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Praveen. C, CTFD Division, NAL, Bangalore First Prev Next Last Go Back Full Screen Close Quit
Finite Volume Method
Praveen. C
Computational and Theoretical Fluid Dynamics Division
National Aerospace Laboratories
Bangalore 560 017
email: [email protected]
Workshop on Advances in Computational Fluid Flow and Heat TransferAnnamalai University
October 17-18, 2005
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Topics to be covered
1. Conservation Laws
2. Finite volume method
3. Types of finite volumes
4. Flux functions
5. Spatial discretization schemes
6. Higher order schemes
7. Boundary conditions
8. Accuracy and stability
9. Computational issues
10. References
Hyperbolic equations, Compressible flow, unstructured grid schemes
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Conservation Laws and FVM
• Basic laws of physics are conservation laws - mass, momentum, energy
• Differential form∂U
∂t+
∂f
∂x+
∂g
∂y+
∂h
∂z= 0
U - conserved variables
f, g, h - flux vector
• Compressible flows - shocks and other discontinuities
• Classical solution may not exist
• Integral form (using divergence theorem)
∂
∂t
∫Ω
Udxdydz +
∮∂Ω
(fnx + gny + hnz)dS = 0
Rate of change of U in Ω = - (Net flux across the boundary of Ω)
⇓Starting point for finite volume method
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• Discontinuities are a consequence of conservation laws
• Rankine-Hugoniot jump conditions [9, 10]
(fnx + gny + hnz)R − (fnx + gny + hnz)L = s(UR − UL)
n
U
U
L
RShock
• Solution satisfying integral form - weak solution
• Definition (Weak solution)
1. Satisfies the differential form in smooth regions
2. Satisfies jump condition across discontinuities
• Hyperbolic conservation laws - non-uniqueness
• Limit of a dissipative model: Navier-Stokes → Euler
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• Entropy condition - second law of thermodynamics
• Entropy satisfying weak solution - unique (Kruzkov)
• Conservative scheme (FVM) - correct shock location (Warnecke)
• Useful for solving equations with discontinuous coefficients
• FVM can be applied on arbitrary grids - structured and unstructured
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• Entropy condition - second law of thermodynamics
• Entropy satisfying weak solution - unique (Kruzkov)
• Conservative scheme (FVM) - correct shock location (Warnecke)
• Useful for solving equations with discontinuous coefficients
• FVM can be applied on arbitrary grids - structured and unstructured
Praveen. C, CTFD Division, NAL, Bangalore First Prev Next Last Go Back Full Screen Close Quit
• Entropy condition - second law of thermodynamics
• Entropy satisfying weak solution - unique (Kruzkov)
• Conservative scheme (FVM) - correct shock location (Warnecke)
• Useful for solving equations with discontinuous coefficients
• FVM can be applied on arbitrary grids - structured and unstructured
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FVM in 1-D
• Divide computational domain [a, b] into N cells
a = x1/2 < x3/2 < . . . < xN+1/2 = b
Ci = [xi−1/2, xi+1/2]
Ci
hi−3/2 i−1/2 i+1/2 i+3/2i
• Conservation law for cell Ci
∂
∂t
∫ xi+1/2
xi−1/2
Udx + f (xi+1/2, t)− f (xi−1/2, t) = 0
• Cell average value
Ui(t) =1
hi
∫ xi+1/2
xi−1/2
U(x, t)dx
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• Conservation law for cell Ci
hidUi
dt+ f (xi+1/2, t)− f (xi−1/2, t) = 0
U
U
i+1/2
i
i+1
• Riemann problem at each interface
• Numerical flux function (Godunov approach)
Fi+1/2(t) = F (Ui(t), Ui+1(t))
• Semi-discrete update equation (ODE system)
dUi
dt= − 1
hi[Fi+1/2(t)− Fi−1/2(t)]
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• Method of lines approach
– Discretize in space
– Integrate the ODE system in time
• Explicit Euler scheme [ Uni ≈ U(xi, t
n) ]
Ui(tn+1)− Ui(t
n)
∆t= − 1
hi[Fi+1/2(t
n)− Fi−1/2(tn)]
⇓
Un+1i = Un
i −∆t
hi[F n
i+1/2 − F ni−1/2]
• Conservation: Telescopic collapse of fluxes∑i
hidUi
dt= −
∑i
[Fi+1/2(t)− Fi−1/2(t)]
= −[f (b, t)− f (a, t)]
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Numerical Flux Function
• Simple averaging
Fi+1/2 = f ((Ui + Ui+1)/2) or Fi+1/2 = (fi + fi+1)/2
• Equivalent to central differencing
dUi
dt+
1
hi(fi+1 − fi−1) = 0 (unstable)
• Two approaches
1. Central differencing with artificial dissipation [13]
Fi+1/2 =1
2(fi + fi+1)− di+1/2
2. Upwind flux formula [9, 10, 13, 20, 22]
FVS: Fi+1/2 = f+(Ui) + f−(Ui+1)
FDS: Fi+1/2 =1
2(fi + fi+1)−
1
2[(∆f )−i+1/2 − (∆f )+i+1/2]
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• Example: convection-diffusion equation
∂U
∂t+
∂f
∂x= 0, f = aU − ν
∂U
∂x
Fi+1/2 = aUi+1/2 − ν∂U
∂x
∣∣∣∣i+1/2
• Upwind definition of interfacial state
Ui+1/2 =
Ui if a ≥ 0
Ui+1 if a < 0
• Central-difference for viscous term
∂U
∂x
∣∣∣∣i+1/2
=Ui+1 − Ui
xi+1 − xi
• Upwind numerical flux
Fi+1/2 =1
2(aUi + aUi+1)−
|a|2
(Ui+1 − Ui)− νUi+1 − Ui
xi+1 − xi
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Significance of conservative scheme
• Inviscid Burgers equation
∂U
∂t+
∂
∂x
(U 2
2
)= 0, f (U) =
U 2
2
• Rankine-Hugoniot condition
fR − fL = s(UR − UL) =⇒ s =1
2(UL + UR)
• Non-conservative form∂U
∂t+ U
∂U
∂x= 0
• Upwind scheme (assume U ≥ 0)
Un+1i − Un
i
∆t+ Un
i
Uni − Un
i−1
h= 0
or
Un+1i = Un
i −∆t
hUn
i (Uni − Un
i−1)
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• Initial condition
U(x, 0) =
1 if x < 0
0 if x > 0
• Numerical solution
Uni = U o
i =⇒ stationary shock
• Exact solution (shock speed = 1/2)
U(x, t) =
1 if x < t/2
0 if x > t/2
• Conservation form from physical considerations
U∂U
∂t+ U
∂
∂x
(U 2
2
)= 0
or∂
∂t
(U 2
2
)+
∂
∂x
(U 3
3
)= 0
• Jump conditions not identical: s = 23
(U2
L+ULUR+U2R
UL+UR
)
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Higher order scheme in 1-D
• Constant-in-cell representation
i−3/2 i−1/2 i+1/2 i+3/2
Ci−1
Ci+1
Ci
• First order accurate
|Ui − U(xi)| = O(h)
h = maxi
hi
• Reconstruction - evolution - projection
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Higher order scheme in 1-D
• Reconstruct the variation within a cell
i−3/2 i−1/2 i+1/2 i+3/2
Ci−1
Ci+1
Ci
Left state
Rightstate
• Linear reconstruction
U(x) = Ui + si(x− xi), x ∈ [xi−1/2, xi+1/2]
• Biased interpolant
ULi+1/2 = Ui + si(xi+1/2 − xi), UR
i+1/2 = Ui+1 + si+1(xi+1/2 − xi+1),
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• Flux for higher order scheme
Fi+1/2 = F (Ui, Ui+1)
• Reconstruction variables
1. Conserved variables - conservative
2. Characteristic variables - better upwinding but costly
3. Primitive variables (ρ, u, p) - computationally cheap
• Unsteady flows - reconstruction must preserve conservation
1
hi
∫Ci
U(x)dx = Ui
• Gradients for reconstruction: backward, forward, central difference
si,b =Ui − Ui−1
xi − xi−1, si,f =
Ui+1 − Ui
xi+1 − xi, si,c =
Ui+1 − Ui−1
xi+1 − xi−1
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• Flux for higher order scheme
Fi+1/2 = F (ULi+1/2, U
Ri+1/2)
• Reconstruction variables
1. Conserved variables - conservative
2. Characteristic variables - better upwinding but costly
3. Primitive variables (ρ, u, p) - computationally cheap
• Unsteady flows - reconstruction must preserve conservation
1
hi
∫Ci
U(x)dx = Ui
• Gradients for reconstruction: backward, forward, central difference
si,b =Ui − Ui−1
xi − xi−1, si,f =
Ui+1 − Ui
xi+1 − xi, si,c =
Ui+1 − Ui−1
xi+1 − xi−1
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• Solution with discontinuity
1
1/2
i−1 i i+1
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• Central-difference: Non-monotone reconstruction
1
1/2
i−1 i i+1
• Limited gradients [9, 12]
si = Limiter(si,b, si,f , si,c)
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FVM in 2-D
• Divide computational domain into disjoint polygonal cells, Ω = ∪iCi
• Integral form for cell Ci
∂
∂t
∫Ci
Udxdy +
∮∂Ci
(fnx + gny)dS = 0
• Cell average value
Ui(t) =1
|Ci|
∫Ci
U(x, y, t)dxdy, |Ci| = area of Ci
• Cell connectivity: N(i) = j : Cj and Ci share a common face
Ci Ci
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∮∂Ci
(fnx + gny)dS =∑
j∈N(i)
∫Ci∩Cj
(fnx + gny)dS
• Approximate flux integral by quadrature
C
n
C
i
j
ij
∫Ci∩Cj
(fnx + gny)dS ≈ Fij∆Sij
• Semi-discrete update equation
|Ci|dUi
dt= −
∑j∈N(i)
Fij∆Sij
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• Numerical flux function
Fij = F (Ui, Uj, nij)
• Properties of flux function
1. Consistency
F (U,U, n) = f (U)nx + g(U)ny
2. Conservation
F (V, U,−n) = −F (U, V, n)
3. Continuity
‖F (UL, UR, n)− F (U,U, n)‖ ≤ C max (‖UL − U‖, ‖UR − U‖)
• Flux functions [10, 13, 20, 22]
– FVS: Steger-Warming, Van Leer, KFVS, AUSM
– FDS: Godunov, Roe, Engquist-Osher
• Integrate in time using a Runge-Kutta scheme [5, 12]
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Grids and Finite Volumes
• Elements in 2-D
• Elements in 3-D
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• Boundary layers - prism and hexahedra
• Cell-centered and vertex-centered scheme [5, 18, 21]
• Median (dual) cell
– join centroid to mid-point of sides
– well-defined for any triangulation
Centroid
Mid−point
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• Voronoi cell
– join circum-center to mid-point of sides
– smooth area variation
– not defined for obtuse triangles
• Containment circle tessalation
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Median and containment-circle tessalation
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• Stretched triangles - median dual and containment-circle
• Containment-circle finite volume
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• Turbulent flow over RAE2822 airfoil: vertex-centered scheme
Mach = 0.729, α = 2.31 deg, Re = 6.5 million
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Higher order scheme in 2-D
• Bi-linear reconstruction in cell Ci
U(x, y) = Ui + ai(x− xi) + bi(y − yi), (x, y) ∈ Ci
U
U L
R
C
C
i
j
• Define left/right states
UL = Ui + ai(xij − xi) + bi(yij − yi)
UR = Uj + aj(xij − xj) + bj(yij − yj)
• Flux for higher order scheme
Fij = F (UL, UR, nij)
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• Gradient estimation using
1. Green-Gauss theorem
2. Least squares fitting
• Green-Gauss theorem ∫Ci
∇Udxdy =
∮∂Ci
UndS
• Approximate surface integral by quadrature
∇Ui ≈1
|Ci|∑face
∫face
UndS
• Face value
Uface =1
2(UL + UR)
• Non-uniform cells
Uface = αUL + (1− α)UR, α ∈ (0, 1)
• Accuracy can degrade for non-uniform grids [4, 6, 8, 14]
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• Least-squares reconstruction [3, 5]
1
2
3
4
0
Uo + ao(xj − xo) + bo(yj − yo) = Uj, j = 1, 2, 3, 4
• Over-determined system of equations - solve by least-squares fit
min∑
j
[Uj − Uo − ao(xj − xo)− bo(yj − yo)]2, wrt ao, bo
ao =∑
j
αj(Uj − Uo), bo =∑
j
βj(Uj − Uo)
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• Limited reconstruction
– Cell-centered: Min-max [3, 5], Venkatakrishnan [5], ENO-type [1, 14]
– Vertex-centered: edge-based limiter [17]
• Min-max limiter
Umin ≤ Uo + ao(xj − xo) + bo(yj − yo) ≤ Umax, j = 1, 2, 3, 4
(ao, bo)←− (φao, φbo), φ ∈ [0, 1]
– Very dissipative - smeared shocks
– Performance degrades on coarse grids
– Stalled convergence - limit cycle
– Useful for flows with large discontinuities
• Venkatakrishnan limiter
– Smooth modification of min-max limiter
– Better control - depends on cell size
– Better convergence properties
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• Vertex-centered cell: Edge-based limiter
L R
i i+1i−1
i+2
UL = Ui +1
2Limiter
[(Ui+1 − Ui),
|PiPi+1||PiPi−1|
(Ui − Ui−1)
]• Using vertex-gradients
UL = Ui +1
2Limiter
[(Ui+1 − Ui), (~Pi+1 − ~Pi) · ∇Ui
]• Van-albada limiter
Limiter(a, b) =(a2 + ε)b + (b2 + ε)a
a2 + b2 + 2ε, ε 1
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Higher order flux quadrature
U L1
U R1
U L2
U R2
C
C
i
j
• Quadratic reconstruction in cell Ci
U(x, y) = Ui + ai(x− xi) + bi(y − yi)
+ ci(x− xi)2 + di(x− xi)(y − yi) + ei(y − yi)
2
• 2-point Gauss quadrature for flux
Fij = ω1F (UL1 , UR
1 , nij) + ω2F (UL2 , UR
2 , nij)
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Discretization of viscous flux
• Viscous terms
∇ · µ∇u
• Finite volume discretization∫Ci
(∇ · µ∇u)dV =
∮∂Ci
(µ∇u · n)dS
• Simple averaging
∇uij =1
2(∇ui +∇uj)
– Odd-even decoupling on quadrilateral/hexahedral cells
– Large stencil size
• 1-D case: ut = uxx
un+1i = un
i +∆t
2h(un
i−2 − 2uni + un
i+2)
• Correction for decoupling problem [5]
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• Green-Gauss theorem for auxiliary volume
Face−centered volume
i
j
• Least-squares gradients
– Quadratic reconstruction: gradients and hessian [3]
– Face-centered least-squares
• Vertex-centered scheme
– Galerkin approximation on triangles/tetrahedra
– Nearest neighbour stencil
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Turbulence models
• Reynolds-average Navier-Stokes equations - need turbulence models
• Differential equation based models: k − ε, k − ω, Spalart-Allmaras
• Turbulence quantities must remain positive
• Discretize using first order upwind finite volume method
Example: Spalart-Allmaras model∫Ci
∇ · (νu)dV ≈∑
j∈N(i)
[(uij · nij)+νi + (uij · nij)
−νj]∆Sij
(·)± =(·)± |(·)|
2, uij =
1
2(ui + uj)
• Coupled or de-coupled approach
• Stiffness problem - positivity preserving implicit methods
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Boundary conditions
• Cell-centered approach
1. Ghost cell
2. Flux boundary condition
w
g
Boundary
Ghost cell
• Inviscid flow (slip flow - zero normal velocity)
ρg = ρw, pg = pw, ug = uw, vg = −vw
• Viscous flow (noslip flow - zero velocity)
ρg = ρw, pg = pw, ug = −uw, vg = −vw
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• Boundary flux depends on pressure only
F (Uw, Ug, n) = function of p only
• Flux boundary condition
(~F · n)wall = p[0, nx, ny, 0]>
1. Extrapolate pressure from interior cells
2. Solve normal momentum equation [2]
• Vertex-centered approach - flux boundary condition
• Boundary cell in vertex-centered scheme
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Accuracy and Stability
• FVM with linear reconstruction - second order accurate on uniform and
smooth grids
• On non-uniform grids =⇒ formally first order accurate
• Local truncation error not a good indicator of global error [22]
• r’th order reconstruction and ng Gaussian points for flux quadrature - accu-
racy is min(r, 2ng) [19]
• Semi-discrete scheme
dUi
dt=
∑j∈N(i)
aij(Uj − Ui), aij ≥ 0
• Local Extremum Diminishing (LED) property - maxima do not increase and
minima do not decrease (Jameson)
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• If Ui is a local maximum =⇒ Uj − Ui ≤ 0
dUi
dt=
∑j∈N(i)
aij(Uj − Ui) ≤ 0 =⇒ Ui does not increase
• Fully discrete scheme
Un+1i = (1−∆t
∑j
aij)Uni +
∑j
aijUnj , ∆t ≤ 1∑
j aij
• Convex linear combination
minj∈N(i)
Unj ≤ Un+1
i ≤ maxj∈N(i)
Unj
• Prevents oscillations (Gibbs phenomenon) near discontinuities
• Stable in maximum norm
minj
Unj ≤ Un+1
i ≤ maxj
Unj
• Elliptic equations - discrete maximum principle
minj∈∂Ω
Uj ≤ Ui ≤ maxj∈∂Ω
Uj
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Data structures and Programming
• Data structure for FVM
– Coordinates of vertices
– Indices of vertices forming each cell
• Cell-based updating
------------------------------------------------------
for cell = 1 to Ncell
FluxDiv = 0
for face = 1 to Nface(cell)
cellNeighbour = CellNeighbour(cell, face)
flux = NumFlux(cell, cellNeighbour)
FluxDiv += flux
end
Unew(cell) = Uold(cell) - dt*FluxDiv
end------------------------------------------------------
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• Face-based updating
------------------------------------------------------
FluxDiv(:) = 0
for face = 1 to Nface
LeftCell = FaceCell(face,1)
RightCell = FaceCell(face,2)
flux = NumFlux(LeftCell, RightCell)
FluxDiv(LeftCell) += flux
FluxDiv(RightCell) -= flux
end
Unew(:) = Uold(:) - dt*FluxDiv(:)
------------------------------------------------------
• Flux computations reduced by half - speed-up of two
• Other geometric quantities - cell centroids, face areas, face normals, face
centroids
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References
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[13] Hirsch Ch., Numerical Computation of Internal and External Flows, Vol. 2,
Wiley, 1989.
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