J Fluid Mech 720:104–139

    Article  Google Scholar 

  • Berkooz G, Titi ES (1993) Galerkin projections and the proper orthogonal decomposition for equivariant equations. Phys Lett A 174:94–102

    Article  MathSciNet  Google Scholar 

  • Berkooz G, Holmes P, Lumley JL (1993) The proper orthogonal decomposition in the analysis of turbulent flows. Ann Rev Fluid Mech 25:539–575

    Article  MathSciNet  Google Scholar 

  • Billant P, Chomaz JM (2001) Self-similarity of strongly stratified inviscid flows. Phys Fluids 13:1645

    Article  MathSciNet  Google Scholar 

  • Brunton SL, Kutz JN (2019) Data-driven science and engineering: machine learning, dynamical systems, and control. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Clyne J, Rast M (2005) A prototype discovery environment for analyzing and visualizing terascale turbulent fluid flow simulations. Electron Imaging 2005:284–294

    Google Scholar 

  • Clyne J, Mininni P, Norton A, Rast M (2007) Interactive desktop analysis of high resolution simulations: application to turbulent plume dynamics and current sheet formation. New J Phys 9:301

    Article  Google Scholar 

  • Deusebio E, Caulfield CP, Taylor JR (2015) The intermittency boundary in stratified plane couette flow. J Fluid Mech 781:298–329

    Article  MathSciNet  Google Scholar 

  • Diamessis PJ, Domaradzki JA, Hesthaven JS (2005) A spectral multidomain penalty method model for the simulation of high Reynolds number localized incompressible stratified turbulence. J Comput Phys 202:298–322

    Article  MathSciNet  Google Scholar 

  • Diamessis PJ, Gurka R, Liberzon A (2010) Spatial characterization of vortical structures and internal waves in a stratified turbulent wake using proper orthogonal decomposition. Phys Fluids 22(8):086601

    Article  Google Scholar 

  • Diamessis PJ, Spedding GR, Domaradzki JA (2011) Similarity scaling and vorticity structure in high-Reynolds-number stably stratified turbulent wakes. J Fluid Mech 671:52–95

    Article  MathSciNet  Google Scholar 

  • Halawa B (2020) Three-dimensional visualization and reduced-order representation of stratied turbulent wakes at varying Reynolds number. Master’s thesis, University of Calgary

  • Halawa B, Merhi S, Tang C, Zhou Q (2020) Three-dimensional visualization of stratified turbulent wakes at varying Reynolds number. J Vis 23:437–447

    Article  Google Scholar 

  • He C, Liu Y (2017) Proper orthogonal decomposition of time-resolved LIF visualization: scalar mixing in a round jet. J Vis 20:789–815

    Article  Google Scholar 

  • Kirby M, Armbruster D (1992) Reconstructing phase space from PDE simulations. Z Angew Math Phys 43(6):999–1022

    Article  MathSciNet  Google Scholar 

  • Kundu PK, Cohen IM, Dowling DR (2012) Fluid mechanics, 5th edn. Academic Press, New York

    MATH  Google Scholar 

  • Lui HF, Wolf WR (2019) Construction of reduced-order models for fluid flows using deep feedforward neural networks. J Fluid Mech 872:963–994

    Article  MathSciNet  Google Scholar 

  • Mansfield D (1996) The use of potential vorticity as an operational forecast tool. Meteorol Appl 3:195–210

    Article  Google Scholar 

  • Portwood GD, de Bruyn Kops SM, Taylor JR, Salehipour H, Caulfield CP (2016) Robust identification of dynamically distinct regions in stratified turbulence. J Fluid Mech 807:R2

    Article  MathSciNet  Google Scholar 

  • Riley JJ, Lelong MP (2000) Fluid motions in the presence of strong stable stratification. Ann Rev Fluid Mech 32:613–657

    Article  MathSciNet  Google Scholar 

  • Rotunno R, Grubišić V, Smolarkiewicz PK (1999) Vorticity and potential vorticity in mountain wakes. J Atmos Sci 36:2796–2810

    Article  MathSciNet  Google Scholar 

  • Rowe KL, Diamessis PJ, Zhou Q (2020) Internal gravity wave radiation from a stratified turbulent wake. J Fluid Mech 888:A25

    Article  MathSciNet  Google Scholar 

  • Rowley CW, Marsden JE (2000) Reconstruction equations and the Karhunen–Loève expansion for systems with symmetry. Physica D 142:1–19

    Article  MathSciNet  Google Scholar 

  • San O, Maulik R, Ahmed M (2019) An artificial neural network framework for reduced order modeling of transient flows. Commun Nonlinear Sci 77:271–287

    Article  MathSciNet  Google Scholar 

  • Schmid PJ (2010) Dynamic mode decomposition of numerical and experimental data. J Fluid Mech 656:5–28

    Article  MathSciNet  Google Scholar 

  • Shaw J, Stastna M (2019) Feature identification in time-indexed model output. PLoS ONE 14(12):e0225439

    Article  Google Scholar 

  • Sirovich L (1987) Turbulence and the dynamics of coherent structures. Quant Appl Math 45(3):561–590

    Article  MathSciNet  Google Scholar 

  • Spedding GR (1997) The evolution of initially turbulent bluff-body wakes at high internal Froude number. J Fluid Mech 337:283–301

    Article  Google Scholar 

  • Spedding GR (2014) Wake signature detection. Ann Rev Fluid Mech 46:273–302

    Article  MathSciNet  Google Scholar 

  • Srinivasan P, Guastoni L, Azizpour H, Schlatter P, Vinuesa R (2019) Predictions of turbulent shear flows using deep neural networks. Phys Rev Fluids 4(5):054603

    Article  Google Scholar 

  • Taira K, Brunton SL, Dawson ST, Rowley CW, Colonius T, McKeon BJ, Schmidt OT, Gordeyev S, Theofilis V, Ukeiley LS (2017) Modal analysis of fluid flows: an overview. AAIA J 55:4013–4041

    Article  Google Scholar 

  • Wang Y, Qian J, Song H, Pant K, Yang HQ, Li X, Grismer MJ, Camberos JA, Fahroo F (2014) Feature extraction from massive, dynamic computational data based on proper orthogonal decomposition and feature mining. J Vis 17:363–372

    Article  Google Scholar 

  • Zhou Q (2015) Far-field evolution of turbulence-emitted internal waves and Reynolds number effects on a localized stratified turbulent flow. Ph.d. thesis, Cornell University, Ithaca, New York

  • Zhou Q, Diamessis PJ (2016) Surface manifestation of internal waves emitted by submerged localized stratified turbulence. J Fluid Mech 798:505–539

    Article  MathSciNet  Google Scholar 

  • Zhou Q, Diamessis PJ (2019) Large-scale characteristics of stratified wake turbulence at varying Reynolds number. Phys Rev Fluids 4:084802

    Article  Google Scholar 

Download references

Acknowledgements

Support by the Natural Sciences and Engineering Research Council of Canada (NSERC) through a Discover Grant (RGPIN-2018-04329) awarded to QZ is gratefully acknowledged. This research was enabled in part by support provided by the Advanced Research Computing (ARC) cluster at the University of Calgary and by Compute Canada (www.computecanada.ca). Additional support for CX and QZ was provided by the Marine Environmental Observation, Prediction and Response (MEOPAR) network of Canada through an early career faculty grant.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chengzhu Xu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Halawa, B., Xu, C. & Zhou, Q. Reduced-order representation of stratified wakes by proper orthogonal decomposition utilizing translational symmetry. J Vis 24, 485–499 (2021). https://doi.org/10.1007/s12650-020-00726-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12650-020-00726-y

Keywords

Navigation