This section is intended to give a rough measure of the performance that users may expect from ADflow. It is intended to give users some indication if their simulation is performing as well as it should be.
In general, simulations should be run on the minimum number of processors possible. This will generally mean that memory usage will be at, or near the maximum per core on the specific machine you are using. Smaller processor counts minimize parallel losses, especially from the breakdown of the effectiveness of the ASM/ILU preconditioner. A strong scaling study for ADflow can be found in Table 7 of this paper.
For the following analysis options:
SA turbulence model
ADflow will be able to run ~40,000 cells/GB of main memory. This figure includes some additional overhead for the mesh movement algorithm as well. Increasing the amount of ILU fill, the ASM overlap, or subspace size will increase memory usage. The viscPC option will increase the the total memory by a factor of approximately 2-3. Storing dRdw will increase memory usage by about 50%. Increasing any of these options will increase the memory usage and reduce the number of cells that will fit in 1GB.
Run-time performance can vary widely depending on the particular case one is solving. A useful, but somewhat imperfect metric that can be used to measure solution performance is: CPPH = # of cells converged/(proc * hour). We will arbitrary say a solution is converged when the L2 norm of the residual (totalR in the monitoring output) has dropped by 8 orders of magnitude. This is equivalent of setting L2convergence=1e-8 with the NKSolver.
For simple cases with good meshes (think isolated wing with a pyHyp) mesh and a modest number of cells (<1M), CPPH can exceed 1 million. As a concrete example:
4 processors (Desktop machine)
400 sec solution time
CPPH = 1 012 500
For much larger and more difficult case, such as complete configuration: wing, body, nacelle, pylon, h-stab and v-stab, the CPPH may be much lower ~200,000. Another example:
5 200 000 cell mesh
1200 sec solution time
CPPH = 244 000
These values are typical of using the NKSolver in transonic flow from freestream condition with a reasonably efficient Runge-Kutta/DADI start-up procedure before the NKSolver starts. For optimization, subsequent solutions should take less time, often taking less than 1/2 of the time towards the end of an optimization.
In general, Euler cases will require somewhat less memory than the RANS cases. However, with the matrix-free adjoint solver the fraction reduction will not be much more than 5/6 of the RANS memory. Run time for Euler cases will be typically be 2-5 times smaller than an equivalently sized RANS case (number of cells).