INTERPOLATION SCHEMES AND FLUX LIMITERS IN FINITE VOLUME METHOD.

A brief explanation of Interpolation Schemes & Flux Limiters in Finite Volume Method;

Some of the interpolation schemes used are Linear, quadratic ,etc interpolation schemes.

These methods are used toget to more realistic results as the engineering problems basically uses the real world environment.Here in FVM the use of volume instead of  a point makes that close to real problems and governing equation needs to be solved for that.

Flux limiters basically used in high resolution schemes, to avoid the random and violent oscillations that can occur with high order spatial discretization schemes due to shocks, discontinuities or sharp changes in the solution domain which is not really much appreciated..The logic behind the construction of flux limiter schemes is to limit the spatial derivatives to to values which are close to the real world datas.

Basically Flux removes the diffusion problems arises there.

Now let us explore them in detail for better understanding.

A. Interpolation Schemes in Finite Volume Method:

The approximation of surface and volume integrals may require values of the variable at locations other than the computational nodes of the CV. Values at these locations are obtained using interpolation formulae. Some of the possibilities are:  

1. Upwind Interpolation Scheme: (UDS)

The name upwind refers to the assumption that we are going to use the node that is upwind or upstream of the given node. it is generally used for convection dominated problems (i.e. High Peclet Number Pe>1 flows). For the east face of a quantity it is given as:

`f_e = {(f_P if (v.n)_e > 0) , (f_E if (v.n)_e < 0):}`

The interface values are given by:

for `v > 0`, we have : `u i − 1 2 ≈ u i − 1 ; u i + 1 2 ≈ u i`

`I c ≈ v (u i − u i − 1)/ Δ x`(Backward Differencing).

for `v < 0`, we have : `u i − 1 2 ≈ u i ; u i + 1 2 ≈ u i + 1`

`I c ≈ v (u i + 1 − u i ) / Δx` (Forward Differencing).

The resulting taylor series expansion is:

vu_(i+1/2) = vu_i - v(Deltax)/2*((delu)/(delx))_(i+1/2)
where the second term is the leading truncation error which resembles a diffusive flux (Numerical Diffusion)

Upwind Differencing Scheme is the only approximation that satisfies boundedness criterion unconditionally. It is first order accurate in space.

2. Central Differencing Approximation: (CDS)

Approximate value of the variable at the Control Volume face centre by linear interpolation of the values at two nearest computational nodes:

`f_e = f_Elambda_e + f_P(1-lambda_e) where lambda_e = (x_e - x_P)/(x_E - x_P)`

The linear interpolation is equivalent to the use of central difference formula of the first order derivative, and hence, this scheme is also termed as Central Difference Scheme (CDS).

Interpolation Polynomial:

p_1(x) = u_L*(x_R - x)/(x_R - x_L) + u_R*(x - x_L)/(x_R - x_L)

Average Interface Values are given by:

u_(i-1/2) ~~ (u_(i-1)+u_i)/2 and u_(i+1/2) ~~ (u_i + u_(i+1))/2

I_c ~~ v*(u_(i+1) - u_(i-1))/(2Deltax) Central Differencing

Taylor Series Approximation:

u_(i+1) = u_(i+1/2) + (Deltax)/2*((delu)/(delx))_(i+1/2) + (Deltax)^2/8*((del^2u)/(delx^2))_(i+1/2) + ....

u_i = u_(i+1/2) - (Deltax)/2*((delu)/(delx))_(i+1/2) + (Deltax)^2/8*((del^u)/(delx^2))_(i+1/2) + ....

 From the above expressions we have:

u_(i+1/2) = (u_i + u_(i+1))/2 - (Deltax)^2/8*((del^2u)/(delx^2))_(i+1/2)+...

This approximation is second order accurate. It is the simplest and most widely used interpolation forevaluation of gradients required for evaluation of diffusive fluxes.

3. Quadratic Upwind Differencing Scheme: (QUICK)

QUICK Stands for Quadratic Upwind Interpolation scheme for Convective Kinematics. It is a third order flux approximation. Although it has second order overall accuracy. It approximates the valuess of the variable at CV face centre by Quadratic Interpolation of the values at three nearest computational nodes (one Downstream node D and two upstream nodes U and UU)

f_e = f_E + g_1(f_D- f_U) + g_2(f_(UU) - f_U)

where g1 and g2 are given by,

`g_1 = ((x_e - x_U)(x_e - x_(UU)))/((x_(UU) - x_U)(x_(UU) - x_D))`

`g_2 = ((x_e - x_U)(x_e - x_D))/((x_(UU) - x_U)(x_(UU) - x_D))`

Other Interpolation schemes:

1. Hybrid Interpolation Schemes: (CDS and UDS)
2. Total Variation Diminishing (TVD) Scheme

1. Apart from QUICK all other interpolation schemes would produce inconsistent fluxes when quadratic interpolation formula is used. The results obtained from the schemes do not satisfy the overall equation. An example for the same is given below:

Piecewise Linear Interpolation functions must be used for evaluating the fluxes consistently as shown below:

 B. FLUX LIMITERS:

For convective fluid flow it has been observed that low - order schemes are usually stable but quite dissipative in nature around points of discontinuity or shocks while the higher order schemes are unstable in nature and show oscillations in the vicinity of discontinuity. Highly accurate and Oscillation free schemes are known as High Resolution Schemes.

Flux limiter functions are used to tune high order and low order schemes in such a way that the resulting scheme gives a high order accuracy in the smooth region of the flow and maintains first order accuracy in the vicinity of shocks and discontinuities. For such a scheme TVD (Total Variation Diminishing) criteria is maintained. Their basic forms are shown below:

`F(u_(i+1/2)) = f^(low)(i+1/2) - phi(r_i)(f^(low)(i+1/2) - f^(high)(i+1/2))`

`F(u_(i-1/2)) = f^(low)(i-1/2) -phi(r_(i-1))(f^(low)(i-1/2) - f^(high)(i-1/2))`

where f'low' is low precision flux (1st order accurate)

where f'high' is high precision flux (higher order accurate)

phi(r) =  Flux limiter function where r = (u_i-u_(i-1))/(u_(i+1) - u_i)

Common Forms of Flux Limiter Functions: CHARM, HCUS, HQUICK, Koren, minmod etc.