integrisbrad
Mechanical
- Feb 5, 2013
- 2
I've submitted this question to ANSYS help, and I'm still working with them, but haven't received an answer. I'm new to ANSYS within the month, and I've not done anything with APDL, just the GUI in Workbench.
I've used Nastran before, and if you used the same constraints, you could solve a large number of different load events very quickly, as it decomposed the stiffness matrix only once. However, if you changed constraints, it would need to redo the stiffness matrix calcs.
I can't seem to find a way to mimic this behavior in ANSYS. It always seems to re-run the entire solution for each different load. I've tried separating out into a multiple system model and also tried just a single system with different loads applied in different steps. The only difference between the two is that the multiple system rewrites the input file every time. If I understand what is going on behind the scenes, if I'm getting my described desired behavior, I should only see the "solving the mathematical model" message once and then multiple "writing results files" messages.
Am I wrong? Any advice would be appreciated.
I've used Nastran before, and if you used the same constraints, you could solve a large number of different load events very quickly, as it decomposed the stiffness matrix only once. However, if you changed constraints, it would need to redo the stiffness matrix calcs.
I can't seem to find a way to mimic this behavior in ANSYS. It always seems to re-run the entire solution for each different load. I've tried separating out into a multiple system model and also tried just a single system with different loads applied in different steps. The only difference between the two is that the multiple system rewrites the input file every time. If I understand what is going on behind the scenes, if I'm getting my described desired behavior, I should only see the "solving the mathematical model" message once and then multiple "writing results files" messages.
Am I wrong? Any advice would be appreciated.