lid velocity = 0.000106281, prandtl # = 1., grashof # = 1. 0 SNES Function norm 1.036007954337e-02 0 KSP Residual norm 2.388589583549e+00 1 KSP Residual norm 5.715829806981e-01 2 KSP Residual norm 4.623679005936e-02 3 KSP Residual norm 1.143381177646e-02 4 KSP Residual norm 2.015139840224e-03 5 KSP Residual norm 4.356196119798e-04 6 KSP Residual norm 4.240953066710e-05 7 KSP Residual norm 8.848315297175e-06 1 SNES Function norm 9.854304971115e-06 0 KSP Residual norm 3.868049496775e-05 1 KSP Residual norm 7.693574326868e-06 2 KSP Residual norm 1.059429116239e-06 3 KSP Residual norm 4.004524784804e-07 4 KSP Residual norm 1.050186948327e-07 5 KSP Residual norm 5.073180513583e-08 6 KSP Residual norm 2.510513776297e-08 7 KSP Residual norm 1.211886495400e-08 8 KSP Residual norm 1.911963112131e-09 9 KSP Residual norm 3.005260864225e-10 2 SNES Function norm 3.117674497824e-10 0 KSP Residual norm 3.005042584730e-10 1 KSP Residual norm 1.120673922713e-10 2 KSP Residual norm 3.288439453292e-11 3 KSP Residual norm 5.822504321413e-12 4 KSP Residual norm 2.486684466178e-12 5 KSP Residual norm 1.198858055503e-12 6 KSP Residual norm 6.255669709502e-13 7 KSP Residual norm 1.544647758005e-13 8 KSP Residual norm 4.592122224907e-14 9 KSP Residual norm 4.984149547392e-15 10 KSP Residual norm 8.905129652955e-16 3 SNES Function norm 1.045594761851e-14 SNES Object: 4 MPI processes type: newtonls maximum iterations=50, maximum function evaluations=10000 tolerances: relative=1e-08, absolute=1e-50, solution=1e-08 total number of linear solver iterations=26 total number of function evaluations=4 norm schedule ALWAYS Jacobian is built using colored finite differences on a DMDA SNESLineSearch Object: 4 MPI processes type: bt interpolation: cubic alpha=1.000000e-04 maxlambda=1.000000e+00, minlambda=1.000000e-12 tolerances: relative=1.000000e-08, absolute=1.000000e-15, lambda=1.000000e-08 maximum iterations=40 KSP Object: 4 MPI processes type: gmres restart=30, using classical (unmodified) Gram-Schmidt orthogonalization with no iterative refinement happy breakdown tolerance=1e-30 maximum iterations=10000, initial guess is zero tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning using PRECONDITIONED norm type for convergence test PC Object: 4 MPI processes type: mg type is MULTIPLICATIVE, levels=6 cycles=v Cycles per PCApply=1 Not using Galerkin computed coarse grid matrices Coarse grid solver -- level 0 ------------------------------- KSP Object: (mg_coarse_) 4 MPI processes type: preonly maximum iterations=10000, initial guess is zero tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning not checking for convergence PC Object: (mg_coarse_) 4 MPI processes type: redundant First (color=0) of 4 PCs follows KSP Object: (mg_coarse_redundant_) 1 MPI process type: preonly maximum iterations=10000, initial guess is zero tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning not checking for convergence PC Object: (mg_coarse_redundant_) 1 MPI process type: lu out-of-place factorization tolerance for zero pivot 2.22045e-14 using diagonal shift on blocks to prevent zero pivot [INBLOCKS] matrix ordering: nd factor fill ratio given 5., needed 1.875 Factored matrix: Mat Object: (mg_coarse_redundant_) 1 MPI process type: seqaij rows=64, cols=64, bs=4 package used to perform factorization: petsc total: nonzeros=1920, allocated nonzeros=1920 using I-node routines: found 16 nodes, limit used is 5 linear system matrix, which is also used to construct the preconditioner: Mat Object: 1 MPI process type: seqaij rows=64, cols=64, bs=4 total: nonzeros=1024, allocated nonzeros=1024 total number of mallocs used during MatSetValues calls=0 using I-node routines: found 16 nodes, limit used is 5 linear system matrix, which is also used to construct the preconditioner: Mat Object: 4 MPI processes type: mpiaij rows=64, cols=64, bs=4 total: nonzeros=1024, allocated nonzeros=1024 total number of mallocs used during MatSetValues calls=0 Down solver (pre-smoother) on level 1 ------------------------------- KSP Object: (mg_levels_1_) 4 MPI processes type: chebyshev Chebyshev polynomial of first kind eigenvalue targets used: min 0.148269, max 1.63095 eigenvalues estimated via gmres: min 0.144902, max 1.48269 eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1] KSP Object: (mg_levels_1_esteig_) 4 MPI processes type: gmres restart=30, using classical (unmodified) Gram-Schmidt orthogonalization with no iterative refinement happy breakdown tolerance=1e-30 maximum iterations=10, initial guess is zero tolerances: relative=1e-12, absolute=1e-50, divergence=10000. left preconditioning using PRECONDITIONED norm type for convergence test estimating eigenvalues using a noisy random number generated right-hand side maximum iterations=2, nonzero initial guess tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning not checking for convergence PC Object: (mg_levels_1_) 4 MPI processes type: sor type = local_symmetric, iterations = 1, local iterations = 1, omega = 1. linear system matrix, which is also used to construct the preconditioner: Mat Object: 4 MPI processes type: mpiaij rows=196, cols=196, bs=4 total: nonzeros=3472, allocated nonzeros=3472 total number of mallocs used during MatSetValues calls=0 Up solver (post-smoother) same as down solver (pre-smoother) Down solver (pre-smoother) on level 2 ------------------------------- KSP Object: (mg_levels_2_) 4 MPI processes type: chebyshev Chebyshev polynomial of first kind eigenvalue targets used: min 0.149178, max 1.64096 eigenvalues estimated via gmres: min 0.0843938, max 1.49178 eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1] KSP Object: (mg_levels_2_esteig_) 4 MPI processes type: gmres restart=30, using classical (unmodified) Gram-Schmidt orthogonalization with no iterative refinement happy breakdown tolerance=1e-30 maximum iterations=10, initial guess is zero tolerances: relative=1e-12, absolute=1e-50, divergence=10000. left preconditioning using PRECONDITIONED norm type for convergence test estimating eigenvalues using a noisy random number generated right-hand side maximum iterations=2, nonzero initial guess tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning not checking for convergence PC Object: (mg_levels_2_) 4 MPI processes type: sor type = local_symmetric, iterations = 1, local iterations = 1, omega = 1. linear system matrix, which is also used to construct the preconditioner: Mat Object: 4 MPI processes type: mpiaij rows=676, cols=676, bs=4 total: nonzeros=12688, allocated nonzeros=12688 total number of mallocs used during MatSetValues calls=0 Up solver (post-smoother) same as down solver (pre-smoother) Down solver (pre-smoother) on level 3 ------------------------------- KSP Object: (mg_levels_3_) 4 MPI processes type: chebyshev Chebyshev polynomial of first kind eigenvalue targets used: min 0.146454, max 1.61099 eigenvalues estimated via gmres: min 0.0659153, max 1.46454 eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1] KSP Object: (mg_levels_3_esteig_) 4 MPI processes type: gmres restart=30, using classical (unmodified) Gram-Schmidt orthogonalization with no iterative refinement happy breakdown tolerance=1e-30 maximum iterations=10, initial guess is zero tolerances: relative=1e-12, absolute=1e-50, divergence=10000. left preconditioning using PRECONDITIONED norm type for convergence test estimating eigenvalues using a noisy random number generated right-hand side maximum iterations=2, nonzero initial guess tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning not checking for convergence PC Object: (mg_levels_3_) 4 MPI processes type: sor type = local_symmetric, iterations = 1, local iterations = 1, omega = 1. linear system matrix, which is also used to construct the preconditioner: Mat Object: 4 MPI processes type: mpiaij rows=2500, cols=2500, bs=4 total: nonzeros=48400, allocated nonzeros=48400 total number of mallocs used during MatSetValues calls=0 Up solver (post-smoother) same as down solver (pre-smoother) Down solver (pre-smoother) on level 4 ------------------------------- KSP Object: (mg_levels_4_) 4 MPI processes type: chebyshev Chebyshev polynomial of first kind eigenvalue targets used: min 0.141089, max 1.55197 eigenvalues estimated via gmres: min 0.044097, max 1.41089 eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1] KSP Object: (mg_levels_4_esteig_) 4 MPI processes type: gmres restart=30, using classical (unmodified) Gram-Schmidt orthogonalization with no iterative refinement happy breakdown tolerance=1e-30 maximum iterations=10, initial guess is zero tolerances: relative=1e-12, absolute=1e-50, divergence=10000. left preconditioning using PRECONDITIONED norm type for convergence test estimating eigenvalues using a noisy random number generated right-hand side maximum iterations=2, nonzero initial guess tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning not checking for convergence PC Object: (mg_levels_4_) 4 MPI processes type: sor type = local_symmetric, iterations = 1, local iterations = 1, omega = 1. linear system matrix, which is also used to construct the preconditioner: Mat Object: 4 MPI processes type: mpiaij rows=9604, cols=9604, bs=4 total: nonzeros=188944, allocated nonzeros=188944 total number of mallocs used during MatSetValues calls=0 Up solver (post-smoother) same as down solver (pre-smoother) Down solver (pre-smoother) on level 5 ------------------------------- KSP Object: (mg_levels_5_) 4 MPI processes type: chebyshev Chebyshev polynomial of first kind eigenvalue targets used: min 0.127956, max 1.40751 eigenvalues estimated via gmres: min 0.0380398, max 1.27956 eigenvalues estimated using gmres with transform: [0. 0.1; 0. 1.1] KSP Object: (mg_levels_5_esteig_) 4 MPI processes type: gmres restart=30, using classical (unmodified) Gram-Schmidt orthogonalization with no iterative refinement happy breakdown tolerance=1e-30 maximum iterations=10, initial guess is zero tolerances: relative=1e-12, absolute=1e-50, divergence=10000. left preconditioning using PRECONDITIONED norm type for convergence test estimating eigenvalues using a noisy random number generated right-hand side maximum iterations=2, nonzero initial guess tolerances: relative=1e-05, absolute=1e-50, divergence=10000. left preconditioning not checking for convergence PC Object: (mg_levels_5_) 4 MPI processes type: sor type = local_symmetric, iterations = 1, local iterations = 1, omega = 1. linear system matrix, which is also used to construct the preconditioner: Mat Object: 4 MPI processes type: mpiaij rows=37636, cols=37636, bs=4 total: nonzeros=746512, allocated nonzeros=746512 total number of mallocs used during MatSetValues calls=0 Up solver (post-smoother) same as down solver (pre-smoother) linear system matrix, which is also used to construct the preconditioner: Mat Object: 4 MPI processes type: mpiaij rows=37636, cols=37636, bs=4 total: nonzeros=746512, allocated nonzeros=746512 total number of mallocs used during MatSetValues calls=0 Number of SNES iterations = 3