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Geometrically parametrised reduced order models for studying the hysteresis of the Coanda effect in finite element-based incompressible fluid dynamics

Published: 18 July 2024 Publication History

Abstract

This article presents a general reduced order model (ROM) framework for addressing fluid dynamics problems involving time-dependent geometric parametrisations. The framework integrates Proper Orthogonal Decomposition (POD) and Empirical Cubature Method (ECM) hyper-reduction techniques to effectively approximate incompressible computational fluid dynamics simulations. To demonstrate the applicability of this framework, we investigate the behaviour of a planar contraction-expansion channel geometry exhibiting bifurcating solutions known as the Coanda effect. By introducing time-dependent deformations to the channel geometry, we observe hysteresis phenomena in the solution.
The paper provides a detailed formulation of the framework, including the stabilised finite elements full order model (FOM) and ROM, with a particular focus on the considerations related to geometric parametrisation. Subsequently, we present the results obtained from the simulations, analysing the solution behaviour in a phase space for the fluid velocity at a probe point, considered as the Quantity of Interest (QoI). Through qualitative and quantitative evaluations of the ROMs and hyper-reduced order models (HROMs), we demonstrate their ability to accurately reproduce the complete solution field and the QoI.
While HROMs offer significant computational speedup, enabling efficient simulations, they do exhibit some errors, particularly for testing trajectories. However, their value lies in applications where the detection of the Coanda effect holds paramount importance, even if the selected bifurcation branch is incorrect. Alternatively, for more precise results, HROMs with lower speedups can be employed.

Highlights

General reduced order model (ROM) framework for time-dependent geometric parametrisations.
Study of the hysteresis of the Coanda Effect for a contraction-expansion channel in a ROM context.
Presentation of the empirical cubature method (ECM) hyper-reduction algorithm for elements selection.

References

[1]
N.A. Ahmed, Coanda Effect: Flow Phenomenon and Applications, CRC Press, 2019.
[2]
C. Allery, S. Guérin, A. Hamdouni, A. Sakout, Experimental and numerical pod study of the coanda effect used to reduce self-sustained tones, Mech. Res. Commun. 31 (1) (2004) 105–120.
[3]
A. Ambrosetti, G. Prodi, A Primer of Nonlinear Analysis, vol. 34, Cambridge University Press, 1995.
[4]
J. Barnett, C. Farhat, Quadratic approximation manifold for mitigating the Kolmogorov barrier in nonlinear projection-based model order reduction, J. Comput. Phys. 464 (2022).
[5]
A. Beckert, H. Wendland, Multivariate interpolation for fluid-structure-interaction problems using radial basis functions, Aerosp. Sci. Technol. 5 (2) (2001) 125–134.
[6]
S. Boyd, S.P. Boyd, L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004.
[7]
S. Chaturantabut, D.C. Sorensen, Nonlinear model reduction via discrete empirical interpolation, SIAM J. Sci. Comput. 32 (5) (2010) 2737–2764.
[8]
W. Cherdron, F. Durst, J.H. Whitelaw, Asymmetric flows and instabilities in symmetric ducts with sudden expansions, J. Fluid Mech. 84 (1) (1978) 13–31.
[9]
J. Chung, G. Hulbert, A time integration algorithm for structural dynamics with improved numerical dissipation: the generalized-α method, 1993.
[10]
R. Codina, Stabilized finite element approximation of transient incompressible flows using orthogonal subscales, Comput. Methods Appl. Mech. Eng. 191 (39–40) (2002) 4295–4321.
[11]
R. Codina, S. Badia, J. Baiges, J. Principe, Variational multiscale methods in computational fluid dynamics, Encycl. Comput. Mech. (2018) 1–28.
[12]
J. Donea, A. Huerta, Finite Element Methods for Flow Problems, John Wiley & Sons, 2003.
[13]
Z. Drmac, S. Gugercin, A new selection operator for the discrete empirical interpolation method—improved a priori error bound and extensions, SIAM J. Sci. Comput. 38 (2) (2016) A631–A648.
[14]
C. Eckart, G. Young, The approximation of one matrix by another of lower rank, Psychometrika 1 (3) (1936) 211–218.
[15]
C. Farhat, P. Avery, T. Chapman, J. Cortial, Dimensional reduction of nonlinear finite element dynamic models with finite rotations and energy-based mesh sampling and weighting for computational efficiency, Int. J. Numer. Methods Eng. 98 (9) (2014) 625–662.
[16]
C. Farhat, T. Chapman, P. Avery, Structure-preserving, stability, and accuracy properties of the energy-conserving sampling and weighting method for the hyper reduction of nonlinear finite element dynamic models, Int. J. Numer. Methods Eng. 102 (5) (2015) 1077–1110.
[17]
C. Farhat, S. Grimberg, A. Manzoni, A. Quarteroni, et al., Computational Bottlenecks for Proms: Precomputation and Hyperreduction. Model Order Reduction, De Gruyter, Berlin, 2020, pp. 181–244.
[18]
V.M. Ferrándiz, P. Bucher, R. Zorrilla, R. Rossi, A. Cornejo, jcotela, M.A. Celigueta, J. Maria, tteschemacher, C. Roig, M. Masó, S. Warnakulasuriya, G. Casas, M. Núñez, P. Dadvand, S. Latorre, I. de Pouplana, J.I. González, F. Arrufat, riccardotosi, AFranci, A. Ghantasala, P. Wilson, dbaumgaertner, B. Chandra, A. Geiser, K.B. Sautter, I. Lopez, lluís, J. Gárate, Kratosmultiphysics/kratos: Release 9.3, Feb. 2023.
[19]
J. Hernández, A multiscale method for periodic structures using domain decomposition and ecm-hyperreduction, Comput. Methods Appl. Mech. Eng. 368 (2020).
[20]
J.A. Hernandez, M.A. Caicedo, A. Ferrer, Dimensional hyper-reduction of nonlinear finite element models via empirical cubature, Comput. Methods Appl. Mech. Eng. 313 (2017) 687–722.
[21]
M. Hess, A. Alla, A. Quaini, G. Rozza, M. Gunzburger, A localized reduced-order modeling approach for pdes with bifurcating solutions, Comput. Methods Appl. Mech. Eng. 351 (2019) 379–403.
[22]
M.W. Hess, A. Quaini, G. Rozza, Reduced basis model order reduction for Navier–Stokes equations in domains with walls of varying curvature, Int. J. Comput. Fluid Dyn. 34 (2) (2020) 119–126.
[23]
M.W. Hess, A. Quaini, G. Rozza, A spectral element reduced basis method for Navier–Stokes equations with geometric variations, in: Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2018, 2020, pp. 561–571.
[24]
M.W. Hess, A. Quaini, G. Rozza, Data-driven enhanced model reduction for bifurcating models in computational fluid dynamics, in: ECCOMAS Congress 2022, 2022.
[25]
J.S. Hesthaven, G. Rozza, B. Stamm, et al., Certified Reduced Basis Methods for Parametrized Partial Differential Equations, vol. 590, Springer, 2016.
[26]
J.A. Hernández, J.R. Bravo, S. Ares de Parga Cecm, A continuous empirical cubature method with application to the dimensional hyperreduction of parameterized finite element models, Comput. Methods Appl. Mech. Eng. 418 (2024).
[27]
S. Jain, P. Tiso, J.B. Rutzmoser, D.J. Rixen, A quadratic manifold for model order reduction of nonlinear structural dynamics, Comput. Struct. 188 (2017) 80–94.
[28]
E.N. Karatzas, M. Nonino, F. Ballarin, G. Rozza, A reduced order cut finite element method for geometrically parametrized steady and unsteady Navier–Stokes problems, Comput. Math. Appl. 116 (2022) 140–160.
[29]
E.N. Karatzas, G. Stabile, L. Nouveau, G. Scovazzi, G. Rozza, A reduced-order shifted boundary method for parametrized incompressible Navier–Stokes equations, Comput. Methods Appl. Mech. Eng. 370 (2020).
[30]
M. Khamlich, F. Pichi, G. Rozza, Model order reduction for bifurcating phenomena in fluid-structure interaction problems, Int. J. Numer. Methods Fluids 94 (10) (2022) 1611–1640.
[31]
J. Lai, D. Lu, Effect of wall inclination on the mean flow and turbulence characteristics in a two-dimensional wall jet, Int. J. Heat Fluid Flow 17 (4) (1996) 377–385.
[32]
T. Lassila, A. Manzoni, A. Quarteroni, G. Rozza, Model order reduction in fluid dynamics: challenges and perspectives, in: Reduced Order Methods for Modeling and Computational Reduction, 2014, pp. 235–273.
[33]
K. Lee, K.T. Carlberg, Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders, J. Comput. Phys. 404 (2020).
[34]
M. Lombardi, N. Parolini, A. Quarteroni, G. Rozza, Numerical simulation of sailing boats: dynamics, fsi, and shape optimization, in: Variational Analysis and Aerospace Engineering: Mathematical Challenges for Aerospace Design: Contributions from a Workshop Held at the School of Mathematics in Erice, Italy, Springer, 2012, pp. 339–377.
[35]
A. Manzoni, Reduced models for optimal control, shape optimization and inverse problems in haemodynamics, Technical report, EPFL 2012.
[36]
Melendo, A.; Coll, A.; Pasenau, M.; Escolano, E.; Monros, A. (2018) : www.gidhome.com Online.
[37]
J. Mizushima, Y. Shiotani, Transitions and instabilities of flow in a symmetric channel with a suddenly expanded and contracted part, J. Fluid Mech. 434 (2001) 355–369.
[38]
B. Newman, The deflection of plane jets by adjacent boundaries-coanda effect, in: Boundary Layer and Flow Control, 1961.
[39]
M.S. Oliveira, L.E. Rodd, G.H. McKinley, M.A. Alves, Simulations of extensional flow in microrheometric devices, Microfluid. Nanofluid. 5 (6) (2008) 809–826.
[40]
Z. Pan, H. Bao, J. Huang, Subspace dynamic simulation using rotation-strain coordinates, ACM Trans. Graph. 34 (6) (2015) 1–12.
[41]
F. Pichi, F. Ballarin, G. Rozza, J.S. Hesthaven, An artificial neural network approach to bifurcating phenomena in computational fluid dynamics, Comput. Fluids 254 (2023).
[42]
M. Pintore, F. Pichi, M. Hess, G. Rozza, C. Canuto, Efficient computation of bifurcation diagrams with a deflated approach to reduced basis spectral element method, Adv. Comput. Math. 47 (1) (2021) 1–39.
[43]
G. Pitton, A. Quaini, G. Rozza, Computational reduction strategies for the detection of steady bifurcations in incompressible fluid-dynamics: applications to coanda effect in cardiology, J. Comput. Phys. 344 (2017) 534–557.
[44]
G. Pitton, G. Rozza, On the application of reduced basis methods to bifurcation problems in incompressible fluid dynamics, J. Sci. Comput. 73 (1) (2017) 157–177.
[45]
A. Quaini, R. Glowinski, S. Čanić, Symmetry breaking and preliminary results about a Hopf bifurcation for incompressible viscous flow in an expansion channel, Int. J. Comput. Fluid Dyn. 30 (1) (2016) 7–19.
[46]
A. Quarteroni, G. Rozza, Numerical solution of parametrized Navier–Stokes equations by reduced basis methods, Numer. Methods Partial Differ. Equ. 23 (4) (2007) 923–948.
[47]
Romor, F.; Stabile, G.; Rozza, G. (2022): Non-linear manifold rom with convolutional autoencoders and reduced over-collocation method. arXiv preprint arXiv:2203.00360.
[48]
T.W. Sederberg, S.R. Parry, Free-form deformation of solid geometric models, in: Proceedings of the 13th Annual Conference on Computer Graphics and Interactive Techniques, 1986, pp. 151–160.
[49]
L. Sirovich, Turbulence and the dynamics of coherent structures. I. Coherent structures, Q. Appl. Math. 45 (3) (1987) 561–571.
[50]
A. Skotnicka-Siepsiak, Pressure distribution on a flat plate in the context of the phenomenon of the coanda effect hysteresis, Sci. Rep. 12 (1) (2022) 1–13.
[51]
I.J. Sobey, P.G. Drazin, Bifurcations of two-dimensional channel flows, J. Fluid Mech. 171 (1986) 263–287.
[52]
G. Stabile, F. Ballarin, G. Zuccarino, G. Rozza, A reduced order variational multiscale approach for turbulent flows, Adv. Comput. Math. 45 (2019) 2349–2368.
[53]
G. Stabile, M. Zancanaro, G. Rozza, Efficient geometrical parametrization for finite-volume-based reduced order methods, Int. J. Numer. Methods Eng. 121 (12) (2020) 2655–2682.
[54]
R. Tezaur, F. As'ad, C. Farhat, Robust and globally efficient reduction of parametric, highly nonlinear computational models and real time online performance, Comput. Methods Appl. Mech. Eng. 399 (2022).
[55]
M. Tezzele, N. Demo, A. Mola, G. Rozza PyGeM, Python geometrical morphing, in: Software Impacts, 2020.
[56]
M. Tezzele, F. Salmoiraghi, A. Mola, G. Rozza, Dimension reduction in heterogeneous parametric spaces with application to naval engineering shape design problems, Adv. Model. Simul. Eng. Sci. 5 (1) (2018) 1–19.
[57]
P. Tiso, R. Dedden, D. Rixen, A Modified Discrete Empirical Interpolation Method for Reducing Non-linear Structural Finite Element Models, in: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. 55973, American Society of Mechanical Engineers, 2013.
[58]
A.E. Tomás, E.S. Quintana-Ortí, Tall-and-skinny qr factorization with approximate Householder reflectors on graphics processors, J. Supercomput. 76 (11) (2020) 8771–8786.
[59]
R. Wille, H. Fernholz, Report on the first European mechanics colloquium, on the coanda effect, J. Fluid Mech. 23 (4) (1965) 801–819.
[60]
M. Yano, A.T. Patera, An lp empirical quadrature procedure for reduced basis treatment of parametrized nonlinear pdes, Comput. Methods Appl. Mech. Eng. 344 (2019) 1104–1123.

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Published In

cover image Journal of Computational Physics
Journal of Computational Physics  Volume 509, Issue C
Jul 2024
579 pages

Publisher

Academic Press Professional, Inc.

United States

Publication History

Published: 18 July 2024

Author Tags

  1. Coanda effect
  2. Hysteresis
  3. Geometric parametrisation
  4. ECM hyper-reduction
  5. Proper orthogonal decomposition

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