is this low hanging fruit? (real time water simulation better than our baked sims)

https://drive.google.com/file/d/0B9N6z_bRVUMmbWU0UW13ZFoyTDA/view

"In this paper we introduce a novel micropolar material model forthe simulation of turbulent inviscid uids. The governing equations

are solved by using the concept of Smoothed Particle Hydrody-
namics (SPH). As already investigated in previous works, SPH uid

simulations suer from numerical diusion which leads to a lower
vorticity, a loss in turbulent details and nally in less realistic results.
To solve this problem we propose a micropolar uid model. The

micropolar uid model is a generalization of the classical Navier-
Stokes equations, which are typically used in computer graphics

to simulate uids. In contrast to the classical Navier-Stokes model,
micropolar uids have a microstructure and therefore consider the
rotational motion of uid particles. In addition to the linear velocity
eld these uids also have a eld of microrotation which represents

existing vortices and provides a source for new ones. However, clas-
sical micropolar materials are viscous and the translational and the

rotational motion are coupled in a dissipative way. Since our goal

is to simulate turbulent uids, we introduce a novel modied mi-
cropolar material for inviscid uids with a non-dissipative coupling.

Our model can generate realistic turbulences, is linear and angular
momentum conserving, can be easily integrated in existing SPH
simulation methods and its computational overhead is negligible."

In our last experiment we compared the computa-
tional eort of our method and CSPH in a breaking dam scenario

with 1M uid particles and three static Stanford dragons (see Fig-
ure 6). The times were measured on an Intel Xeon E5-2683 processor

with 2.1 GHz and 16 cores. The results show that most of the com-
putation time in a simulation step is required for the neighborhood

search and the implicit pressure solver. The additional computa-
tions for the microrotations in our micropolar uid model have

only a linear time complexity. In the dragon scenario this results in
a negligible computational overhead of only 5% compared to the
classical SPH approach. In larger scenarios as the river scene (see
Figure 1, left) the computational overhead is even lower since the
neighborhood search and the pressure solver require proportionally
more time in more complex scenarios.

How did you deduct from that, it was real time simulation ?
You can use a realtime render engine to render baked meshes of a simulation.
No mention of a GPU, it looks like a baked simulation.

Is it just me or is the water not looking right ?,the white foam (as something from a large scale) and the changing of borders of the water stream which would rather be a future of some small stream…