Variational multiscale method

The variational multiscale method (VMS) is a technique used for deriving models and numerical methods for multiscale phenomena.[1] The VMS framework has been mainly applied to design stabilized finite element methods in which stability of the standard Galerkin method is not ensured both in terms of singular perturbation and of compatibility conditions with the finite element spaces.[2]

Stabilized methods are getting increasing attention in computational fluid dynamics because they are designed to solve drawbacks typical of the standard Galerkin method: advection-dominated flows problems and problems in which an arbitrary combination of interpolation functions may yield to unstable discretized formulations.[3][4] The milestone of stabilized methods for this class of problems can be considered the Streamline Upwind Petrov-Galerkin method (SUPG), designed during 80s for convection dominated-flows for the incompressible Navier–Stokes equations by Brooks and Hughes.[5][6] Variational Multiscale Method (VMS) was introduced by Hughes in 1995.[7] Broadly speaking, VMS is a technique used to get mathematical models and numerical methods which are able to catch multiscale phenomena;[1] in fact, it is usually adopted for problems with huge scale ranges, which are separated into a number of scale groups.[8] The main idea of the method is to design a sum decomposition of the solution as , where is denoted as coarse-scale solution and it is solved numerically, whereas represents the fine scale solution and is determined analytically eliminating it from the problem of the coarse scale equation.[1]

The abstract framework edit

Abstract Dirichlet problem with variational formulation edit

Consider an open bounded domain   with smooth boundary  , being   the number of space dimensions. Denoting with   a generic, second order, nonsymmetric differential operator, consider the following boundary value problem:[4]

 
 

being   and   given functions. Let   be the Hilbert space of square-integrable functions with square-integrable derivatives:[4]

 

Consider the trial solution space   and the weighting function space   defined as follows:[4]

 
 

The variational formulation of the boundary value problem defined above reads:[4]

 ,

being   the bilinear form satisfying  ,   a bounded linear functional on   and   is the   inner product.[2] Furthermore, the dual operator   of   is defined as that differential operator such that  .[7]

Variational multiscale method edit

 
One dimensional representation of  ,   and  

In VMS approach, the function spaces are decomposed through a multiscale direct sum decomposition for both   and   into coarse and fine scales subspaces as:[1]

 

and

 

Hence, an overlapping sum decomposition is assumed for both   and   as:

 ,

where   represents the coarse (resolvable) scales and   the fine (subgrid) scales, with  ,  ,   and  . In particular, the following assumptions are made on these functions:[1]

 

With this in mind, the variational form can be rewritten as

 

and, by using bilinearity of   and linearity of  ,

 

Last equation, yields to a coarse scale and a fine scale problem:

 
 

or, equivalently, considering that   and  :

 
 

By rearranging the second problem as  , the corresponding Euler–Lagrange equation reads:[7]

 

which shows that the fine scale solution   depends on the strong residual of the coarse scale equation  .[7] The fine scale solution can be expressed in terms of   through the Green's function  :

 

Let   be the Dirac delta function, by definition, the Green's function is found by solving  

 

Moreover, it is possible to express   in terms of a new differential operator   that approximates the differential operator   as [1]

  with  . In order to eliminate the explicit dependence in the coarse scale equation of the sub-grid scale terms, considering the definition of the dual operator, the last expression can be substituted in the second term of the coarse scale equation:[1]

 

Since   is an approximation of  , the Variational Multiscale Formulation will consist in finding an approximate solution   instead of  . The coarse problem is therefore rewritten as:[1]

 

being

 

Introducing the form [7]

 

and the functional

 ,

the VMS formulation of the coarse scale equation is rearranged as:[7]

 

Since commonly it is not possible to determine both   and  , one usually adopt an approximation. In this sense, the coarse scale spaces   and   are chosen as finite dimensional space of functions as:[1]

 

and

 

being   the Finite Element space of Lagrangian polynomials of degree   over the mesh built in   .[4] Note that   and   are infinite-dimensional spaces, while   and   are finite-dimensional spaces.

Let   and   be respectively approximations of   and  , and let   and   be respectively approximations of   and  . The VMS problem with Finite Element approximation reads:[7]

 

or, equivalently:

 

VMS and stabilized methods edit

Consider an advection–diffusion problem:[4]

 

where   is the diffusion coefficient with   and   is a given advection field. Let   and  ,  ,  .[4] Let  , being   and  .[1] The variational form of the problem above reads:[4]

 

being

 

Consider a Finite Element approximation in space of the problem above by introducing the space   over a grid   made of   elements, with  .

The standard Galerkin formulation of this problem reads[4]

 

Consider a strongly consistent stabilization method of the problem above in a finite element framework:

 

for a suitable form   that satisfies:[4]

 

The form   can be expressed as  , being   a differential operator such as:[1]

 

and   is the stabilization parameter. A stabilized method with   is typically referred to multiscale stabilized method . In 1995, Thomas J.R. Hughes showed that a stabilized method of multiscale type can be viewed as a sub-grid scale model where the stabilization parameter is equal to

 

or, in terms of the Green's function as

 

which yields the following definition of  :

 [7]

Stabilization Parameter Properties edit

For the 1-d advection diffusion problem, with an appropriate choice of basis functions and  , VMS provides a projection in the approximation space.[9] Further, an adjoint-based expression for   can be derived,[10]

 

where   is the element wise stabilization parameter,   is the element wise residual and the adjoint   problem solves,

 

In fact, one can show that the   thus calculated allows one to compute the linear functional   exactly.[10]

VMS turbulence modeling for large-eddy simulations of incompressible flows edit

The idea of VMS turbulence modeling for Large Eddy Simulations(LES) of incompressible Navier–Stokes equations was introduced by Hughes et al. in 2000 and the main idea was to use - instead of classical filtered techniques - variational projections.[11][12]

Incompressible Navier–Stokes equations edit

Consider the incompressible Navier–Stokes equations for a Newtonian fluid of constant density   in a domain   with boundary  , being   and   portions of the boundary where respectively a Dirichlet and a Neumann boundary condition is applied ( ):[4]

 

being   the fluid velocity,   the fluid pressure,   a given forcing term,   the outward directed unit normal vector to  , and   the viscous stress tensor defined as:

 

Let   be the dynamic viscosity of the fluid,   the second order identity tensor and   the strain-rate tensor defined as:

 

The functions   and   are given Dirichlet and Neumann boundary data, while   is the initial condition.[4]

Global space time variational formulation edit

In order to find a variational formulation of the Navier–Stokes equations, consider the following infinite-dimensional spaces:[4]

 
 
 

Furthermore, let   and  . The weak form of the unsteady-incompressible Navier–Stokes equations reads:[4] given  ,

 
 

where   represents the   inner product and   the   inner product. Moreover, the bilinear forms  ,   and the trilinear form   are defined as follows:[4]

 

Finite element method for space discretization and VMS-LES modeling edit

In order to discretize in space the Navier–Stokes equations, consider the function space of finite element

 

of piecewise Lagrangian Polynomials of degree   over the domain   triangulated with a mesh   made of tetrahedrons of diameters  ,  . Following the approach shown above, let introduce a multiscale direct-sum decomposition of the space   which represents either   and  :[13]

 

being

 

the finite dimensional function space associated to the coarse scale, and

 

the infinite-dimensional fine scale function space, with

 ,
 

and

 .

An overlapping sum decomposition is then defined as:[12][13]

 

By using the decomposition above in the variational form of the Navier–Stokes equations, one gets a coarse and a fine scale equation; the fine scale terms appearing in the coarse scale equation are integrated by parts and the fine scale variables are modeled as:[12]

 

In the expressions above,   and   are the residuals of the momentum equation and continuity equation in strong forms defined as:

 

while the stabilization parameters are set equal to:[13]

 

where   is a constant depending on the polynomials's degree  ,   is a constant equal to the order of the backward differentiation formula (BDF) adopted as temporal integration scheme and   is the time step.[13] The semi-discrete variational multiscale multiscale formulation (VMS-LES) of the incompressible Navier–Stokes equations, reads:[13] given  ,

 

being

 

and

 

The forms   and   are defined as:[13]

 

From the expressions above, one can see that:[13]

  • the form   contains the standard terms of the Navier–Stokes equations in variational formulation;
  • the form   contain four terms:
  1. the first term is the classical SUPG stabilization term;
  2. the second term represents a stabilization term additional to the SUPG one;
  3. the third term is a stabilization term typical of the VMS modeling;
  4. the fourth term is peculiar of the LES modeling, describing the Reynolds cross-stress.

See also edit

References edit

  1. ^ a b c d e f g h i j k Hughes, T.J.R.; Scovazzi, G.; Franca, L.P. (2004). "Chapter 2: Multiscale and Stabilized Methods". In Stein, Erwin; de Borst, René; Hughes, Thomas J.R. (eds.). Encyclopedia of Computational Mechanics. John Wiley & Sons. pp. 5–59. ISBN 0-470-84699-2.
  2. ^ a b Codina, R.; Badia, S.; Baiges, J.; Principe, J. (2017). "Chapter 2: Variational Multiscale Methods in Computational Fluid Dynamics". In Stein, Erwin; de Borst, René; Hughes, Thomas J.R. (eds.). Encyclopedia of Computational Mechanics Second Edition. John Wiley & Sons. pp. 1–28. ISBN 9781119003793.
  3. ^ Masud, Arif (April 2004). "Preface". Computer Methods in Applied Mechanics and Engineering. 193 (15–16): iii–iv. doi:10.1016/j.cma.2004.01.003.
  4. ^ a b c d e f g h i j k l m n o p Quarteroni, Alfio (2017-10-10). Numerical models for differential problems (Third ed.). Springer. ISBN 978-3-319-49316-9.
  5. ^ Brooks, Alexander N.; Hughes, Thomas J.R. (September 1982). "Streamline upwind/Petrov-Galerkin formulations for convection dominated flows with particular emphasis on the incompressible Navier–Stokes equations". Computer Methods in Applied Mechanics and Engineering. 32 (1–3): 199–259. Bibcode:1982CMAME..32..199B. doi:10.1016/0045-7825(82)90071-8.
  6. ^ Masud, Arif; Calderer, Ramon (3 February 2009). "A variational multiscale stabilized formulation for the incompressible Navier–Stokes equations". Computational Mechanics. 44 (2): 145–160. Bibcode:2009CompM..44..145M. doi:10.1007/s00466-008-0362-3. S2CID 7036642.
  7. ^ a b c d e f g h Hughes, Thomas J.R. (November 1995). "Multiscale phenomena: Green's functions, the Dirichlet-to-Neumann formulation, subgrid scale models, bubbles and the origins of stabilized methods". Computer Methods in Applied Mechanics and Engineering. 127 (1–4): 387–401. Bibcode:1995CMAME.127..387H. doi:10.1016/0045-7825(95)00844-9.
  8. ^ Rasthofer, Ursula; Gravemeier, Volker (27 February 2017). "Recent Developments in Variational Multiscale Methods for Large-Eddy Simulation of Turbulent Flow". Archives of Computational Methods in Engineering. 25 (3): 647–690. doi:10.1007/s11831-017-9209-4. hdl:20.500.11850/129122. S2CID 29169067.
  9. ^ Hughes, T.J.; Sangalli, G. (2007). "Variational multiscale analysis: the fine-scale Green's function, projection, optimization, localization, and stabilized methods". SIAM Journal on Numerical Analysis. 45 (2). SIAM: 539–557.
  10. ^ a b Garg, V.V.; Stogner, R. (2019). "Local enhancement of functional evaluation and adjoint error estimation for variational multiscale formulations". Computer Methods in Applied Mechanics and Engineering. 354. Elsevier: 119–142.
  11. ^ Hughes, Thomas J.R.; Mazzei, Luca; Jansen, Kenneth E. (May 2000). "Large Eddy Simulation and the variational multiscale method". Computing and Visualization in Science. 3 (1–2): 47–59. doi:10.1007/s007910050051. S2CID 120207183.
  12. ^ a b c Bazilevs, Y.; Calo, V.M.; Cottrell, J.A.; Hughes, T.J.R.; Reali, A.; Scovazzi, G. (December 2007). "Variational multiscale residual-based turbulence modeling for large eddy simulation of incompressible flows". Computer Methods in Applied Mechanics and Engineering. 197 (1–4): 173–201. Bibcode:2007CMAME.197..173B. doi:10.1016/j.cma.2007.07.016.
  13. ^ a b c d e f g Forti, Davide; Dedè, Luca (August 2015). "Semi-implicit BDF time discretization of the Navier–Stokes equations with VMS-LES modeling in a High Performance Computing framework". Computers & Fluids. 117: 168–182. doi:10.1016/j.compfluid.2015.05.011.