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Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Multiscale Basis Functions for Iterative Domain DecompositionProcedures

A. Francisco1, V. Ginting2, F. Pereira3 and J. Rigelo2

1Department Mechanical EngineeringFederal Fluminense University, Volta Redonda, RJ 27255-125, Brazil

afrancisco@metal.eeimvr.u!.br

2Department of MathematicsUniversity of Wyoming, Laramie, WY 82071-3036, USA

{vginting,jrigelo}@uwyo.edu

3Department of Mathematics and School of Energy ResourcesUniversity of Wyoming, Laramie, WY 82071-3036, USA

lpereira@uwyo.edu

Support: DOE: DE-FE0004832/DE-SC0004982; NSF: DMS-1016283;Center for Fundamentals of Subsurface Flow(UW).

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Outline:

1 Motivation: Domain Decomposition Method

2 The Multiscale Mixed Method (MuMM)

3 Numerical Results

4 Conclusions and Future Work

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Motivation

We are concerned withthe development ofnumerical proceduresfor the fast andaccurate approximationof subsurface flows thatcan take advantage ofheterogeneousprocessing units.

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Motivation

Incorporate fine scale information into a coarse scalediscretization, without solving it directly.

Coarse Domain Decomposition

Our iterative procedure does not use MPI in each iteration.

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Model Problem

Our model problem is a second order linear elliptic equationwritten as a first order system

!.u = f (x), where u = "k(x)!p in !, (1)

p = pb on "D , u.! = ub on "N . (2)

Here ! is a bounded domain with a Lipschitz boundary"! = "D # "N , "D $ "N = %.

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Domain Decomposition

The domain ! is divided into a non-overlapping partition{!j}:

! =!M

j=1!j ; !j $ !k = %, j &= k .

Motivation: Non-overlapping iterative DDM based on theRobin boundary conditon.

J. Douglas, Jr., P. J. Paes Leme, J. E. Roberts, and J. Wang,A parallel iterative procedure applicable to the approximatesolution of second order partial di!erential equations by mixedfinite element methods, Numer. Math., 65 (1993) 95–108.

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Domain Decomposition (cont.)

Mixed finite element space: hybridized Raviart-Thomas.

Procedure: subdomain = element.

Degrees of freedom (for each !j) :

p, u! and #! , $ = L,R ,B ,T .h

B

T

L R

Then for a single element, the discrete form of the Poisson’sequation is given by

uL + uR + uB + uT = fh, (3)

u! " 2

hk(p " #!) = 0. (4)

(5)

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Domain Decomposition (cont.)

Robin Interface Condition:

#! = %!(u! + u!!) + #!! , where $ = L,R ,B ,T . (6)

!j !k $ - $!

L

T

R

T

B

R

L

B

"jk

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Domain Decomposition (cont.)

Douglas, Jr. et al. parallel iterative scheme:

1 Set an initial guess: {p0, u0! , #0!}.2 For all red elements, update {p, u! , #!}, using [3, 4, 5].

3 For all black elements, compute {p, u! , #!}, by solving[3, 4, 5], using the updated values from the red elements.

4 Check for convergence.

new

old

old

oldold

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Domain Decomposition (cont.)

o

o o

o

n

o

o o

o

n

subdomain: one element larger subdomain

Convergence is established.

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

The Multiscale Basis Functions Formulation

Consider a subdomain !j . Let &ji = (ui! , #i! , p

i )j , i = 1, ..., 4N,be the basis functions associated with this subdomain.

"%LuL+#L = 1 '0

0 0 0 0

0

0

0

0

0000

0

0

&j1

B. Ganis and I. Yotov, Implementation of a mortar mixed finiteelement method using a multiscale flux basis, Computer Methods inApplied Mechanics and Engineering, 198 (2009) 3989-3998.

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

The Multiscale Basis Functions Formulation

Given the Robin boundary values Aji , the solution for thePoisson equation is given by

S"j =4N"

i=1

Aji&ji j3

N

N

A

Aj1

j2A

where, for i = 1, ..., 4N,&ji = (ui! , #

i! , p

i )j are the canonical basis functions.

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

The Multiscale Basis Functions Formulation

Advantage :

Avoid the direct solution of the local problems.

Problem :

We have to compute 4N basis functions for each subdomain!

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

MuMM: A Modified iteration

Introduce an intermediate scale H, h ( H ( H.

Based on an average Robin condition:

Aji = "%LuTL +uBL

2 +"TL +"BL

2h

TB

Aji H

H

!j !k

Goal: To reduce the number of basis functions.

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

MuMM: A Modified iteration

The solution is given by, for example: S"j =

4N/2"

i=1

Aji &ji .

"%LuL+#L = 1 '

0

0 0

0 0

0

0

&j1

A

N

Aj2

j1

2D: Douglas, Jr. et al. iteration; 3D: CG preconditioned withthe AMG.

Solution in the fine grid: post-processing.

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

MuMM: A Modified iteration

Remarks :

Flux conservation is maintained in the H scale.

The balance between numerical accuracy and numericale#ciency is determined by the choice of

span{&ji} ) span{&ji}.

Extreme cases:

H = h: Douglas, Jr. et al. iteration.H = H: 4 basis functions/subdomain.

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Example 1: kmax/kmin = 176

Example: 2D problem with a fine grid of 220* 60,coarse grid of 11* 3 (subdomains of 20* 20).

Permeability model: SPE10 model, where k(x) = exp(' ((x)).

20 40 60 80 100 120 140 160 180 200 220

5

10

15

20

25

30

35

40

45

50

55

60

5

10

15

20

25

The physical transport of fluids is given by solving:

)#c#t + u.!c = 0, with I .C .+ B .C . given.

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

H = H

H = H/4

H = H/2

fine grid

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Tracer cut curves

The fraction of the tracer in the produced fluid is given by

F(t) =

#!"out

c u.n dS#!"out

u.n dS.

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Relative Errors :

Relative error = !uMuMM"ufine!maxi,j!ufine! .

Figure :From top to bottom:

4, 8 and 16 basis functions.

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Example 2: kmax/kmin = 1408.

Example: 2D problem with a fine grid of 220* 60,coarse grid of 11* 3 (subdomains of 20* 20).

Permeability model: SPE10 model. We consider 16 basisfunctions.

MuMM fine grid

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Example 2: Tracer cut curve and permeability field

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Conclusions

Properties :

uL + uR + uB + uT = fh holds in the fine grid.

Sources and sinks are naturally incorporated in the procedure.

All local problems are positive definite.

Global information is not needed.

Straightforward implementation in 2 and 3D.

Fits well in CPU-GPU clusters.

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Future Work

3D implementation on GPUs.

Extension to multiphase/Compositional flows.

Adaptivity (basis functions not altered).

Enrichment of basis functions.

Thank you!!

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

References

J. Douglas, Jr., P. J. Paes Leme, J. E. Roberts, and J. Wang,A parallel iterative procedure applicable to the approximatesolution of second order partial di!erential equations by mixedfinite element methods, Numer. Math., 65 (1993) 95–108.

B. Ganis and I. Yotov, Implementation of a mortar mixed finiteelement method using a multiscale flux basis, ComputerMethods in Applied Mechanics and Engineering, 198 (2009)3989-3998.

Vegard Kippe . Jorg E. Aarnes. Knut-Andreas Lie, Acomparison of multiscale methods for elliptic problems inporous media flow, Comput Geosci, (2008) 12:377-398.

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Variational Formulation

The pressure and velocity spaces for the global problem [1, 2] are:

W = L2(!) and Vr = {v + H(div ;!)| v.! = r on "N},

where H(div ;!) = {v + (L2(!))2| div v + L2(!)}.

The global weak form is giving by finding {p,u} + W * Vr suchthat

(K"1u, u)" " (p, div u)" = 0, u + V0, (7)

(div u, p)" = (f , p)", p + W . (8)

Motivation: Domain Decomposition MethodThe Multiscale Mixed Method (MuMM)

Numerical ResultsConclusions and Future Work

Variational Formulation

Similarly, define the spaces for each subdomain !j by

Wj = {w |!j | w + W (!)},

Vr ,j = {v + H(div ;!j) | v.!j = r on "!j $ "N}.

The weak formulation are given by seeking {pj ,uj} + Wj * Vr ,j

such that

(div u, p)"j = (f , p)"j , p + Wj ,

(K"1u, u)"j " (p, div u)"j +"

j #=k

< p, u.!j >#jk=

"M"

j

< pb, u.!j >#"j$#D , u + V0,j ,

where "jk = "kj = "!j $ "!k .