(Difference between revisions)
 Revision as of 03:02, 3 October 2005 (view source)Zxaar (Talk | contribs)← Older edit Revision as of 05:16, 3 October 2005 (view source)Zxaar (Talk | contribs) Newer edit → Line 11: Line 11: == Value at Face == == Value at Face == + === Cell Based === There are many ways of estimating value of $\phi$ at face.
There are many ways of estimating value of $\phi$ at face.
# Weighted interpolation: $\phi _f = w\phi _1 + \left( {1 - w} \right)\phi _0$ # Weighted interpolation: $\phi _f = w\phi _1 + \left( {1 - w} \right)\phi _0$ #Arithmatic Average: $\phi _f = 0.5 \left( \phi _1 + \phi _0 \right)$ #Arithmatic Average: $\phi _f = 0.5 \left( \phi _1 + \phi _0 \right)$ + + ===Node Based === + == Note == == Note == The above mentioned gradients are sometimes called '''unlimited gradients''' since the face value obtained from them can exceed the bounding cell values. For this reason, for implementing higher order schemes, it becomes important to restrict them, so as not to introduce over and undershoot of variables. The above mentioned gradients are sometimes called '''unlimited gradients''' since the face value obtained from them can exceed the bounding cell values. For this reason, for implementing higher order schemes, it becomes important to restrict them, so as not to introduce over and undershoot of variables.

## Contents

From Green-Gauss theorem:

$\int\limits_V {\nabla \phi dV = } \oint\limits_A {\phi dA}$

Written in discrete form:

$\left( {\nabla \phi } \right)_0 = {1 \over V}\sum\limits_f {\bar \phi _f A_f }$

## Value at Face

### Cell Based

There are many ways of estimating value of $\phi$ at face.

1. Weighted interpolation: $\phi _f = w\phi _1 + \left( {1 - w} \right)\phi _0$
2. Arithmatic Average: $\phi _f = 0.5 \left( \phi _1 + \phi _0 \right)$

## Note

The above mentioned gradients are sometimes called unlimited gradients since the face value obtained from them can exceed the bounding cell values. For this reason, for implementing higher order schemes, it becomes important to restrict them, so as not to introduce over and undershoot of variables.