A question about the setting of 'n' in the gaussian_function(C, r, n, sigma).

[WangOne1]

I found that the default setting for 'n' in gaussian_function(C, r, n, sigma) is 1,such as "near_wake_deficit = gaussian_function(C, r, 1, np.sqrt(0.5))", which might reference the super-Gaussian wake model (n ≥ 2,Blondel and Cathelain - 2020 - An alternative form of the super-Gaussian wind ...). Could you tell me which literature references the choice of 1 or the reasoning behind this choice when Standard model n=2?

[WangOne1]

and sigma default sqrt（0.5），then result=C（x）exp（-r），why do this

[misi9170]

Hi @WangOne1 ,

Thanks for the question, and sorry that it took me a while to get around to answering it. I think that the confusion here is that r in the gaussian_function is in fact $r^2$ as normally defined in a Gaussian wake model. You'll notice that r comes from the rC function, and there it is computed as the hypotenuse squared (rather than the hypotenuse). Simplifying the expression, it is essentially $r := y^2 + z^2$ . This is admittedly confusing when cross-referencing with papers supplying the "usual" definition of $r$ as the hypotenuse---I will try to add some documentation to clarify that (#1105).

Does that answer your question?

Misha

[WangOne1]

Thank you very much！I got it！
You and your team have done an outstanding job on FLORIS! I am using FLORIS as a tool to write a paper. Could I ask two more questions if that's convenient?
（1）Why Floris use Iref=0.07 when wind speed is equal to 15m/s(differ from IEC 61400-1 suggested)？ Is any reference article or more details document favoring this.
（2）I have found that many wake visualization plots do not have a colorbar and instead display contour lines, which can lead to sharp ends when setting up a colorbar. I have also noticed negative velocities in the near wake region, which could potentially mislead readers if included in the paper. Do you have any suggestions on how to address this issue? I am currently exploring solutions myself.


If the problem description is not clear enough, please correct me, and I will ask the question more specifically in the "Question" section. Thank you very much for the help your team has provided me with. I hope to have the opportunity to make a contribution in the related field.

Wang

[misi9170]

Hi Wang,

Thank you, and great to hear you are using FLORIS in your work! You might find that you can get the desired behavior in terms of minimum colors by supplying thelevels argument to visualize_cut_plane, for instance, levels=[1, 2, 3, 4, 5, 6, 7, 8]. However, control over how the colorbar looks is not straightforward from visualize_cut_plane, unfortunately (see further discussion in #772)---to make final figures for publication, you may be best to copy the code from visualize_cut_plane and make modifications to it directly (using ax.tricontourf and/or line_contour_cut_plane).

I think the answer to your question about the "low" default value for Iref is in the docstring for assign_ti_using_IEC_method(). We have found that with the wake models in FLORIS, the results don't seem to reflect reality when the TI is as high as 0.15. Let me know if that doesn't answer your question though.

Misha

[misi9170]

I have now updated the variable names in wake_velocity/gauss.py and wake_deflection/gauss.py from r (or similar) to r_squared (or similar) in #1171 to hopefully improve the clarity on this. The form of the models is not changed.