{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "ChEn-5310: Computational Continuum Transport Phenomena Spring 2021 UMass Lowell; Prof. V. F. de Almeida **13Apr21**\n", "\n", "# 12. Peclet Bulk-Coupled 1D with Dirichlet Boundary Conditions\n", "$ \n", " \\newcommand{\\Amtrx}{\\boldsymbol{\\mathsf{A}}}\n", " \\newcommand{\\Bmtrx}{\\boldsymbol{\\mathsf{B}}}\n", " \\newcommand{\\Mmtrx}{\\boldsymbol{\\mathsf{M}}}\n", " \\newcommand{\\Imtrx}{\\boldsymbol{\\mathsf{I}}}\n", " \\newcommand{\\Pmtrx}{\\boldsymbol{\\mathsf{P}}}\n", " \\newcommand{\\Lmtrx}{\\boldsymbol{\\mathsf{L}}}\n", " \\newcommand{\\Umtrx}{\\boldsymbol{\\mathsf{U}}}\n", " \\newcommand{\\Smtrx}{\\boldsymbol{\\mathsf{S}}}\n", " \\newcommand{\\xvec}{\\boldsymbol{\\mathsf{x}}}\n", " \\newcommand{\\avec}{\\boldsymbol{\\mathsf{a}}}\n", " \\newcommand{\\bvec}{\\boldsymbol{\\mathsf{b}}}\n", " \\newcommand{\\cvec}{\\boldsymbol{\\mathsf{c}}}\n", " \\newcommand{\\rvec}{\\boldsymbol{\\mathsf{r}}}\n", " \\newcommand{\\fvec}{\\boldsymbol{\\mathsf{f}}}\n", " \\newcommand{\\mvec}{\\boldsymbol{\\mathsf{m}}}\n", " \\newcommand{\\gvec}{\\boldsymbol{\\mathsf{g}}}\n", " \\newcommand{\\zerovec}{\\boldsymbol{\\mathsf{0}}}\n", " \\newcommand{\\norm}[1]{\\bigl\\lVert{#1}\\bigr\\rVert}\n", " \\newcommand{\\transpose}[1]{{#1}^\\top}\n", " \\DeclareMathOperator{\\rank}{rank}\n", " \\newcommand{\\Reals}{\\mathbb{R}}\n", " \\newcommand{\\thetavec}{\\boldsymbol{\\theta}}\n", "$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Table of Contents\n", "* [Objectives](#obj)\n", "1. [Plotting Functions](#plotting)\n", "

\n", "1. [Problem Statement](#problem)\n", " + [Strong Form](#dbcstrong)\n", " + [Source Coupling](#sourcecoupling)\n", " + [Weak Form](#dbcweak)\n", "

\n", "1. [Problem Solution](#solution)\n", " + [Code a Source Term Kernel](#sourcekernel)\n", " + [Compile and Link Application](#compile)\n", " + [Input File](#dbcinput)\n", " + [Run Application](#dbcrun)\n", "

\n", "1. [Linear Lagrange FEM Results](#dbclinearfemresults)\n", " + [Compute Error](#linearerror)\n", "

\n", "1. [Quadratic Lagrange FEM Results](#dbcquadfemresults)\n", " + [Compute Error](#quaderror)\n", "

\n", "1. [High Peclet Number](#highpe)\n", " + [Quadratic Lagrange FEM Results](#highperesults1)\n", " - [Compute Error](#highperesults1error)\n", " + [Quadratic Lagrange FEM Results](#highperesults2)\n", " - [Compute Error](#highperesults2error)\n", "

\n", "1. [Application Tree](#tree)\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [Objectives](#toc)\n", "\n", "+ Introduce bulk-coupling in the context of a Peclet problem studied in the notebook series 09/10/11; all past notebooks must be thoroughly reviewed.\n", "+ Present the Galerkin weak form of the Peclet 1D problem with two unknown variables as described below ([OneNote notes here](https://studentuml-my.sharepoint.com/:o:/g/personal/valmor_dealmeida_uml_edu/Eib-vZHIpRlPlOMtz0Gf_asBegEFKsl9dOK4nHyDbgSeUA?e=sLu1td)).\n", "+ Some initial code is provided in the course repository but no full source code is given out. A significant effort in programing is often necessary to learn the subject well. However the material in this course is helpful with this task. Hands-on work during lectures will try to fill in existing gap. The steps in this notebook are necessary for a basic understanding of the subject. \n", "+ The reader is supposed to consult the [`MOOSE source documentation`](https://mooseframework.inl.gov/source/index.html) to fill in gaps in reproducing the steps below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [Plotting Functions](#toc)\n", "\n", "This is an auxiliary section for holding plotting functions used later." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "code_folding": [ 2 ] }, "outputs": [], "source": [ "'''Plot function for FEM Solution'''\n", "\n", "def plot_solution(df, \n", " title='No Title', \n", " u1_legend='no u1 legend',\n", " u2_legend='no u2 legend',\n", " u1_flux_legend='no u1 flux legend',\n", " u2_flux_legend='no u2 flux legend'):\n", " \n", " import matplotlib.pyplot as plt\n", " %matplotlib inline\n", " plt.style.use('dark_background')\n", "\n", " (fig, ax1) = plt.subplots(1, figsize=(15, 6))\n", "\n", " ax1.plot(df['x'], df['u'],'r*-',label=u1_legend)\n", " \n", " if 'u2' in df.columns:\n", " ax1.plot(df['x'], df['u2'],'r*--',label=u2_legend)\n", "\n", " ax1.set_xlabel(r'$x$ [cm]', fontsize=18)\n", " ax1.set_ylabel(r'$u_h(x)$ [g/cc]', fontsize=18, color='red')\n", " ax1.tick_params(axis='y', labelcolor='red', labelsize=14)\n", " ax1.tick_params(axis='x', labelsize=14)\n", " ax1.legend(loc='center left', fontsize=12)\n", " #ax1.set_ylim(0,1)\n", " ax1.grid(True)\n", "\n", " if 'diffFluxU_x' in df.columns:\n", " # create a twin x axis to be shared\n", " ax2 = ax1.twinx()\n", "\n", " ax2.plot(df['x'], df['diffFluxU_x'],'*-', color='yellow', label=u1_flux_legend)\n", " \n", " if 'diffFluxU2_x' in df.columns:\n", " ax2.plot(df['x'], df['diffFluxU2_x'],'*--', color='yellow', label=u2_flux_legend)\n", "\n", " ax2.set_ylabel(r\"$q_h(x)$ [g/cm$^2$-s]\", fontsize=16, color='yellow')\n", " ax2.tick_params(axis='y', labelcolor='yellow', labelsize=14)\n", " ax2.legend(loc='center right', fontsize=12)\n", " #ax2.set_ylim(0,2)\n", " #ax2.grid(True)\n", "\n", " plt.title(title, fontsize=20)\n", " fig.tight_layout() # otherwise the right y-label is slightly clipped\n", " plt.show()\n", " print('')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [Problem Statement](#toc)\n", "\n", "There exists two kinds of coupling of unknown variables (or fields), one kind is when the fields share the same domain $\\Omega$ and are coupled tightly over the entire $\\Omega$. This is the kind that will be studied here. The other kind is when the fields do not share the same domain $\\Omega$ and the coupling takes place on a portion of the boundary of $\\Omega$ shared by the fields. This latter case will be described in a future notebook.\n", "\n", "The following sections describe an extension of the Peclet problem described in previous notebooks for the case when two fields are coupled through a source term." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Strong Form of Problem Statement](#toc)\n", "\n", "Solve the Peclet model problem. Find $u_1:[a,b]\\subset\\Reals\\rightarrow\\Reals$ and $u_2:[a,b]\\subset\\Reals\\rightarrow\\Reals$ for $D_1 > 0$ and $D_2 > 0$ such that:\n", "\n", "\\begin{align*}\n", " v\\, u_1' &= -\\bigl(-D_1\\, u_1'\\bigr)'(x) + S(u_1, u_2) \\quad \\forall \\quad x\\in [a,b], \\\\\n", " u_1(a) &= A_1, \\\\\n", " u_1(b) &= B_1.\n", "\\end{align*}\n", "\n", "and\n", "\n", "\\begin{align*}\n", " v\\, u_2' &= -\\bigl(-D_2\\, u_2'\\bigr)'(x) - S(u_1, u_2) \\quad \\forall \\quad x\\in [a,b], \\\\\n", " u_2(a) &= A_2, \\\\\n", " u_2(b) &= B_2.\n", "\\end{align*}\n", "\n", "The *diffusion flux* associated to the quantity $u_i, \\, \\ i=1,2$ is denoted $q_i := -D_i\\,u_i'$, and it is often of interest as a derived quantity. Here a point-wise *convective sink (or sweep)* is given by $v\\,u_i'$. There exists two Peclet numbers: \n", "\n", " + Peclet number: $\\frac{v\\,L}{D_i}$. \n", "\n", "whose effects has been described in earlier notebooks.\n", "\n", "Likewise in the single-field Peclet 1-D problem (Notebook 09), the values of the dependent variables are given on the two end points of the domain (*essential* boundary conditions or *Dirichlet boundary conditions*)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Source Coupling](#toc)\n", "\n", "Consider the following source coupling:\n", "\n", "\\begin{align*}\n", " S(u_1, u_2) = S_1 - h_1\\,\\bigl(u_1-u_1^*\\bigr) - \\Bigl( S_2 - h_2\\, \\bigl(u_2 - u_2^*\\bigr) \\Bigr)\n", "\\end{align*}\n", "\n", "where $S_i$ is a fixed source (sink) for each field, $h_i$ is a transfer coefficient, and $u_i^*$ is a saturation value." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Weak Form of the Problem Statement](#toc)\n", "\n", "The Galerkin weak formulation is as follows. Find $u_1 \\in H^1\\!\\bigl([a,b]\\bigr)$ and $u_2 \\in H^1\\!\\bigl([a,b]\\bigr)$\n", "so that \n", "\n", "\\begin{align*}\n", " \\int\\limits_a^b v\\, u_1'(x)\\, w(x)\\,dx + \\int\\limits_a^b D\\, u_1'(x)\\,w'(x)\\,dx - \\int\\limits_a^b S(u_1, u_2)\\,w(x)\\,dx &= 0 \\quad \\forall \\quad w \\in H^1_0\\!\\bigl([a,b]\\bigr), \\text{and}\n", " \\\\\n", " \\int\\limits_a^b v\\, u_2'(x)\\, w(x)\\,dx + \\int\\limits_a^b D\\, u_2'(x)\\,w'(x)\\,dx + \\int\\limits_a^b S(u_1, u_2)\\,w(x)\\,dx &= 0 \\quad \\forall \\quad w \\in H^1_0\\!\\bigl([a,b]\\bigr),\n", "\\end{align*}\n", "\n", "where $H^1\\!\\bigl([a,b]\\bigr) := \\bigl\\{ u:[a,b]\\subset\\Reals\\rightarrow \\Reals \\mid \\int_a^b u'^2\\,dx < \\infty\\bigr\\}$ and $H^1_0\\!\\bigl([a,b]\\bigr) := \\bigl\\{ w \\mid w \\in H^1(a,b), w(a) = 0, w(b) =0 \\bigr\\}$. Both function sets as just defined are Hilbert spaces. The function $w$ is called a test function. Because $w$, $u_1$, $u_2$ are sought in very similar sets of functions, this weak form is called Galerkin's weak form.\n", "\n", "The new form of the source term is the key term to be computed here. Since the MOOSE framework performs the integration, and provides the implementation of the test function, we need to provide the integrand of the integral, that is, the kernel. Therefore the kernel needed is an expansion of what has been covered so far in the course:\n", "\n", " 1. $\\pm S(u_1, u_2)\\,w(x)$.\n", " \n", "The kernels are to be evaluated at quadrature points provided by the MOOSE framework." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [Problem Solution](#toc)\n", "\n", "We will leverage the Peclet 1D development (Notebook 09) to modify the source term where the coupling takes place." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Code a Source Term Kernel](#toc)\n", "\n", "1. Starting from the Peclet 1D development, the source kernel file `SourceTerm.h` can be modified as follows:\n", " ```c++\n", " /// The variables associated to the source\n", " const Real _sourceS;\n", " const Real _transferCoeff;\n", " const Real _saturation;\n", "\n", " const VariableValue & _uCoupled;\n", " const Real _sourceSCoupled;\n", " const Real _transferCoeffCoupled;\n", " const Real _saturationCoupled;\n", " ```\n", "1. Likewise the `SourceTerm.C` class implementation can be also changed to:\n", "\n", "Residual:\n", " ```c++\n", "Real\n", "SourceTerm::computeQpResidual()\n", "{\n", " return ( (_sourceS - _transferCoeff * (_u[_qp] - _saturation)) \\\n", " -(_sourceSCoupled -_transferCoeffCoupled * (_uCoupled[_qp] - _saturationCoupled)) ) \\\n", " * _test[_i][_qp];\n", "}\n", "\n", " ```\n", "Jacobian diagonal:\n", " ```c++\n", " Real\n", " SourceTerm::computeQpJacobian()\n", " {\n", " return (- _transferCoeff + _transferCoeffCoupled) * _phi[_j][_qp] * _test[_i][_qp];\n", " }\n", " ```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Compile and Link Application](#toc)\n", "\n", "1. Make sure you are in the problem project:\n", " + `cd ../..`\n", " + `pwd`\n", "1. You should see: `..../engy5310p1`\n", "1. Compile and link the application\n", " + `make`\n", "1. If all is sucessfull you should see among other things in the screen output:\n", " + Linking Library `.../engy5310p1/lib/libengy5310p1-opt.la...`\n", " + Linking Executable `.../engy5310p1/engy5310p1-opt...`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Input File](#toc)\n", "\n", "The newly revised source kernel can now be used for both fields:\n", "\n", "```\n", " [source-term-1]\n", " type = SourceTerm\n", " variable = u1 # add to produced quantity\n", " sourceS = ${replace source_s_1}\n", " transferCoeff = ${replace source_transfer_coeff_1}\n", " saturation = ${replace source_saturation_1}\n", " coupledVariable = u2\n", " sourceSCoupled = ${replace source_s_2}\n", " transferCoeffCoupled = ${replace source_transfer_coeff_2}\n", " saturationCoupled = ${replace source_saturation_2}\n", " []\n", " [source-term-2]\n", " type = SourceTerm\n", " variable = u2 # add to produced quantity\n", " sourceS = ${replace source_s_2}\n", " transferCoeff = ${replace source_transfer_coeff_2}\n", " saturation = ${replace source_saturation_2}\n", " coupledVariable = u1\n", " sourceSCoupled = ${replace source_s_1}\n", " transferCoeffCoupled = ${replace source_transfer_coeff_1}\n", " saturationCoupled = ${replace source_saturation_1}\n", " []\n", "```\n", "On the working examples below a cleaner version with defaults is used to avoid writing entries with zero values.\n", "Additional blocks in the input file can be used to create the weak form for both variables as described below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Run Application](#toc)\n", "\n", "1. In the `engy5310p1/` directory run the application with the Linux shell command:\n", " + `./engy5310p1-opt -i input.hit`\n", "2. Compare your results with this notebook results below.\n", "3. Return here to follow instructions on how to implement the calculation of the total energy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [Linear Lagrange FEM](#toc)\n", "\n", "Solve problem with parameter values:\n", "\n", "> + $a = 0$ cm\n", "> + $b = 25$ cm\n", "\n", "> + Pe$_\\text{ave} = 10$\n", "\n", "| $u_1$ **Parameter** | **Value** | $u_2$ **Parameter** | **Value** |\n", "|:-------------------:|:----------:|:-------------------:|:---------:|\n", "| $A_1$ | 3 g/cc | $A_2$ | 0 g/cc |\n", "| $B_1$ | 0 g/cc | $B_2$ | 0 g/cc |\n", "| $D_1$ | 0.1 cm^2/s | $D_2$ | 0.5 cm^2/s |\n", "| $S_1$ | $1\\times 10^{-3}$ g/cc-s | $S_2$ | 0 g/cc-s |\n", "| $h_1$ | $5\\times 10^{-3}$ cm/s | $h_2$ | 0 cm/s |\n", "| $u_1^*$ | 1.5 g/cc | $u_2^*$ | 0 g/cc |\n", "\n", "FEM parameters:\n", "\n", "> + Basis Functions: First Order Lagrangian\n", "> + num. of finite elements: 20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Comment: This data tests the case when there is only transfer from $u_1$ to $u_2$. Other cases will be presented in another notebook.**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "'''Domain'''\n", "\n", "x_a = 0\n", "x_b = 25\n", "\n", "x_length = x_b - x_a" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "'''Parameters and data'''\n", "\n", "Pe_ave = 10 # mildly convective dominated\n", "\n", "diff_coeff_1 = 0.1\n", "source_s_1 = 1e-3\n", "source_transfer_coeff_1 = 5e-3\n", "source_saturation_1 = 1.0\n", "\n", "diff_coeff_2 = 0.5\n", "\n", "velocity = (Pe_ave * (diff_coeff_1+diff_coeff_2)/2/x_length, 0, 0) # length scale is the x length\n", "\n", "u_a = 3\n", "u_b = 0\n", "\n", "u2_a = 0\n", "u2_b = 0" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "code_folding": [ 13 ] }, "outputs": [], "source": [ "'''FEM Solution'''\n", "\n", "n_felem = 20\n", "\n", "order = 'first'\n", "\n", "n_plot_pts = n_felem + 1\n", "\n", "try: \n", " from engy_5310.toolkit import write_engy5310_p1_1d_input_file \n", "except ModuleNotFoundError:\n", " assert False, 'You need to provide your own code here. Bailing out.'\n", "\n", "write_engy5310_p1_1d_input_file(x_left=x_a, x_right=x_b, \n", " u_left=u_a, u_right=u_b, \n", " diff_coeff=diff_coeff_1, source_s=source_s_1,\n", " source_transfer_coeff=source_transfer_coeff_1, \n", " source_saturation=source_saturation_1,\n", " u2_left=u2_a, u2_right=u2_b, \n", " diff_coeff_2=diff_coeff_2,\n", " velocity=velocity, \n", " n_felem=n_felem, order=order, \n", " n_plot_pts=n_plot_pts,\n", " compute_diffusion_flux=True,\n", " solver='fdp-newt-full')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Engy-5310 Problem 1: Poisson 1D FEM\r\n", "# UMass Lowell Nuclear Chemical Engineering\r\n", "# Prof. Valmor F. de Almeida\r\n", "# 02May21 18:24:20\r\n", "\r\n", "# Parameters\r\n", "xmin = 0.00000e+00\r\n", "xmax = 2.50000e+01\r\n", "diff_coeff = 1.00000e-01\r\n", "source_s = 1.00000e-03\r\n", "source_transfer_coeff = 5.00000e-03\r\n", "source_saturation = 1.00000e+00\r\n", "u_left = 3.00000e+00\r\n", "u_right = 0.00000e+00\r\n", "u2_left = 0.00000e+00\r\n", "u2_right = 0.00000e+00\r\n", "diff_coeff_2 = 5.00000e-01\r\n", "velocity = '1.20000e-01 0.00000e+00 0.00000e+00'\r\n", "\r\n", "[Problem]\r\n", " type = FEProblem\r\n", " coord_type = XYZ\r\n", "[]\r\n", "\r\n", "[Mesh]\r\n", " [omega-1d]\r\n", " type = GeneratedMeshGenerator\r\n", " dim = 1\r\n", " xmin = ${replace xmin}\r\n", " xmax = ${replace xmax}\r\n", " nx = 20\r\n", " []\r\n", "[]\r\n", "\r\n", "[Variables]\r\n", " [u]\r\n", " order = first\r\n", " family = lagrange\r\n", " initial_condition = ${fparse (u_right+u_left)/2}\r\n", " []\r\n", " [u2]\r\n", " order = first\r\n", " family = lagrange\r\n", " initial_condition = ${fparse (u2_right+u2_left)/2}\r\n", " []\r\n", "[]\r\n", "\r\n", "[AuxVariables]\r\n", " [diffFluxU]\r\n", " order = CONSTANT\r\n", " family = MONOMIAL_VEC\r\n", " []\r\n", " [diffFluxU2]\r\n", " order = CONSTANT\r\n", " family = MONOMIAL_VEC\r\n", " []\r\n", " [diffFluxU_x]\r\n", " order = CONSTANT\r\n", " family = MONOMIAL\r\n", " []\r\n", " [diffFluxU2_x]\r\n", " order = CONSTANT\r\n", " family = MONOMIAL\r\n", " []\r\n", "[]\r\n", "\r\n", "[Kernels]\r\n", " [diffusion-term]\r\n", " type = DiffusionTerm\r\n", " variable = u # produced quantity\r\n", " diffCoeff = ${replace diff_coeff}\r\n", " []\r\n", " [source-term]\r\n", " type = SourceTerm\r\n", " variable = u # add to produced quantity\r\n", " sourceS = ${replace source_s}\r\n", " transferCoeff = ${replace source_transfer_coeff}\r\n", " saturation = ${replace source_saturation}\r\n", " coupledVariable = u2\r\n", " []\r\n", " [convection-term]\r\n", " type = ConvectionTerm\r\n", " variable = u # produced quantity\r\n", " velocity = ${replace velocity}\r\n", " []\r\n", " [diffusion-term-2]\r\n", " type = DiffusionTerm\r\n", " variable = u2 # produced quantity\r\n", " diffCoeff = ${replace diff_coeff_2}\r\n", " []\r\n", " [source-term-2]\r\n", " type = SourceTerm\r\n", " variable = u2 # add to produced quantity\r\n", " coupledVariable = u\r\n", " sourceSCoupled = ${replace source_s}\r\n", " transferCoeffCoupled = ${replace source_transfer_coeff}\r\n", " saturationCoupled = ${replace source_saturation}\r\n", " []\r\n", " [convection-term-2]\r\n", " type = ConvectionTerm\r\n", " variable = u2 # produced quantity\r\n", " velocity = ${replace velocity}\r\n", " []\r\n", "[]\r\n", "\r\n", "[AuxKernels]\r\n", " [diffusion-flux]\r\n", " execute_on = timestep_end\r\n", " type = DiffusionFlux\r\n", " field = u\r\n", " diffCoeff = ${replace diff_coeff}\r\n", " variable = diffFluxU # produced quantity\r\n", " []\r\n", " [diffusion-flux-x]\r\n", " execute_on = timestep_end\r\n", " type = VectorVariableComponentAux\r\n", " variable = diffFluxU_x # produced quantity\r\n", " component = x\r\n", " vector_variable = diffFluxU \r\n", " []\r\n", " [diffusion-flux-2]\r\n", " execute_on = timestep_end\r\n", " type = DiffusionFlux\r\n", " field = u2\r\n", " diffCoeff = ${replace diff_coeff_2}\r\n", " variable = diffFluxU2 # produced quantity\r\n", " []\r\n", " [diffusion-flux-x-2]\r\n", " execute_on = timestep_end\r\n", " type = VectorVariableComponentAux\r\n", " variable = diffFluxU2_x # produced quantity\r\n", " component = x\r\n", " vector_variable = diffFluxU2 \r\n", " []\r\n", "[]\r\n", "\r\n", "[BCs]\r\n", " [entry-u]\r\n", " type = DirichletBC\r\n", " variable = u\r\n", " boundary = left\r\n", " value = ${replace u_left}\r\n", " []\r\n", " [entry-u2]\r\n", " type = DirichletBC\r\n", " variable = u2\r\n", " boundary = left\r\n", " value = ${replace u2_left}\r\n", " []\r\n", " [exit-u]\r\n", " type = DirichletBC\r\n", " variable = u\r\n", " boundary = right\r\n", " value = ${replace u_right}\r\n", " []\r\n", " [exit-u2]\r\n", " type = DirichletBC\r\n", " variable = u2\r\n", " boundary = right\r\n", " value = ${replace u2_right}\r\n", " []\r\n", "[]\r\n", "\r\n", "[Preconditioning]\r\n", " active = 'fdp-newt-full'\r\n", " [fdp-newt-full]\r\n", " type = FDP\r\n", " full = true\r\n", " solve_type = 'NEWTON'\r\n", " petsc_options_iname = '-pc_type -mat_fd_coloring_err -mat_fd_type'\r\n", " petsc_options_value = 'lu 1.000e-06 ds'\r\n", " []\r\n", "[]\r\n", "\r\n", "[Executioner]\r\n", " type = Steady\r\n", "[]\r\n", "\r\n", "[VectorPostprocessors]\r\n", " [omega-data]\r\n", " type = LineValueSampler\r\n", " execute_on = 'timestep_end final'\r\n", " variable = 'u u2 diffFluxU_x diffFluxU2_x' # output data\r\n", " start_point = '${replace xmin} 0 0'\r\n", " end_point = '${replace xmax} 0 0'\r\n", " num_points = 21\r\n", " sort_by = id\r\n", " []\r\n", "[]\r\n", "\r\n", "[Outputs]\r\n", " console = true\r\n", " [csv]\r\n", " type = CSV\r\n", " file_base = 'output'\r\n", " execute_on = 'final'\r\n", " []\r\n", "[]\r\n" ] } ], "source": [ "'''Display MOOSE input file created'''\n", "\n", "!cat engy5310p1/input.hit" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Framework Information:\n", "MOOSE Version: git commit a7f499ed31 on 2021-04-30\n", "LibMesh Version: 27141d18f3137f77e33cdb3d565fd38ebfbfc46f\n", "PETSc Version: 3.15.0\n", "SLEPc Version: 3.14.2\n", "Current Time: Sun May 2 18:24:20 2021\n", "Executable Timestamp: Sun May 2 16:17:43 2021\n", "\n", "Parallelism:\n", " Num Processors: 1\n", " Num Threads: 1\n", "\n", "Mesh: \n", " Parallel Type: replicated\n", " Mesh Dimension: 1\n", " Spatial Dimension: 1\n", " Nodes: \n", " Total: 21\n", " Local: 21\n", " Elems: \n", " Total: 20\n", " Local: 20\n", " Num Subdomains: 1\n", " Num Partitions: 1\n", "\n", "Nonlinear System:\n", " Num DOFs: 42\n", " Num Local DOFs: 42\n", " Variables: { \"u\" \"u2\" } \n", " Finite Element Types: \"LAGRANGE\" \n", " Approximation Orders: \"FIRST\" \n", "\n", "Auxiliary System:\n", " Num DOFs: 80\n", " Num Local DOFs: 80\n", " Variables: { \"diffFluxU\" \"diffFluxU2\" } { \"diffFluxU_x\" \"diffFluxU2_x\" } \n", " Finite Element Types: \"MONOMIAL_VEC\" \"MONOMIAL\" \n", " Approximation Orders: \"CONSTANT\" \"CONSTANT\" \n", "\n", "Execution Information:\n", " Executioner: Steady\n", " Solver Mode: NEWTON\n", " PETSc Preconditioner: lu \n", " MOOSE Preconditioner: FDP\n", "\n", " 0 Nonlinear |R| = \u001b[32m2.090894e-01\u001b[39m\n", " 0 Linear |R| = \u001b[32m2.090894e-01\u001b[39m\n", " 1 Linear |R| = \u001b[32m3.244198e-16\u001b[39m\n", " 1 Nonlinear |R| = \u001b[32m1.387873e-08\u001b[39m\n", " 0 Linear |R| = \u001b[32m1.387873e-08\u001b[39m\n", " 1 Linear |R| = \u001b[32m3.268868e-23\u001b[39m\n", " 2 Nonlinear |R| = \u001b[32m1.982895e-16\u001b[39m\n", "\u001b[32m Solve Converged!\u001b[39m\n", "WARNING! There are options you set that were not used!\n", "WARNING! could be spelling mistake, etc!\n", "There is one unused database option. It is:\n", "Option left: name:-i value: engy5310p1/input.hit\n" ] } ], "source": [ "'''Run Engy5310P1 MOOSE App'''\n", "\n", "!engy5310p1/engy5310p1-opt -i engy5310p1/input.hit" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "code_folding": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "'''Show FEM Solution'''\n", "\n", "import pandas as pd\n", "df = pd.read_csv('output_omega-data_0002.csv')\n", " \n", "plot_solution(df, title='Peclet Coupled Variables w/ Dirichlet BC FEM Solution', \n", " u1_legend=r'$u_1$ Linear Lagrange', u2_legend=r'$u_2$ Linear Lagrange',\n", " u1_flux_legend=r'$u_1$ Diff. Flux Constant Monomial',\n", " u2_flux_legend=r'$u_2$ Diff. Flux Constant Monomial')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Comments:**\n", "\n", "1. The *concentration* of $u_1$ decreases as $u_2$ increases since there is only transfer from 1 to 2." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [FEM Error](#toc)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'coming...'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'''Error Compared to Exact Dimensionless Solution'''\n", "\n", "'''coming...'''" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Comments:**\n", "\n", "1. TBA\n", "1. TBA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [Quadratic Lagrange FEM](#toc)\n", "\n", "Solve problem with the same parameter values as above.\n", "\n", "FEM parameters:\n", "\n", "> + Basis Functions: Second Order Lagrangian\n", "> + num. of finite elements: 20" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "code_folding": [ 13 ] }, "outputs": [], "source": [ "'''FEM Solution'''\n", "\n", "n_felem = 20\n", "\n", "order = 'second'\n", "\n", "n_plot_pts = 2*n_felem + 1\n", "\n", "try: \n", " from engy_5310.toolkit import write_engy5310_p1_1d_input_file \n", "except ModuleNotFoundError:\n", " assert False, 'You need to provide your own code here. Bailing out.'\n", "\n", "write_engy5310_p1_1d_input_file(x_left=x_a, x_right=x_b, \n", " u_left=u_a, u_right=u_b, \n", " diff_coeff=diff_coeff_1, source_s=source_s_1,\n", " source_transfer_coeff=source_transfer_coeff_1, \n", " source_saturation=source_saturation_1,\n", " u2_left=u2_a, u2_right=u2_b, \n", " diff_coeff_2=diff_coeff_2,\n", " velocity=velocity, \n", " n_felem=n_felem, order=order, \n", " n_plot_pts=n_plot_pts,\n", " compute_diffusion_flux=True,\n", " solver='fdp-newt-full')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Engy-5310 Problem 1: Poisson 1D FEM\r\n", "# UMass Lowell Nuclear Chemical Engineering\r\n", "# Prof. Valmor F. de Almeida\r\n", "# 02May21 18:24:21\r\n", "\r\n", "# Parameters\r\n", "xmin = 0.00000e+00\r\n", "xmax = 2.50000e+01\r\n", "diff_coeff = 1.00000e-01\r\n", "source_s = 1.00000e-03\r\n", "source_transfer_coeff = 5.00000e-03\r\n", "source_saturation = 1.00000e+00\r\n", "u_left = 3.00000e+00\r\n", "u_right = 0.00000e+00\r\n", "u2_left = 0.00000e+00\r\n", "u2_right = 0.00000e+00\r\n", "diff_coeff_2 = 5.00000e-01\r\n", "velocity = '1.20000e-01 0.00000e+00 0.00000e+00'\r\n", "\r\n", "[Problem]\r\n", " type = FEProblem\r\n", " coord_type = XYZ\r\n", "[]\r\n", "\r\n", "[Mesh]\r\n", " [omega-1d]\r\n", " type = GeneratedMeshGenerator\r\n", " dim = 1\r\n", " xmin = ${replace xmin}\r\n", " xmax = ${replace xmax}\r\n", " nx = 20\r\n", " elem_type = edge3\r\n", " []\r\n", "[]\r\n", "\r\n", "[Variables]\r\n", " [u]\r\n", " order = second\r\n", " family = lagrange\r\n", " initial_condition = ${fparse (u_right+u_left)/2}\r\n", " []\r\n", " [u2]\r\n", " order = second\r\n", " family = lagrange\r\n", " initial_condition = ${fparse (u2_right+u2_left)/2}\r\n", " []\r\n", "[]\r\n", "\r\n", "[AuxVariables]\r\n", " [diffFluxU]\r\n", " order = FIRST\r\n", " family = MONOMIAL_VEC\r\n", " []\r\n", " [diffFluxU2]\r\n", " order = FIRST\r\n", " family = MONOMIAL_VEC\r\n", " []\r\n", " [diffFluxU_x]\r\n", " order = FIRST\r\n", " family = MONOMIAL\r\n", " []\r\n", " [diffFluxU2_x]\r\n", " order = FIRST\r\n", " family = MONOMIAL\r\n", " []\r\n", "[]\r\n", "\r\n", "[Kernels]\r\n", " [diffusion-term]\r\n", " type = DiffusionTerm\r\n", " variable = u # produced quantity\r\n", " diffCoeff = ${replace diff_coeff}\r\n", " []\r\n", " [source-term]\r\n", " type = SourceTerm\r\n", " variable = u # add to produced quantity\r\n", " sourceS = ${replace source_s}\r\n", " transferCoeff = ${replace source_transfer_coeff}\r\n", " saturation = ${replace source_saturation}\r\n", " coupledVariable = u2\r\n", " []\r\n", " [convection-term]\r\n", " type = ConvectionTerm\r\n", " variable = u # produced quantity\r\n", " velocity = ${replace velocity}\r\n", " []\r\n", " [diffusion-term-2]\r\n", " type = DiffusionTerm\r\n", " variable = u2 # produced quantity\r\n", " diffCoeff = ${replace diff_coeff_2}\r\n", " []\r\n", " [source-term-2]\r\n", " type = SourceTerm\r\n", " variable = u2 # add to produced quantity\r\n", " coupledVariable = u\r\n", " sourceSCoupled = ${replace source_s}\r\n", " transferCoeffCoupled = ${replace source_transfer_coeff}\r\n", " saturationCoupled = ${replace source_saturation}\r\n", " []\r\n", " [convection-term-2]\r\n", " type = ConvectionTerm\r\n", " variable = u2 # produced quantity\r\n", " velocity = ${replace velocity}\r\n", " []\r\n", "[]\r\n", "\r\n", "[AuxKernels]\r\n", " [diffusion-flux]\r\n", " execute_on = timestep_end\r\n", " type = DiffusionFlux\r\n", " field = u\r\n", " diffCoeff = ${replace diff_coeff}\r\n", " variable = diffFluxU # produced quantity\r\n", " []\r\n", " [diffusion-flux-x]\r\n", " execute_on = timestep_end\r\n", " type = VectorVariableComponentAux\r\n", " variable = diffFluxU_x # produced quantity\r\n", " component = x\r\n", " vector_variable = diffFluxU \r\n", " []\r\n", " [diffusion-flux-2]\r\n", " execute_on = timestep_end\r\n", " type = DiffusionFlux\r\n", " field = u2\r\n", " diffCoeff = ${replace diff_coeff_2}\r\n", " variable = diffFluxU2 # produced quantity\r\n", " []\r\n", " [diffusion-flux-x-2]\r\n", " execute_on = timestep_end\r\n", " type = VectorVariableComponentAux\r\n", " variable = diffFluxU2_x # produced quantity\r\n", " component = x\r\n", " vector_variable = diffFluxU2 \r\n", " []\r\n", "[]\r\n", "\r\n", "[BCs]\r\n", " [entry-u]\r\n", " type = DirichletBC\r\n", " variable = u\r\n", " boundary = left\r\n", " value = ${replace u_left}\r\n", " []\r\n", " [entry-u2]\r\n", " type = DirichletBC\r\n", " variable = u2\r\n", " boundary = left\r\n", " value = ${replace u2_left}\r\n", " []\r\n", " [exit-u]\r\n", " type = DirichletBC\r\n", " variable = u\r\n", " boundary = right\r\n", " value = ${replace u_right}\r\n", " []\r\n", " [exit-u2]\r\n", " type = DirichletBC\r\n", " variable = u2\r\n", " boundary = right\r\n", " value = ${replace u2_right}\r\n", " []\r\n", "[]\r\n", "\r\n", "[Preconditioning]\r\n", " active = 'fdp-newt-full'\r\n", " [fdp-newt-full]\r\n", " type = FDP\r\n", " full = true\r\n", " solve_type = 'NEWTON'\r\n", " petsc_options_iname = '-pc_type -mat_fd_coloring_err -mat_fd_type'\r\n", " petsc_options_value = 'lu 1.000e-06 ds'\r\n", " []\r\n", "[]\r\n", "\r\n", "[Executioner]\r\n", " type = Steady\r\n", "[]\r\n", "\r\n", "[VectorPostprocessors]\r\n", " [omega-data]\r\n", " type = LineValueSampler\r\n", " execute_on = 'timestep_end final'\r\n", " variable = 'u u2 diffFluxU_x diffFluxU2_x' # output data\r\n", " start_point = '${replace xmin} 0 0'\r\n", " end_point = '${replace xmax} 0 0'\r\n", " num_points = 41\r\n", " sort_by = id\r\n", " []\r\n", "[]\r\n", "\r\n", "[Outputs]\r\n", " console = true\r\n", " [csv]\r\n", " type = CSV\r\n", " file_base = 'output'\r\n", " execute_on = 'final'\r\n", " []\r\n", "[]\r\n" ] } ], "source": [ "'''Display MOOSE input file created'''\n", "\n", "!cat engy5310p1/input.hit" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Framework Information:\n", "MOOSE Version: git commit a7f499ed31 on 2021-04-30\n", "LibMesh Version: 27141d18f3137f77e33cdb3d565fd38ebfbfc46f\n", "PETSc Version: 3.15.0\n", "SLEPc Version: 3.14.2\n", "Current Time: Sun May 2 18:24:22 2021\n", "Executable Timestamp: Sun May 2 16:17:43 2021\n", "\n", "Parallelism:\n", " Num Processors: 1\n", " Num Threads: 1\n", "\n", "Mesh: \n", " Parallel Type: replicated\n", " Mesh Dimension: 1\n", " Spatial Dimension: 1\n", " Nodes: \n", " Total: 41\n", " Local: 41\n", " Elems: \n", " Total: 20\n", " Local: 20\n", " Num Subdomains: 1\n", " Num Partitions: 1\n", "\n", "Nonlinear System:\n", " Num DOFs: 82\n", " Num Local DOFs: 82\n", " Variables: { \"u\" \"u2\" } \n", " Finite Element Types: \"LAGRANGE\" \n", " Approximation Orders: \"SECOND\" \n", "\n", "Auxiliary System:\n", " Num DOFs: 160\n", " Num Local DOFs: 160\n", " Variables: { \"diffFluxU\" \"diffFluxU2\" } { \"diffFluxU_x\" \"diffFluxU2_x\" } \n", " Finite Element Types: \"MONOMIAL_VEC\" \"MONOMIAL\" \n", " Approximation Orders: \"FIRST\" \"FIRST\" \n", "\n", "Execution Information:\n", " Executioner: Steady\n", " Solver Mode: NEWTON\n", " PETSc Preconditioner: lu \n", " MOOSE Preconditioner: FDP\n", "\n", " 0 Nonlinear |R| = \u001b[32m4.871389e-01\u001b[39m\n", " 0 Linear |R| = \u001b[32m4.871389e-01\u001b[39m\n", " 1 Linear |R| = \u001b[32m9.612425e-16\u001b[39m\n", " 1 Nonlinear |R| = \u001b[32m3.801380e-07\u001b[39m\n", " 0 Linear |R| = \u001b[32m3.801380e-07\u001b[39m\n", " 1 Linear |R| = \u001b[32m8.456680e-21\u001b[39m\n", " 2 Nonlinear |R| = \u001b[32m6.874920e-15\u001b[39m\n", "\u001b[32m Solve Converged!\u001b[39m\n", "WARNING! There are options you set that were not used!\n", "WARNING! could be spelling mistake, etc!\n", "There is one unused database option. It is:\n", "Option left: name:-i value: engy5310p1/input.hit\n" ] } ], "source": [ "'''Run Engy5310P1 MOOSE App'''\n", "\n", "!engy5310p1/engy5310p1-opt -i engy5310p1/input.hit" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "code_folding": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "'''Show FEM Solution'''\n", "\n", "import pandas as pd\n", "df = pd.read_csv('output_omega-data_0002.csv')\n", " \n", "plot_solution(df, title='Peclet Coupled Variables w/ Dirichlet BC FEM Solution', \n", " u1_legend=r'$u_1$ Quadratic Lagrange', u2_legend=r'$u_2$ Quadratic Lagrange',\n", " u1_flux_legend=r'$u_1$ Diff. Flux Linear Monomial',\n", " u2_flux_legend=r'$u_2$ Diff. Flux Linear Monomial')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [FEM Error](#toc)\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'comming...'" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'''Error Compared to Exact Dimensionless Solution'''\n", "\n", "'''comming...'''" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Comments:**\n", "\n", "1. TBA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [High Peclet Number](#toc)\n", "\n", "Highly convective problems may lead to numerical difficulties. Below, enough finite elements are chosen so to avoid oscillations. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Quadratic Lagrange FEM](#toc)\n", "\n", "Solve problem with the same parameter values above.\n", "\n", "FEM parameters:\n", "\n", "> + Basis Functions: Second Order Lagrangian\n", "> + num. of finite elements: 10" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "'''Parameters and data'''\n", "\n", "Pe_ave = 50 # convective dominated\n", "\n", "velocity = (Pe_ave * (diff_coeff_1+diff_coeff_2)/2/x_length, 0, 0) # length scale is the x length" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "code_folding": [ 13 ] }, "outputs": [], "source": [ "'''FEM Solution'''\n", "\n", "n_felem = 10\n", "\n", "order = 'second'\n", "\n", "n_plot_pts = 2*n_felem + 1\n", "\n", "try: \n", " from engy_5310.toolkit import write_engy5310_p1_1d_input_file \n", "except ModuleNotFoundError:\n", " assert False, 'You need to provide your own code here. Bailing out.'\n", "\n", "write_engy5310_p1_1d_input_file(x_left=x_a, x_right=x_b, \n", " u_left=u_a, u_right=u_b, \n", " diff_coeff=diff_coeff_1, source_s=source_s_1,\n", " source_transfer_coeff=source_transfer_coeff_1, \n", " source_saturation=source_saturation_1,\n", " u2_left=u2_a, u2_right=u2_b, \n", " diff_coeff_2=diff_coeff_2,\n", " velocity=velocity, \n", " n_felem=n_felem, order=order, \n", " n_plot_pts=n_plot_pts,\n", " compute_diffusion_flux=True,\n", " solver='fdp-newt-full')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Engy-5310 Problem 1: Poisson 1D FEM\r\n", "# UMass Lowell Nuclear Chemical Engineering\r\n", "# Prof. Valmor F. de Almeida\r\n", "# 02May21 18:24:22\r\n", "\r\n", "# Parameters\r\n", "xmin = 0.00000e+00\r\n", "xmax = 2.50000e+01\r\n", "diff_coeff = 1.00000e-01\r\n", "source_s = 1.00000e-03\r\n", "source_transfer_coeff = 5.00000e-03\r\n", "source_saturation = 1.00000e+00\r\n", "u_left = 3.00000e+00\r\n", "u_right = 0.00000e+00\r\n", "u2_left = 0.00000e+00\r\n", "u2_right = 0.00000e+00\r\n", "diff_coeff_2 = 5.00000e-01\r\n", "velocity = '6.00000e-01 0.00000e+00 0.00000e+00'\r\n", "\r\n", "[Problem]\r\n", " type = FEProblem\r\n", " coord_type = XYZ\r\n", "[]\r\n", "\r\n", "[Mesh]\r\n", " [omega-1d]\r\n", " type = GeneratedMeshGenerator\r\n", " dim = 1\r\n", " xmin = ${replace xmin}\r\n", " xmax = ${replace xmax}\r\n", " nx = 10\r\n", " elem_type = edge3\r\n", " []\r\n", "[]\r\n", "\r\n", "[Variables]\r\n", " [u]\r\n", " order = second\r\n", " family = lagrange\r\n", " initial_condition = ${fparse (u_right+u_left)/2}\r\n", " []\r\n", " [u2]\r\n", " order = second\r\n", " family = lagrange\r\n", " initial_condition = ${fparse (u2_right+u2_left)/2}\r\n", " []\r\n", "[]\r\n", "\r\n", "[AuxVariables]\r\n", " [diffFluxU]\r\n", " order = FIRST\r\n", " family = MONOMIAL_VEC\r\n", " []\r\n", " [diffFluxU2]\r\n", " order = FIRST\r\n", " family = MONOMIAL_VEC\r\n", " []\r\n", " [diffFluxU_x]\r\n", " order = FIRST\r\n", " family = MONOMIAL\r\n", " []\r\n", " [diffFluxU2_x]\r\n", " order = FIRST\r\n", " family = MONOMIAL\r\n", " []\r\n", "[]\r\n", "\r\n", "[Kernels]\r\n", " [diffusion-term]\r\n", " type = DiffusionTerm\r\n", " variable = u # produced quantity\r\n", " diffCoeff = ${replace diff_coeff}\r\n", " []\r\n", " [source-term]\r\n", " type = SourceTerm\r\n", " variable = u # add to produced quantity\r\n", " sourceS = ${replace source_s}\r\n", " transferCoeff = ${replace source_transfer_coeff}\r\n", " saturation = ${replace source_saturation}\r\n", " coupledVariable = u2\r\n", " []\r\n", " [convection-term]\r\n", " type = ConvectionTerm\r\n", " variable = u # produced quantity\r\n", " velocity = ${replace velocity}\r\n", " []\r\n", " [diffusion-term-2]\r\n", " type = DiffusionTerm\r\n", " variable = u2 # produced quantity\r\n", " diffCoeff = ${replace diff_coeff_2}\r\n", " []\r\n", " [source-term-2]\r\n", " type = SourceTerm\r\n", " variable = u2 # add to produced quantity\r\n", " coupledVariable = u\r\n", " sourceSCoupled = ${replace source_s}\r\n", " transferCoeffCoupled = ${replace source_transfer_coeff}\r\n", " saturationCoupled = ${replace source_saturation}\r\n", " []\r\n", " [convection-term-2]\r\n", " type = ConvectionTerm\r\n", " variable = u2 # produced quantity\r\n", " velocity = ${replace velocity}\r\n", " []\r\n", "[]\r\n", "\r\n", "[AuxKernels]\r\n", " [diffusion-flux]\r\n", " execute_on = timestep_end\r\n", " type = DiffusionFlux\r\n", " field = u\r\n", " diffCoeff = ${replace diff_coeff}\r\n", " variable = diffFluxU # produced quantity\r\n", " []\r\n", " [diffusion-flux-x]\r\n", " execute_on = timestep_end\r\n", " type = VectorVariableComponentAux\r\n", " variable = diffFluxU_x # produced quantity\r\n", " component = x\r\n", " vector_variable = diffFluxU \r\n", " []\r\n", " [diffusion-flux-2]\r\n", " execute_on = timestep_end\r\n", " type = DiffusionFlux\r\n", " field = u2\r\n", " diffCoeff = ${replace diff_coeff_2}\r\n", " variable = diffFluxU2 # produced quantity\r\n", " []\r\n", " [diffusion-flux-x-2]\r\n", " execute_on = timestep_end\r\n", " type = VectorVariableComponentAux\r\n", " variable = diffFluxU2_x # produced quantity\r\n", " component = x\r\n", " vector_variable = diffFluxU2 \r\n", " []\r\n", "[]\r\n", "\r\n", "[BCs]\r\n", " [entry-u]\r\n", " type = DirichletBC\r\n", " variable = u\r\n", " boundary = left\r\n", " value = ${replace u_left}\r\n", " []\r\n", " [entry-u2]\r\n", " type = DirichletBC\r\n", " variable = u2\r\n", " boundary = left\r\n", " value = ${replace u2_left}\r\n", " []\r\n", " [exit-u]\r\n", " type = DirichletBC\r\n", " variable = u\r\n", " boundary = right\r\n", " value = ${replace u_right}\r\n", " []\r\n", " [exit-u2]\r\n", " type = DirichletBC\r\n", " variable = u2\r\n", " boundary = right\r\n", " value = ${replace u2_right}\r\n", " []\r\n", "[]\r\n", "\r\n", "[Preconditioning]\r\n", " active = 'fdp-newt-full'\r\n", " [fdp-newt-full]\r\n", " type = FDP\r\n", " full = true\r\n", " solve_type = 'NEWTON'\r\n", " petsc_options_iname = '-pc_type -mat_fd_coloring_err -mat_fd_type'\r\n", " petsc_options_value = 'lu 1.000e-06 ds'\r\n", " []\r\n", "[]\r\n", "\r\n", "[Executioner]\r\n", " type = Steady\r\n", "[]\r\n", "\r\n", "[VectorPostprocessors]\r\n", " [omega-data]\r\n", " type = LineValueSampler\r\n", " execute_on = 'timestep_end final'\r\n", " variable = 'u u2 diffFluxU_x diffFluxU2_x' # output data\r\n", " start_point = '${replace xmin} 0 0'\r\n", " end_point = '${replace xmax} 0 0'\r\n", " num_points = 21\r\n", " sort_by = id\r\n", " []\r\n", "[]\r\n", "\r\n", "[Outputs]\r\n", " console = true\r\n", " [csv]\r\n", " type = CSV\r\n", " file_base = 'output'\r\n", " execute_on = 'final'\r\n", " []\r\n", "[]\r\n" ] } ], "source": [ "'''Display MOOSE input file created'''\n", "\n", "!cat engy5310p1/input.hit" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Framework Information:\n", "MOOSE Version: git commit a7f499ed31 on 2021-04-30\n", "LibMesh Version: 27141d18f3137f77e33cdb3d565fd38ebfbfc46f\n", "PETSc Version: 3.15.0\n", "SLEPc Version: 3.14.2\n", "Current Time: Sun May 2 18:24:23 2021\n", "Executable Timestamp: Sun May 2 16:17:43 2021\n", "\n", "Parallelism:\n", " Num Processors: 1\n", " Num Threads: 1\n", "\n", "Mesh: \n", " Parallel Type: replicated\n", " Mesh Dimension: 1\n", " Spatial Dimension: 1\n", " Nodes: \n", " Total: 21\n", " Local: 21\n", " Elems: \n", " Total: 10\n", " Local: 10\n", " Num Subdomains: 1\n", " Num Partitions: 1\n", "\n", "Nonlinear System:\n", " Num DOFs: 42\n", " Num Local DOFs: 42\n", " Variables: { \"u\" \"u2\" } \n", " Finite Element Types: \"LAGRANGE\" \n", " Approximation Orders: \"SECOND\" \n", "\n", "Auxiliary System:\n", " Num DOFs: 80\n", " Num Local DOFs: 80\n", " Variables: { \"diffFluxU\" \"diffFluxU2\" } { \"diffFluxU_x\" \"diffFluxU2_x\" } \n", " Finite Element Types: \"MONOMIAL_VEC\" \"MONOMIAL\" \n", " Approximation Orders: \"FIRST\" \"FIRST\" \n", "\n", "Execution Information:\n", " Executioner: Steady\n", " Solver Mode: NEWTON\n", " PETSc Preconditioner: lu \n", " MOOSE Preconditioner: FDP\n", "\n", " 0 Nonlinear |R| = \u001b[32m9.005892e-01\u001b[39m\n", " 0 Linear |R| = \u001b[32m9.005892e-01\u001b[39m\n", " 1 Linear |R| = \u001b[32m1.085705e-15\u001b[39m\n", " 1 Nonlinear |R| = \u001b[32m1.660617e-07\u001b[39m\n", " 0 Linear |R| = \u001b[32m1.660617e-07\u001b[39m\n", " 1 Linear |R| = \u001b[32m1.928338e-22\u001b[39m\n", " 2 Nonlinear |R| = \u001b[32m4.792070e-16\u001b[39m\n", "\u001b[32m Solve Converged!\u001b[39m\n", "WARNING! There are options you set that were not used!\n", "WARNING! could be spelling mistake, etc!\n", "There is one unused database option. It is:\n", "Option left: name:-i value: engy5310p1/input.hit\n" ] } ], "source": [ "'''Run Engy5310P1 MOOSE App'''\n", "\n", "!engy5310p1/engy5310p1-opt -i engy5310p1/input.hit" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "code_folding": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "'''Show FEM Solution'''\n", "\n", "import pandas as pd\n", "df = pd.read_csv('output_omega-data_0002.csv')\n", " \n", "plot_solution(df, title='Peclet Coupled Variables w/ Dirichlet BC FEM Solution', \n", " u1_legend=r'$u_1$ Quadratic Lagrange', u2_legend=r'$u_2$ Quadratic Lagrange',\n", " u1_flux_legend=r'$u_1$ Diff. Flux Linear Monomial',\n", " u2_flux_legend=r'$u_2$ Diff. Flux Linear Monomial')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Comments:**\n", "\n", "As convection dominates, the boundary layer sharpens and numerical instability appears in the form of oscillations. Either an increase of number of finite element basis functions or mesh adaptivity could resolve the boundary layer as demonstrated below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### [Compute Error](#toc)\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'coming...'" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'''Flux Error Compared to Exact Dimensionless Solution'''\n", "\n", "'''coming...'''" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### [Quadratic Lagrange FEM](#toc)\n", "\n", "Solve problem with the same parameter values above.\n", "\n", "FEM parameters:\n", "\n", "> + Basis Functions: Second Order Lagrangian\n", "> + num. of finite elements: 20" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "code_folding": [ 16 ] }, "outputs": [], "source": [ "'''FEM Solution'''\n", "\n", "n_felem = 20\n", "\n", "x_bias = .5 # mesh adaptivity\n", "\n", "order = 'second'\n", "\n", "n_plot_pts = 2*n_felem + 1\n", "n_plot_pts *= 2\n", "\n", "try: \n", " from engy_5310.toolkit import write_engy5310_p1_1d_input_file \n", "except ModuleNotFoundError:\n", " assert False, 'You need to provide your own code here. Bailing out.'\n", "\n", "write_engy5310_p1_1d_input_file(x_left=x_a, x_right=x_b, \n", " u_left=u_a, u_right=u_b, \n", " diff_coeff=diff_coeff_1, source_s=source_s_1,\n", " source_transfer_coeff=source_transfer_coeff_1, \n", " source_saturation=source_saturation_1,\n", " u2_left=u2_a, u2_right=u2_b, \n", " diff_coeff_2=diff_coeff_2,\n", " velocity=velocity, \n", " n_felem=n_felem, order=order, \n", " x_bias=x_bias,\n", " n_plot_pts=n_plot_pts,\n", " compute_diffusion_flux=True,\n", " solver='fdp-newt-full')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# Engy-5310 Problem 1: Poisson 1D FEM\r\n", "# UMass Lowell Nuclear Chemical Engineering\r\n", "# Prof. Valmor F. de Almeida\r\n", "# 02May21 18:24:23\r\n", "\r\n", "# Parameters\r\n", "xmin = 0.00000e+00\r\n", "xmax = 2.50000e+01\r\n", "diff_coeff = 1.00000e-01\r\n", "source_s = 1.00000e-03\r\n", "source_transfer_coeff = 5.00000e-03\r\n", "source_saturation = 1.00000e+00\r\n", "u_left = 3.00000e+00\r\n", "u_right = 0.00000e+00\r\n", "u2_left = 0.00000e+00\r\n", "u2_right = 0.00000e+00\r\n", "diff_coeff_2 = 5.00000e-01\r\n", "velocity = '6.00000e-01 0.00000e+00 0.00000e+00'\r\n", "\r\n", "[Problem]\r\n", " type = FEProblem\r\n", " coord_type = XYZ\r\n", "[]\r\n", "\r\n", "[Mesh]\r\n", " [omega-1d]\r\n", " type = GeneratedMeshGenerator\r\n", " dim = 1\r\n", " xmin = ${replace xmin}\r\n", " xmax = ${replace xmax}\r\n", " nx = 20\r\n", " elem_type = edge3\r\n", " bias_x = 5.000e-01\r\n", " []\r\n", "[]\r\n", "\r\n", "[Variables]\r\n", " [u]\r\n", " order = second\r\n", " family = lagrange\r\n", " initial_condition = ${fparse (u_right+u_left)/2}\r\n", " []\r\n", " [u2]\r\n", " order = second\r\n", " family = lagrange\r\n", " initial_condition = ${fparse (u2_right+u2_left)/2}\r\n", " []\r\n", "[]\r\n", "\r\n", "[AuxVariables]\r\n", " [diffFluxU]\r\n", " order = FIRST\r\n", " family = MONOMIAL_VEC\r\n", " []\r\n", " [diffFluxU2]\r\n", " order = FIRST\r\n", " family = MONOMIAL_VEC\r\n", " []\r\n", " [diffFluxU_x]\r\n", " order = FIRST\r\n", " family = MONOMIAL\r\n", " []\r\n", " [diffFluxU2_x]\r\n", " order = FIRST\r\n", " family = MONOMIAL\r\n", " []\r\n", "[]\r\n", "\r\n", "[Kernels]\r\n", " [diffusion-term]\r\n", " type = DiffusionTerm\r\n", " variable = u # produced quantity\r\n", " diffCoeff = ${replace diff_coeff}\r\n", " []\r\n", " [source-term]\r\n", " type = SourceTerm\r\n", " variable = u # add to produced quantity\r\n", " sourceS = ${replace source_s}\r\n", " transferCoeff = ${replace source_transfer_coeff}\r\n", " saturation = ${replace source_saturation}\r\n", " coupledVariable = u2\r\n", " []\r\n", " [convection-term]\r\n", " type = ConvectionTerm\r\n", " variable = u # produced quantity\r\n", " velocity = ${replace velocity}\r\n", " []\r\n", " [diffusion-term-2]\r\n", " type = DiffusionTerm\r\n", " variable = u2 # produced quantity\r\n", " diffCoeff = ${replace diff_coeff_2}\r\n", " []\r\n", " [source-term-2]\r\n", " type = SourceTerm\r\n", " variable = u2 # add to produced quantity\r\n", " coupledVariable = u\r\n", " sourceSCoupled = ${replace source_s}\r\n", " transferCoeffCoupled = ${replace source_transfer_coeff}\r\n", " saturationCoupled = ${replace source_saturation}\r\n", " []\r\n", " [convection-term-2]\r\n", " type = ConvectionTerm\r\n", " variable = u2 # produced quantity\r\n", " velocity = ${replace velocity}\r\n", " []\r\n", "[]\r\n", "\r\n", "[AuxKernels]\r\n", " [diffusion-flux]\r\n", " execute_on = timestep_end\r\n", " type = DiffusionFlux\r\n", " field = u\r\n", " diffCoeff = ${replace diff_coeff}\r\n", " variable = diffFluxU # produced quantity\r\n", " []\r\n", " [diffusion-flux-x]\r\n", " execute_on = timestep_end\r\n", " type = VectorVariableComponentAux\r\n", " variable = diffFluxU_x # produced quantity\r\n", " component = x\r\n", " vector_variable = diffFluxU \r\n", " []\r\n", " [diffusion-flux-2]\r\n", " execute_on = timestep_end\r\n", " type = DiffusionFlux\r\n", " field = u2\r\n", " diffCoeff = ${replace diff_coeff_2}\r\n", " variable = diffFluxU2 # produced quantity\r\n", " []\r\n", " [diffusion-flux-x-2]\r\n", " execute_on = timestep_end\r\n", " type = VectorVariableComponentAux\r\n", " variable = diffFluxU2_x # produced quantity\r\n", " component = x\r\n", " vector_variable = diffFluxU2 \r\n", " []\r\n", "[]\r\n", "\r\n", "[BCs]\r\n", " [entry-u]\r\n", " type = DirichletBC\r\n", " variable = u\r\n", " boundary = left\r\n", " value = ${replace u_left}\r\n", " []\r\n", " [entry-u2]\r\n", " type = DirichletBC\r\n", " variable = u2\r\n", " boundary = left\r\n", " value = ${replace u2_left}\r\n", " []\r\n", " [exit-u]\r\n", " type = DirichletBC\r\n", " variable = u\r\n", " boundary = right\r\n", " value = ${replace u_right}\r\n", " []\r\n", " [exit-u2]\r\n", " type = DirichletBC\r\n", " variable = u2\r\n", " boundary = right\r\n", " value = ${replace u2_right}\r\n", " []\r\n", "[]\r\n", "\r\n", "[Preconditioning]\r\n", " active = 'fdp-newt-full'\r\n", " [fdp-newt-full]\r\n", " type = FDP\r\n", " full = true\r\n", " solve_type = 'NEWTON'\r\n", " petsc_options_iname = '-pc_type -mat_fd_coloring_err -mat_fd_type'\r\n", " petsc_options_value = 'lu 1.000e-06 ds'\r\n", " []\r\n", "[]\r\n", "\r\n", "[Executioner]\r\n", " type = Steady\r\n", "[]\r\n", "\r\n", "[VectorPostprocessors]\r\n", " [omega-data]\r\n", " type = LineValueSampler\r\n", " execute_on = 'timestep_end final'\r\n", " variable = 'u u2 diffFluxU_x diffFluxU2_x' # output data\r\n", " start_point = '${replace xmin} 0 0'\r\n", " end_point = '${replace xmax} 0 0'\r\n", " num_points = 82\r\n", " sort_by = id\r\n", " []\r\n", "[]\r\n", "\r\n", "[Outputs]\r\n", " console = true\r\n", " [csv]\r\n", " type = CSV\r\n", " file_base = 'output'\r\n", " execute_on = 'final'\r\n", " []\r\n", "[]\r\n" ] } ], "source": [ "'''Display MOOSE input file created'''\n", "\n", "!cat engy5310p1/input.hit" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Framework Information:\n", "MOOSE Version: git commit a7f499ed31 on 2021-04-30\n", "LibMesh Version: 27141d18f3137f77e33cdb3d565fd38ebfbfc46f\n", "PETSc Version: 3.15.0\n", "SLEPc Version: 3.14.2\n", "Current Time: Sun May 2 18:24:23 2021\n", "Executable Timestamp: Sun May 2 16:17:43 2021\n", "\n", "Parallelism:\n", " Num Processors: 1\n", " Num Threads: 1\n", "\n", "Mesh: \n", " Parallel Type: replicated\n", " Mesh Dimension: 1\n", " Spatial Dimension: 1\n", " Nodes: \n", " Total: 41\n", " Local: 41\n", " Elems: \n", " Total: 20\n", " Local: 20\n", " Num Subdomains: 1\n", " Num Partitions: 1\n", "\n", "Nonlinear System:\n", " Num DOFs: 82\n", " Num Local DOFs: 82\n", " Variables: { \"u\" \"u2\" } \n", " Finite Element Types: \"LAGRANGE\" \n", " Approximation Orders: \"SECOND\" \n", "\n", "Auxiliary System:\n", " Num DOFs: 160\n", " Num Local DOFs: 160\n", " Variables: { \"diffFluxU\" \"diffFluxU2\" } { \"diffFluxU_x\" \"diffFluxU2_x\" } \n", " Finite Element Types: \"MONOMIAL_VEC\" \"MONOMIAL\" \n", " Approximation Orders: \"FIRST\" \"FIRST\" \n", "\n", "Execution Information:\n", " Executioner: Steady\n", " Solver Mode: NEWTON\n", " PETSc Preconditioner: lu \n", " MOOSE Preconditioner: FDP\n", "\n", " 0 Nonlinear |R| = \u001b[32m1.690715e+04\u001b[39m\n", " 0 Linear |R| = \u001b[32m1.690715e+04\u001b[39m\n", " 1 Linear |R| = \u001b[32m4.774424e-12\u001b[39m\n", " 1 Nonlinear |R| = \u001b[32m3.952108e-06\u001b[39m\n", "\u001b[32m Solve Converged!\u001b[39m\n", "WARNING! There are options you set that were not used!\n", "WARNING! could be spelling mistake, etc!\n", "There is one unused database option. It is:\n", "Option left: name:-i value: engy5310p1/input.hit\n" ] } ], "source": [ "'''Run Engy5310P1 MOOSE App'''\n", "\n", "!engy5310p1/engy5310p1-opt -i engy5310p1/input.hit" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "code_folding": [ 5 ] }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "'''Show FEM Solution'''\n", "\n", "import pandas as pd\n", "df = pd.read_csv('output_omega-data_0002.csv')\n", " \n", "plot_solution(df, title='Peclet Coupled Variables w/ Dirichlet BC FEM Solution', \n", " u1_legend=r'$u_1$ Quadratic Lagrange', u2_legend=r'$u_2$ Quadratic Lagrange',\n", " u1_flux_legend=r'$u_1$ Diff. Flux Linear Monomial',\n", " u2_flux_legend=r'$u_2$ Diff. Flux Linear Monomial')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Comments:**\n", "\n", "1. The mesh adaptivity with refinement on the right side of the domain captures the exit boundary layer. An increase of the number of finite element would also correct the oscillation issue but this strategy is often undesirable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### [Compute Error](#toc)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'coming'" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'''Error Compared to Exact Dimensionless Solution'''\n", "\n", "'''coming'''" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [Application Tree](#toc)\n", "\n", "This tree printout helps the understanding of various pieces of the `MOOSE` application repository created after all the above steps including future implementations in the notebooks following the present one that covers various boundary conditions." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[01;34mengy5310p1\u001b[00m\r\n", "├── LICENSE\r\n", "├── Makefile\r\n", "├── README.md\r\n", "├── \u001b[01;34m__pycache__\u001b[00m\r\n", "│   └── chigger.cpython-38.pyc\r\n", "├── \u001b[01;34mbuild\u001b[00m\r\n", "│   ├── \u001b[01;34mheader_symlinks\u001b[00m\r\n", "│   │   ├── \u001b[01;36mBoundaryEnergy.h\u001b[00m -> /home/dealmeida/OneDrive/uml-courses/engy-5310/2021-01-05-spring/jupynb-repo/notebooks/engy5310p1/include/postprocessors/BoundaryEnergy.h\r\n", "│   │   ├── \u001b[01;36mBulkEnergy.h\u001b[00m -> /home/dealmeida/OneDrive/uml-courses/engy-5310/2021-01-05-spring/jupynb-repo/notebooks/engy5310p1/include/postprocessors/BulkEnergy.h\r\n", "│   │   ├── \u001b[01;36mConvectionTerm.h\u001b[00m -> /home/dealmeida/OneDrive/uml-courses/engy-5310/2021-01-05-spring/jupynb-repo/notebooks/engy5310p1/include/kernels/ConvectionTerm.h\r\n", "│   │   ├── \u001b[01;36mDiffusionFlux.h\u001b[00m -> /home/dealmeida/OneDrive/uml-courses/engy-5310/2021-01-05-spring/jupynb-repo/notebooks/engy5310p1/include/auxkernels/DiffusionFlux.h\r\n", "│   │   ├── \u001b[01;36mDiffusionFluxComponent.h\u001b[00m -> /home/dealmeida/OneDrive/uml-courses/engy-5310/2021-01-05-spring/jupynb-repo/notebooks/engy5310p1/include/auxkernels/DiffusionFluxComponent.h\r\n", "│   │   ├── \u001b[01;36mDiffusionTerm.h\u001b[00m -> /home/dealmeida/OneDrive/uml-courses/engy-5310/2021-01-05-spring/jupynb-repo/notebooks/engy5310p1/include/kernels/DiffusionTerm.h\r\n", "│   │   ├── \u001b[01;36mEngy5310P1App.h\u001b[00m -> /home/dealmeida/OneDrive/uml-courses/engy-5310/2021-01-05-spring/jupynb-repo/notebooks/engy5310p1/include/base/Engy5310P1App.h\r\n", "│   │   ├── \u001b[01;36mInterfaceDiffusion.h\u001b[00m -> /home/dealmeida/OneDrive/uml-courses/engy-5310/2021-01-05-spring/jupynb-repo/notebooks/engy5310p1/include/interfkernels/InterfaceDiffusion.h\r\n", "│   │   ├── \u001b[01;36mInterfaceNormalFluxContinuity.h\u001b[00m -> /home/dealmeida/OneDrive/uml-courses/engy-5310/2021-01-05-spring/jupynb-repo/notebooks/engy5310p1/include/interfkernels/InterfaceNormalFluxContinuity.h\r\n", "│   │   ├── \u001b[01;36mInterfacePartition.h\u001b[00m -> /home/dealmeida/OneDrive/uml-courses/engy-5310/2021-01-05-spring/jupynb-repo/notebooks/engy5310p1/include/interfkernels/InterfacePartition.h\r\n", "│   │   ├── \u001b[01;36mNormalFluxBC.h\u001b[00m -> /home/dealmeida/OneDrive/uml-courses/engy-5310/2021-01-05-spring/jupynb-repo/notebooks/engy5310p1/include/bcs/NormalFluxBC.h\r\n", "│   │   └── \u001b[01;36mSourceTerm.h\u001b[00m -> /home/dealmeida/OneDrive/uml-courses/engy-5310/2021-01-05-spring/jupynb-repo/notebooks/engy5310p1/include/kernels/SourceTerm.h\r\n", "│   └── \u001b[01;34munity_src\u001b[00m\r\n", "│   ├── auxkernels_Unity.C\r\n", "│   ├── auxkernels_Unity.x86_64-pc-linux-gnu.opt.lo\r\n", "│   ├── auxkernels_Unity.x86_64-pc-linux-gnu.opt.lo.d\r\n", "│   ├── bcs_Unity.C\r\n", "│   ├── bcs_Unity.x86_64-pc-linux-gnu.opt.lo\r\n", "│   ├── bcs_Unity.x86_64-pc-linux-gnu.opt.lo.d\r\n", "│   ├── interfkernels_Unity.C\r\n", "│   ├── interfkernels_Unity.x86_64-pc-linux-gnu.opt.lo\r\n", "│   ├── interfkernels_Unity.x86_64-pc-linux-gnu.opt.lo.d\r\n", "│   ├── kernels_Unity.C\r\n", "│   ├── kernels_Unity.x86_64-pc-linux-gnu.opt.lo\r\n", "│   ├── kernels_Unity.x86_64-pc-linux-gnu.opt.lo.d\r\n", "│   ├── postprocessors_Unity.C\r\n", "│   ├── postprocessors_Unity.x86_64-pc-linux-gnu.opt.lo\r\n", "│   └── postprocessors_Unity.x86_64-pc-linux-gnu.opt.lo.d\r\n", "├── \u001b[01;32mengy5310p1-opt\u001b[00m\r\n", "├── \u001b[01;34minclude\u001b[00m\r\n", "│   ├── \u001b[01;34mauxkernels\u001b[00m\r\n", "│   │   ├── DiffusionFlux.h\r\n", "│   │   └── DiffusionFluxComponent.h\r\n", "│   ├── \u001b[01;34mbase\u001b[00m\r\n", "│   │   └── Engy5310P1App.h\r\n", "│   ├── \u001b[01;34mbcs\u001b[00m\r\n", "│   │   └── NormalFluxBC.h\r\n", "│   ├── \u001b[01;34minterfkernels\u001b[00m\r\n", "│   │   ├── InterfaceDiffusion.h\r\n", "│   │   ├── InterfaceNormalFluxContinuity.h\r\n", "│   │   ├── InterfaceNormalFluxContinuity.h~\r\n", "│   │   ├── InterfacePartition.h\r\n", "│   │   └── InterfaceReaction\r\n", "│   ├── \u001b[01;34mkernels\u001b[00m\r\n", "│   │   ├── ConvectionTerm.h\r\n", "│   │   ├── ConvectionTerm.h~\r\n", "│   │   ├── DiffusionTerm.h\r\n", "│   │   ├── SourceTerm.h\r\n", "│   │   └── SourceTerm.h~\r\n", "│   └── \u001b[01;34mpostprocessors\u001b[00m\r\n", "│   ├── BoundaryEnergy.h\r\n", "│   └── BulkEnergy.h\r\n", "├── input-test.hit\r\n", "├── input.hit\r\n", "├── \u001b[01;34mlib\u001b[00m\r\n", "│   ├── \u001b[01;32mlibengy5310p1-opt.la\u001b[00m\r\n", "│   ├── \u001b[01;36mlibengy5310p1-opt.so\u001b[00m -> \u001b[01;32mlibengy5310p1-opt.so.0.0.0\u001b[00m\r\n", "│   ├── \u001b[01;36mlibengy5310p1-opt.so.0\u001b[00m -> \u001b[01;32mlibengy5310p1-opt.so.0.0.0\u001b[00m\r\n", "│   └── \u001b[01;32mlibengy5310p1-opt.so.0.0.0\u001b[00m\r\n", "├── mesh_test.i\r\n", "├── partition-test.i\r\n", "├── \u001b[01;34msrc\u001b[00m\r\n", "│   ├── \u001b[01;34mauxkernels\u001b[00m\r\n", "│   │   ├── DiffusionFlux.C\r\n", "│   │   └── DiffusionFluxComponent.C\r\n", "│   ├── \u001b[01;34mbase\u001b[00m\r\n", "│   │   ├── Engy5310P1App.C\r\n", "│   │   ├── Engy5310P1App.x86_64-pc-linux-gnu.opt.lo\r\n", "│   │   └── Engy5310P1App.x86_64-pc-linux-gnu.opt.lo.d\r\n", "│   ├── \u001b[01;34mbcs\u001b[00m\r\n", "│   │   ├── NormalFluxBC.C\r\n", "│   │   └── NormalFluxBC.C~\r\n", "│   ├── \u001b[01;34minterfkernels\u001b[00m\r\n", "│   │   ├── InterfaceDiffusion.C\r\n", "│   │   ├── InterfaceNormalFluxContinuity.C\r\n", "│   │   ├── InterfacePartition.C\r\n", "│   │   └── InterfaceReaction\r\n", "│   ├── \u001b[01;34mkernels\u001b[00m\r\n", "│   │   ├── ConvectionTerm.C\r\n", "│   │   ├── ConvectionTerm.C~\r\n", "│   │   ├── DiffusionTerm.C\r\n", "│   │   ├── DiffusionTerm.C~\r\n", "│   │   ├── SourceTerm.C\r\n", "│   │   └── SourceTerm.C~\r\n", "│   ├── main.C\r\n", "│   ├── main.C~\r\n", "│   ├── main.x86_64-pc-linux-gnu.opt.lo\r\n", "│   ├── main.x86_64-pc-linux-gnu.opt.lo.d\r\n", "│   └── \u001b[01;34mpostprocessors\u001b[00m\r\n", "│   ├── BoundaryEnergy.C\r\n", "│   └── BulkEnergy.C\r\n", "├── subdom_test2.i\r\n", "├── \u001b[01;34mtest\u001b[00m\r\n", "│   └── \u001b[01;34mlib\u001b[00m\r\n", "│   ├── \u001b[01;32mlibengy5310p1_test-opt.la\u001b[00m\r\n", "│   ├── \u001b[01;36mlibengy5310p1_test-opt.so\u001b[00m -> \u001b[01;32mlibengy5310p1_test-opt.so.0.0.0\u001b[00m\r\n", "│   ├── \u001b[01;36mlibengy5310p1_test-opt.so.0\u001b[00m -> \u001b[01;32mlibengy5310p1_test-opt.so.0.0.0\u001b[00m\r\n", "│   └── \u001b[01;32mlibengy5310p1_test-opt.so.0.0.0\u001b[00m\r\n", "└── vtkviz.py\r\n", "\r\n", "21 directories, 85 files\r\n" ] } ], "source": [ "!tree engy5310p1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "220.75px" }, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }