JuliaFEM.jl/docs/tutorials/2016-02-07-hyperelastic-beam.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using JuliaFEM\n",
    "\n",
    "using JuliaFEM.Core: Node, Element, Seg2, Tri3, Quad4, Hex8\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, aster_create_elements\n",
    "\n",
    "import JuliaFEM.Core: solve_linear_system\n",
    "\n",
    "using PyPlot\n",
    "\n",
    "ENV[\"COLUMNS\"] = 300"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperelastic beam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Found 10 element sets: BEAM_LEFT, BEAM_TOP, BEAM, RING_OUTER, RING_INNER, RING_TOP_LEFT, BEAM_RIGHT, RING_TOP_RIGHT, BEAM_BOTTOM, RING\n",
      "INFO: bc3: # of master elements: 63\n",
      "INFO: bc3: # of slave elements: 69\n",
      "INFO: normal tangential for first slave element\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array(Float64,(2,2)) 2x2 Array{Float64,2}"
     ]
    }
   ],
   "source": [
    "function get_hyperelastic_beam_problem(meshfile=\"/geometry/2d_hyperelastic_beam/MESH.med\")\n",
    "\n",
    "    mesh = parse_aster_med_file(Pkg.dir(\"JuliaFEM\")*meshfile)\n",
    "    \n",
    "    body1 = Problem(Elasticity, \"beam\", 2)\n",
    "    body1_elements = aster_create_elements(mesh, :BEAM, :QU4)\n",
    "    update!(body1_elements, \"youngs modulus\", 2880.0)\n",
    "    update!(body1_elements, \"poissons ratio\", 1/3)\n",
    "    push!(body1, body1_elements...)\n",
    "\n",
    "    body2 = Problem(Elasticity, \"ring\", 2)\n",
    "    body2_elements = aster_create_elements(mesh, :RING, (:TR3, :QU4))\n",
    "    #body2_elements = aster_create_elements(mesh, :RING, :QU4)\n",
    "    update!(body2_elements, \"youngs modulus\", 2880.0)\n",
    "    update!(body2_elements, \"poissons ratio\", 1/3)\n",
    "    push!(body2, body2_elements...)\n",
    "\n",
    "    # boundary conditions\n",
    "    bc1 = Problem(Dirichlet, \"SUPPORT\", 2, \"displacement\")\n",
    "    bc1_elements_1 = aster_create_elements(mesh, :BEAM_LEFT, :SE2)\n",
    "    update!(bc1_elements_1, \"displacement 1\", 0.0)\n",
    "    update!(bc1_elements_1, \"displacement 2\", 0.0)\n",
    "    push!(bc1, bc1_elements_1...)\n",
    "    bc1_elements_2 = aster_create_elements(mesh, :BEAM_RIGHT, :SE2)\n",
    "    update!(bc1_elements_2, \"displacement 1\", 0.0)\n",
    "    update!(bc1_elements_2, \"displacement 2\", 0.0)\n",
    "    push!(bc1, bc1_elements_2...)\n",
    "\n",
    "    bc2 = Problem(Dirichlet, \"SUPPORT ring\", 2, \"displacement\")\n",
    "    bc2_elements_1 = aster_create_elements(mesh, :RING_TOP_LEFT, :SE2)\n",
    "    bc2_elements_2 = aster_create_elements(mesh, :RING_TOP_RIGHT, :SE2)\n",
    "    update!(bc2_elements_1, \"displacement 1\",  0.0)\n",
    "    update!(bc2_elements_1, \"displacement 2\",  -2.0)\n",
    "    update!(bc2_elements_2, \"displacement 1\",  0.0)\n",
    "    update!(bc2_elements_2, \"displacement 2\",  -2.0)\n",
    "    push!(bc2, bc2_elements_1...)\n",
    "    push!(bc2, bc2_elements_2...)\n",
    "    \n",
    "    # contact between bodies\n",
    "    bc3 = Problem(Mortar, \"sliding ocntact\", 2, \"displacement\")\n",
    "    bc3_slave_elements = aster_create_elements(mesh, :BEAM_TOP, :SE2)\n",
    "    bc3_master_elements = aster_create_elements(mesh, :RING_OUTER, :SE2)\n",
    "    info(\"bc3: # of master elements: $(length(bc3_master_elements))\")\n",
    "    info(\"bc3: # of slave elements: $(length(bc3_slave_elements))\")\n",
    "    calculate_normal_tangential_coordinates!(bc3_slave_elements, 0.0)\n",
    "    update!(bc3_slave_elements, \"master elements\", bc3_master_elements)\n",
    "    push!(bc3, bc3_slave_elements...)\n",
    "    push!(bc3, bc3_master_elements...)\n",
    "    info(\"normal tangential for first slave element\")\n",
    "    Q = bc3_slave_elements[1](\"normal-tangential coordinates\", [0.0], 0.0)\n",
    "    dump(Q)\n",
    "\n",
    "    return body1, body2, bc1, bc2, bc3\n",
    "end\n",
    "\n",
    "body1, body2, bc1, bc2, bc3 = get_hyperelastic_beam_problem();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Found 10 element sets: BEAM_LEFT, BEAM_TOP, BEAM, RING_OUTER, RING_INNER, RING_TOP_LEFT, BEAM_RIGHT, RING_TOP_RIGHT, BEAM_BOTTOM, RING\n",
      "INFO: bc3: # of master elements: 63\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ":\n",
      " 0.0  -1.0\n",
      " 1.0   0.0\n",
      "Array(Float64,(2,2)) 2x2 Array{Float64,2}:\n",
      " 0.0  -1.0\n",
      " 1.0   0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: bc3: # of slave elements: 69\n",
      "INFO: normal tangential for first slave element\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: Int64[]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.482158899307251 seconds. norm = 32.98484500494083\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,145,146]\n",
      "INFO: UMFPACK: solved in 0.017026185989379883 seconds. norm = 32.29737154275384\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.014234066009521484 seconds. norm = 32.33387399057165\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,143,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.021646976470947266 seconds. norm = 32.699038077640864\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,142,143,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.010354042053222656 seconds. norm = 33.258179193127965\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,141,142,143,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.012367010116577148 seconds. norm = 33.927981181900996\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,141,142,143,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.0136871337890625 seconds. norm = 34.629575285095385\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,141,142,143,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.020704984664916992 seconds. norm = 35.36372079765873\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,141,142,143,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.013571977615356445 seconds. norm = 36.05313416585338\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [126,127,128,129,130,131,132,133,134,135,136,137,138,139,140]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,141,142,143,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.020359039306640625 seconds. norm = 36.68889887835854\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [127,128,129,130,131,132,133,134,135,136,137,138,139,140]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,141,142,143,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.015437126159667969 seconds. norm = 37.24847423019135\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [128,129,130,131,132,133,134,135,136,137,138,139,140]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,141,142,143,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.01300501823425293 seconds. norm = 37.73476317547798\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [129,130,131,132,133,134,135,136,137,138,139,140]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,141,142,143,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.013777017593383789 seconds. norm = 38.14401610389244\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [130,131,132,133,134,135,136,137,138,139,140]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,141,142,143,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.01752495765686035 seconds. norm = 38.45905535427587\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [131,132,133,134,135,136,137,138,139,140]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,141,142,143,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.018970012664794922 seconds. norm = 38.70723549357263\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [132,133,134,135,136,137,138,139,140]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,141,142,143,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.016641855239868164 seconds. norm = 38.89875943043454\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [133,134,135,136,137,138,139,140]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,141,142,143,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.02564692497253418 seconds. norm = 39.028247200511615\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n",
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [134,135,136,137,138,139,140]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,141,142,143,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.013334035873413086 seconds. norm = 39.0805994302936\n",
      "INFO: solving linear system of 5 problems.\n",
      "INFO: PDASS: Starting primal-dual active set strategy to determine active constraints\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "body1, body2, bc1, bc2, bc3 = get_hyperelastic_beam_problem()\n",
    "solver = Solver(\"solver beam problem\")\n",
    "push!(solver, body1, body2, bc1, bc2, bc3)\n",
    "body1.properties.formulation = :plane_stress\n",
    "body2.properties.formulation = :plane_stress\n",
    "#bc3.properties.normal_condition = :Contact\n",
    "#bc3.properties.tangential_condition = :Slip\n",
    "bc3.properties.inequality_constraints = true\n",
    "#bc3.properties.minimum_distance = 2.0\n",
    "#bc3.properties.store_debug_info = true\n",
    "solver.nonlinear_system_max_iterations = 20\n",
    "#solver.is_linear_system = true\n",
    "call(solver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7fbbe044b350>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.0,35.0,-2.0,10.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: PDASS: contact nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146]\n",
      "INFO: PDASS: active nodes: [134,135,136,137,138,139,140]\n",
      "INFO: PDASS: inactive nodes: [5,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,141,142,143,144,145,146]\n",
      "INFO: UMFPACK: solved in 0.013537883758544922 seconds. norm = 39.0805994302936\n",
      "INFO: Converged in 19 iterations.\n"
     ]
    }
   ],
   "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 = 0\n",
    "        xi1b = 0\n",
    "        try\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",
    "        catch\n",
    "            info(\"failed get construct segmentation\")\n",
    "        end\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",
    "            continue\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, time=0.0)\n",
    "    info(\"plot segmentation\")\n",
    "    for (i, element) in enumerate(get_elements(contact_problem))\n",
    "        haskey(element, \"master elements\") || continue\n",
    "        plot_contact_segmentation(element, time)\n",
    "    end\n",
    "end\n",
    "\n",
    "\n",
    "figure(figsize=(12, 4))\n",
    "plot_it(body1; show_undeformed=false, equal_axis=false, show_node_ids=false)\n",
    "plot_it(body2; show_undeformed=false, equal_axis=false, show_node_ids=false)\n",
    "#plot_segmentation(bc3)\n",
    "#xlim(-0.1, 1.1)\n",
    "#ylim(0.30, 0.52)\n",
    "#axis(\"off\")\n",
    "\n",
    "element = nothing\n",
    "for element in get_elements(bc3)\n",
    "    continue\n",
    "    haskey(element, \"master elements\") || continue\n",
    "    haskey(element, \"active nodes\") || continue\n",
    "    A = element[\"active nodes\"]\n",
    "    X = element(\"geometry\", [0.0], 0.0)\n",
    "    u = element(\"displacement\", [0.0], 0.0)\n",
    "\n",
    "    G = element[\"G\"].data\n",
    "    isapprox(G, [0.0, 0.0, 0.0, 0.0]) && continue\n",
    "    println(A.data)\n",
    "    println(element[\"c\"].data[1:2:end])\n",
    "    x = X + u\n",
    "    plot(x[1], x[2], \"ro\")\n",
    "end\n",
    "axis(\"off\")"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Julia 0.4.3",
   "language": "julia",
   "name": "julia-0.4"
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  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}