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
