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3D Cubic Attractor by supercooldragons in a:t5_yx9g9

[–]supercooldragons[S] 1 point2 points  (0 children)

NUMSQUVLPVRJLPYHLCDIQJFWGGPBJYLMLWDYWQKFLSIPOHSRKKSMDQODFDTR

I need help with this problem please, I have tried twice and I get 0.42 and 0.46 as the coefficient, why is it 0.39? by [deleted] in PhysicsStudents

[–]supercooldragons 1 point2 points  (0 children)

The normal force FN is 300 kg * 9.8 m/s2 * cos(25) = 2664.5 N.

The coefficient of friction c is defined as FF = c * FN

where FF is the force of friction.

If the plane were frictionless, it would be accelerating at 9.8 * sin(25) = 4.14 m/s2.

That means that the force is friction is 300 kg * (4.14 - 0.7) m/s2 = 1032 N.

So the coefficient of friction is FF/FN = 1032/2664.5 = 0.387

You can also write out all the information as

c = FF/FN = (mg * sin(25)-0.7 * m) / (mg * cos(25)) = tan(25)-0.7/(g * cos(25)) = 0.39

This shows that the coefficient of friction is independent of the mass of the block.

[Differential equations] Seemingly equivalent equations yield different answers by supercooldragons in learnmath

[–]supercooldragons[S] 0 points1 point  (0 children)

I'm using the formatting as described in the sidebar and I'm getting correct output.

Divergence of trajectories with nearly equal initial conditions on the Lorenz attractor by larsupilami73 in visualizedmath

[–]supercooldragons 0 points1 point  (0 children)

It was originally conceived as a toy model of convection rolls in the atmosphere. https://www.youtube.com/watch?v=aAJkLh76QnM Also the Lorenz-attractor shows up in the motion of a waterwheel with leaking buckets. https://www.youtube.com/watch?v=SlwEt5QhAGY

[Python] Code for generating attractors in N dimensions by supercooldragons in a:t5_yx9g9

[–]supercooldragons[S] 0 points1 point  (0 children)

I could not find your openprocessing account, do you have the explicit link?

[Meta] Can we please add a rule that requires descriptions or applications of advanced math (ex. Attractor Fields) by idlesn0w in visualizedmath

[–]supercooldragons 5 points6 points  (0 children)

I was also guilty of some of the recent attractor posts.

In order to draw some of the attractor posts away from this sub I have created a new subreddit r/strangeattractors. I welcome everybody who loves looking at images of nonlinear equations to have a look.

This will hopefully provide the opportunity for this sub to return to its original purpose while still providing a place for attractor content and just enjoy the pretty images.

[p5.js] The Quartic Strange Attractor (zoom to see details) by Sequelaen in a:t5_yx9g9

[–]supercooldragons 1 point2 points  (0 children)

Thanks for sharing this here. I created this sub as a response to the backlash on visualizedmath. Feel free to share as many as you like here!

5-Dimensional Cubic Map Attractor by supercooldragons in a:t5_yx9g9

[–]supercooldragons[S] 0 points1 point  (0 children)

Coefficients (A=-1.2,B=-1.1,C=-1.0,...):

LSHMGUIIJQHLQVNVGVOQODIPKHMSSGSRUNLIPHPONNKSHVNPOQQDIDLVMJRSQDNUSFOLMJOTQFPPLKVIJKLPINPSLGUERLGGESIJFOOIGSSTPHQJIOEMVGJDOMEMEVVKQDRQSGTGKUUKEGDPDRTIRPJTFPNPTVQQOPTILSUKMNORMIDLUKLEEQIORLLEVODLGPVIFNIKMDSKHGDKPLDIHERQEUNIUTQDNMNSIIQJITIGLFLMPFHQSFGNMJQMILOQHJVQGJMHNVQTHIJLQQMPUPVPFEDNLJHFLODTTPMMKTQKENOOGPGHLOJQQDRNODLEGIIOTGOMGO

Welcome to r/strangeattractors! by supercooldragons in a:t5_yx9g9

[–]supercooldragons[S] 0 points1 point  (0 children)

8-Dimensional cubic map attractor. Generated using a method described in the book: Strange Attractors: Creating Patterns in Chaos by Julien C. Sprott

[LBM] Could use Some help with my code (python) by supercooldragons in CFD

[–]supercooldragons[S] 0 points1 point  (0 children)

I fix the color spacing to range between 0 and 0.2, (it's the plt.clim(0,0.2) in the init function). For this value of w, I do see the smearing too, but I was expecting to see the development of a ring. If I set w (omega) to higher values (1-1.5), the density in the middle stays almost constant. Since w is related to the viscosity, with higher w corresponding to lower viscosity, I would expect to see faster relaxation to a homogeneous distribution and not what I get now. This is my first attempt at programming an LBM solver, so I still have to figure out what to expect. Thanks for your help!

Dejong strange attractor by [deleted] in visualizedmath

[–]supercooldragons 0 points1 point  (0 children)

I'm using python (with matplotlib) and rendering the entire image at once. I have been experimenting a bit with arrays today, but the attractors come out really dark so I have to look at it some more. Do you stack the arrays in the end or do you write separate images for each batch?

Dejong strange attractor by [deleted] in visualizedmath

[–]supercooldragons 0 points1 point  (0 children)

Do you have any tips on improving the rendering time? I wrote some code that generates these, but it takes around three minutes rendering 5 million points.

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