JuliaFEM.jl/docs/tutorials/2016-02-03-curved-interface.ipynb

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       "300"
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   ],
   "source": [
    "using JuliaFEM\n",
    "\n",
    "using JuliaFEM.Core: Node, Element, Seg2, Tri3, Quad4\n",
    "using JuliaFEM.Core: Problem, FieldProblem, BoundaryProblem, Dirichlet, Elasticity, Mortar\n",
    "using JuliaFEM.Core: Solver, SparseMatrixCOO\n",
    "using JuliaFEM.Core: get_elements, update!, calculate_normal_tangential_coordinates!,\n",
    "get_connectivity, get_field_assembly, get_boundary_problems, handle_overconstraint_error!\n",
    "\n",
    "using JuliaFEM.Preprocess: parse_aster_med_file\n",
    "\n",
    "import JuliaFEM.Core: solve_linear_system\n",
    "\n",
    "using PyPlot\n",
    "\n",
    "ENV[\"COLUMNS\"] = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "plot_segmentation (generic function with 1 method)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "function plot_it(field_problem, scaling_factor=1, time=0.0; show_undeformed=true,\n",
    "    show_deformed=true, show_node_ids=true, equal_axis=true, xmin=-1000, ymin=-1000, xmax=1000, ymax=1000)\n",
    "\n",
    "    function in_window(X)\n",
    "        xmin < X[1] < xmax || return false\n",
    "        ymin < X[2] < ymax || return false\n",
    "        return true\n",
    "    end\n",
    "\n",
    "    for element in field_problem.elements\n",
    "        conn = get_connectivity(element)\n",
    "        X = element(\"geometry\", time)\n",
    "        \n",
    "        u = element(\"displacement\", time)\n",
    "        x = X + scaling_factor*u\n",
    "\n",
    "        if show_undeformed\n",
    "            # undeformed\n",
    "            for i=1:length(X)\n",
    "                in_window(X[i]) || continue\n",
    "                px1 = X[i][1]\n",
    "                py1 = X[i][2]\n",
    "                px2 = X[mod(i,length(X))+1][1]\n",
    "                py2 = X[mod(i,length(X))+1][2]\n",
    "                PyPlot.plot([px1, px2], [py1, py2], \"k-\", alpha=0.5, label=\"undeformed\")\n",
    "            end\n",
    "        end\n",
    "\n",
    "        if show_deformed\n",
    "            # deformed\n",
    "            for i=1:length(x)\n",
    "                in_window(x[i]) || continue\n",
    "                px1 = x[i][1]\n",
    "                py1 = x[i][2]\n",
    "                px2 = x[mod(i,length(x))+1][1]\n",
    "                py2 = x[mod(i,length(x))+1][2]\n",
    "                PyPlot.plot([px1, px2], [py1, py2], \"g-\", alpha=0.5, label=\"deformed\")\n",
    "            end\n",
    "        end\n",
    "\n",
    "        if show_node_ids\n",
    "            for (i, c) in enumerate(conn)\n",
    "                in_window(x[i]) || continue\n",
    "                PyPlot.text(x[i][1], x[i][2], \"$c\")\n",
    "            end\n",
    "        end\n",
    "        \n",
    "    end\n",
    "\n",
    "    if equal_axis\n",
    "        PyPlot.axis(\"equal\")\n",
    "    end\n",
    "    #PyPlot.grid()\n",
    "\n",
    "end\n",
    "\n",
    "using JuliaFEM.Core: project_from_master_to_slave, project_from_slave_to_master\n",
    "\n",
    "function plot_normals(contact_problem, time, scale=3)\n",
    "    for element in get_elements(contact_problem)\n",
    "        X1 = element(\"geometry\", [-1.0], time)\n",
    "        X2 = element(\"geometry\", [ 1.0], time)\n",
    "        n1 = element(\"normal-tangential coordinates\", [-1.0], time)\n",
    "        n2 = element(\"normal-tangential coordinates\", [ 1.0], time)\n",
    "        plot([X1[1], X1[1]+scale*n1[1]], [X1[2], X1[2]+scale*n1[2]], \"-b\")\n",
    "        plot([X2[1], X2[1]+scale*n2[1]], [X2[2], X2[2]+scale*n2[2]], \"-b\")\n",
    "    end\n",
    "end\n",
    "\n",
    "function plot_contact_segmentation(slave_element, time)\n",
    "    for master_element in slave_element[\"master elements\"]\n",
    "        xi1a = project_from_master_to_slave(slave_element, master_element, [-1.0])\n",
    "        xi1b = project_from_master_to_slave(slave_element, master_element, [ 1.0])\n",
    "        xi1 = clamp([xi1a xi1b], -1.0, 1.0)\n",
    "        l = 1/2*(xi1[2]-xi1[1])\n",
    "        if abs(l) < 1.0e-9\n",
    "            #info(\"no contribution\")\n",
    "        end\n",
    "        Xs1 = slave_element(\"geometry\", [xi1[1]], time)\n",
    "        Xs2 = slave_element(\"geometry\", [xi1[2]], time)\n",
    "        xi2a = project_from_slave_to_master(slave_element, master_element, [xi1[1]])\n",
    "        xi2b = project_from_slave_to_master(slave_element, master_element, [xi1[2]])\n",
    "        Xm1 = master_element(\"geometry\", xi2a, time)\n",
    "        Xm2 = master_element(\"geometry\", xi2b, time)\n",
    "        plot([Xs1[1], Xm1[1]], [Xs1[2], Xm1[2]], \"-r\", alpha=0.3)\n",
    "        plot([Xs2[1], Xm2[1]], [Xs2[2], Xm2[2]], \"-r\", alpha=0.3)\n",
    "    end\n",
    "end\n",
    "\n",
    "function plot_segmentation(contact_problem)\n",
    "    for (i, element) in enumerate(get_elements(contact_problem))\n",
    "        haskey(element, \"master elements\") || continue\n",
    "        plot_contact_segmentation(element, 0.0)\n",
    "    end\n",
    "end\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: normal direction on slave side = [0.8944271909999159 0.4472135954999579]\n"
     ]
    }
   ],
   "source": [
    "function get_test_problem_1()\n",
    "\n",
    "    gap = [0.5, 0.0]\n",
    "    nodes = Dict{Int64, Node}(\n",
    "        1 => [0.0, 0.0],\n",
    "        2 => [2.0, 0.0],\n",
    "        3 => [1.0, 2.0],\n",
    "        4 => [0.0, 2.0],\n",
    "        5 => [2.0, 0.0]+gap,\n",
    "        6 => [4.0, 0.0]+gap,\n",
    "        7 => [4.0, 1.0]+gap,\n",
    "        8 => [2.0, 1.0]+gap)\n",
    "\n",
    "    E = 288.0\n",
    "    nu = 1.0/3.0\n",
    "\n",
    "    # field problem is plane stress linear elasticity\n",
    "    element1 = Quad4([1, 2, 3, 4])\n",
    "    element2 = Quad4([5, 6, 7, 8])\n",
    "    update!([element1, element2], \"geometry\", nodes)\n",
    "    update!([element1, element2], \"youngs modulus\", E)\n",
    "    update!([element1, element2], \"poissons ratio\", nu)\n",
    "    field_problem = Problem(Elasticity, \"block\", 2)\n",
    "    field_problem.properties.formulation = :plane_stress\n",
    "    push!(field_problem, element1, element2)\n",
    "\n",
    "    # dirichlet boundary conditions\n",
    "    bc1 = Seg2([1, 4])\n",
    "    bc2 = Seg2([6, 7])\n",
    "    update!([bc1, bc2], \"geometry\", nodes)\n",
    "    update!(bc1, \"displacement 1\",  0.0)\n",
    "    update!(bc1, \"displacement 2\",  0.0)\n",
    "    update!(bc2, \"displacement 1\",  0.0)\n",
    "    update!(bc2, \"displacement 2\",  0.0)\n",
    "    # name, unknown field, unknown field dimension\n",
    "    boundary_problem_1 = Problem(Dirichlet, \"ends\", 2, \"displacement\")\n",
    "    push!(boundary_problem_1, bc1, bc2)\n",
    "\n",
    "    # symmetry boundary condition\n",
    "    bc3 = Seg2([1, 2])\n",
    "    bc4 = Seg2([5, 6])\n",
    "    update!([bc3, bc4], \"geometry\", nodes)\n",
    "    update!([bc3, bc4], \"displacement 2\", 0.0)\n",
    "    boundary_problem_2 = Problem(Dirichlet, \"symmetry x-line\", 2, \"displacement\")\n",
    "    push!(boundary_problem_2, bc3, bc4)\n",
    "    \n",
    "    # mortar boundary condition between blocks (set left side as non-mortar side)\n",
    "    bc5 = Seg2([3, 2])\n",
    "    bc6 = Seg2([8, 5])\n",
    "    update!([bc5, bc6], \"geometry\", nodes)\n",
    "    calculate_normal_tangential_coordinates!(bc5, 0.0)\n",
    "    bc5[\"master elements\"] = [bc6]\n",
    "    boundary_problem_3 = Problem(Mortar, \"contact between bodies\", 2, \"displacement\")\n",
    "    push!(boundary_problem_3, bc5, bc6)\n",
    "    nt = bc5(\"normal-tangential coordinates\", [0.0], 0.0)[:,1]\n",
    "    info(\"normal direction on slave side = $(nt')\")\n",
    "\n",
    "    return field_problem, boundary_problem_1, boundary_problem_2, boundary_problem_3\n",
    "end\n",
    "field_problem, boundary_problem_1, boundary_problem_2, boundary_problem_3 = get_test_problem_1();"
   ]
  },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: normal direction on slave side = [0.8944271909999159 0.4472135954999579]\n",
      "INFO: solving linear system of 4 problems.\n",
      "INFO: System is overconstrained by 2 dofs.\n",
      "INFO: Overconstrained_nodes: 1, 6.\n",
      "INFO: Following dofs already constrained: 2, 12.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 1 with dofs 1, 2:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 1: ⋯ + 1.0*λ₁ ⋯\n",
      "INFO: dof 2: ⋯ + 1.0*λ₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 1: 1.0*u₁ = 0.0\n",
      "INFO: dof 2: 1.0*u₂ = 0.0 <-- overconstrained dof\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 2: ⋯ + 1.0*λ₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 2: 1.0*u₂ = 0.0 <-- overconstrained dof\n",
      "INFO: rank = 1, dofs = 1\n",
      "INFO: algorithm 1 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 2: 1.0*u₂ = 0.0\n",
      "INFO: \n",
      "INFO: \n",
      "INFO: SUMMARY for node id 6 with dofs 11, 12:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 11: ⋯ + 0.5*λ₁₁ ⋯\n",
      "INFO: dof 12: ⋯ + 0.5*λ₁₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 11: 0.5*u₁₁ = 0.0\n",
      "INFO: dof 12: 0.5*u₁₂ = 0.0 <-- overconstrained dof\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 12: ⋯ + 1.0*λ₁₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 12: 1.0*u₁₂ = 0.0 <-- overconstrained dof\n",
      "INFO: rank = 1, dofs = 1\n",
      "INFO: algorithm 1 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 12: 1.0*u₁₂ = 0.0\n",
      "INFO: \n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 2.\n",
      "INFO: Following dofs already constrained: 4.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 2 with dofs 3, 4:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 4: ⋯ + 1.0*λ₄ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 4: 1.0*u₄ = 0.0 <-- overconstrained dof\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 3: ⋯ + 0.57*λ₃ ⋯\n",
      "INFO: dof 4: ⋯ + 0.57*λ₄ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 3: 0.51*u₃ + 0.255*u₄ - 0.383*u₉ - 0.191*u₁₀ - 0.128*u₁₅ - 0.064*u₁₆ = 0.319\n",
      "INFO: dof 4: 0.255*u₃ - 0.51*u₄ - 0.191*u₉ + 0.383*u₁₀ - 0.064*u₁₅ + 0.128*u₁₆ = 0.0 <-- overconstrained dof\n",
      "INFO: ----- Related equations -----\n",
      "INFO: dof 3: 0.51*u₃ + 0.255*u₄ - 0.383*u₉ - 0.191*u₁₀ - 0.128*u₁₅ - 0.064*u₁₆ = 0.319\n",
      "INFO: dof 10: 1.0*u₁₀ = 0.0\n",
      "INFO: algorithm 1 solved issue? false\n",
      "INFO: algorithm 2 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 4: 0.255*u₃ - 0.51*u₄ - 0.191*u₉ + 0.383*u₁₀ - 0.064*u₁₅ + 0.128*u₁₆ = 0.0\n",
      "INFO: \n",
      "INFO: UMFPACK: solved in 1.722184181213379 seconds. norm = 0.7572029587976586\n",
      "INFO: solving linear system of 4 problems.\n"
     ]
    },
    {
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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f43da840390>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "field_problem, boundary_problem_1, boundary_problem_2, boundary_problem_3 = get_test_problem_1()\n",
    "solver = Solver(\"solve block problem\")\n",
    "push!(solver, field_problem, boundary_problem_1, boundary_problem_2, boundary_problem_3)\n",
    "#solver.is_linear_system = true\n",
    "call(solver)\n",
    "plot_it(field_problem)\n",
    "plot_segmentation(boundary_problem_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: System is overconstrained by 2 dofs.\n",
      "INFO: Overconstrained_nodes: 1, 6.\n",
      "INFO: Following dofs already constrained: 2, 12.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 1 with dofs 1, 2:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 1: ⋯ + 1.0*λ₁ ⋯\n",
      "INFO: dof 2: ⋯ + 1.0*λ₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 1: 1.0*u₁ = 0.0\n",
      "INFO: dof 2: 1.0*u₂ = 0.0 <-- overconstrained dof\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 2: ⋯ + 1.0*λ₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 2: 1.0*u₂ = 0.0 <-- overconstrained dof\n",
      "INFO: rank = 1, dofs = 1\n",
      "INFO: algorithm 1 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 2: 1.0*u₂ = 0.0\n",
      "INFO: \n",
      "INFO: \n",
      "INFO: SUMMARY for node id 6 with dofs 11, 12:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 11: ⋯ + 0.5*λ₁₁ ⋯\n",
      "INFO: dof 12: ⋯ + 0.5*λ₁₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 11: 0.5*u₁₁ = 0.0\n",
      "INFO: dof 12: 0.5*u₁₂ = 0.0 <-- overconstrained dof\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 12: ⋯ + 1.0*λ₁₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 12: 1.0*u₁₂ = 0.0 <-- overconstrained dof\n",
      "INFO: rank = 1, dofs = 1\n",
      "INFO: algorithm 1 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 12: 1.0*u₁₂ = 0.0\n",
      "INFO: \n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 2.\n",
      "INFO: Following dofs already constrained: 4.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 2 with dofs 3, 4:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 4: ⋯ + 1.0*λ₄ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 4: 1.0*u₄ = 0.0 <-- overconstrained dof\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 3: ⋯ + 0.57*λ₃ ⋯\n",
      "INFO: dof 4: ⋯ + 0.57*λ₄ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 3: 0.51*u₃ + 0.255*u₄ - 0.383*u₉ - 0.191*u₁₀ - 0.128*u₁₅ - 0.064*u₁₆ = 0.319\n",
      "INFO: dof 4: 0.255*u₃ - 0.51*u₄ - 0.191*u₉ + 0.383*u₁₀ - 0.064*u₁₅ + 0.128*u₁₆ = 0.0 <-- overconstrained dof\n",
      "INFO: ----- Related equations -----\n",
      "INFO: dof 3: 0.51*u₃ + 0.255*u₄ - 0.383*u₉ - 0.191*u₁₀ - 0.128*u₁₅ - 0.064*u₁₆ = 0.319\n",
      "INFO: dof 10: 1.0*u₁₀ = 0.0\n",
      "INFO: algorithm 1 solved issue? false\n",
      "INFO: algorithm 2 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 4: 0.255*u₃ - 0.51*u₄ - 0.191*u₉ + 0.383*u₁₀ - 0.064*u₁₅ + 0.128*u₁₆ = 0.0\n",
      "INFO: \n",
      "INFO: UMFPACK: solved in 0.002077817916870117 seconds. norm = 0.7572029587976586\n",
      "INFO: Converged in 2 iterations.\n"
     ]
    }
   ],
   "source": [
    "@assert isapprox(norm(field_problem.assembly.u), 0.7572029587976579)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: [0.24895296909147305 0.18847925373355964]\n",
      "INFO: [-0.12546065165888792 0.0]\n"
     ]
    }
   ],
   "source": [
    "info(field_problem.elements[1](\"displacement\", [ 1.0, -1.0], 0.0)')\n",
    "info(field_problem.elements[2](\"displacement\", [-1.0, -1.0], 0.0)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Curved interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Found 5 element sets: SYM23, LOWER_TO_UPPER, LOAD, SYM13, UPPER_TO_LOWER\n",
      "INFO: created 170 field elements.\n",
      "INFO: slave element UPPER_TO_LOWER: midpoint = [0.4147584594687138,0.40581289439367096], nt coordinate = [0.06698025853929859 -0.995493336386047\n",
      " 0.995493336386047 0.06698025853929859]\n",
      "INFO: # of master elements: 5\n",
      "INFO: # of slave elements: 6\n",
      "INFO: solving linear system of 4 problems.\n",
      "INFO: System is overconstrained by 1 dofs.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function curved_interface_problem(meshfile=\"/geometry/2d_curved_interface/MESH_SPARSE.med\")\n",
    "\n",
    "    mesh = parse_aster_med_file(Pkg.dir(\"JuliaFEM\")*meshfile)\n",
    "    \n",
    "    field_problem = Problem(Elasticity, \"two parts with curved interface\", 2)\n",
    "    field_problem.properties.formulation = :plane_stress\n",
    "\n",
    "    # field problems\n",
    "    mapping = Dict(:QU4 => Quad4, :TR3 => Tri3)\n",
    "    for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
    "        haskey(mapping, eltype) || continue\n",
    "        element = mapping[eltype](elcon)\n",
    "        update!(element, \"geometry\", mesh[\"nodes\"])\n",
    "        element[\"youngs modulus\"] = 2880.0\n",
    "        element[\"poissons ratio\"] = 1/3\n",
    "        push!(field_problem, element)\n",
    "    end\n",
    "    for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
    "        eltype == :SE2 || continue\n",
    "        elset == :LOAD || continue\n",
    "        element = Seg2(elcon)\n",
    "        update!(element, \"geometry\", mesh[\"nodes\"])\n",
    "        update!(element, \"displacement traction force 2\", -100.0)\n",
    "        push!(field_problem, element)\n",
    "    end\n",
    "\n",
    "    # boundary conditions\n",
    "    sym13 = Problem(Dirichlet, \"SYM13\", 2, \"displacement\")\n",
    "    sym23 = Problem(Dirichlet, \"SYM23\", 2, \"displacement\")\n",
    "\n",
    "    for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
    "        eltype == :SE2 || continue\n",
    "        elset == :SYM13 || continue\n",
    "        element = Seg2(elcon)\n",
    "        update!(element, \"geometry\", mesh[\"nodes\"])\n",
    "        push!(sym13, element)\n",
    "    end\n",
    "    for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
    "        eltype == :SE2 || continue\n",
    "        elset == :SYM23 || continue\n",
    "        element = Seg2(elcon)\n",
    "        update!(element, \"geometry\", mesh[\"nodes\"])\n",
    "        push!(sym23, element)\n",
    "    end\n",
    "\n",
    "    update!(get_elements(sym13), \"displacement 2\", 0.0)\n",
    "    update!(get_elements(sym23), \"displacement 1\", 0.0)\n",
    "\n",
    "    info(\"created $(length(get_elements(field_problem))) field elements.\")\n",
    "#    info(\"created $(length(get_elements(boundary_problem))) boundary elements.\")\n",
    "\n",
    "    slave_surface = :UPPER_TO_LOWER\n",
    "    master_surface = :LOWER_TO_UPPER\n",
    "\n",
    "    contact = Problem(Mortar, \"tie contact between blocks\", 2, \"displacement\")\n",
    "    master_elements = Element[]\n",
    "    for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
    "        eltype == :SE2 || continue\n",
    "        elset == master_surface || continue\n",
    "        element = Seg2(elcon)\n",
    "        update!(element, \"geometry\", mesh[\"nodes\"])\n",
    "        push!(master_elements, element)\n",
    "        push!(contact, element)\n",
    "    end\n",
    "\n",
    "    first = true\n",
    "    slave_elements = Element[]\n",
    "    for (elid, (eltype, elset, elcon)) in mesh[\"connectivity\"]\n",
    "        eltype == :SE2 || continue\n",
    "        elset == slave_surface || continue\n",
    "        element = Seg2(elcon)\n",
    "        update!(element, \"geometry\", mesh[\"nodes\"])\n",
    "        element[\"master elements\"] = master_elements\n",
    "        push!(slave_elements, element)\n",
    "        push!(contact, element)\n",
    "    end\n",
    "    calculate_normal_tangential_coordinates!(slave_elements, 0.0)\n",
    "    if first\n",
    "        element = slave_elements[1]\n",
    "        Q = element(\"normal-tangential coordinates\", [0.0], 0.0)\n",
    "        X = element(\"geometry\", [0.0], 0.0)\n",
    "        info(\"slave element $slave_surface: midpoint = $X, nt coordinate = $Q\")\n",
    "        first = false\n",
    "    end\n",
    "\n",
    "    info(\"# of master elements: $(length(master_elements))\")\n",
    "    info(\"# of slave elements: $(length(slave_elements))\")\n",
    "\n",
    "    return field_problem, sym13, sym23, contact\n",
    "\n",
    "end\n",
    "\n",
    "field_problem, sym13, sym23, contact = curved_interface_problem()\n",
    "solver = Solver(\"solver curved interface\")\n",
    "\n",
    "push!(solver, field_problem, sym13, sym23, contact)\n",
    "\n",
    "#solver.is_linear_system = true\n",
    "call(solver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f43d83f3210>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(-0.1,1.1,0.3,0.52)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Overconstrained_nodes: 47.\n",
      "INFO: Following dofs already constrained: 93.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 47 with dofs 93, 94:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 93: ⋯ + 0.062*λ₉₃ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 93: 0.062*u₉₃ = 0.0 <-- overconstrained dof\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 93: ⋯ + 0.085*λ₉₃ ⋯\n",
      "INFO: dof 94: ⋯ + 0.085*λ₉₄ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 93: 0.027*u₉ - 0.081*u₁₀ - 0.0*u₁₁ - 0.0*u₁₂ + 0.027*u₉₃ + 0.081*u₉₄ = 0.0 <-- overconstrained dof\n",
      "INFO: dof 94: 0.081*u₉ - 0.027*u₁₀ + 0.0*u₁₁ - 0.0*u₁₂ - 0.081*u₉₃ + 0.027*u₉₄ = -0.0\n",
      "INFO: ----- Related equations -----\n",
      "INFO: dof 94: 0.081*u₉ - 0.027*u₁₀ + 0.0*u₁₁ - 0.0*u₁₂ - 0.081*u₉₃ + 0.027*u₉₄ = -0.0\n",
      "INFO: dof 9: 0.062*u₉ = 0.0\n",
      "INFO: algorithm 1 solved issue? false\n",
      "INFO: algorithm 2 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 93: 0.027*u₉ - 0.081*u₁₀ - 0.0*u₁₁ - 0.0*u₁₂ + 0.027*u₉₃ + 0.081*u₉₄ = 0.0\n",
      "INFO: \n",
      "INFO: UMFPACK: solved in 0.0029261112213134766 seconds. norm = 0.2117161697202773\n",
      "INFO: solving linear system of 4 problems.\n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 47.\n",
      "INFO: Following dofs already constrained: 93.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 47 with dofs 93, 94:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 93: ⋯ + 0.062*λ₉₃ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 93: 0.062*u₉₃ = 0.0 <-- overconstrained dof\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 93: ⋯ + 0.085*λ₉₃ ⋯\n",
      "INFO: dof 94: ⋯ + 0.085*λ₉₄ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 93: 0.027*u₉ - 0.081*u₁₀ - 0.0*u₁₁ - 0.0*u₁₂ + 0.027*u₉₃ + 0.081*u₉₄ = 0.0 <-- overconstrained dof\n",
      "INFO: dof 94: 0.081*u₉ - 0.027*u₁₀ + 0.0*u₁₁ - 0.0*u₁₂ - 0.081*u₉₃ + 0.027*u₉₄ = -0.0\n",
      "INFO: ----- Related equations -----\n",
      "INFO: dof 94: 0.081*u₉ - 0.027*u₁₀ + 0.0*u₁₁ - 0.0*u₁₂ - 0.081*u₉₃ + 0.027*u₉₄ = -0.0\n",
      "INFO: dof 9: 0.062*u₉ = 0.0\n",
      "INFO: algorithm 1 solved issue? false\n",
      "INFO: algorithm 2 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 93: 0.027*u₉ - 0.081*u₁₀ - 0.0*u₁₁ - 0.0*u₁₂ + 0.027*u₉₃ + 0.081*u₉₄ = 0.0\n",
      "INFO: \n",
      "INFO: UMFPACK: solved in 0.002628803253173828 seconds. norm = 0.2117161697202773\n",
      "INFO: Converged in 2 iterations.\n"
     ]
    }
   ],
   "source": [
    "figure(figsize=(12, 4))\n",
    "plot_it(field_problem; show_undeformed=false, equal_axis=false, show_node_ids=false)\n",
    "xlim(-0.1, 1.1)\n",
    "ylim(0.30, 0.52)\n",
    "axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Found 6 element sets: SYM23, BLOCK_TO_CYLINDER, CYLINDER_TO_BLOCK, CYLINDER, GROUND, BLOCK\n",
      "INFO: # of master elements: 27\n"
     ]
    }
   ],
   "source": [
    "using JuliaFEM.Preprocess: aster_create_elements\n",
    "\n",
    "function create_hertzian_problem(meshfile=\"/geometry/2d_cylinder_roller_contact/MESH_REFINED_3.med\")\n",
    "\n",
    "    mesh = parse_aster_med_file(Pkg.dir(\"JuliaFEM\")*meshfile)\n",
    "    \n",
    "    body1 = Problem(Elasticity, \"cylinder\", 2)\n",
    "    body1_elements = aster_create_elements(mesh, :CYLINDER, :TR3)\n",
    "    body1.properties.formulation = :plane_strain\n",
    "    update!(body1_elements, \"youngs modulus\", 210e3)\n",
    "    update!(body1_elements, \"poissons ratio\", 0.3)\n",
    "    push!(body1, body1_elements...)\n",
    "\n",
    "    body2 = Problem(Elasticity, \"block\", 2)\n",
    "    body2_elements = aster_create_elements(mesh, :BLOCK, :TR3)\n",
    "    body2.properties.formulation = :plane_strain\n",
    "    update!(body2_elements, \"youngs modulus\", 70.0e3)\n",
    "    update!(body2_elements, \"poissons ratio\", 0.3)\n",
    "    push!(body2, body2_elements...)\n",
    "\n",
    "    # boundary conditions\n",
    "    bc1 = Problem(Dirichlet, \"ground\", 2, \"displacement\")\n",
    "    bc1_elements = aster_create_elements(mesh, :GROUND, :SE2)\n",
    "    update!(bc1_elements, \"displacement\", 0.0)\n",
    "    push!(bc1, bc1_elements...)\n",
    "\n",
    "    bc2 = Problem(Dirichlet, \"symmetry line\", 2, \"displacement\")\n",
    "    bc2_elements = aster_create_elements(mesh, :SYM23, :SE2)\n",
    "    update!(bc2_elements, \"displacement 1\", 0.0)\n",
    "    push!(bc2, bc2_elements...)\n",
    "\n",
    "    # contact\n",
    "    bc3 = Problem(Mortar, \"contact interface\", 2, \"displacement\")\n",
    "    bc3_slave_elements = aster_create_elements(mesh, :CYLINDER_TO_BLOCK, :SE2)\n",
    "    bc3_master_elements = aster_create_elements(mesh, :BLOCK_TO_CYLINDER, :SE2)\n",
    "    update!(bc3_slave_elements, \"master elements\", bc3_master_elements)\n",
    "    calculate_normal_tangential_coordinates!(bc3_slave_elements, 0.0)\n",
    "    push!(bc3, bc3_slave_elements...)\n",
    "    push!(bc3, bc3_master_elements...)\n",
    "    info(\"# of master elements: $(length(bc3_master_elements))\")\n",
    "    info(\"# of slave elements: $(length(bc3_slave_elements))\")\n",
    "    fel = bc3_slave_elements[1]\n",
    "    n = fel(\"normals\", [0.0], 0.0)\n",
    "    X1 = fel(\"geometry\", [-1.0], 0.0)\n",
    "    X2 = fel(\"geometry\", [ 1.0], 0.0)\n",
    "    info(\"normal direction of first contact element: $n\")\n",
    "    info(\"element geometry: from $X1 to $X2\")\n",
    "\n",
    "    return body1, body2, bc1, bc2, bc3\n",
    "\n",
    "end\n",
    "create_hertzian_problem();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: # of slave elements: 37\n",
      "INFO: normal direction of first contact element: [0.33004993336440486,-0.9438504633679171]\n",
      "INFO: element geometry: from [17.19187882421571,203.04854312703935] to [15.811388300842912,202.56583509747463]\n",
      "INFO: Found 6 element sets: SYM23, BLOCK_TO_CYLINDER, CYLINDER_TO_BLOCK, CYLINDER, GROUND, BLOCK\n",
      "INFO: # of master elements: 27\n",
      "INFO: # of slave elements: 37\n",
      "INFO: normal direction of first contact element: [0.33004993336440486,-0.9438504633679171]\n",
      "INFO: element geometry: from [17.19187882421571,203.04854312703935] to [15.811388300842912,202.56583509747463]\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: Hacking nodal force.\n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 11.\n",
      "INFO: Following dofs already constrained: 21.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 11 with dofs 21, 22:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 21: ⋯ + 5.0*λ₂₁ ⋯\n",
      "INFO: dof 22: ⋯ + 5.0*λ₂₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 21: 5.0*u₂₁ = 0.0 <-- overconstrained dof\n",
      "INFO: dof 22: 5.0*u₂₂ = 0.0\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 21: ⋯ + 5.0*λ₂₁ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 21: 5.0*u₂₁ = 0.0 <-- overconstrained dof\n",
      "INFO: rank = 1, dofs = 1\n",
      "INFO: algorithm 1 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 21: 1.0*u₂₁ = 0.0\n",
      "INFO: \n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [490,491,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596]\n",
      "INFO: PDASS: active nodes: [491]\n",
      "INFO: PDASS: inactive nodes: [490,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596]\n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 491.\n",
      "INFO: Following dofs already constrained: 981.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 491 with dofs 981, 982:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 981: ⋯ + 0.1*λ₉₈₁ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 981: 0.1*u₉₈₁ = -0.0 <-- overconstrained dof\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 981: ⋯ + 0.1*λ₉₈₁ ⋯\n",
      "INFO: dof 982: ⋯ + 0.1*λ₉₈₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 981: 0.0*u₁₁₂ + 0.0*u₁₁₃ - 0.0*u₁₁₄ - 0.0*u₁₁₅ + 0.1*u₁₁₆ + 0.0*u₉₈₁ - 0.1*u₉₈₂ = -0.0 <-- overconstrained dof\n",
      "INFO: dof 982: 0.0*u₁₁₁ + 0.0*u₁₁₃ + 0.0*u₁₁₄ - 0.1*u₁₁₅ - 0.0*u₁₁₆ + 0.1*u₉₈₁ + 0.0*u₉₈₂ = -0.0\n",
      "INFO: ----- Related equations -----\n",
      "INFO: dof 982: 0.0*u₁₁₁ + 0.0*u₁₁₃ + 0.0*u₁₁₄ - 0.1*u₁₁₅ - 0.0*u₁₁₆ + 0.1*u₉₈₁ + 0.0*u₉₈₂ = -0.0\n",
      "INFO: dof 115: 0.1*u₁₁₅ = 0.0\n",
      "INFO: algorithm 1 solved issue? false\n",
      "INFO: algorithm 2 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 981: 0.0*u₁₁₂ + 0.0*u₁₁₃ - 0.0*u₁₁₄ - 0.0*u₁₁₅ + 0.1*u₁₁₆ + 0.0*u₉₈₁ - 0.1*u₉₈₂ = -0.0\n",
      "INFO: \n",
      "INFO: UMFPACK: solved in 0.14606285095214844 seconds. norm = 85.08623527509272\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: Hacking nodal force.\n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 11.\n",
      "INFO: Following dofs already constrained: 21.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 11 with dofs 21, 22:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 21: ⋯ + 5.0*λ₂₁ ⋯\n",
      "INFO: dof 22: ⋯ + 5.0*λ₂₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 21: 5.0*u₂₁ = 0.0 <-- overconstrained dof\n",
      "INFO: dof 22: 5.0*u₂₂ = 0.0\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 21: ⋯ + 5.0*λ₂₁ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 21: 5.0*u₂₁ = 0.0 <-- overconstrained dof\n",
      "INFO: rank = 1, dofs = 1\n",
      "INFO: algorithm 1 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 21: 1.0*u₂₁ = 0.0\n",
      "INFO: \n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [490,491,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596]\n",
      "INFO: PDASS: active nodes: [491,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596]\n",
      "INFO: PDASS: inactive nodes: [490,561,562,563,564,565,566,567,568,569,570,571,572]\n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 491.\n",
      "INFO: Following dofs already constrained: 981.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 491 with dofs 981, 982:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 981: ⋯ + 0.1*λ₉₈₁ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 981: 0.1*u₉₈₁ = -0.0 <-- overconstrained dof\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 981: ⋯ + 0.1*λ₉₈₁ ⋯\n",
      "INFO: dof 982: ⋯ + 0.1*λ₉₈₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 981: 0.0*u₁₁₂ + 0.0*u₁₁₃ - 0.0*u₁₁₄ - 0.0*u₁₁₅ + 0.1*u₁₁₆ + 0.0*u₉₈₁ - 0.1*u₉₈₂ = -0.0 <-- overconstrained dof\n",
      "INFO: dof 982: 0.0*u₁₁₁ + 0.0*u₁₁₃ + 0.0*u₁₁₄ - 0.1*u₁₁₅ - 0.0*u₁₁₆ + 0.1*u₉₈₁ + 0.0*u₉₈₂ = -0.0\n",
      "INFO: ----- Related equations -----\n",
      "INFO: dof 982: 0.0*u₁₁₁ + 0.0*u₁₁₃ + 0.0*u₁₁₄ - 0.1*u₁₁₅ - 0.0*u₁₁₆ + 0.1*u₉₈₁ + 0.0*u₉₈₂ = -0.0\n",
      "INFO: dof 115: 0.1*u₁₁₅ = 0.0\n",
      "INFO: algorithm 1 solved issue? false\n",
      "INFO: algorithm 2 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 981: 0.0*u₁₁₂ + 0.0*u₁₁₃ - 0.0*u₁₁₄ - 0.0*u₁₁₅ + 0.1*u₁₁₆ + 0.0*u₉₈₁ - 0.1*u₉₈₂ = -0.0\n",
      "INFO: \n",
      "INFO: UMFPACK: solved in 0.026980876922607422 seconds. norm = 59.83136581257375\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: Hacking nodal force.\n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 11.\n",
      "INFO: Following dofs already constrained: 21.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 11 with dofs 21, 22:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 21: ⋯ + 5.0*λ₂₁ ⋯\n",
      "INFO: dof 22: ⋯ + 5.0*λ₂₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 21: 5.0*u₂₁ = 0.0 <-- overconstrained dof\n",
      "INFO: dof 22: 5.0*u₂₂ = 0.0\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 21: ⋯ + 5.0*λ₂₁ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 21: 5.0*u₂₁ = 0.0 <-- overconstrained dof\n",
      "INFO: rank = 1, dofs = 1\n",
      "INFO: algorithm 1 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 21: 1.0*u₂₁ = 0.0\n",
      "INFO: \n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [490,491,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596]\n",
      "INFO: PDASS: active nodes: [491,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596]\n",
      "INFO: PDASS: inactive nodes: [490,561,562,563,564,565,566,567,568,569,570,571,572,573,574]\n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 491.\n",
      "INFO: Following dofs already constrained: 981.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 491 with dofs 981, 982:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 981: ⋯ + 0.1*λ₉₈₁ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 981: 0.1*u₉₈₁ = -0.0 <-- overconstrained dof\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 981: ⋯ + 0.1*λ₉₈₁ ⋯\n",
      "INFO: dof 982: ⋯ + 0.1*λ₉₈₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 981: 0.0*u₁₁₂ + 0.0*u₁₁₃ - 0.0*u₁₁₄ - 0.0*u₁₁₅ + 0.1*u₁₁₆ + 0.0*u₉₈₁ - 0.1*u₉₈₂ = -0.0 <-- overconstrained dof\n",
      "INFO: dof 982: 0.0*u₁₁₁ + 0.0*u₁₁₃ + 0.0*u₁₁₄ - 0.1*u₁₁₅ - 0.0*u₁₁₆ + 0.1*u₉₈₁ + 0.0*u₉₈₂ = -0.0\n",
      "INFO: ----- Related equations -----\n",
      "INFO: dof 982: 0.0*u₁₁₁ + 0.0*u₁₁₃ + 0.0*u₁₁₄ - 0.1*u₁₁₅ - 0.0*u₁₁₆ + 0.1*u₉₈₁ + 0.0*u₉₈₂ = -0.0\n",
      "INFO: dof 115: 0.1*u₁₁₅ = 0.0\n",
      "INFO: algorithm 1 solved issue? false\n",
      "INFO: algorithm 2 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 981: 0.0*u₁₁₂ + 0.0*u₁₁₃ - 0.0*u₁₁₄ - 0.0*u₁₁₅ + 0.1*u₁₁₆ + 0.0*u₉₈₁ - 0.1*u₉₈₂ = -0.0\n",
      "INFO: \n",
      "INFO: UMFPACK: solved in 0.02696514129638672 seconds. norm = 52.776611096750806\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: Hacking nodal force.\n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 11.\n",
      "INFO: Following dofs already constrained: 21.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 11 with dofs 21, 22:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 21: ⋯ + 5.0*λ₂₁ ⋯\n",
      "INFO: dof 22: ⋯ + 5.0*λ₂₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 21: 5.0*u₂₁ = 0.0 <-- overconstrained dof\n",
      "INFO: dof 22: 5.0*u₂₂ = 0.0\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 21: ⋯ + 5.0*λ₂₁ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 21: 5.0*u₂₁ = 0.0 <-- overconstrained dof\n",
      "INFO: rank = 1, dofs = 1\n",
      "INFO: algorithm 1 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 21: 1.0*u₂₁ = 0.0\n",
      "INFO: \n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [490,491,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596]\n",
      "INFO: PDASS: active nodes: [491,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596]\n",
      "INFO: PDASS: inactive nodes: [490,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576]\n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 491.\n",
      "INFO: Following dofs already constrained: 981.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 491 with dofs 981, 982:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 981: ⋯ + 0.1*λ₉₈₁ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 981: 0.1*u₉₈₁ = -0.0 <-- overconstrained dof\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 981: ⋯ + 0.1*λ₉₈₁ ⋯\n",
      "INFO: dof 982: ⋯ + 0.1*λ₉₈₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 981: 0.0*u₁₁₂ + 0.0*u₁₁₃ - 0.0*u₁₁₄ - 0.0*u₁₁₅ + 0.1*u₁₁₆ + 0.0*u₉₈₁ - 0.1*u₉₈₂ = -0.0 <-- overconstrained dof\n",
      "INFO: dof 982: 0.0*u₁₁₁ + 0.0*u₁₁₃ + 0.0*u₁₁₄ - 0.1*u₁₁₅ - 0.0*u₁₁₆ + 0.1*u₉₈₁ + 0.0*u₉₈₂ = -0.0\n",
      "INFO: ----- Related equations -----\n",
      "INFO: dof 982: 0.0*u₁₁₁ + 0.0*u₁₁₃ + 0.0*u₁₁₄ - 0.1*u₁₁₅ - 0.0*u₁₁₆ + 0.1*u₉₈₁ + 0.0*u₉₈₂ = -0.0\n",
      "INFO: dof 115: 0.1*u₁₁₅ = 0.0\n",
      "INFO: algorithm 1 solved issue? false\n",
      "INFO: algorithm 2 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 981: 0.0*u₁₁₂ + 0.0*u₁₁₃ - 0.0*u₁₁₄ - 0.0*u₁₁₅ + 0.1*u₁₁₆ + 0.0*u₉₈₁ - 0.1*u₉₈₂ = -0.0\n",
      "INFO: \n",
      "INFO: UMFPACK: solved in 0.02712392807006836 seconds. norm = 48.47259883552261\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: Hacking nodal force.\n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 11.\n",
      "INFO: Following dofs already constrained: 21.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 11 with dofs 21, 22:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 21: ⋯ + 5.0*λ₂₁ ⋯\n",
      "INFO: dof 22: ⋯ + 5.0*λ₂₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 21: 5.0*u₂₁ = 0.0 <-- overconstrained dof\n",
      "INFO: dof 22: 5.0*u₂₂ = 0.0\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 21: ⋯ + 5.0*λ₂₁ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 21: 5.0*u₂₁ = 0.0 <-- overconstrained dof\n",
      "INFO: rank = 1, dofs = 1\n",
      "INFO: algorithm 1 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 21: 1.0*u₂₁ = 0.0\n",
      "INFO: \n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [490,491,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596]\n",
      "INFO: PDASS: active nodes: [491,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596]\n",
      "INFO: PDASS: inactive nodes: [490,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578]\n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 491.\n",
      "INFO: Following dofs already constrained: 981.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 491 with dofs 981, 982:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 981: ⋯ + 0.1*λ₉₈₁ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 981: 0.1*u₉₈₁ = -0.0 <-- overconstrained dof\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 981: ⋯ + 0.1*λ₉₈₁ ⋯\n",
      "INFO: dof 982: ⋯ + 0.1*λ₉₈₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 981: 0.0*u₁₁₂ + 0.0*u₁₁₃ - 0.0*u₁₁₄ - 0.0*u₁₁₅ + 0.1*u₁₁₆ + 0.0*u₉₈₁ - 0.1*u₉₈₂ = -0.0 <-- overconstrained dof\n",
      "INFO: dof 982: 0.0*u₁₁₁ + 0.0*u₁₁₃ + 0.0*u₁₁₄ - 0.1*u₁₁₅ - 0.0*u₁₁₆ + 0.1*u₉₈₁ + 0.0*u₉₈₂ = -0.0\n",
      "INFO: ----- Related equations -----\n",
      "INFO: dof 982: 0.0*u₁₁₁ + 0.0*u₁₁₃ + 0.0*u₁₁₄ - 0.1*u₁₁₅ - 0.0*u₁₁₆ + 0.1*u₉₈₁ + 0.0*u₉₈₂ = -0.0\n",
      "INFO: dof 115: 0.1*u₁₁₅ = 0.0\n",
      "INFO: algorithm 1 solved issue? false\n",
      "INFO: algorithm 2 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 981: 0.0*u₁₁₂ + 0.0*u₁₁₃ - 0.0*u₁₁₄ - 0.0*u₁₁₅ + 0.1*u₁₁₆ + 0.0*u₉₈₁ - 0.1*u₉₈₂ = -0.0\n",
      "INFO: \n",
      "INFO: UMFPACK: solved in 0.03039407730102539 seconds. norm = 46.17282278249895\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: Hacking nodal force.\n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 11.\n",
      "INFO: Following dofs already constrained: 21.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 11 with dofs 21, 22:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 21: ⋯ + 5.0*λ₂₁ ⋯\n",
      "INFO: dof 22: ⋯ + 5.0*λ₂₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 21: 5.0*u₂₁ = 0.0 <-- overconstrained dof\n",
      "INFO: dof 22: 5.0*u₂₂ = 0.0\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 21: ⋯ + 5.0*λ₂₁ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 21: 5.0*u₂₁ = 0.0 <-- overconstrained dof\n",
      "INFO: rank = 1, dofs = 1\n",
      "INFO: algorithm 1 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 21: 1.0*u₂₁ = 0.0\n",
      "INFO: \n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [490,491,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596]\n",
      "INFO: PDASS: active nodes: [491,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596]\n",
      "INFO: PDASS: inactive nodes: [490,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579]\n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 491.\n",
      "INFO: Following dofs already constrained: 981.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 491 with dofs 981, 982:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 981: ⋯ + 0.1*λ₉₈₁ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 981: 0.1*u₉₈₁ = -0.0 <-- overconstrained dof\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 981: ⋯ + 0.1*λ₉₈₁ ⋯\n",
      "INFO: dof 982: ⋯ + 0.1*λ₉₈₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 981: 0.0*u₁₁₂ + 0.0*u₁₁₃ - 0.0*u₁₁₄ - 0.0*u₁₁₅ + 0.1*u₁₁₆ + 0.0*u₉₈₁ - 0.1*u₉₈₂ = -0.0 <-- overconstrained dof\n",
      "INFO: dof 982: 0.0*u₁₁₁ + 0.0*u₁₁₃ + 0.0*u₁₁₄ - 0.1*u₁₁₅ - 0.0*u₁₁₆ + 0.1*u₉₈₁ + 0.0*u₉₈₂ = -0.0\n",
      "INFO: ----- Related equations -----\n",
      "INFO: dof 982: 0.0*u₁₁₁ + 0.0*u₁₁₃ + 0.0*u₁₁₄ - 0.1*u₁₁₅ - 0.0*u₁₁₆ + 0.1*u₉₈₁ + 0.0*u₉₈₂ = -0.0\n",
      "INFO: dof 115: 0.1*u₁₁₅ = 0.0\n",
      "INFO: algorithm 1 solved issue? false\n",
      "INFO: algorithm 2 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 981: 0.0*u₁₁₂ + 0.0*u₁₁₃ - 0.0*u₁₁₄ - 0.0*u₁₁₅ + 0.1*u₁₁₆ + 0.0*u₉₈₁ - 0.1*u₉₈₂ = -0.0\n",
      "INFO: \n",
      "INFO: UMFPACK: solved in 0.032618045806884766 seconds. norm = 45.75796140962765\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: Hacking nodal force.\n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 11.\n",
      "INFO: Following dofs already constrained: 21.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 11 with dofs 21, 22:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 21: ⋯ + 5.0*λ₂₁ ⋯\n",
      "INFO: dof 22: ⋯ + 5.0*λ₂₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 21: 5.0*u₂₁ = 0.0 <-- overconstrained dof\n",
      "INFO: dof 22: 5.0*u₂₂ = 0.0\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 21: ⋯ + 5.0*λ₂₁ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 21: 5.0*u₂₁ = 0.0 <-- overconstrained dof\n",
      "INFO: rank = 1, dofs = 1\n",
      "INFO: algorithm 1 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 21: 1.0*u₂₁ = 0.0\n",
      "INFO: \n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [490,491,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596]\n",
      "INFO: PDASS: active nodes: [491,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596]\n",
      "INFO: PDASS: inactive nodes: [490,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580]\n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 491.\n",
      "INFO: Following dofs already constrained: 981.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 491 with dofs 981, 982:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 981: ⋯ + 0.1*λ₉₈₁ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 981: 0.1*u₉₈₁ = -0.0 <-- overconstrained dof\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 981: ⋯ + 0.1*λ₉₈₁ ⋯\n",
      "INFO: dof 982: ⋯ + 0.1*λ₉₈₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 981: 0.0*u₁₁₂ + 0.0*u₁₁₃ - 0.0*u₁₁₄ - 0.0*u₁₁₅ + 0.1*u₁₁₆ + 0.0*u₉₈₁ - 0.1*u₉₈₂ = -0.0 <-- overconstrained dof\n",
      "INFO: dof 982: 0.0*u₁₁₁ + 0.0*u₁₁₃ + 0.0*u₁₁₄ - 0.1*u₁₁₅ - 0.0*u₁₁₆ + 0.1*u₉₈₁ + 0.0*u₉₈₂ = -0.0\n",
      "INFO: ----- Related equations -----\n",
      "INFO: dof 982: 0.0*u₁₁₁ + 0.0*u₁₁₃ + 0.0*u₁₁₄ - 0.1*u₁₁₅ - 0.0*u₁₁₆ + 0.1*u₉₈₁ + 0.0*u₉₈₂ = -0.0\n",
      "INFO: dof 115: 0.1*u₁₁₅ = 0.0\n",
      "INFO: algorithm 1 solved issue? false\n",
      "INFO: algorithm 2 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 981: 0.0*u₁₁₂ + 0.0*u₁₁₃ - 0.0*u₁₁₄ - 0.0*u₁₁₅ + 0.1*u₁₁₆ + 0.0*u₉₈₁ - 0.1*u₉₈₂ = -0.0\n",
      "INFO: \n",
      "INFO: UMFPACK: solved in 0.037216901779174805 seconds. norm = 45.34459038168802\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: Hacking nodal force.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import JuliaFEM.Core: boundary_assembly_posthook!, field_assembly_posthook!\n",
    "\n",
    "\"\"\" Hack y direction nodal force. \"\"\"\n",
    "function field_assembly_posthook!(solver::Solver, K::SparseMatrixCSC, f::SparseMatrixCSC)\n",
    "    info(\"Hacking nodal force.\")\n",
    "    f[2*(450-1)+2] = -35.0e3/2\n",
    "end\n",
    "\n",
    "body1, body2, bc1, bc2, bc3 = create_hertzian_problem()\n",
    "solver = Solver(\"roller contact\")\n",
    "#solver.is_linear_system = true\n",
    "solver.nonlinear_system_max_iterations = 20\n",
    "bc3.properties.inequality_constraints = true\n",
    "#bc3.properties.normal_condition = :Contact\n",
    "#bc3.properties.tangential_condition = :Slip\n",
    "push!(solver, body1, body2, bc1, bc2, bc3)\n",
    "solver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f43d78a3690>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(199,202)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 11.\n",
      "INFO: Following dofs already constrained: 21.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 11 with dofs 21, 22:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 21: ⋯ + 5.0*λ₂₁ ⋯\n",
      "INFO: dof 22: ⋯ + 5.0*λ₂₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 21: 5.0*u₂₁ = 0.0 <-- overconstrained dof\n",
      "INFO: dof 22: 5.0*u₂₂ = 0.0\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 21: ⋯ + 5.0*λ₂₁ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 21: 5.0*u₂₁ = 0.0 <-- overconstrained dof\n",
      "INFO: rank = 1, dofs = 1\n",
      "INFO: algorithm 1 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 21: 1.0*u₂₁ = 0.0\n",
      "INFO: \n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [490,491,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596]\n",
      "INFO: PDASS: active nodes: [491,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596]\n",
      "INFO: PDASS: inactive nodes: [490,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580]\n",
      "INFO: System is overconstrained by 1 dofs.\n",
      "INFO: Overconstrained_nodes: 491.\n",
      "INFO: Following dofs already constrained: 981.\n",
      "INFO: \n",
      "INFO: SUMMARY for node id 491 with dofs 981, 982:\n",
      "INFO: ----- Current constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 981: ⋯ + 0.1*λ₉₈₁ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 981: 0.1*u₉₈₁ = -0.0 <-- overconstrained dof\n",
      "INFO: ----- New constraint -----\n",
      "INFO: lambda coefficients in C1 matrix are:\n",
      "INFO: dof 981: ⋯ + 0.1*λ₉₈₁ ⋯\n",
      "INFO: dof 982: ⋯ + 0.1*λ₉₈₂ ⋯\n",
      "INFO: rows in constraint matrix C2 & D\n",
      "INFO: dof 981: 0.0*u₁₁₂ + 0.0*u₁₁₃ - 0.0*u₁₁₄ - 0.0*u₁₁₅ + 0.1*u₁₁₆ + 0.0*u₉₈₁ - 0.1*u₉₈₂ = -0.0 <-- overconstrained dof\n",
      "INFO: dof 982: 0.0*u₁₁₁ + 0.0*u₁₁₃ + 0.0*u₁₁₄ - 0.1*u₁₁₅ - 0.0*u₁₁₆ + 0.1*u₉₈₁ + 0.0*u₉₈₂ = -0.0\n",
      "INFO: ----- Related equations -----\n",
      "INFO: dof 982: 0.0*u₁₁₁ + 0.0*u₁₁₃ + 0.0*u₁₁₄ - 0.1*u₁₁₅ - 0.0*u₁₁₆ + 0.1*u₉₈₁ + 0.0*u₉₈₂ = -0.0\n",
      "INFO: dof 115: 0.1*u₁₁₅ = 0.0\n",
      "INFO: algorithm 1 solved issue? false\n",
      "INFO: algorithm 2 solved issue? true\n",
      "INFO: fixed: new setting is\n",
      "INFO: dof 981: 0.0*u₁₁₂ + 0.0*u₁₁₃ - 0.0*u₁₁₄ - 0.0*u₁₁₅ + 0.1*u₁₁₆ + 0.0*u₉₈₁ - 0.1*u₉₈₂ = -0.0\n",
      "INFO: \n",
      "INFO: UMFPACK: solved in 0.027383089065551758 seconds. norm = 45.34459038168802\n",
      "INFO: Converged in 8 iterations.\n"
     ]
    }
   ],
   "source": [
    "fig = PyPlot.figure(figsize=(8,4))\n",
    "plot_it(body1, 1.0; show_node_ids=false, show_undeformed=false,\n",
    "        show_deformed=true, xmin=-1, xmax=30, ymin=190, ymax=210)\n",
    "plot_it(body2, 1.0; show_node_ids=false, show_undeformed=false,\n",
    "        show_deformed=true, xmin=-1, xmax=30, ymin=190, ymax=210)\n",
    "#plot_normals(bc3, 0.0, 1)\n",
    "#grid(\"off\")\n",
    "#axis(\"off\")\n",
    "xlim(0, 10)\n",
    "ylim(199, 202)\n",
    "#legend(loc=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "@assert isapprox(norm(body1.assembly.u), 45.34459038168802)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pmax = 3585.362230884501\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: pn[1] = 3662.35094080636\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f43da89b850>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0,10)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function get_acc_sol()\n",
    "    B = 1\n",
    "    E1 = 210.0e3\n",
    "    E2 = 70.0e3\n",
    "    nu1 = 0.3\n",
    "    nu2 = 0.3\n",
    "    F = 35.0e3\n",
    "    E = 2*E1*E2/(E2*(1-nu1^2) + E1*(1-nu2^2))\n",
    "    R = 50.0\n",
    "    pmax = sqrt(F*E/(2*pi*B*R))\n",
    "    println(\"pmax = $pmax\")\n",
    "    a = sqrt(8*F*R/(pi*B*E))\n",
    "    acc_x = linspace(1.0e-10, a)\n",
    "    acc_p = pmax*sqrt(1-acc_x/a)\n",
    "    return acc_x, acc_p\n",
    "end\n",
    "acc_x, acc_p = get_acc_sol()\n",
    "\n",
    "x = Float64[]\n",
    "pn = Float64[]\n",
    "#xi = linspace(-1, 1, 5)\n",
    "xi = [-1.0, 0.0, 1.0]\n",
    "\n",
    "for element in get_elements(bc3)\n",
    "    haskey(element, \"master elements\") || continue\n",
    "    for k in xi\n",
    "        X = element(\"geometry\", [k], 0.0)\n",
    "        u = element(\"displacement\", [k], 0.0)\n",
    "        pxy = element(\"reaction force\", [k], 0.0)\n",
    "        Q = element(\"normal-tangential coordinates\", [k], 0.0)\n",
    "        pnt = Q'*pxy\n",
    "        push!(x, X[1])\n",
    "        push!(pn, pnt[1])\n",
    "        #plot(X[1], pnt[1], \"bo\")\n",
    "    end\n",
    "end\n",
    "\n",
    "P = sortperm(x)\n",
    "figure(figsize=(5, 5))\n",
    "x = x[P]\n",
    "pn = pn[P]\n",
    "info(\"pn[1] = $(pn[1])\")\n",
    "plot(x, pn, \"k.\")\n",
    "plot(acc_x, acc_p, \"-g\")\n",
    "#    plot(X[1], pxy[1], \"ro\", label=\"x direction\")\n",
    "#    plot(X[1], pxy[2], \"go\", label=\"y direction\")\n",
    "#    plot(X[1], pnt[2], \"bo\", label=\"tangential direction\")\n",
    "xlim(0, 10)\n",
    "#ylim(0, 4200)\n",
    "#legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-8378.501456957838,671.4667011944753)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minimum(bc3.assembly.la), maximum(bc3.assembly.la)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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