{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Micromagnetic standard problem 1\n", "\n", "**Authors**: Marijan Beg, Ryan A. Pepper, and Hans Fangohr\n", "\n", "**Date**: 12 December 2016\n", "\n", "## Problem specification\n", "\n", "The simulated sample is a thin film cuboid with dimensions:\n", "\n", "- length $l_{x} = 2 \\,\\mu\\text{m}$,\n", "- width $l_{y} = 1 \\,\\mu\\text{m}$, and\n", "- thickness $l_{z} = 20 \\,\\text{nm}$.\n", "\n", "The material parameters (similar to permalloy) are:\n", "\n", "- exchange energy constant $A = 1.3 \\times 10^{-11} \\,\\text{J/m}$,\n", "- magnetisation saturation $M_\\text{s} = 8 \\times 10^{5} \\,\\text{A/m}$.\n", "\n", "Apart from the symmetric exchange and demagnetisation energies, uniaxial anisotropy energy is also present with\n", "\n", "- $K = 0.5 \\times 10^{3} \\,\\text{J/m}^{3}$ and\n", "- $\\mathbf{u} = (1, 0, 0)$.\n", "\n", "More details on standard problem 1 specifications can be found in Ref. 1.\n", "\n", "## Simulation\n", "\n", "In the first step, we import the required `discretisedfield` and `oommfc` modules." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import discretisedfield as df\n", "import oommfc as oc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can set all required geometry and material parameters." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Geometry\n", "lx = 2e-6 # x dimension of the sample(m)\n", "ly = 1e-6 # y dimension of the sample (m)\n", "lz = 20e-9 # sample thickness (m)\n", "\n", "# Material parameters\n", "Ms = 8e5 # saturation magnetisation (A/m)\n", "A = 1.3e-11 # exchange energy constant (J/m)\n", "K = 0.5e3 # uniaxial anisotropy constant (J/m**3)\n", "u = (1, 0, 0) # uniaxial anisotropy axis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can create a system object with `stdprob1` name." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "system = oc.System(name=\"stdprob1\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to completely define the micromagnetic system, we need to provide:\n", "\n", "1. hamiltonian $\\mathcal{H}$\n", "2. dynamics $\\text{d}\\mathbf{m}/\\text{d}t$\n", "3. magnetisation $\\mathbf{m}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Hamiltonian:** In the next step, we add energy terms to the Hamiltonian. We add exchange, demagnetisation, and uniaxial anisotropy terms. Although we need to simulate the hysteresis loop with applied magnetic field, we do not add the Zeeman energy contribution to the Hamilotnian because the driver will take care of that." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\mathcal{H}=A (\\nabla \\mathbf{m})^{2}-K_{1} (\\mathbf{m} \\cdot \\mathbf{u})^{2}-\\frac{1}{2}\\mu_{0}M_\\text{s}\\mathbf{m} \\cdot \\mathbf{H}_\\text{d}$" ], "text/plain": [ "Exchange(A=1.3e-11, name=\"exchange\") + UniaxialAnisotropy(K1=500.0, K2=0, u=(1, 0, 0), name=\"uniaxialanisotropy\") + Demag(name=\"demag\")" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "system.hamiltonian = oc.Exchange(A) + oc.UniaxialAnisotropy(K, u) + oc.Demag()\n", "system.hamiltonian" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Dynamics:** In this standard problem, we do not care about the magnetisation dynamics. All we need is the minimum energy magnetisation configuration. Because of that, we will be using the `MinDriver` which does not need dynamics equation to be specified." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\frac{\\partial \\mathbf{m}}{\\partial t}=0$" ], "text/plain": [] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "system.dynamics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Magnetisation:** The system is initialised in the $(10, 1, 0)$ direction [1]. Firstly, we create the mesh by providing two points `p1` and `p2` between which the mesh domain spans and the size of a discretisation cell. We choose the discretisation to be $(20, 20, 20) \\,\\text{nm}$." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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zc3OG0T5xUiM/6Uv99Oo9x3EwmUyL/qzlSBOUS2VBoI4WtfalNi+an5+H1+tV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\nCwsWLFgYIrAcsgULFiwMEfw/qgz3LPBvoMAAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "# Create a mesh object.\n", "mesh = oc.Mesh(p1=(0, 0, 0), p2=(lx, ly, lz), cell=(20e-9, 20e-9, 20e-9))\n", "mesh" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, the magnetisation field can be fefined." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "system.m = df.Field(mesh, value=(-10, -1, 0), norm=Ms)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Hysteresis simulation:** Before the hysteresis simulations are carried out, maximum and minimum external magnetic field values at which the system should be relaxed must be created. These values are taken from the standard problem specification [1]. Also, we specify that between $H_\\text{min}$ and $H_\\text{max}$ we want $n=50$ steps." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Hmax = (50e-3/oc.mu0, 0.87275325e-3/oc.mu0, 0)\n", "Hmin = (-50e-3/oc.mu0, -0.87275325e-3/oc.mu0, 0)\n", "n = 50" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we drive the system using `HysteresisDriver`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2017/5/18 12:48: Calling OOMMF (stdprob1/stdprob1.mif) ... [29.7s]\n" ] } ], "source": [ "hd = oc.HysteresisDriver()\n", "hd.drive(system, Hmax=Hmax, Hmin=Hmin, n=n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hysteresis loop plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After obtaining the average magnetisation at different external magnetic field values, hysteresis loop is plotted. In this tutorial, we extract the x component of magnetisation and external magnetic field from the system's datatable and plot it using `matplotlib`." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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c/ThwxyTbNwAfCZZ/AFx7huN3AzeWM0aROOgPhl3pUEKRCNCT8iIxdqrJSwlF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\niJSEEoqIiJSEEoqIiJTE/wLitGlLIFvcmQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "Bx = system.dt[\"Bx\"].as_matrix()\n", "mx = system.dt[\"mx\"].as_matrix()\n", "\n", "plt.plot(Bx*1e-3, mx)\n", "plt.grid()\n", "plt.xlim([Hmin[0]*oc.mu0, Hmax[0]*oc.mu0])\n", "plt.xlabel(\"Bx (T)\")\n", "plt.ylabel(\"mx\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "[1] µMAG Site Directory: http://www.ctcms.nist.gov/~rdm/mumag.org.html" ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }