Accessing the components of the resolved Reynolds stress tensor in a LES
In a LES in Fluent, I can evaluate some of the Reynolds stresses via the "data sampling for time statistics" option.
My problem is that only the shear stresses (uv, uw, vw) are created. I am missing the normal components (uu, vv, ww) of the Reynolds stress tensor. Am I missing something or do I really have to use a UDF to evaluate the normal components? 
Any ideas?

You should really look better... does RMS mean anything to you?

Yes it does.
But I thought that a root mean square for a velocity component with a nonzero mean value is not the same as the reynols stress, where only the fluctuating part is considered. In fact the RMS of a constant velocity in laminar flow is the velocity itself, so it cannot be the a reynolds stress. 
Your doubt is understandable, indeed the Fluent manual exactly report this definition for those quantities. However, if you compare them with your own statistics (in the most simple case by sampling the velocity in a point) you will see that, as a matter of fact, they are RMS of fluctuations. Also, if you look at the RMS of your streamwise (main) velocity component, it clearly is in term of fluctuations (with respect to the mean). Consider that this is also true for the non RMS statistics.

Since I didnt find a definition in the fluent manual, i assumed the "usual" definitions for the statistics.
Thanks for the clarification. 
Sorry to bother you again, but where exactly did you find the information?
I searched again through the fluent manual but couldnt find any hint about the calculation of RMS statistics. 
It depends from the version of the manual. For the latest Fluent versions, check the users guide, field function definition, alphabetical listing or using the solver, performing timedependent calculations, postprocessing...
However, it just says that they are root mean squares of the variables, no fluctuation (with respect to the mean) is ever mentioned. 
This is where my confusion came from.
Anyway, thanks a lot for sharing your insight. 
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