Hi Zach,

If you only use the surf-flux anti-aliasing, defining the quadrature degree for 
the line interfaces is sufficient. If you use the other anti-aliasing options 
(flux, div-flux) you need to specify it for the quad element type as well.

Yes, as far I as I’m aware, that is how the high-order meshes work in PyFR.

-Niki

On 21 Mar 2017, at 20:44, Zach Davis 
<[email protected]<mailto:[email protected]>> wrote:

Hi Niki,

Yes, I was referring to the source terms.  If I turn on surf-flux 
anti-aliasing, do I need to define the quadrature degree order and quadrature 
rule type for both the line interfaces and quad element type, or is defining it 
for only the quad element type necessary?

Are you saying when we send you higher order meshes, the additional “shape 
points” created in elevating the mesh order are then used to define a 
polynomial that PyFR in turn uses to represent the curvature of the element?

Best Regards,


[Pointwise, Inc.]
Zach Davis
Pointwise®, Inc.
Sr. Engineer, Sales & Marketing
213 South Jennings Avenue
Fort Worth, TX 76104-1107

E: [email protected]<mailto:[email protected]>
P: (817) 377-2807 x1202
F: (817) 377-2799
[http://www.pointwise.com/images/twitter-circle-icon.png]<https://www.twitter.com/RcktMan78>
 [http://www.pointwise.com/images/youtube-circle-icon.png] 
<https://www.youtube.com/cfdmeshing>  
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<https://www.facebook.com/pointwise>  
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<https://www.github.com/pointwise>


On Mar 21, 2017, at 12:38 PM, Niki Loppi 
<[email protected]<mailto:[email protected]>> wrote:


Hi Zach,

You mean the source terms? On a GPU they seem to increase the simulation time 
by less than 8%. Since they turn off at t=5 anyway, you can restart the 
simulation without them from 5.00.pyfrs to reduce the cost.

Turning the surf-flux anti-aliasing on and increasing the quad-deg order under 
the surface element-types is equivalent to sub-sampling the flux polynomial at 
quadrature points along the linear interfaces for more accurate integration. 
The curvature of elements is achieved with polynomial representations defined 
by shape points and that information comes from the high-order mesh.

The algorithm is formulated to be as compute intensive as possible to leverage 
the arithmetic capabilities of GPUs/co-processors. Other incompressible 
approaches normally rely on (partly) implicit time stepping and a global matrix 
inversion which makes them memory intensive/bandwidth bound and thereby less 
attractive for GPUs. You can find more about the GPU motivation in the original 
PyFR paper.

http://www.sciencedirect.com/science/article/pii/S0010465514002549


Niki

On 20/03/17 21:57, Zach Davis wrote:
Hi Niki,

So, with the artificial compressibility terms turned on, the simulation would 
require about 40 hours on 4 cores using the OpenMP backend with the new values 
for dt and pseudo-dt.  Turning them off requires only 15 hours to run the 
solver for a 3rd order solution.  Are they necessary?  Is there more I can read 
about why this formulation of yours favors GPUs?

I’m getting to curving in upcoming runs, but wanted a linear mesh to begin 
with. for comparison  This leads into another question I have.  I know PyFR 
inserts solution points via quadrature rules for each element type which are 
primarily used in integration over the element.  Does PyFR also insert points 
along the interfaces of the elements as well for computing fluxes?  What if 
these points already exists as in when we elevate the order of the mesh?

Best Regards,

[Pointwise, Inc.]
Zach Davis
Pointwise®, Inc.
Sr. Engineer, Sales & Marketing
213 South Jennings Avenue
Fort Worth, TX 76104-1107

E: [email protected]<mailto:[email protected]>
P: (817) 377-2807 x1202
F: (817) 377-2799
[http://www.pointwise.com/images/twitter-circle-icon.png]<https://www.twitter.com/RcktMan78>
 [http://www.pointwise.com/images/youtube-circle-icon.png] 
<https://www.youtube.com/cfdmeshing>  
[http://www.pointwise.com/images/facebook-circle-icon.png] 
<https://www.facebook.com/pointwise>  
[http://www.pointwise.com/images/github-circle-icon.png] 
<https://www.github.com/pointwise>


On Mar 20, 2017, at 3:40 PM, Niki Loppi 
<[email protected]<mailto:[email protected]>> wrote:


Hi Zach,

The gmsh to .pyfrm converted mesh produced the same result as the direct .pyrfm 
conversion, so the Pointwise writer is working fine. Additionally, no 
anti-aliasing is needed, that was my mistake.

Sometimes anti-aliasing (over sampling) is needed for the non-linear flux 
calculation in areas that are under-resolved. This is not the case in this 
simulation and actually the resolution is too high near the cylinder for P=4 
which causes the major limitation in the explicit pseudo-time step size. The 
high-order CFL criterion is dependent on the polynomial order, which is why you 
cant use the standard CFL expression. The solution is to make the cylinder 
surface elements curved and increase their size to efficiently use higher 
orders with less restrictive time-steps.

With P=3 you can get a stable combination with ac-zeta=4.0, dt = 0.001, 
pseudo-dt = 0.0005 which results in ~6 hour simulation with a single FirePro 
S10,000GPU. Please note that this artificial compressibility approach is 
formulated to leverage arithmetic capabilities of GPUs in order to make it 
competitive against codes that are based on global Poisson solve.

-Niki



On 20/03/17 17:39, Zach Davis wrote:
Hi Niki,

Thanks for catching my time stepping error.  I transposed the time step values 
that were reported in the paper incorrectly.  In choosing an appropriate time 
step, the CFL criteria seems to suggest that I need to select something less 
than1.0*(min dS spacing/vel).  Since I’m normalizing the u velocity to 1.0, 
then I think my time step should correspond directly to my minimum dS spacing.  
I can see what that minimum spacing is directly in Pointwise (~0.0583), but it 
appears that you need to back off from that substantially.

If I try to run this on 4 cores using the OpenMP backend, PyFR estimates that 
it would take about 375 hours to simulate 60 seconds.  This lead to my earlier 
comment about this taking awhile.  I would try using the OpenCL backend, but it 
currently isn’t working for me.  I also think this mesh is small enough that I 
wouldn’t notice much of speedup difference.

I don’t have any understanding of what antialiasing accomplishes in PyFR, or 
how to set it up appropriately.  My initial impression was that it would be 
used in conjunction with shock capturing simulations in order to smooth the 
gradients across elements in the vicinity of a shock or other strong 
discontinuity in the control volume.  If you’re employing it for this case, 
then perhaps my assumption is wrong.  Are there references you can point me to 
that discuss this topic a bit further?

The Gmsh file for this mesh is attached.  Thanks so much for working through 
this with me!

Best Regards,


[Pointwise, Inc.]
Zach Davis
Pointwise®, Inc.
Sr. Engineer, Sales & Marketing
213 South Jennings Avenue
Fort Worth, TX 76104-1107

E: [email protected]<mailto:[email protected]>
P: (817) 377-2807 x1202
F: (817) 377-2799
[http://www.pointwise.com/images/twitter-circle-icon.png]<https://www.twitter.com/RcktMan78>
 [http://www.pointwise.com/images/youtube-circle-icon.png] 
<https://www.youtube.com/cfdmeshing>  
[http://www.pointwise.com/images/facebook-circle-icon.png] 
<https://www.facebook.com/pointwise>  
[http://www.pointwise.com/images/github-circle-icon.png] 
<https://www.github.com/pointwise>


enc

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On Mar 20, 2017, at 11:50 AM, Niki Loppi 
<[email protected]<mailto:[email protected]>> wrote:


Hi Zach,

The backward Euler, bdf2, and bdf3 are backward difference schemes, which are 
iterated explicitly in pseudo time. This is because fully implicit approaches 
in high-order are unattractive for GPUs due to their memory limitations. 
Therefore, the CFL is limited by the stability of the explicit RK4. You have 
specified the pseudo time step larger than the real time step, which is why the 
simulation is almost guaranteed blow-up.

The source terms are not the issue. They are just used to prevent the 
reflection of the initial pseudo wave to make the initial transient phase 
faster and cleaner. You should be able to run the case without them, but then 
in the transient phase you are left with bouncing pressure waves. The 
coordinates +/-5 and +/-25 are from the origin, which is in the middle of the 
cylinder.

Yes, the paper says that the factor typically ranges from 1 to 4, but there is 
much more literature on this. Its  appropriate value is affected by many 
different aspects, such as length scales in the simulation and the grid you are 
using. Moreover, it should have different values the viscous and advection 
dominated areas. Since the optimal value for beta should be temporally and 
spatially varying, the best option for now is just to find a good global value 
and stable time steps for each configuration heuristically. The value ac-zeta = 
6 seemed to work fine in my configuration.

I managed to start the simulation with bdf2/rk4, ac-zeta = 4.0, dt = 0.0002, 
pseudo-dt = 0.0001 and 7th order flux anti-aliasing, but simulation blows up 
starting from the boundaries (picture attached). Just to be sure that the 
boundary definitions work correctly in the Pointwise pyfrm writer, could you 
write your mesh as Gmsh .msh and send it to me.

Thanks,

Niki





On 17/03/17 18:16, Zach Davis wrote:
Hi Niki,

I’ve modified my 2_d cylinder mesh to use a diameter of 1.  I’ve also brought 
in the control volume boundaries to align more with what was covered in the 
paper reference you provided.  I’ve attached the mesh and configuration files 
I’m using in the hopes that you might have some time to help me troubleshoot, 
so I can better understand the process involved.  The solver is diverging, and 
i’ve tried increasing the max number of sub-iterations, decreasing the time 
step, and so forth.  I suspect it may have to do again with the source terms 
which I still don’t quite understand (are your +/-5 and +/-25 from the cylinder 
boundary or from the control volume boundaries?).

Also the paper suggests that the artificial compressibility factor they use is 
1.25 and typically ranges from 1 to 4.  Could you explain why you opted for a 
factor of 6?  Lastly, is there a difference in how one should choose a time 
step and pseudo time step depending on whether the backward euler, bdf2, and 
bdf3 schemes are used?  I assume the former is an explicit scheme; whereas, the 
latter two are implicit?  Thanks!

Best Regards,



Zach

enc










On Mar 16, 2017, at 12:47 PM, Niki Loppi 
<[email protected]<mailto:[email protected]>> wrote:


Zach,

Yes, I saw that in your original .vtu file. However, when I started the 
simulation from scratch with your mesh, it did not reproduce the error. 
However, I did repartition it into two. Did you try it without partitioning?

The cylinder diameter is not defined in the .ini file, but my mesh was 
generated so that D=1 and domain length -5<x<30 units. According to Paraview, 
your D=~285 and domain -2500<x<20000 units. Therefore, some of the values in 
the .ini file including the source terms should be scaled to match the 
dimensionless quantities. For example, now your Reynolds number is 
Re=1*285/0.008 = 35625 and one flow through would take 22500 time units. In the 
original file Re = 1*1/0.008=125 and flow through time 35 units.

-Niki


On 16/03/17 16:43, Zach Davis wrote:
Niki,

You aren’t seeing this in Paraview (see attached screenshot)?  Also, where is 
the cylinder diameter defined within the *.ini file for this case.  Are you 
referring to your setup of the source terms?

Best Regards,

[Pointwise, Inc.]
Zach Davis
Pointwise®, Inc.
Sr. Engineer, Sales & Marketing
213 South Jennings Avenue
Fort Worth, TX 76104-1107

E: [email protected]<mailto:[email protected]>
P: (817) 377-2807 x1202
F: (817) 377-2799


enc

<Mail Attachment.png>

On Mar 16, 2017, at 8:22 AM, Niki Loppi 
<[email protected]<mailto:[email protected]>> wrote:


Hi Zach,

I tried running your case for couple of outputs and did not experience the odd 
behaviour in your .vtu file (attachment). However, in your mesh the dimensions 
are scaled differently. For instance, the cylinder diameter is D=~285, while 
the values in the .ini file are specified for D=1 used in my mesh.

Cheers,

Niki

On 15/03/17 20:27, Zach Davis wrote:
Hi Niki,

Thanks for the explanation.  I’ll look into this method a bit further based on 
the paper reference you provided.  Something seems to be amiss with solution 
files.  I’ve attached the *.pyfrm *.vtu and *.ini files I have generated for 
this case; though, the mesh is one of my own creation.  If someone could 
explain what’s happening with the *.vtu file based on the *.pyfrm mesh input, 
then that would be appreciated.

Best Regards,


[Pointwise, Inc.]
Zach Davis
Pointwise®, Inc.
Sr. Engineer, Sales & Marketing
213 South Jennings Avenue
Fort Worth, TX 76104-1107

E: [email protected]<mailto:[email protected]>
P: (817) 377-2807 x1202
F: (817) 377-2799

enc










On Mar 15, 2017, at 8:42 AM, Niki Loppi 
<[email protected]<mailto:[email protected]>> wrote:


Hi Zach,

AC stands for the method of artificial compressibility. Instead of relying on a 
Poisson based projection, the system is driven towards a divergence free state 
by introducing artificial pressure waves through the continuity equation. The 
formulation preserves the hyperbolic nature of the system, but destroys the 
time accuracy, which is then recovered with dual time stepping. For the ac 
formulation you can refer to

http://www.sciencedirect.com/science/article/pii/S0021999116001686

The artificial compressibility factor ac-zeta is the coefficient of the fluxes 
in the continuity equation. This results in characteristics

V + c,  V, V - c,

where c = sqrt(V^2 + ac-zeta) is the pseudo speed of sound. Thus, in the 
current implementation ac-zeta is the free parameter that is used to downscale 
the speed of the pseudo-waves to globally reduce the pseudo system stiffness. 
The parameter is something that one can experiment with, typical values varying 
from 1.25 - 10 times the freestream velocity. Currently, I am looking into 
making ac-zeta and pseudo-dt spatially and temporally varying.

The source terms specify a sponge region near the domain edges (|y|>5, x<-5, 
x>25) to damp the initial pressure wave that is generated when the simulation 
is started from scratch. Please note that the sponge turns off at t=5 because 
of the (1 - tanh(1.5*(t - 5.0)))*0.5 coefficient. You can see how the sponge 
works if you write the solution files before t=5.

The plugin [soln-plugin-pseudostats] is used to output the residual of the 
pseudo time problem to monitor the divergence. The [soln-plugin-residual] on 
the other hand computes the "residual"  of two consecutive real time steps. The 
[soln-plugin-fluidforce] plugin can be used with the ac systems.

Coarsening the mesh and increasing the order is something that would be 
beneficial, especially when using a polynomial multigrid for accelerating the 
pseudo time problem. P-multigrid should be added in the next release.

Thanks,

Niki





On 14/03/17 23:37, Zach Davis wrote:
Tuesday, 14 March 2017



Peter & Freddie,

I believe the mesh export issue from Pointwise using the PyFR exporter has been 
resolved in PyFR 1.6.  I still seem to have issues running using the OpenCL 
backend which persistently complains about an invalid workgroup size.  It use 
to work at one point, but something has changed in the intervening releases 
which is causing problems for me at least.  I’ve tried adjusting the values per 
Freddie’s guidance to no avail.  He also suggested a tool that might be helpful 
in determining the appropriate workgroup size needed for my card.  
Unfortunately that tool seems to be NVIDIA card specific requiring installation 
of NVIDIA software that won’t run on my machine.  I’m using an AMD card 
instead, and the tool won’t compile due to missing dependencies.  It’s not a 
pressing matter, but just something that I thought you both might want to be 
aware of.

Nikki,

I’ve looked over your incompressible 2d-cylinder case, and I was wondering if 
you could elaborate a bit, or point me to some reference, about how you came up 
with the source terms you’re using in the input file.  It also appears that the 
[soln-plugin-pseudostats] is used in place of the [soln-plugin-residual] 
namelist for incompressible cases—is that right?  Does the 
[soln-plugin-fluidforce] namelist still work for the ac-navier-stokes solver?  
Another question if you don’t mind—what is this artificial compressibility 
factor, ac-zeta and why is value of 6.0 used?  Oh, and what does the ac prefix 
stand for?  Thanks!

I think it would be interesting to see how coarse of a higher-order mesh could 
be made for this case while increasing the polynomial solution basis such that 
you essentially recover the linear mesh spacing in each element, and see if you 
could capture one or more vortices within a single element with any noticeable 
diffusion over time.

Best Regards,


[Pointwise, Inc.]
Zach Davis
Pointwise®, Inc.
Sr. Engineer, Sales & Marketing
213 South Jennings Avenue
Fort Worth, TX 76104-1107

E: [email protected]<mailto:[email protected]>
P: (817) 377-2807 x1202
F: (817) 377-2799


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