{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "Code provided under BSD 3-Clause license, all other content under a Creative Commons Attribution license, CC-BY 4.0. (c) 2023 Francesco Mario Antonio Mitrotta." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Investigation of the Equilibrium Paths of the Unreinforced Box Beam\n", "\n", "In our previous notebooks we have established that the equilibrium path of a box beam under a concetrated tip load is always stable and resembles the natural path of a broken supercritical pitchfork. In the case of Euler's column, the natural path is accompanied by the presence of a complementary path that cannot be reached from the ground state. Consequently, a legitimate question arises: is there any complementary path also in the case of our box beam?\n", "\n", "A clue on the existence of complimentary paths for the box beam comes from the sensitivity study of the nonlinear analysis parameters that we carried out for the [box beam reinforced with ribs](08_Nonlinear_Buckling_Analysis_of_a_Box_Beam_Reinforced_with_Ribs.ipynb#nonlinear-analysis-parameters) and for the [one reinforced with ribs and stiffeners](12_Nonlinear_Buckling_Analysis_of_a_Box_Beam_Reinforced_with_Ribs_and_Stiffeners.ipynb#nonlinear-analysis-parameters). In those cases we observed some differences in the load-displacement curves obtained by using different nonlinear analysis parameters. We initially attributed those differences to the varying accuracy of the solution, caused in turn by the different parameters used for the analysis. However, is it possible that the equilibrium points of those curves actually belong to different equilibrium paths?\n", "\n", "In this notebook we are going to investigate the possible presence of other paths in the equilibrium manifold of the unreinforced box beam, and we will play with the nonlinear analysis parameters to find those paths.\n", "\n", "* [Setup of the numerical model](#numerical-model)\n", "* [Variation of nonlinear analysis parameters](#nonlinear-analysis-parameters)\n", " * [Error function](#error-function)\n", " * [Convergence tolerance](#convergence-tolerance)\n", " * [Initial load increment](#initial-load-increment)\n", "* [Verification of the existence of other equilibrium paths](#verification)\n", "* [Visualization of 3D equilibrium diagram and of deformation shapes](#visualization)\n", "* [Conclusion](#conclusion)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt # import matplotlib\n", "import tol_colors as tc # import package for colorblind-friendly colors\n", "import os\n", "\n", "# Set default plot settings\n", "plt.rc(\n", " \"axes\", prop_cycle=plt.cycler(\"color\", list(tc.tol_cset(\"bright\")))\n", ")\n", "plt.rcParams[\"figure.dpi\"] = 120 # set default dpi of figures\n", "AMPLIFICATION_FACTOR = 200 # define amplification factor for displacements\n", "\n", "# Enable interactive plots\n", "%matplotlib widget\n", "\n", "# Constants - Nastran subcase IDs\n", "FIRST_SUBCASE_ID = 1\n", "SECOND_SUBCASE_ID = 2\n", "\n", "# Constants - Nastran card IDs\n", "FORCE_SET_ID = 11\n", "LOAD_SET_ID = 12\n", "\n", "# Constants - displacement and load component indices\n", "Z_INDEX = 2 # translation along z-axis\n", "\n", "# Define path to analysis directory\n", "analysis_directory_name = (\n", " \"14_Investigation_of_the_Equilibrium_Paths_of_the_Unreinforced_Box_Beam\"\n", ")\n", "ANALYSIS_DIRECTORY_PATH = os.path.join(os.getcwd(), \"analyses\", analysis_directory_name)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "## Setup of the numerical model \n", "\n", "***\n", "\n", "Let's consider the same unreinforced box beam used in our previous notebooks.\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Box beam dimensions:\n", "- width: 1.0 m\n", "- length: 4.5 m\n", "- height: 0.2 m\n", "- wall thickness: 4.0 mm\n" ] } ], "source": [ "# Define geometry\n", "AR = 9.0 # aspect ratio - 2*b/w (the length of the box beam corresponds to half the span of the CRM wing)\n", "w = 1e3 # width [mm]\n", "l = AR * w / 2 # length [mm]\n", "non_dimensional_height = 0.2 # h/w\n", "h = w * non_dimensional_height # box height [mm]\n", "non_dimensional_thickness = 1 / 50 # t/h\n", "t = h * non_dimensional_thickness # shell thickness [mm]\n", "print(\n", " f\"\"\"\n", "Box beam dimensions:\n", "- width: {w/1e3:.1f} m\n", "- length: {l/1e3:.1f} m\n", "- height: {h/1e3:.1f} m\n", "- wall thickness: {t:.1f} mm\"\"\"\n", ")\n", "\n", "# Define material\n", "rho = 2780e-12 # density [ton/mm^3]\n", "E = 73.1e3 # Young's modulus [MPa]\n", "nu = 0.3 # Poisson's ratio" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Let's define the mesh using a shell element of 59.9 mm, as found in a previous [mesh convergence study](04_Nonlinear_Buckling_Analysis_of_a_Box_Beam.ipynb#mesh-convergence). We use the function `mesh_box_with_pyvista` from the `box_beam_utils` module and then we call the function `create_base_bdf_input` from the same module, which generates a `BDF` object of our box beam with material properties, nodes, elements, boundary conditions and output files defaults. Finally, we print a summary of the cards of our `BDF` object." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---BDF Statistics---\n", "SOL None\n", "\n", "bdf.spcs[1]: 1\n", " SPC1: 1\n", "\n", "bdf.params: 0\n", " PARAM : 1\n", "\n", "bdf.nodes: 0\n", " GRID : 3388\n", "\n", "bdf.elements: 0\n", " CQUAD4 : 3344\n", "\n", "bdf.properties: 0\n", " PSHELL : 1\n", "\n", "bdf.materials: 0\n", " MAT1 : 1\n", "\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "subcase=0 already exists...skipping\n" ] } ], "source": [ "from resources import box_beam_utils\n", "\n", "shell_element_length = 59.9 # [mm]\n", "box_beam_mesh = box_beam_utils.mesh_box_beam(\n", " width=w, length=l, height=h, element_length=shell_element_length\n", ")\n", "nodes_coordinates_array = box_beam_mesh.points\n", "nodes_connectivity_matrix = box_beam_mesh.faces.reshape(-1, 5)[:, 1:]\n", "box_beam_bdf = box_beam_utils.create_base_bdf_input(\n", " young_modulus=E,\n", " poisson_ratio=nu,\n", " density=rho,\n", " shell_thickness=t,\n", " nodes_xyz_array=nodes_coordinates_array,\n", " nodes_connectivity_matrix=nodes_connectivity_matrix,\n", ")\n", "print(box_beam_bdf.get_bdf_stats())" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Now we rigidly constrain the tip section of the box beam with `RBE2` elements and we add a concentrated force of $1$ N in the positive $z$-direction. We apply this force at the center of the section by calling the function `add_unitary_force` from the `pynastran_utils` module, which by default applies a uniformly distributed force of $1$ N over the indicated nodes by means of `FORCE` cards." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "import numpy as np\n", "from resources import (\n", " pynastran_utils,\n", ") # module with useful functions to work with pyNastran objects\n", "\n", "# Add master node of tip section\n", "tip_master_node_id = np.size(nodes_coordinates_array, 0) + 1\n", "box_beam_bdf.add_grid(tip_master_node_id, [w / 2, l, 0.0])\n", "\n", "# Add RBE2 element to make tip section rigid\n", "nodes_ids = np.arange(1, np.size(nodes_coordinates_array, 0) + 1)\n", "tip_nodes_ids = nodes_ids[nodes_coordinates_array[:, 1] == l]\n", "rbe2_eid = len(box_beam_bdf.elements) + 1\n", "box_beam_bdf.add_rbe2(rbe2_eid, tip_master_node_id, \"123456\", tip_nodes_ids)\n", "\n", "# Add concentrated force\n", "concentrated_force_direction = [0.0, 0.0, 1.0]\n", "pynastran_utils.add_uniform_force(\n", " bdf=box_beam_bdf,\n", " nodes_ids=[tip_master_node_id],\n", " set_id=FORCE_SET_ID,\n", " direction_vector=concentrated_force_direction,\n", ")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Finally we set up the arc-length method with default parameters calling the function `set_up_arc_length_method` from the `pynastran_utils` module." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "pynastran_utils.set_up_arc_length_method(\n", " box_beam_bdf\n", ") # set up SOL 106 with arc-length method using default parameters" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "## Variation of nonlinear analysis parameters \n", "\n", "***\n", "\n", "How can we find whether any complementary path exist in the equilibrium manifold of our box beam? As mentioned earlier, we can get a clue from our previous sensitivity studies on the nonlinear analysis parameters. For the box beams reinforced with ribs and with ribs and stiffeners we observed different load-displacement curves when varying certain nonlinear analysis parameters. The idea here is to select those parameters and for each value leading to a different final equilibrium point we run a new analysis with two subcases. In the first subcase we use the same set of parameters to reproduce the final equilibrium point and in the second subcase we unload the structure with a very fine arc-length increment size, with the aim of tracking the equilibrium path with relatively high accuracy. In this way we will be able to verify whether the final equilibrium points belong to a different path than the natural one.\n", "\n", "When we carried out the sensitivity study on the nonlinear analysis parameters for the [unreinforced box beam](05_Sensitivity_Study_of_SOL_106_Nonlinear_Analysis_Parameters.ipynb), we only loaded the structure up to the buckling load predicted by SOL 105, $P_\\text{SOL 105}$, instead of twice its value, and consequently we did not observe any influence of the parameters on the load-displacement curves. For this reason, we are going to perform the sensitivity study again, this time loading the structure with $P/P_\\text{SOL 105}=2$, until we find a parameter whose variation results in different load-displacement curves. Once the parameter has been identified, we'll try to run an analysis with two subcases as described earlier.\n", "\n", "To perform the sensitivity study let's first define a subcase with an applied load of $P/P_\\text{SOL 105}=2$. We recall the buckling load predicted by SOL 105 for our box beam, add a `LOAD` card, and call the function `create_static_load_subcase` from the `pynastran_utils` module." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "SOL_105_BUCKLING_LOAD = 1654.0 # [N]\n", "box_beam_bdf.add_load(\n", " sid=LOAD_SET_ID,\n", " scale=1.0,\n", " scale_factors=[2 * SOL_105_BUCKLING_LOAD],\n", " load_ids=[FORCE_SET_ID],\n", ") # add LOAD card to define load set\n", "pynastran_utils.create_static_load_subcase(\n", " bdf=box_beam_bdf, subcase_id=FIRST_SUBCASE_ID, load_set_id=LOAD_SET_ID\n", ") # create subcase with defined load set" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### Error function \n", "\n", "Let's start by considering different combinations of error functions:\n", "- load and energy (default);\n", "- load and energy with vector component checking;\n", "- load and displacement;\n", "- load and displacement with vector component checking;\n", "- load, energy and displacement;\n", "- load, energy and displacement with vector component checking." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "error_functions = [\"PW\", \"PWV\", \"PU\", \"PUV\", \"PWU\", \"PWUV\"]" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "To run the analyses with the different parameter settings, we first define the name of the analysis directory and then we define the function `plot_load_displacement_curve` to run the analysis and plot the resulting load-displacement curve on an already existing figure. We monitor the tip displacement $u_{z,\\,tip}$ and nondimensionalize it with the length $l$ of the box beam." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "from pyNastran.op2.op2 import read_op2 # function to read Nastran op2 files\n", "\n", "\n", "def plot_load_displacement_curve(\n", " bdf_input, filename, axes, marker_style, line_label, run_flag=True\n", "):\n", " # Run analysis\n", " pynastran_utils.run_analysis(\n", " directory_path=ANALYSIS_DIRECTORY_PATH,\n", " bdf=bdf_input,\n", " filename=filename,\n", " run_flag=run_flag,\n", " )\n", " # Read load and displacement history from op2 file\n", " op2_filepath = os.path.join(ANALYSIS_DIRECTORY_PATH, filename + \".op2\")\n", " op2 = read_op2(op2_filename=op2_filepath, debug=None)\n", " _, p, disp = pynastran_utils.read_load_displacement_history_from_op2(\n", " op2=op2, node_ids=[tip_master_node_id]\n", " )\n", " # Plot load-displacement curve on input axes\n", " axes.plot(\n", " disp[tip_master_node_id][FIRST_SUBCASE_ID][:, Z_INDEX] / l,\n", " p[FIRST_SUBCASE_ID][:, Z_INDEX] / SOL_105_BUCKLING_LOAD,\n", " marker=marker_style,\n", " linestyle=\"-\",\n", " label=line_label,\n", " )" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Let's find the load-equilibrium curves for the different combinations of error functions." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Nastran job nonlinear_analysis_error_function_PW.bdf completed\n", "Wall time: 15.0 s\n", "Nastran job nonlinear_analysis_error_function_PWV.bdf completed\n", "Wall time: 13.0 s\n", "Nastran job nonlinear_analysis_error_function_PU.bdf completed\n", "Wall time: 18.0 s\n", "Nastran job nonlinear_analysis_error_function_PUV.bdf completed\n", "Wall time: 19.0 s\n", "Nastran job nonlinear_analysis_error_function_PWU.bdf completed\n", "Wall time: 18.0 s\n", "Nastran job nonlinear_analysis_error_function_PWUV.bdf completed\n", "Wall time: 19.0 s\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7f13c9f267aa4acbb28dda1338cb1ef2", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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