From 179a042983e9e2006bcd1c2ba95f8bb803ae0350 Mon Sep 17 00:00:00 2001 From: claudio perez <50180406+claudioperez@users.noreply.github.com> Date: Fri, 26 Apr 2024 09:53:41 -0700 Subject: [PATCH 1/9] cmp - add `OpenSeesError`, clean `getTangent` --- src/opensees/openseespy.py | 40 +++++++++++++++++++++++--------------- 1 file changed, 24 insertions(+), 16 deletions(-) diff --git a/src/opensees/openseespy.py b/src/opensees/openseespy.py index e1cffd6fd9..babba3617a 100644 --- a/src/opensees/openseespy.py +++ b/src/opensees/openseespy.py @@ -19,6 +19,11 @@ # from this module. All of these are dynamically # resolved by the function __getattr__ below. __all__ = [ +# + "tcl" + "OpenSeesError" + +# OpenSeesPy attributes "uniaxialMaterial", "testUniaxialMaterial", "setStrain", @@ -232,18 +237,19 @@ "partition", "pressureConstraint", "domainCommitTag", - "runFOSMAnalysis", +# "runFOSMAnalysis", "findDesignPoint", "runFORMAnalysis", "getLSFTags", "runImportanceSamplingAnalysis", "IGA", "NDTest", - - "tcl" ] -def _as_str_arg(arg): +class OpenSeesError(Exception): + pass + +def _as_str_arg(arg, name: str = None): """ Convert arg to a string that represents Tcl semantics. @@ -251,14 +257,18 @@ def _as_str_arg(arg): if isinstance(arg, list): return f"{{{' '.join(_as_str_arg(a) for a in arg)}}}" - elif isinstance(arg, bool): - return str(int(arg)) +# elif isinstance(arg, bool): +# if name is None: +# return str(int(arg)) +# else: +# return f"-{name.replace('_','-')}" elif isinstance(arg, dict): return "{\n" + "\n".join([ f"{cmd} " + " ".join(_as_str_arg(a) for a in val) for cmd, val in kwds - ]) + "}" + ]) + "}" + else: return str(arg) @@ -293,18 +303,19 @@ def _str_call(self, proc_name: str, *args, **kwds)->str: strings. """ - tcl_args = [_as_str_arg(i) for i in args] - tcl_args += [ - f"-{key} " + _as_str_arg(val) + tcl_args = (_as_str_arg(i) for i in args) + tcl_kwds = ( + (f"-{key.replace('_','-')}" if val else "") if isinstance(val, bool) + else f"-{key} " + _as_str_arg(val) for key, val in kwds.items() - ] - cmd = f"{proc_name} " + " ".join(tcl_args) + ) + cmd = f"{proc_name} {' '.join(tcl_args)} {' '.join(tcl_kwds)}" # TODO: make sure errors print nicely try: ret = self.eval(cmd) except Exception as e: - raise e + raise OpenSeesError() from e if ret is None or ret == "": return None @@ -421,10 +432,7 @@ def getResidual(self): def getTangent(self): import numpy as np - # TODO -# self._openseespy.eval("system FullGeneral") A = np.array(self._openseespy._str_call("printA", "-ret")) -# self._openseespy.eval("system ProfileSPD") return A.reshape([int(np.sqrt(len(A)))]*2) def __getattr__(self, name: str): From 4cc394ccd29d4bd5a9eb03499a59f9957d94ef03 Mon Sep 17 00:00:00 2001 From: claudio perez <50180406+claudioperez@users.noreply.github.com> Date: Fri, 26 Apr 2024 09:54:39 -0700 Subject: [PATCH 2/9] cmp - update `libg3` --- src/libg3 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/libg3 b/src/libg3 index 2315273dc3..002f7d7f1e 160000 --- a/src/libg3 +++ b/src/libg3 @@ -1 +1 @@ -Subproject commit 2315273dc35b048522b95188f1af88c50ae64b07 +Subproject commit 002f7d7f1e77a043e65ee3255952bc05f5ad4509 From 2a204615dae4a5fbffca295fc43244560f68bde2 Mon Sep 17 00:00:00 2001 From: claudio perez <50180406+claudioperez@users.noreply.github.com> Date: Fri, 26 Apr 2024 09:59:44 -0700 Subject: [PATCH 3/9] cmp - removed `eigen` submodule in `libg3` --- src/libg3 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/libg3 b/src/libg3 index 002f7d7f1e..504256b578 160000 --- a/src/libg3 +++ b/src/libg3 @@ -1 +1 @@ -Subproject commit 002f7d7f1e77a043e65ee3255952bc05f5ad4509 +Subproject commit 504256b5782246afc43c647842095970399de4c8 From cff7778f43563a5e5f8973a1ef0fa6c430fab449 Mon Sep 17 00:00:00 2001 From: claudio perez <50180406+claudioperez@users.noreply.github.com> Date: Fri, 26 Apr 2024 10:07:21 -0700 Subject: [PATCH 4/9] cmp - fix moved FFI --- src/libg3 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/libg3 b/src/libg3 index 504256b578..3862aff99a 160000 --- a/src/libg3 +++ b/src/libg3 @@ -1 +1 @@ -Subproject commit 504256b5782246afc43c647842095970399de4c8 +Subproject commit 3862aff99a9985899676e87c0957e8c7266d6ac9 From 86b242b3883f06b0614039c50d7054579eed40b6 Mon Sep 17 00:00:00 2001 From: claudio perez <50180406+claudioperez@users.noreply.github.com> Date: Fri, 26 Apr 2024 10:18:16 -0700 Subject: [PATCH 5/9] cmp - add `OpenSeesFFI.h` --- src/libg3 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/libg3 b/src/libg3 index 3862aff99a..c67d2eb941 160000 --- a/src/libg3 +++ b/src/libg3 @@ -1 +1 @@ -Subproject commit 3862aff99a9985899676e87c0957e8c7266d6ac9 +Subproject commit c67d2eb9413e03c7e6831de32faa8004f772bd10 From cf8a69851acb4c0e967d098b9982d5be6cddb5a2 Mon Sep 17 00:00:00 2001 From: claudio perez <50180406+claudioperez@users.noreply.github.com> Date: Fri, 26 Apr 2024 11:41:24 -0700 Subject: [PATCH 6/9] cmp - try fix for MacOS --- src/libg3 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/libg3 b/src/libg3 index c67d2eb941..57899c2aef 160000 --- a/src/libg3 +++ b/src/libg3 @@ -1 +1 @@ -Subproject commit c67d2eb9413e03c7e6831de32faa8004f772bd10 +Subproject commit 57899c2aef6e6f51ef6f9940769c41d253802507 From 56ccfd0c06dbb7149ffbba131e85f02730820490 Mon Sep 17 00:00:00 2001 From: claudio perez <50180406+claudioperez@users.noreply.github.com> Date: Fri, 26 Apr 2024 11:44:31 -0700 Subject: [PATCH 7/9] cmp - try fix for MacOS --- src/libg3 | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/libg3 b/src/libg3 index 57899c2aef..4308e21504 160000 --- a/src/libg3 +++ b/src/libg3 @@ -1 +1 @@ -Subproject commit 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z$eyo^$VBJuY1SG)2rOZ!~LN;VNb?G@~-dvR=K7=NDVIJ+teXTFnVPx8&4r~C${keGZ1yB(0U zX`4nb(4-&kPljy|z4xRhTQlPDzPPXm9#2r-E<(T8Himm1F7CwT}Z$LLGRB?%l#p_+n@KFe}*qU{?23hAM3!e4jk*iu?`&Tz_AYe L|DXe~ALIW40C **NOTE** Before running this notebook, you must install the external dependencies.\n", + "> To to this, uncomment the following cell by removing the leading `#` character, execute the cell,\n", + "> then replace the `#` character to prevent it from running again. Once everything is installed you may\n", + "> re-run the commented-out cell to hide the text generated by the installation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cff8c256-be06-444c-9a97-57435a6c7ee3", + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install -Ur requirements.txt" + ] + }, { "cell_type": "markdown", "id": "b6909664-24e3-401c-b176-49ee45890e58", "metadata": {}, "source": [ + "\n", + "\n", + "OpenSees is a finite element analysis platform that is\n", + "developed by researchers in the field of earthquake engineering, and made freely available. \n", + "Rather than exposing a graphical user interface like Abaqus or SAP2000, OpenSees can be used directly from several general purpose programming languages. This gives users much greater control for dynamically generating models, conducting an analysis, and performing otherwise cumbersome tasks like topology optimization, reliability studies and training artificial intelligence models.\n", + "\n", + "In this problem, we investigate how this programmatic interface can be used to solve a highly nonlinear problem. Consider the shallow arch shown below (Clarke and Hancock, 1990):\n", "![Shallow arch](./images/ClarkeHancock.png)\n", + "This problem exhibits several critical points along the solution path,\n", + "and requires the use of a sophisticated algorithm to properly traverse these.\n", + "Most finite element analysis programs do not implement algorithms that are capable of solving such problems, so in this study, we will investigate how you can implement one yourself. At the end of the notebook we demonstrate how you might, alternatively solve this using a built-in feature of OpenSees.\n", + "\n", + "We will perform the analysis by creating an OpenSees `Model` data structure, and using\n", + "its \"methods\" to perform various tasks. A \"method\" is simply a function that\n", + "is bound to a particular instance of a data structure. In this case, the `Model`\n", + "data structure encapsulates the geometry and state (i.e., the current values of solution variables) of our structural model. Some methods that you might see being used in this notebook include (you wont need to make any changes to the use of any of these):\n", + "- `Model.integrator(...)` This method configures the iteration strategy to be performed\n", + " in the next increment\n", + "- `Model.analyze(n)` This method applies `n` increments, and between each increment, performs Newton-Raphson iterations.\n", "\n", - "> Clarke, M.J. and Hancock, G.J. (1990) ‘A study of incremental‐iterative strategies for non‐linear analyses’, International Journal for Numerical Methods in Engineering, 29(7), pp. 1365–1391. Available at: https://doi.org/10.1002/nme.1620290702.\n" + "The creation of the `Model` performed by the `arch_model` helper function from the file [`arch.py`](./arch.py):" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 29, "id": "3e1fbcfd-1fac-4288-9698-cb05ad680ef7", "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", "from arch import arch_model" ] }, + { + "cell_type": "markdown", + "id": "8dddfcd5-a90d-4e1c-b639-fd8ba529f531", + "metadata": {}, + "source": [ + "We'll also find the following imports convenient:" + ] + }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 25, "id": "0cc60ce7-c1c3-4739-b0d4-6913cc3453e3", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "try:\n", - " import scienceplots\n", - " plt.style.use([\"science\", \"notebook\"])\n", - "except:\n", - " pass" + "from numpy import sign\n", + "from numpy.linalg import det" ] }, { "cell_type": "markdown", - "id": "4f629e2c-5f3f-4765-b056-2637f6cc30ee", + "id": "69b03d70-02a6-4882-91e5-9ed61af975e6", "metadata": {}, "source": [ - "
\n", - "
Solution 1
\n", - "Iterative strategy: Constant load (Section 3.1)\n", - "Load incrementation strategy: Direct incrementation of the load parameter (Section 4.1.1)\n", - "
\n", - "
Solution 2
\n", - "Iterative strategy: Constant vertical displacement under the load, $v_6$ (Section 3.2)\n", - "Load incrementation strategy: Incrementation of the displacement component $v_6$ (Section 4.1.2)\n", - "
\n", - "
Solution 3
\n", - "Iterative strategy: Constant arc-length (Section 3.3)\n", - "Load incrementation strategy: Incrementation of the arc-length (Section 4.1.3)\n", - "
\n", - "
Solution 4
\n", - "Iterative strategy: Minimum unbalanced displacement norm (Section 3.5)\n", - "Load incrementation strategy: Incrementation of the arc-length (Section 4.1.3)\n", - "
\n", - "
Solution 5
\n", - "Iterative strategy: Constant weighted response (Section 3.7, equation (39))\n", - "Load incrementation strategy: Incrementation of the arc-length (Section 4.1.3)\n", - "
\n", - "
Solution 6
\n", - "Iterative strategy: Minimum unbalanced force norm (Section 3.6)\n", - "Load incrementation strategy: Using the current stiffness parameter (Section 4.2, equation (57))\n", - "
\n", - "
Solution 7
\n", - "Iterative strategy: Minimum unbalanced force norm (Section 3.6)\n", - "Load incrementation strategy: Incrementation of the arc-length (Section 4.1.3)\n", - "
\n", - "
Solution 8
\n", - "Iterative strategy: Constant arc-length (Section 3.3)\n", - "Load incrementation strategy: Using the current stiffness parameter (Section 4.2, equation (57))\n", - "
\n", - "
" + "## Solution Strategy\n", + "\n", + "Our general strategy is implemented in the following function `solve()`.\n", + "This function adopts an incremental approach\n", + "where the load is applied in small steps of varying size. \n", + "The arguments to the function are:\n", + "- `model`: an OpenSees `Model` object\n", + "- `node` : an integer indicating which node to collect results from.\n", + "\n", + "Both of these arguments will be supplied by the `arch_model` helper function mentioned\n", + "above.\n" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "6e2f54b9-4dcc-40d8-b981-be9d55abdfc2", + "execution_count": 23, + "id": "5000bf8a-e442-4368-b09d-2c60a810ed37", "metadata": {}, "outputs": [], "source": [ + "def solve(model, node):\n", + " # Initialize some variables\n", + " xy = [] # Container to hold solution history\n", + " status = 0 # Convergence flag\n", + " data = 1.0 # Explained below\n", + " ds = 45 # Initial arc-length increment\n", + " steps = 110 # Number of steps\n", "\n", - "def solution0(model, mid, dof, step):\n", - " model.integrator(\"LoadControl\", 400.0)\n", + " # Configure the first load increment\n", + " increment(model, ds, data)\n", "\n", + " for step in range(steps):\n", "\n", - "def solution1(model, mid, dof, step):\n", - " Jd = 5\n", - " # Only initialize the integrator on the first step; this\n", - " # means that incrementation will be handled internally\n", - " # by the OpenSees integrator, in addition to iteration.\n", - " if step == 0:\n", - " model.integrator(\"LoadControl\", 400.0, Jd, -800., 800.)\n", + " # 1. Perform Newton-Raphson iterations until convergence\n", + " status = model.analyze(1)\n", + "\n", + " # 2. Store the displacement and load factor\n", + " xy.append([model.nodeDisp(node, 2), model.getTime()])\n", + "\n", + " # 3. If the iterations failed, try cutting\n", + " # the increment arc-length in half\n", + " if status != 0:\n", + " ds /= 2\n", + "\n", + " # 4. Compute new arc-length for the next increment\n", + " ds, data = increment(model, ds, data)\n", "\n", + " return np.array(xy).T" + ] + }, + { + "cell_type": "markdown", + "id": "516c98cd-8770-40b6-9feb-3382189c95ae", + "metadata": {}, + "source": [ + "## Problem Statement\n", "\n", - "def solution2(model, mid, dof, step, dx):\n", - " Jd = 5\n", - " if step == 0:\n", - " model.integrator(\"DisplacementControl\", mid, dof, dx, Jd)\n", + "Each time the load is incremented, the `analyze()` method of the model is invoked\n", + "and Newton-Raphson iterations are performed \n", + "to find the displacements which equilibriate the current load. \n", + "Each increment is configured by the following function `increment()` which takes the\n", + "following arguments:\n", + "- `ds` The initial arc-length increment for the *last* load step\n", + "- `det_past` A history variable" + ] + }, + { + "cell_type": "markdown", + "id": "913cf616-4257-4a31-aad1-85e31718e4ec", + "metadata": {}, + "source": [ + "The function additionally returns the following variables:\n", + "- `ds` the initial arc-length increment for the *upcoming* load step, which is currently the same as the input.\n", + "- `det_pres` An update to the history variable, which is currently the same as the input.\n", "\n", + "In order to pass through critical points, the \"Arc-length\" method is used to adapt the\n", + "load increment at each iteration. However, if you run the notebook as-is, you will find that the algorithm is unable to pass over the first critical point. Note how the displacement begins oscillating in **Plot 2** below.\n", "\n", - "def norm_control(model, mid, dof, step):\n", - " dof = 2\n", - " Jd = 15\n", - " if step == 0:\n", - " model.integrator(\"MinUnbalDispNorm\", 1.0, Jd, -10, 10)\n", + "To circumvent this issue, we propose implementing Crisfield's strategy\n", + "for determining the loading direction of the increment.\n", + "To this end you are asked to do the following:\n", "\n", - "def arc_control(model, mid, dof, step):\n", - " if step == 0:\n", - " model.integrator(\"ArcLength\", 1.0, 0)\n", - "\n" + "> Modify the `increment()` function below to detect whether the sign (i.e., `+` or `-`) of the determinant\n", + "> of the stiffness has changed since the last time `increment` was invoked. If the\n", + "> determinant sign has changed, switch the sign of `ds` and return its modified version.\n", + ">\n", + "> **Hint**: You can use the `Model.getTangent()` method to obtain the current stiffness matrix. This will return an array which can be passed to the `det` function that was imported earlier. For documentation of `det`, see https://numpy.org/doc/stable/reference/generated/numpy.linalg.det.html\n" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "5000bf8a-e442-4368-b09d-2c60a810ed37", + "execution_count": 34, + "id": "6e2f54b9-4dcc-40d8-b981-be9d55abdfc2", "metadata": {}, "outputs": [], "source": [ + "def increment(model, ds, det_past):\n", + " # Make your changes here\n", + " det_pres = 1.0\n", + " ds = ds\n", "\n", - "def analyze(model, mid, incr, steps, *args):\n", - " dof = 2\n", - " model.system(\"ProfileSPD\")\n", - " model.test(\"NormUnbalance\", 1e-6, 25, 9)\n", - " model.algorithm(\"Newton\")\n", - " model.analysis(\"Static\")\n", - " \n", - " xy = []\n", - " for step in range(steps):\n", - " incr(model, mid, dof, step, *args)\n", - " status = model.analyze(1)\n", + " # No changes are needed beyond this point\n", + " model.integrator(\"ArcLength\", ds, 0, exp=0)#0.5)\n", + " return ds, det_pres" + ] + }, + { + "cell_type": "markdown", + "id": "ce6b2663-82d5-4818-b9da-afdb9015cdf5", + "metadata": {}, + "source": [ + "To check your changes, run all the cells of the notebook and verify that the generated **Plot 1** produces the response\n", + "shown in the figure below. When **Plot 1** looks correct, you can export the notebook as a PDF for your submission by going to **File**/**Save and Export Notebook As**/**PDF**.\n", "\n", - " xy.append([model.nodeDisp(mid, dof), model.getTime()])\n", - " # return if analysis failed\n", - " if status != 0:\n", - " break\n", + "![Expected response](./7920.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "4f629e2c-5f3f-4765-b056-2637f6cc30ee", + "metadata": {}, + "source": [ + "## Analysis\n", "\n", - " return np.array(xy).T\n" + "The remainder of this notebook performs the analysis and plots the results." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 26, + "id": "bd255683-4c9f-4a65-b4fa-3f310ce1984d", + "metadata": {}, + "outputs": [], + "source": [ + "model, mid_node = arch_model()\n", + "x, y = solve(model, mid_node)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, "id": "7024dd88-cf13-4c48-a73e-9cdba805dd28", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[400. 900. 900.]\n" - ] - }, { "data": { + "image/png": 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"text/plain": [ - "" + "
" ] }, - "execution_count": 15, "metadata": {}, - "output_type": "execute_result" - }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "try:\n", + " import scienceplots\n", + " plt.style.use(\"steel\")#[\"ieee\", \"science\", \"notebook\"])\n", + "except:\n", + " pass\n", + " \n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.plot(-x, y, \"x\")\n", + "ax.set_title(\"Plot 1: Displacement vs Load Factor\")\n", + "ax.set_ylabel(r\"Load factor $\\lambda$\")\n", + "ax.set_xlabel(\"Displacement $u$\")\n", + "\n", + "ax.set_xlim([0, 1200])\n", + "ax.set_ylim([-800, 3000]);" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "f0b43af2-dc3c-4a40-a1fd-e11e46f23206", + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -189,42 +285,29 @@ } ], "source": [ - "# x, y = solution0(*arch_model(), 6)\n", - "# plt.plot(-x, y, 'x', label=\"S0\")\n", - "\n", - "x, y = analyze(*arch_model(), solution1, 6)\n", - "plt.plot(-x, y, 'x', label=\"S1\")\n", - "print(y)\n", - "\n", - "x, y = analyze(*arch_model(), solution2, 7, -150)\n", - "plt.plot(-x, y, 'o', label=\"S2\")\n", - "\n", - "x, y = analyze(*arch_model(), solution2, 600, -1.5)\n", - "plt.plot(-x, y, '-', label=\"S2\")\n", - "\n", - "x, y = analyze(*arch_model(), arc_control, 5000)\n", - "plt.plot(-x, y, \".\", label=\"arc\")\n", - "\n", - "x, y = analyze(*arch_model(), norm_control, 8000)\n", - "plt.plot(-x, y, label=\"norm\")\n", - "\n", - "plt.legend()" + "fix, ax = plt.subplots()\n", + "ax.plot(-x, '-')\n", + "ax.set_title(\"Plot 2: Displacement vs Step Number\")\n", + "ax.set_ylabel(\"Displacement $u$\")\n", + "ax.set_xlabel(\"Analysis step $n$\");" ] }, { "cell_type": "markdown", - "id": "a5092dd9-6a86-4367-83f5-c3cd453c356d", + "id": "21bf025c-2023-4ea6-adf2-56f452f304b5", "metadata": {}, "source": [ - "The following animation of the solution is created in [`Animating.ipynb`](./Animating.ipynb)" + "" ] }, { "cell_type": "markdown", - "id": "21bf025c-2023-4ea6-adf2-56f452f304b5", + "id": "e069d44a-2d54-4263-8b91-70c730ddd912", "metadata": {}, "source": [ - "![](./arch.gif)" + "## References\n", + "\n", + "- Clarke, M.J. and Hancock, G.J. (1990) ‘A study of incremental‐iterative strategies for non‐linear analyses’, International Journal for Numerical Methods in Engineering, 29(7), pp. 1365–1391. Available at: https://doi.org/10.1002/nme.1620290702.\n" ] } ], diff --git a/notebooks/IncrementalAnalysis/requirements.txt b/notebooks/IncrementalAnalysis/requirements.txt index a6338b7340..da44d1214b 100644 --- a/notebooks/IncrementalAnalysis/requirements.txt +++ b/notebooks/IncrementalAnalysis/requirements.txt @@ -1,6 +1,7 @@ -imageio[ffmpeg] +# imageio[ffmpeg] opensees sees mdof SciencePlots +nbconvert[webpdf] From 263ae07ea187589ed9f117c3549397e7cff8fb54 Mon Sep 17 00:00:00 2001 From: claudio perez <50180406+claudioperez@users.noreply.github.com> Date: Sun, 28 Apr 2024 13:11:52 -0700 Subject: [PATCH 9/9] cmp --- notebooks/IncrementalAnalysis/arch.py | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/notebooks/IncrementalAnalysis/arch.py b/notebooks/IncrementalAnalysis/arch.py index 786979092b..49bdf3ee9c 100644 --- a/notebooks/IncrementalAnalysis/arch.py +++ b/notebooks/IncrementalAnalysis/arch.py @@ -6,8 +6,8 @@ def arch_model(): # Define model parameters - L = 5000 - Rise = 500 + L = 5000 + Rise = 500 Offset = 200 # Define material parameters @@ -28,7 +28,7 @@ def arch_model(): ne = 10 nen = 2 nn = ne*(nen-1)+1 - mid = (nn+1)//2 # midpoint node + mid = (nn+1)//2 # midpoint node for i, angle in enumerate(np.linspace(-th/2, th/2, nn)): tag = i + 1 @@ -63,6 +63,16 @@ def arch_model(): # Add a nodal load to the pattern model.load(mid, 0.0, -1.0, 0.0, pattern=1) + + model.system("ProfileSPD") + # model.system("FullGeneral") + # model.system("BandGeneral") + # model.system("Umfpack", det=True) + + model.test("NormUnbalance", 1e-6, 25, 0) + model.algorithm("Newton") + model.analysis("Static") + return model, mid