Controlling Fluid Simulations with Custom Fields in Houdini Master ...

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Figure 10: The geometric representation of the steps inside the Vortex Connementmicrosolver as described in (4.3.3). Red represents the curl eld after step2, green represents the gradient of the curl magnitude eld after step 5, bluerepresents the vortex direction after step 8.18
4.4 Up Res techniqueIn Houdini 10 a new technique was introduced were a low resolution simulationcan be used as the basis for a higher resolution simulation. The key conceptis that you get the general motion from the lower resolution simulation andintroduce high frequency noise into the velocity eld, but only where there isa high curvature. The introduced noise has a low amplitude so it does notinterfere with the general motion coming from the low resolution velocity eld.A cut down version of the pyrosolver is used for the Up Res phase as a lot of theinformation is coming from the low res elds rather than having to be calculatedfrom scratch. Because it is still a high resolution simulation it can take a longtime. The foundations for this Up Res technique were initially built in Houdini9.5 based on [11]. It then became a fully integrated tool supporting Constant,Wavelets and Curl noise. The Gas Up Res solver is also a digital asset, whichmeans it can be modied to support custom user dened elds coming from thelow res simulation or added in to support a certain type of control or new typeof noise. Those elds can then be up-ressed and written to disk at the end ofeach simulation step.4.5 Distributed simulationsRunning high resolution simulations can take a lot of time. Therefore the functionalityhas been built in to be able to split a heavy uid container into slices.These slices represent partitions of the volume. The amount of overlap betweenthe slices that is required for a good distributed simulation depends on the speedof the uid and needs to be set by the user. There are several ways the slicescan be dened, but by default they will partition the space inside the containersimilarly to a binary tree. Each slice can then be calculated on a dierentmachine that is connected to a host machine that is running a tracker. Themachines share the data between them in the overlapping regions through interprocess communication. The speed gains are signicant, but not linear as theextra overlap and communication between machines takes up a bit of additionalprocessing power. The simulation generally runs as fast as the slowest machine.If all the machines have the same specications this is not a problem, but if oneof them is signicantly slower than the others, the faster machines will not beused as eciently.Sidefx has provided Hqueue, a python based renderfarm software to helpwith this distribution. However HQueue was not tested for this project due tolimited administrative permissions. Instead a few shell scripts were written thatremotely start a tracker and the simulations and make use of the exact samedistribution techniques. Again it is convenient that open access is provided tothe distribution techniques without forcing users to use HQueue.19
Page 1 and 2: Controlling Fluid Simulations with
Page 3 and 4: 1 AbstractThere are various dierent
Page 5 and 6: Figure 2: High resolution 2d uid co
Page 7 and 8: Figure 5: The node based operator g
Page 9 and 10: sets, which means they are built fr
Page 11 and 12: • as an axis aligned plane that s
Page 13 and 14: Figure 7: The node tree for the upd
Page 15 and 16: Figure 8: The node tree showing the
Page 17: Figure 9: The node tree in Houdini
Page 21 and 22: • The nal option is with a Gas Fi
Page 23 and 24: Figure 12: Premultiplication proble
Page 25 and 26: Figure 14: The preset smoke solver
Page 27 and 28: Figure 16: A helix curve emitting d
Page 29 and 30: Figure 19: A few stages of the adve
Page 31 and 32: Figure 21: Top: The stages of the a
Page 33 and 34: and pyrosolver work took time. This
Page 35: [10] Christopher Horvath and Willi

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