Dear Niki,
I think the problem is the stiffness of mass conservation convergence in
internal flow cases. Based on my trials, no matter bdf2, euler, tvd-rk3 or
rk4, convergence is facing stiffness. I have attached the convergence
history files in this email, which show convergence stiffness.
Si
Hi Niki,
Excuse me for interrupting you.
I'm afraid that I had doubts about the solution about 2D cylinder with
Re=200. There were frequently errors like "NaNs detected at t = xxx" in
PyFR run when I increased the Reynolds number by only modify the "Uin"
(Re>140, and Re=140 is fine without errors
Hi Will,
Thanks for your message.
I have only run external flows with a range of Reynolds numbers,
including 2D cylinder Re=200, turbulent jet Re=10,000, SD7003 Re=60,000.
All these cases converged ok.
In your previous email, you mentioned that you are using the
forward-euler pseudo-time sc
Dear Niki,
If my understanding is right. It seems that for dual-time AC method in
pyFR, there are difficulties on the convergence of the diffusion term.
Based on my simulations, the pressure residual of the pseudo convergence
file increases with the intensity of the flow diffusion. Very low pre
Dear Niki,
I have just finished a new test with the 2nd order 0.23million mesh (1.8
million in pyFR simulation).. I mapped the results from my previous pyfrs
at 40.95s to the new case starting at t = 0.00s. I used bdf2 in physical
time marching and Euler in pseudo time marching. Delta_t = 0.000
Dear Will,
Thanks for your message. We have applied the ACM solver to any internal
flows, so it is interesting see your progress.
"What is the meaning of ma = 0.2? Do you mean the max velocity/ac-zeta?
I think for ideal incompressible flow, ma = 0."
I meant the Mach number of the real life
Dear Niki,
I am wondering what the exact meaning of those residuals in pseudtstats is,
since if they are mass conservation residuals, I think that would be only
one value instead of u, v ,w ?
If Ma = 0.1 is not crucial, does it mean the values of those residuals are
not important to the resul
Dear Will,
It’s true that very low level of divergence is more difficult to achieve with
ACM than with methods that rely on a global Poisson solve because explicit
smoothers are not as efficient for damping low frequency modes. However, it has
other advantages e.g. it is (strong) scalable unlik
