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Concurrent two-way coupling of global and local models across internal boundaries with non-matching discretizations

  • *Corresponding author: Arif Masud.

    *Corresponding author: Arif Masud.

Arif Masud is William J. and Elaine F. Hall Endowed Professor at the University of Illinois.

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  • Coupling local and global models enables efficient simulation of multiscale systems, where global models capture large-scale behavior and local models, with enhanced physics, resolve finer details over a smaller region. This paper presents a mathematically consistent method for coupling physics-based models of varying fidelity across adjacent, non-overlapping subdomains, even when discretizations do not match at the immersed interdomain interfaces. Incompressible Navier-Stokes equations (NSE) constitute the global model while residual-based turbulence model serves as the local high-fidelity model. In addition, a scalar advection-diffusion equation that models the convection of an active scalar field is appended to the turbulence model in the local domain. This scalar field does not have its complement in the global model, giving rise to unequal number of equations at the immersed boundary between local and global models. Interdomain coupling terms are derived via the Variational Multiscale Discontinuous Galerkin (VMDG) method with new developments in scale representation and efficient fine-scale estimation. While transient laminar flows modeled with NSE in the global domain can be resolved with relatively coarse mesh, turbulent flow calculations in the local model require much finer spatial discretizations as well as smaller time-step for appropriately resolving the turbulent flow physics. The proposed framework also accommodates non-matching meshes at the immersed boundaries. Test problems in 2D and 3D numerically showcase the concurrent two-way coupling of unknown fields across the immersed boundaries. The 3D test presents a case with an unequal number of equations, where the scalar field represents the convection of contaminant concentration. This provides more detailed physics in the local region and highlights its application in climate modeling and atmospheric sciences.

    Mathematics Subject Classification: Primary: 76Dxxm, 76Fxx, 35Gxx; Secondary: 65Mxx.

    Citation:

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  • Figure 1.  Schematic of multi-PDE subdomains

    Figure 2.  Abstract representation of global and local models

    Figure 3.  Edge bubble function defined in the reference domain

    Figure 4.  Schematic diagram of global and local models with different fidelity physics and non-matching interfacial meshes

    Figure 5.  Unequal degrees of freedom between the global and local models across $ \Gamma_I $. (In the 3D case, there are 4 and 5 DOFs in the global and local meshes, respectively.)

    Figure 6.  Quadrature points for the surface integral along the interface $ \Gamma_I $

    Figure 7.  Geometry and boundary conditions for flow around a 2D square

    Figure 8.  Non-matching mesh for flow around a 2D square

    Figure 9.  Velocity and pressure fields computed on the non-matching mesh. (Green box indicates the interface.)

    Figure 10.  Comparison of time-varying drag and lift coefficients for flow around a 2D square at $ Re = 250 $

    Figure 11.  Velocities and pressure along the horizontal centerline. (The dotted vertical lines indicate the position of the interface.)

    Figure 12.  Configuration and dimensions of the computational domain for flow over a 3D block. (The blue inner box indicates the local domain.)

    Figure 13.  Matching and non-matching meshes for flow over a 3D block

    Figure 14.  Vortices around a block in the fine-grid block

    Figure 15.  Mean velocity magnitude computed on matching and non-matching meshes for flow over the 3D block. (The velocity magnitude is normalized by the free-stream velocity $ U_{\infty} $.)

    Figure 16.  Mean pressure coefficient computed on the matching and non-matching meshes for flow over a block

    Figure 17.  Mean pressure coefficient on the surface of the 3D block at $ z/d = 3 $. (The coordinate $ \xi $ represents the distance along the perimeter of the 3D block, as illstrated in the figure.)

    Figure 18.  Mean streamwise velocity and velocity fluctuation along the centerline at $ z/d = $1, 3 in the wake

    Figure 19.  Mean streamwise and spanwise velocities along the transverse axis at $ x/d = $2 and $ z/d = 3 $ in the wake

    Figure 20.  Mean streamwise and spanwise velocity fluctuations along the transverse axis at $ x/d = $2 and $ z/d = 3 $ in the wake

    Figure 21.  Energy spectrum of the streamwise velocity fluctuations along the centerline at $ z/d = $1 and $ z/d = $3 in the wake

    Figure 22.  Energy spectrum of the streamwise velocity fluctuations along the transverse line at $ x/d = $2, 3.5, and $ z/d = 3 $ in the wake region

    Figure 23.  Mass concentration around the 3D block in the fine-grid block over $ \Omega_L $

    Figure 24.  Time-averaged mass concentration along the transverse axis at various positions, $ x/d = $1, 2, 3 in the wake region

    Table 1.  Mean drag coefficient, root-mean-square (RMS) lift coefficient, and Strouhal number for flow around a 2D square at $ Re = 250 $

    $ C_D $ $ C_{L, \text{rms}} $ $ St $
    Main and Scovazzi [29] 1.66 0.66 0.145
    Dalton and Zheng [9] 1.8 0.36 0.16
    Franke et al. [14] 1.67 0.81 0.141
    Saha et al. [42] 1.72 0.193 0.154
    Present (Matching) 1.681 0.845 0.141
    Present (Non-matching) 1.674 0.834 0.139
     | Show Table
    DownLoad: CSV

    Table 2.  Description of the global and local models

    Global model Local model
    Equations NSE NSE+Turbulence
    Dispersion
    Degrees of freedom per node 4 $ ({\mathit{\boldsymbol{u}}}, p) $ 5 $ ({\mathit{\boldsymbol{u}}}, p, c) $
     | Show Table
    DownLoad: CSV
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