Hello, The convergence behavior that you describe is typical of reduced basis convergence: It will plateau after an error reduction of about six orders of magnitude or so. So it sounds like the convergence is working fine in the sense that you got a reduction from 1.3569e7 to 41. When you get the message "Exiting greedy because the same parameters were selected twice" that is another indication that the greedy algorithm has plateaued.
I do not know why the RB solution and FE solution did not match well at the end, though --- that of course indicates that something is wrong. One thought; Did you make sure to use an asymmetric solver, since thermoelasticity is not symmetric? David On Sat, Jan 27, 2018 at 4:13 AM, <[email protected]> wrote: > Hi all, > > > > I made a thermoelasticity model based on the cantilever example, > reduced_basis_ex5, by adding a new temperature variable. At the beginning > of > the basis training procedure, the maximum error bound drops sharply from > 1.35694e+07 to 41 as the dimension of the basis increases from 0 to 5. > After > that, although the basis dimension keeps growing, the error bound stops > decreasing and stays at a certain number. The relative training tolerance > is > set at 1.e-7 and the mesh is a T-shaped pipe. > > > > ---- Basis dimension: 5 ---- > > Performing RB solves on training set > > Maximum error bound is 2.42578 > > > > Performing truth solve at parameter: > > h: 1.055972e+01 > > h_Tinf: 2.472563e+02 > > heat_flux: 4.261782e+01 > > > > ---- Basis dimension: 6 ---- > > Performing RB solves on training set > > Maximum error bound is 2.43818 > > > > Performing truth solve at parameter: > > h: 1.151397e+01 > > h_Tinf: 2.473108e+02 > > heat_flux: 4.481571e+01 > > > > ---- Basis dimension: 7 ---- > > Performing RB solves on training set > > Maximum error bound is 2.44673 > > > > Exiting greedy because the same parameters were selected twice > > > > The RB result obtained from this basis differs a lot from the FEM result. I > searched archives of the mailing list and found that this phenomenon might > result from an overly low training tolerance. However, the initial error > bound being nearly e+07, if I select a less strict tolerance, I will end up > having an unsatisfying error and probably a worse result. Could you please > suggest me some advice? I would be grateful for your response. > > > > Best regards, > > Gauvain > > ------------------------------------------------------------ > ------------------ > Check out the vibrant tech community on one of the world's most > engaging tech sites, Slashdot.org! http://sdm.link/slashdot > _______________________________________________ > Libmesh-users mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/libmesh-users > ------------------------------------------------------------------------------ Check out the vibrant tech community on one of the world's most engaging tech sites, Slashdot.org! http://sdm.link/slashdot _______________________________________________ Libmesh-users mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/libmesh-users
