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Practical implementation aspects of Galerkin reduced order models based on proper orthogonal decomposition for computational fluid dynamics

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Abstract

The significant computational cost of computational fluid dynamics (CFD) is still the major drawback to its use for multi-query analysis or optimization cycles. This paper is intended to present the formulation of a Reduced Order Model (ROM) for CFD through the traditional Galerkin projection of differential equations on the modes obtained by proper orthogonal decomposition (POD). The ROM framework was built considering the projection of the continuous equations, which allows its utilization based solely on the Full Order Model (FOM) results. This strategy enables the independence of the CFD solver. This preliminary research illustrates the application of this technique in three examples of increasing complexity. Firstly, the linear heat equation in a one-dimensional domain, then the one-dimensional Burgers’ equation. Finally, the Navier–Stokes equations in a two-dimensional domain for a backward-facing step flow is analyzed considering different Reynolds numbers. The implementation details are discussed and, using ANSYS CFX® as FOM reference, the results for the incompressible Navier–Stokes are investigated in terms of the accuracy level achieved, depending on the parameters chosen for the snapshots generation and the execution time reduction obtained.

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References

  1. Cordier L, Bergmann M (2003) Proper Orthogonal Decomposition: An Overview, Post-Processing of Experimental and Numerical Data, von Karman Institute for Fluid Dynamics. Lecture Series 2003–04

  2. Holmes P, Lumley JL, Berkooz G, Rowley CW (2012) Turbulence, coherent structures, dynamical systems and symmetry. Cambridge University Press, Cambridge

    Book  Google Scholar 

  3. Noack BR, Schlegel M, Morzynski M, Tadmor G (2012) Galerkin methods for nonlinear dynamics. In: reduced-order modelling for flow control, Springer, New York

  4. Bergman M, Bruneau C, Iollo A (2009) Enablers for robust POD Models. J Comput Phys 228:516–538

    Article  MathSciNet  Google Scholar 

  5. Yamaleev NK, Pathak KA (2013) Nonlinear model reduction for unsteady discontinuous flows. J Comput Phys 245:1–13

    Article  MathSciNet  Google Scholar 

  6. Couplet M, Basdevant C, Sagaut P (2005) Calibrated reduced-order POD-Galerkin system for fluid flow modelling. J Comput Phys 207:192–220

    Article  MathSciNet  Google Scholar 

  7. Lassila T, Manzoni A, Quarteroni A, Rozza G (2014) Model order reduction in fluid dynamics: challenges and perspectives. In: Quarteroni A, Rozza G (eds) Reduced order methods for modeling and computational reduction. Springer, Milano

  8. Wang Z, Akhtar I, Borggaard J, Iliescu T (2012) Proper orthogonal decomposition closure models for turbulent flows: a numerical comparison. Comp Methods Appl Mech Engrg 237–240:10–26

    Article  MathSciNet  Google Scholar 

  9. Chaturantabut S, Sorensen D (2010) Nonlinear model reduction via discrete empirical interpolation. SIAM JSci Comput 32:2737–2764

    Article  MathSciNet  Google Scholar 

  10. Baiges J, Codina R, Idelsohn S (2013) Explicit reduced order models for the stabilized finite element approximation of the incompressible Navier–Stokes equations. Int J Numer Methods Fluids 72(12):1219–1243

    Article  MathSciNet  Google Scholar 

  11. Astrid P (2004) Reduction of process simulation models: a proper orthogonal decomposition approach, Ph.D. Thesis, University of Eindhoven

  12. Carlberg K, Fahrat C, Cortial J, Amsallem D (2013) The GNAT for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows. J Comput Phys 242:623–647

    Article  MathSciNet  Google Scholar 

  13. Xiao D, Fang F, Buchan AG, Pain CC, Navon IM, Du J, Hu G (2014) Non-linear model reduction for the Navier–Stokes equations using residual DEIM method. J Comput Phys 263:1–18

    Article  MathSciNet  Google Scholar 

  14. Dowell EH, Hall KC, Thomas JP, Florea R, Epureanu BI, Heeg J (1999) Reduced order models in unsteady aerodynamics, collection of technical papers—AIAA/ASME/ASCE/AHS/ASC structures, Structural Dynamics and Materials Conference. vol. 1, pp 622–637

  15. Lucia DJ, Beran PS (2003) Projection methods for reduced order models of compressible flows. J Comput Phys 188:252–280

    Article  MathSciNet  Google Scholar 

  16. Lucia DJ, Beran PS, Silva WA (2004) Reduced-order modeling: new approaches for computational physics. Prog Aerosp Sci 40:51–117

    Article  Google Scholar 

  17. Amsallem D, Cortial J, Farhat C (2010) Toward real-time computational-fluid-dynamics-based aeroelastic computations using a database of reduced-order information. AIAA J 48(9):2029–2037

    Article  Google Scholar 

  18. Placzek A, Tran DM, Ohayon R (2011) A nonlinear POD-Galerkin reduced-order model for compressible flows taking into account rigid body motions. Comp Methods Appl Mech Engrg 200:3497–3514

    Article  MathSciNet  Google Scholar 

  19. Noack BR, Morzynski M, Tadmor G (2012) Reduced-order modelling for flow control, CSI Courses and Lectures. springer, New York

    Google Scholar 

  20. Ravindran SS (2000) A reduced-order approach for optimal control of fluids using proper orthogonal decomposition. Int J Numer Meth Fluids 34:425–448

    Article  MathSciNet  Google Scholar 

  21. Ravindran SS (2006) Reduced-order controllers for control of flow past an airfoil. Int J Numer Meth Fluids 50:531–554

    Article  MathSciNet  Google Scholar 

  22. Galletti B, Bruneau CH, Zannetti L, Iollo A (2004) Low-order modelling of laminar flow regimes past a confined square cylinder. J Fluid Mech 503:161–170

    Article  MathSciNet  Google Scholar 

  23. Du J, Fang F, Pain CC, Navon IM, Zhu J, Ham D (2013) POD reduced-order unstructured mesh modelling applied to 2D and 3D fluid flows. Comput Math Appl 65(3):362–379

    Article  MathSciNet  Google Scholar 

  24. Leblond C, Allery C, Inard C (2011) An optimal projection method for the reduced-order modeling of incompressible flows. Comput Methods Appl Mech Engrg 200:2507–2527

    Article  MathSciNet  Google Scholar 

  25. Ma X, Karniadakis GE (2002) A low-dimensional model for simulating three-dimensional cylinder flow. J Fluid Mech 458:181–190

    Article  MathSciNet  Google Scholar 

  26. Zokagoa J-M, Soulaïmani A (2012) A POD-based reduced-order model for free surface shallow water flows over real bathymetries for Monte-Carlo-type applications. Comput Methods Appl Mech Engrg 221–222:1–23

    Article  Google Scholar 

  27. Chatterjee A (2000) An introduction to the proper orthogonal decomposition. Curr Sci 78(7):808–817

    Google Scholar 

  28. Lumley JL (1967) The structure of inhomogeneous turbulent flows In: Yaglom AM, Tatarski VI (eds) Atmospheric turbulence and radio propagation. Nauka, Moscow, p 166–178

  29. Kosambi DD (1944) Statistics in functions space. J Indian Math Soc 7:76–88

    MathSciNet  Google Scholar 

  30. Loève M (1945) Fonctions Aléatoires du Second Ordre. Compte Rend Acad Sci, Paris 220

    Google Scholar 

  31. Karhunen K (1946) Zur spektraltheorie stochastischer Prozesse. Ann Acad Sci Fenn, vol. 37

  32. Pougashev VS (1953) General theory of the correlations of random functions. Izvestya Akad Nauk SSSR Ser. Mat. 17:401–402

    Google Scholar 

  33. Obukhov AM (1941) Energy distribution in the spectrum of a turbulent flow. Izvestya Akad Nauk SSSR Ser Mat 24:3–42

    Google Scholar 

  34. Joliffe IT (1986) Principal Component Analysis. Springer Verlag, New York

    Book  Google Scholar 

  35. Hotelling H (1933) Analysis of a complex statistical variables into principal components. J Educ Psychol 24:417–441

    Article  Google Scholar 

  36. Dumon A, Allery C, Ammar A (2011) Proper general decomposition (PGD) for the resolution of Navier-Stokes equations. J Comput Phys 230:1387–1407

    Article  MathSciNet  Google Scholar 

  37. Cizmas PG, Palacios A, O’Brien T, Syamlal M (2003) Proper orthogonal decomposition of spatio-temporal patterns in fluidized beds. Chem Eng Sci 58:4417–4427

    Article  Google Scholar 

  38. Brenner TA, Fontenot RL, Cizmas PGA, O’Brien TJ, Breault RW (2012) A reduced-order model for heat transfer in multiphase flow and practical aspects of the proper orthogonal decomposition. Comput Chem Eng 43:68–80

    Article  Google Scholar 

  39. Strang G (2009) Introduction to linear algebra. Wellesley University Press, UK

    Google Scholar 

  40. Volkwein, S (2007) POD for nonlinear systems: reduced-order modeling & error estimates, CEA-EDF-INRIA School: model reduction: theory and applications, October 8–10, 2007. Rocquencourt, France

  41. Sirisup S, Karniadakis GE, Yang Y, Rockwell D (2004) Wave structure interaction: simulation driven by quantitative imaging. Pro Royal Society Lond A 460:729–755

  42. Sirovich L (1987) Turbulence and the dynamics of coherent structures, Part I: Coherent Structures, Quarterly of Applied Mathematics, Volume XLV, Number 3, pp 561–571

  43. Sirovich L (1987) Turbulence and the dynamics of coherent structures, Part II: symmetries and transformations, quarterly of applied mathematics, Volume XLV, Number 3, pp 573–58

  44. Sirovich L (1987) Turbulence and the dynamics of coherent structures, Part III: dynamics and scaling, quarterly of applied mathematics, Volume XLV, Number 3, pp 583–590

  45. ANSYS Inc., (2012) ANSYS CFX-Solver Theory Guide Release 14.5

  46. Maliska CR (2004) Transferência de Calor e Mecânica dos Fluidos Computacional. Second Edition, LTC

  47. Armaly BF, Durst F, Pereira JCF, Schönung B (1983) Experimental and theoretical investigation of backward-facing step flow. J Fluid Mech 127:473–496

    Article  Google Scholar 

  48. Biswas G, Breuer M, Durst F (2004) backward-facing step flows for various expansion ratios at low and moderate reynolds numbers. J Fluids Eng 126:362–374

    Article  Google Scholar 

  49. Caiazzo A, Iliescu T, John V, Schyscholowa S (2014) A numerical investigation of velocity-pressure reduced order models for incompressible flows. J Comput Phys 259:598–616

    Article  MathSciNet  Google Scholar 

  50. Noack BR, Papas P, Monkewitz PA (2005) the need for a pressure-term representation in empirical Galerkin models of incompressible shear flows. J Fluid Mech 523:339–365

    Article  MathSciNet  Google Scholar 

  51. Aubry N, Holmes P, Lumley J, Stone E (1988) The dynamics of coherent structures in the wall region of a turbulent boundary layer. J Fluid Mech 192:115–173

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge Petróleo Brasileiro S.A. (Petrobras), CNPq and FAPERJ for the support to this research.

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Correspondence to Daniel F. C. Silva.

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Technical Editor: Francisco Ricardo Cunha.

Appendix A: expansion of Galerkin projection of the Navier–Stokes equations

Appendix A: expansion of Galerkin projection of the Navier–Stokes equations

Considering a domain Ω and the tensors obtained from the Galerkin projections of the Navier–Stokes equations in Sect. 3.1:

$$A_{ij} = - \left\langle {{\varvec{\uppsi}}_{j} \cdot \nabla \overline{{\mathbf{U}}} \,,\,{\varvec{\uppsi}}_{i} } \right\rangle - \left\langle {\overline{{\mathbf{U}}} \cdot \nabla {\varvec{\uppsi}}_{j} \,,\,{\varvec{\uppsi}}_{i} } \right\rangle - \frac{1}{Re}\left\langle {\nabla {\varvec{\uppsi}}_{j} ,\nabla {\varvec{\uppsi}}_{i} } \right\rangle , \, \, i, \, j \, = \, 1, \ldots ,m$$
(A.1)
$$N_{ijk} = - \left\langle {{\varvec{\uppsi}}_{j} \cdot \nabla {\varvec{\uppsi}}_{k} \,,\,{\varvec{\uppsi}}_{i} } \right\rangle , \, i, \, j, \, k \, = \, 1, \ldots ,m$$
(A.2)
$$e_{i} = - \left\langle {\overline{{\mathbf{U}}} \cdot \nabla \overline{{\mathbf{U}}} \,,\,{\varvec{\uppsi}}_{i} } \right\rangle - \frac{1}{Re}\left\langle {\nabla \overline{{\mathbf{U}}} ,\nabla {\varvec{\uppsi}}_{i} } \right\rangle , \, i \, = \, 1, \ldots ,m$$
(A.3)
$$\alpha_{0i} = \left\langle {{\mathbf{U}}_{0} - \overline{{\mathbf{U}}} ,{\varvec{\uppsi}}_{i} } \right\rangle , \, i \, = \, 1, \ldots ,m\,$$
(A.4)

These tensors have to be expanded in the context of two-dimensional velocity fields to be computationally implemented. For the expansion, it is worth recalling:

$${\mathbf{U}} = \,\left( {\,U\,\,\,\,V\,} \right)\,;\;\overline{{\mathbf{U}}} = \,\left( {\,\overline{U} \,\,\,\,\overline{V\,} \,} \right);\;{\mathbf{U}}_{0} = \left( {\,U_{0} \,\,\,\,V_{0} \,} \right)\; {\text \, \rm {and}} \, \;{\varvec{\uppsi}} = \left( {\,\psi_{U} \,\,\,\psi_{V} } \right);$$
(A.5)

and the definition of inner product for two-dimensional functions:

$$\left\langle {{\mathbf{f}},{\mathbf{g}}} \right\rangle = \int\limits_{\varOmega } {\left( {f_{x} g_{x} + f_{y} g_{y} } \right)\,\,dxdy}$$
(A.6)

Firstly, expanding the first order tensors, by starting with the vector which corresponds to the initial condition:

$$\alpha_{0i} = \left\langle {{\mathbf{U}}_{0} - \overline{{\mathbf{U}}} ,{\varvec{\uppsi}}_{i} } \right\rangle = \int\limits_{\varOmega } {\left[ {\left( {U_{0} - \overline{U} } \right)\psi_{Ui}^{{}} + \left( {V_{0} - \overline{V} } \right)\psi_{Vi}^{{}} } \right]\,} dxdy$$
(A.7)

The vector e will be decomposed as \(e_{i} = - e_{1i} - \tfrac{1}{Re}e_{2i}\) and the resulting expansions will be:

$$\begin{gathered} e_{1i} = \left\langle {\overline{{\mathbf{U}}} \cdot \nabla \overline{{\mathbf{U}}} \,,\,{\varvec{\uppsi}}_{i} } \right\rangle = \left\langle {\left( {\overline{U} \,\,\,\,\overline{V} } \right)\,\,\left( {\begin{array}{*{20}c} {\tfrac{{\partial \overline{U} }}{\partial x}} & {\tfrac{{\partial \overline{V} }}{\partial x}} \\ {\tfrac{{\partial \overline{U} }}{\partial y}} & {\tfrac{{\partial \overline{V} }}{\partial y}} \\ \end{array} } \right)\,,\,\left( {\,\psi_{U} \,\,\psi_{V} } \right)_{i} } \right\rangle \hfill \\ e_{1i} = \int\limits_{\varOmega } {\left[ {\left( {\overline{U} \frac{{\partial \overline{U} }}{\partial x} + \overline{V} \frac{{\partial \overline{U} }}{\partial y}} \right)\psi_{Ui}^{{}} + \left( {\overline{U} \frac{{\partial \overline{V} }}{\partial x} + \overline{V} \frac{{\partial \overline{V} }}{\partial y}} \right)\psi_{Vi}^{{}} } \right]\,} dxdy \hfill \\ \end{gathered}$$
(A.8)
$$\begin{gathered} e_{2i} = \left\langle {\nabla \overline{{\mathbf{U}}} ,\nabla {\varvec{\uppsi}}_{i} } \right\rangle = \left\langle {\,\left( {\begin{array}{*{20}c} {\tfrac{{\partial \overline{U} }}{\partial x}} & {\tfrac{{\partial \overline{V} }}{\partial x}} \\ {\tfrac{{\partial \overline{U} }}{\partial y}} & {\tfrac{{\partial \overline{V} }}{\partial y}} \\ \end{array} } \right)\,,\,\,\left( {\begin{array}{*{20}c} {\tfrac{{\partial \psi_{U} }}{\partial x}} & {\tfrac{{\partial \psi_{V} }}{\partial x}} \\ {\tfrac{{\partial \psi_{U} }}{\partial y}} & {\tfrac{{\partial \psi_{V} }}{\partial y}} \\ \end{array} } \right)_{i} \,} \right\rangle \hfill \\ e_{2i} = \int\limits_{\varOmega } {\left[ {\frac{{\partial \overline{U} }}{\partial x}\frac{{\partial \psi_{Ui}^{{}} }}{\partial x} + \frac{{\partial \overline{U} }}{\partial y}\frac{{\partial \psi_{Ui}^{{}} }}{\partial y} + \frac{{\partial \overline{V} }}{\partial x}\frac{{\partial \psi_{Vi}^{{}} }}{\partial x} + \frac{{\partial \overline{V} }}{\partial y}\frac{{\partial \psi_{Vi}^{{}} }}{\partial y}} \right]\,} dxdy \hfill \\ \end{gathered}$$
(A.9)

It is also convenient to decompose the matrix A as \(A_{ij} = - A_{1ij} - A_{2ij} - \tfrac{1}{Re}A_{3ij}\), with each term expanded as follows:

$$\begin{gathered} A_{1ij} = \left\langle {{\varvec{\uppsi}}_{j} \cdot \nabla \overline{{\mathbf{U}}} \,,\,{\varvec{\uppsi}}_{i} } \right\rangle = \left\langle {\left( {\,\psi_{U} \,\,\psi_{V} } \right)_{j} \,\,\left( {\begin{array}{*{20}c} {\tfrac{{\partial \overline{U} }}{\partial x}} & {\tfrac{{\partial \overline{V} }}{\partial x}} \\ {\tfrac{{\partial \overline{U} }}{\partial y}} & {\tfrac{{\partial \overline{V} }}{\partial y}} \\ \end{array} } \right)\,,\,\left( {\,\psi_{U} \,\,\psi_{V} } \right)_{i} } \right\rangle \hfill \\ A_{1ij} = \int\limits_{\varOmega } {\left[ {\left( {\psi_{Uj}^{{}} \frac{{\partial \overline{U} }}{\partial x} + \psi_{Vj}^{{}} \frac{{\partial \overline{U} }}{\partial y}} \right)\psi_{Ui}^{{}} + \left( {\psi_{Uj}^{{}} \frac{{\partial \overline{V} }}{\partial x} + \psi_{Vj}^{{}} \frac{{\partial \overline{V} }}{\partial y}} \right)\psi_{Vi}^{{}} } \right]\,} dxdy \hfill \\ \end{gathered}$$
(A.10)
$$\begin{gathered} A_{2ij} = \left\langle {\overline{{\mathbf{U}}} \cdot \nabla {\varvec{\uppsi}}_{j} \,,\,{\varvec{\uppsi}}_{i} } \right\rangle = \left\langle {\left( {\overline{U} \,\,\,\,\overline{V} } \right)\,\,\left( {\begin{array}{*{20}c} {\tfrac{{\partial \psi_{U} }}{\partial x}} & {\tfrac{{\partial \psi_{V} }}{\partial x}} \\ {\tfrac{{\partial \psi_{U} }}{\partial y}} & {\tfrac{{\partial \psi_{V} }}{\partial y}} \\ \end{array} } \right)_{j} \,,\,\left( {\,\psi_{U} \,\,\psi_{V} } \right)_{i} } \right\rangle \hfill \\ A_{2ij} = \int\limits_{\varOmega } {\left[ {\left( {\overline{U} \frac{{\partial \psi_{Uj}^{{}} }}{\partial x} + \overline{V} \frac{{\partial \psi_{Uj}^{{}} }}{\partial y}} \right)\psi_{Ui}^{{}} + \left( {\overline{U} \frac{{\partial \psi_{Vj}^{{}} }}{\partial x} + \overline{V} \frac{{\partial \psi_{Vj}^{{}} }}{\partial y}} \right)\psi_{Vi}^{{}} } \right]\,} dxdy \hfill \\ \end{gathered}$$
(A.11)
$$\begin{gathered} A_{3ij} = \left\langle {\nabla {\varvec{\uppsi}}_{j} ,\nabla {\varvec{\uppsi}}_{i} } \right\rangle = \left\langle {\,\,\left( {\begin{array}{*{20}c} {\tfrac{{\partial \psi_{U} }}{\partial x}} & {\tfrac{{\partial \psi_{V} }}{\partial x}} \\ {\tfrac{{\partial \psi_{U} }}{\partial y}} & {\tfrac{{\partial \psi_{V} }}{\partial y}} \\ \end{array} } \right)_{j} \,,\,\,\left( {\begin{array}{*{20}c} {\tfrac{{\partial \psi_{U} }}{\partial x}} & {\tfrac{{\partial \psi_{V} }}{\partial x}} \\ {\tfrac{{\partial \psi_{U} }}{\partial y}} & {\tfrac{{\partial \psi_{V} }}{\partial y}} \\ \end{array} } \right)_{i} \,} \right\rangle \hfill \\ A_{3ij} = \int\limits_{\varOmega } {\left[ {\frac{{\partial \psi_{Uj}^{{}} }}{\partial x}\frac{{\partial \psi_{Ui}^{{}} }}{\partial x} + \frac{{\partial \psi_{Uj}^{{}} }}{\partial y}\frac{{\partial \psi_{Ui}^{{}} }}{\partial y} + \frac{{\partial \psi_{Vj}^{{}} }}{\partial x}\frac{{\partial \psi_{Vi}^{{}} }}{\partial x} + \frac{{\partial \psi_{Vj}^{{}} }}{\partial y}\frac{{\partial \psi_{Vi}^{{}} }}{\partial y}} \right]\,} dxdy \hfill \\ \end{gathered}$$
(A.12)

Finally the third order tensor may be written as:

$$\begin{gathered} N_{ijk} = - \left\langle {{\varvec{\uppsi}}_{j} \cdot \nabla {\varvec{\uppsi}}_{k} ,{\varvec{\uppsi}}_{i} } \right\rangle = - \left\langle {\,\left( {\,\psi_{U} \,\,\psi_{V} } \right)_{j} \,\left( {\begin{array}{*{20}c} {\tfrac{{\partial \psi_{U} }}{\partial x}} & {\tfrac{{\partial \psi_{V} }}{\partial x}} \\ {\tfrac{{\partial \psi_{U} }}{\partial y}} & {\tfrac{{\partial \psi_{V} }}{\partial y}} \\ \end{array} } \right)_{k} \,,\,\,\,\left( {\,\psi_{U} \,\,\psi_{V} } \right)_{i} \,\,} \right\rangle \hfill \\ N_{ijk} = - \int\limits_{\varOmega } {\left[ {\left( {\psi_{Uj}^{{}} \frac{{\partial \psi_{Uk}^{{}} }}{\partial x} + \psi_{Vj}^{{}} \frac{{\partial \psi_{Uk}^{{}} }}{\partial y}} \right)\psi_{Ui}^{{}} + \left( {\psi_{Uj}^{{}} \frac{{\partial \psi_{Vk}^{{}} }}{\partial x} + \psi_{Vj}^{{}} \frac{{\partial \psi_{Vk}^{{}} }}{\partial y}} \right)\psi_{Vi}^{{}} } \right]\,} dxdy \hfill \\ \end{gathered}$$
(A.13)

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Silva, D.F.C., Coutinho, A.L.G.A. Practical implementation aspects of Galerkin reduced order models based on proper orthogonal decomposition for computational fluid dynamics. J Braz. Soc. Mech. Sci. Eng. 37, 1309–1327 (2015). https://doi.org/10.1007/s40430-014-0259-3

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