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#Tecplot polar contour plot code#
#Tecplot polar contour plot plus#

#Tecplot polar contour plot code#

The source code is 3 times almost the same thing you can chose which ix1 to remember (from first or second search) they are neighboring pointsĪ little outdated, but I still came across this post. If the wing is sampled with too low point count it can lead to inaccuracy. If too small then for smaller radius of curvature will lower the accuracy this approach is dependent on sufficient point coverage. Too big n will cause slowdowns and if n>pnt.num/4 it could also invalidate output.

Sometimes the closest point is before the last found closest point this depends on the curvature of booth sides. find axis0: midpoint of i0=(0-ix1) i1=find closest from (ix1,pnt.num)įor (i0=0,i1=pnt.num-2 i0+npnt.num-2) i=pnt.num-2 ii=i1 Ĭonstant n=4 is just for safety overlapped search for closest points it should be a fraction of pnt.num. This approach has much much accurate output ( axis0,axis1 are closer together) From this point find the most far point from last axis point this is the leading edge point. Also count only valid closest points (on the opposite side) if none found stop (top image case).

#Tecplot polar contour plot plus#

Have an insane thought about this to find the edge point on the run plus some code tweaking and the outcome is good enough for me :) so first some explaining:Īlgorithm for axis stays the same but instead of ix1 bound use only points that was not yet used.

xxx.reset() clears the array and set xxx.num=0.

xxx.num is the actual used size of the array.

List xxx is just mine dynamic list template the same as double xxx.

Load the polar data into Tecplot in Polar line mode. find axis1: midpoint of i0=(ix1,pnt.num) i1=find closest from (0,ix1) Solution: Since Tecplot doesn’t have the built-in ability to have Polar contour plots each case may require its own special adjustments. This can be done in the same way result is blue axis polylineĬ++ source: List pnt // outline 2D pnts = įor (p0=pnt.dat,p1=p0+2,p2=p1+2,i=2 iix1+2 i-=2)

when done then just find the average between axis0,axis1.

do the same but start from opposite side store as axis1 (dark red).

Do this for all points i=(0-ix1) (Red line)

compute midpoint between them and store it as inaccurate axis0 points.

find closest point to it on the opposite side.

Less precise way but much much faster so: This is doable but with insane complexity

find point, It's normal intersect the opposite normal and divide both normals to the same distance.

Mark the edge point as ix1įor precise geometry you will need intersections of normals casted from each side so: Then mark zones where dx is positive or negative and find the middle between them (usually dx=0 for that zone).

Just find point where the sign of dx is flipping and compute dx(i)=x(i)-x(i-1) Leading edge not correct for nontrivial wings I do not have any skewed wings data as on your image the closest thing I found was this: Well it was quite a few years I do something with wings. However, this would not work in theįollowing exemplary profile, since the smallest x-coordinate is located on the upper surface of the profile.ĭoes anybody have an idea, how I could easily calculate/determine this data? Until now, I have always used the smallest x-coodinate as the leading edge. the profile is not normalized and it can exist in a rotated form.the coordinates always run on the bottom/underside to the leading edge, in the example above at (0,0) and from there back to the trailing edge.the first coordinate is always the trailing edge, in the example above at (x=1, y=0).The only known things about the profile are the lists above and the following facts: Given are airfoil profiles in two lists with the following exemplary coordinates:Įxample: x_Coordinates = I have a little issue with calculating coordinates. Heatmap ( z = z, showscale = False, connectgaps = True, zsmooth = 'best' ), 2, 2 ) fig. Heatmap ( z = z, showscale = False, zsmooth = 'best' ), 2, 1 ) fig. Contour ( z = z, showscale = False, connectgaps = True ), 1, 2 ) fig. Contour ( z = z, showscale = False ), 1, 1 ) fig. Import aph_objs as go from plotly.subplots import make_subplots fig = make_subplots ( rows = 2, cols = 2, subplot_titles = ( 'connectgaps = False', 'connectgaps = True' )) z =, ,, ,, , ] fig.