Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Time Integration with NodalKernels #5883

Closed
friedmud opened this issue Oct 28, 2015 · 4 comments
Closed

Time Integration with NodalKernels #5883

friedmud opened this issue Oct 28, 2015 · 4 comments
Labels
C: Framework P: normal A defect affecting operation with a low possibility of significantly affects. T: defect An anomaly, which is anything that deviates from expectations.

Comments

@friedmud
Copy link
Contributor

NodalKernels were introduced in #5815 but they show issues with high order time integration. We need to get that figured out.
@friedmud friedmud added C: Framework T: defect An anomaly, which is anything that deviates from expectations. P: normal A defect affecting operation with a low possibility of significantly affects. labels Oct 28, 2015
@jwpeterson
Copy link
Member

Specifically, BDF2 worked great but there were issues with Crank-Nicolson and the second-order Dirk scheme...

@friedmud friedmud mentioned this issue Nov 10, 2015
Closed
@friedmud
Copy link
Contributor Author

Got it....

figure_1

It was what @permcody found in #5949

Man... DIRK has signficantly lower error! An order of magnitude lower than even Crank-Nicolson! 3 orders of magnitude lower than regular Backward Euler. Maybe I should use that thing more often!

What are the downsides to it?

friedmud added a commit to friedmud/moose that referenced this issue Nov 11, 2015
@friedmud
Make TimeDerivativeNodalKernel inherit from TimeNodalKernel closes id…
33782ff 
…aholab#5883 closes idaholab#5949
@friedmud friedmud mentioned this issue Nov 11, 2015
Merged
@jwpeterson
Copy link
Member

The error constant (vertical offset of the lines) strongly depends on the test problem and the higher derivatives (in time) of the true solution. So I wouldn't draw too many general conclusions from this one test problem. In that other plot I posted, for example, Crank-Nicolson had the lowest error of the second-order methods:

implicit_plot

The main drawback of the DIRK scheme is that it requires two solves per timestep, the benefit of the scheme over Crank-Nicolson is that it's L-stable (no ringing on stiff problems).

@friedmud
Copy link
Contributor Author

Gotcha. Thanks John!

friedmud added a commit to friedmud/moose that referenced this issue Dec 18, 2015
@friedmud
Make TimeDerivativeNodalKernel inherit from TimeNodalKernel closes id…
867cca4 
…aholab#5883 closes idaholab#5949
friedmud added a commit that referenced this issue Dec 18, 2015
@friedmud
Make TimeDerivativeNodalKernel inherit from TimeNodalKernel closes #5883
afcf3b7 
 closes #5949
friedmud added a commit to friedmud/moose that referenced this issue Dec 19, 2015
@friedmud
Make TimeDerivativeNodalKernel inherit from TimeNodalKernel closes id…
7f75e5b 
…aholab#5883 closes idaholab#5949
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Assignees
No one assigned
Labels
C: Framework P: normal A defect affecting operation with a low possibility of significantly affects. T: defect An anomaly, which is anything that deviates from expectations.
Projects
None yet
Milestone
No milestone
Development

No branches or pull requests
2 participants
@friedmud @jwpeterson