{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "anaconda-cloud": {
      "attach-environment": true,
      "summary": "Magnetno polje v osi tuljave",
      "thumbnail": 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"
    },
    "environment": {
      "channels": [
        "defaults"
      ],
      "dependencies": [
        "_license=1.1",
        "_nb_ext_conf=0.3.0",
        "alabaster=0.7.9",
        "anaconda-clean=1.0.0",
        "anaconda-client=1.5.1",
        "anaconda-navigator=1.3.1",
        "anaconda=4.2.0",
        "argcomplete=1.0.0",
        "astroid=1.4.7",
        "astropy=1.2.1",
        "babel=2.3.4",
        "backports=1.0",
        "beautifulsoup4=4.5.1",
        "bitarray=0.8.1",
        "blaze=0.10.1",
        "bokeh=0.12.2",
        "boto=2.42.0",
        "bottleneck=1.1.0",
        "bzip2=1.0.6",
        "cffi=1.7.0",
        "chest=0.2.3",
        "click=6.6",
        "cloudpickle=0.2.1",
        "clyent=1.2.2",
        "colorama=0.3.7",
        "comtypes=1.1.2",
        "conda-build=2.0.2",
        "conda-env=2.6.0",
        "conda=4.2.13",
        "configobj=5.0.6",
        "console_shortcut=0.1.1",
        "contextlib2=0.5.3",
        "cryptography=1.5",
        "curl=7.49.0",
        "cycler=0.10.0",
        "cython=0.24.1",
        "cytoolz=0.8.0",
        "dask=0.11.0",
        "datashape=0.5.2",
        "decorator=4.0.10",
        "dill=0.2.5",
        "docutils=0.12",
        "dynd-python=0.7.2",
        "entrypoints=0.2.2",
        "et_xmlfile=1.0.1",
        "fastcache=1.0.2",
        "filelock=2.0.6",
        "flask-cors=2.1.2",
        "flask=0.11.1",
        "freetype=2.5.5",
        "get_terminal_size=1.0.0",
        "gevent=1.1.2",
        "glueviz=0.9.1",
        "greenlet=0.4.10",
        "h5py=2.6.0",
        "hdf5=1.8.15.1",
        "heapdict=1.0.0",
        "icu=57.1",
        "idna=2.1",
        "imagesize=0.7.1",
        "ipykernel=4.5.0",
        "ipython=5.1.0",
        "ipython_genutils=0.1.0",
        "ipywidgets=5.2.2",
        "itsdangerous=0.24",
        "jdcal=1.2",
        "jedi=0.9.0",
        "jinja2=2.8",
        "jpeg=8d",
        "jsonschema=2.5.1",
        "jupyter=1.0.0",
        "jupyter_client=4.4.0",
        "jupyter_console=5.0.0",
        "jupyter_core=4.2.0",
        "lazy-object-proxy=1.2.1",
        "libdynd=0.7.2",
        "libpng=1.6.22",
        "libtiff=4.0.6",
        "llvmlite=0.13.0",
        "locket=0.2.0",
        "lxml=3.6.4",
        "markupsafe=0.23",
        "matplotlib=1.5.3",
        "menuinst=1.4.1",
        "mistune=0.7.3",
        "mkl-service=1.1.2",
        "mkl=11.3.3",
        "mpmath=0.19",
        "multipledispatch=0.4.8",
        "nb_anacondacloud=1.2.0",
        "nb_conda=2.0.0",
        "nb_conda_kernels=2.0.0",
        "nbconvert=4.2.0",
        "nbformat=4.1.0",
        "nbpresent=3.0.2",
        "networkx=1.11",
        "nltk=3.2.1",
        "nose=1.3.7",
        "notebook=4.2.3",
        "numba=0.28.1",
        "numexpr=2.6.1",
        "numpy=1.11.1",
        "odo=0.5.0",
        "openpyxl=2.3.2",
        "openssl=1.0.2j",
        "pandas=0.18.1",
        "partd=0.3.6",
        "path.py=8.2.1",
        "pathlib2=2.1.0",
        "patsy=0.4.1",
        "pep8=1.7.0",
        "pickleshare=0.7.4",
        "pillow=3.3.1",
        "pip=8.1.2",
        "pkginfo=1.3.2",
        "ply=3.9",
        "prompt_toolkit=1.0.3",
        "psutil=4.3.1",
        "py=1.4.31",
        "pyasn1=0.1.9",
        "pycosat=0.6.1",
        "pycparser=2.14",
        "pycrypto=2.6.1",
        "pycurl=7.43.0",
        "pyflakes=1.3.0",
        "pygments=2.1.3",
        "pylint=1.5.4",
        "pyopenssl=16.0.0",
        "pyparsing=2.1.4",
        "pyqt=5.6.0",
        "pytables=3.2.2",
        "pytest=2.9.2",
        "python-dateutil=2.5.3",
        "python=3.5.2",
        "pytz=2016.6.1",
        "pywin32=220",
        "pyyaml=3.12",
        "pyzmq=15.4.0",
        "qt=5.6.0",
        "qtawesome=0.3.3",
        "qtconsole=4.2.1",
        "qtpy=1.1.2",
        "requests=2.11.1",
        "rope=0.9.4",
        "ruamel_yaml=0.11.14",
        "scikit-image=0.12.3",
        "scikit-learn=0.17.1",
        "scipy=0.18.1",
        "setuptools=27.2.0",
        "simplegeneric=0.8.1",
        "singledispatch=3.4.0.3",
        "sip=4.18",
        "six=1.10.0",
        "snowballstemmer=1.2.1",
        "sockjs-tornado=1.0.3",
        "sphinx=1.4.6",
        "spyder=3.0.0",
        "sqlalchemy=1.0.13",
        "statsmodels=0.6.1",
        "sympy=1.0",
        "tk=8.5.18",
        "toolz=0.8.0",
        "tornado=4.4.1",
        "traitlets=4.3.0",
        "unicodecsv=0.14.1",
        "vs2015_runtime=14.0.25123",
        "wcwidth=0.1.7",
        "werkzeug=0.11.11",
        "wheel=0.29.0",
        "widgetsnbextension=1.2.6",
        "win_unicode_console=0.5",
        "wrapt=1.10.6",
        "xlrd=1.0.0",
        "xlsxwriter=0.9.3",
        "xlwings=0.10.0",
        "xlwt=1.1.2",
        "zlib=1.2.8",
        {
          "pip": [
            "backports.shutil-get-terminal-size",
            "dynd",
            "rope-py3k",
            "ruamel-yaml-",
            "tables"
          ]
        }
      ],
      "name": "notebook-magnetno-polje-v-osi-tuljave-ipynb"
    },
    "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": "3.7.3"
    },
    "colab": {
      "name": "Copy of Magnetno-polje-v-osi-tuljave_induktivnost.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/osnove/Dodatno/blob/master/Copy_of_Magnetno_polje_v_osi_tuljave_induktivnost.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "CdlZUKx1U5ma",
        "colab_type": "text"
      },
      "source": [
        "# Izračun polja v osi tuljave\n",
        "\n",
        "R je polmer tuljave\n",
        "NI je N*I\n",
        "l je dolžina tuljave (z2-z1)\n",
        "\n",
        "Formula za izračun polja v osi tuljave je:\n",
        "\n",
        "$$ {B_z} =\\frac{{{\\mu _0}NI}}{{2l}}\\left( {\\frac{{{z_2} - z}}{{\\sqrt {{{\\left( {z - {z_2}} \\right)}^2} + {R^2}} }} - \\frac{{({z_1} - z)}}{{\\sqrt {{{\\left( {z - {z_1}} \\right)}^2} + {R^2}} }}} \\right)$$\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JC_wFK1XU5mc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from IPython.display import Image\n",
        "from IPython.core.display import HTML "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSp5PUXJU5mf",
        "colab_type": "code",
        "outputId": "385faba0-b903-48c4-9a8e-abee77fc6b6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        }
      },
      "source": [
        "Image(url= \"https://raw.githubusercontent.com/osnove/Slike/master/oe2_solenoid.png\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://raw.githubusercontent.com/osnove/Slike/master/oe2_solenoid.png\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tG3dPtmr-zOw",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0lmnMLTjU5mi",
        "colab_type": "code",
        "outputId": "2f0cec91-5106-438c-d805-bf09eb4f3122",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        }
      },
      "source": [
        "import numpy as np\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "NI=6000   # tok\n",
        "R=5e-2     # polmer\n",
        "mi0=4*np.pi*1e-7  # permeabilnost\n",
        "z1=-5e-2\n",
        "z2=5e-2\n",
        "l=z2-z1    # dolžina\n",
        "\n",
        "z=np.arange(-15,15,0.11)*1e-2\n",
        "\n",
        "B=mi0*NI/(2*l)*((z2-z)/np.sqrt((z-z2)**2+R**2)-(z1-z)/np.sqrt((z-z1)**2+R**2))\n",
        "plt.figure(figsize=(15,10))\n",
        "plt.title('Polje v osi tuljave',fontsize=18)\n",
        "plt.xlabel('Razdalja / cm',fontsize=18)\n",
        "plt.ylabel('Gostota magnetnega pretoka / mT',fontsize=18)\n",
        "plt.tick_params(labelsize=18)\n",
        "\n",
        "plt.plot(z*1e2,B*1e3)\n",
        "plt.grid()\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uf3xqJonElccNyDqK6qGvnjiILcWlXP+Mo4qSVNUsipKkcpm/cgN3TZnPxw/p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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ADs7wmsU5ml",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\t"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ahQHtEZvBBxM",
        "colab_type": "text"
      },
      "source": [
        "## Izračun induktivnosti \n",
        "po http://info.ee.surrey.ac.uk/Workshop/advice/coils/air_coils.html\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AlsGFnN3U5mn",
        "colab_type": "code",
        "outputId": "ce445b7d-1b2f-46aa-a0c6-257e5f5b4d30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        }
      },
      "source": [
        "Image(url= \"https://raw.githubusercontent.com/osnove/Slike/master/mult_layer_air_coil.png\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://raw.githubusercontent.com/osnove/Slike/master/mult_layer_air_coil.png\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEHZIRz8Bxpw",
        "colab_type": "code",
        "outputId": "ae6f81dc-a24a-4b73-fcf8-78d27db9963e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        }
      },
      "source": [
        "Image(url= \"https://raw.githubusercontent.com/osnove/Slike/master/Brooks_ratio.png\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<img src=\"https://raw.githubusercontent.com/osnove/Slike/master/Brooks_ratio.png\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1aqoxe8lU5mp",
        "colab_type": "code",
        "outputId": "217742cb-416b-488a-ac2b-7ba06521f9ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "d= 0.05  # premer tuljave\n",
        "c=0.03  # debelina (višina) ovojev\n",
        "b=0.10   # dolžina tuljave\n",
        "N=1000    # število ovojev\n",
        "\n",
        "\n",
        "a=(d+c)/2 # srednji polmer v m\n",
        "\n",
        "S1 = (c/(2*a))**2 \n",
        "\n",
        "\n",
        "L = 4E-7*np.pi*a*N**2*((0.5+S1/12)*np.log(8/S1) - 0.84834+0.2041*S1)\n",
        "L   # v Henryjih\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06274480469336968"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YZQ-4wvpIthv",
        "colab_type": "text"
      },
      "source": [
        "## Izračun polja s sumacijo po višini"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-dchQoWcDnsa",
        "colab_type": "code",
        "outputId": "28fa01e8-57bc-4d15-c477-bd06adac5adf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "I=6 # Tok v žici\n",
        "NI=N*I # A-ovoji\n",
        "nl=15 # Number of layers (debelina žice * nl = c)\n",
        "Il=NI/nl  # tok v enem layerju\n",
        "Ball=0\n",
        "\n",
        "def B2(r,z):\n",
        "\t# This function returns B for specific radius and position z \n",
        "  B2=mi0*Il/(2*l)*((z2-z)/np.sqrt((z-z2)**2+r**2)-(z1-z)/np.sqrt((z-z1)**2+r**2))\n",
        "  return B2\n",
        " \n",
        "for i in range(1,nl):\n",
        "  r=(d/2)+i/nl*c\n",
        "  Bl=B2(r,0)\n",
        "  Ball=Ball+Bl \n",
        "  print(Bl)\n",
        "\n",
        "print(\"Polje v sredini je \",Ball*1e3, \"mT\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.004422886933014146\n",
            "0.004348121006897477\n",
            "0.004272076884079683\n",
            "0.0041952044484340045\n",
            "0.0041179087729422\n",
            "0.0040405500882946055\n",
            "0.003963444883078566\n",
            "0.0038868678558097542\n",
            "0.0038110544684693097\n",
            "0.003736203886890232\n",
            "0.003662482130919684\n",
            "0.0035900252936793744\n",
            "0.003518942722485669\n",
            "0.0034493200830061493\n",
            "Polje v sredini je  55.015089458000865 mT\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4GvTH0BLaLE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}