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October 14, 2001, 05:05 
error estimation for grid independence

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I want to know if fluent has any capability to measure the error using the xy plot which contains two curves say pressure distribution curves. If the answer is no,can anybody tell me if there are any tools/softwares available to do this and also if it is available on the net. thanks and regards, santosh 

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October 16, 2001, 06:11 
Re: error estimation for grid independence

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No, to my knowledge, Fluent does not have such an error analysis option. What you can do is write (in the "Plot/XY plot" panel, check "write to file" box) the two curves, i.e. get 2 ascii files containing 2 columns each (e.g. distance vs pressure_gradient) and read them in a spreadsheet (e.g. Excel). Then you can do further postprocessing in order to calculate the error between the two runs.


October 16, 2001, 07:25 
Re: error estimation for grid independence

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thank u for the reply,dimitris.
one more thing,may sound silly,since the grid resolution is different for the two xy files the vector size will be different.How can I calculate the error between two different size vectors? thanks and regards, santosh 

October 17, 2001, 16:06 
Re: error estimation for grid independence

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If the geometry is the same in both meshes you will have two curves (in XY plot) starting and ending at the same x values but with different number and distribution of points. Now there are two solutions:
1. You calculate a fit for both of them (e.g. of a polynomial form in Excel or in another data handling program) and from their analytical form calculate the error at certain x values of your choice. 2. For certain x values of your choice interpolate (say a linearly) from the two neighbouring values in order to find the y value at that point for both results. Then find the relative error. for example you may have these 2 data sets: set 1 set 2 x y x y 1.1 3.4 0.98 3.6 2.1 4.5 1.3 4.1 3.1 4.4 2.5 4.6 so if you want to find the error in x=2 you interpolate between points 1 & 2 for dataset 1 and between points 2 & 3 of dataset 2. You find the two interpolated values and then find the error in x=2. 

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