{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/SalgadoHUB/Classical_Mechanics_II/blob/main/Tareas/Tarea2_Inercia_Crookes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "69JpzZ3Wz3yc"
   },
   "source": [
    "# Matriz de inercia de un radiómetro de Crookes\n",
    "\n",
    "Nuestro radiómetro (molinillo de luz) consiste en cuatro aspas de mica, unidas por alambres ideales (sin masa), que pueden pivotar en torno a un punto de apoyo. Idealmente, el molino gira en tornoa un eje vertical, como se muestra en la siguiente figura:\n",
    "\n",
    "![esquema.png](data:image/png;base64,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)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZwBQSANh0-sB"
   },
   "source": [
    "Comenzamos calculando los momentos de inercia de cada placa de mica respecto a sus centros de masas, y luego aplicaremos Steiner para trasladar los momentos de inercia al punto de apoyo.\n",
    "\n",
    "## Momento de inercia de placa rectangular\n",
    "\n",
    "Cada placa tiene lado $L$ y espesor despreciable. Como ejes principales tomamos dos direcciones en las diagonales (una horizontal, otra vertical) y la dirección perpendicular a cada placa. El eje vertical será $e_3$, los otros dos dependerán de la orientación de las placas.\n",
    "\n",
    "## - Diagonal vertical\n",
    "\n",
    "Tomamos elementos longitudinales situados a distancia $x$ del eje, de espesor $dx$, y longitud $h$. Esta longitud está limitada por los bordes rectos de la placa. Por la derecha, la ecuación de la recta que define el borde superior es:\n",
    "\n",
    "\\begin{equation}\n",
    "y_d = \\frac{L}{\\sqrt{2}} - x.\n",
    "\\end{equation}\n",
    "\n",
    "Podemos calcular la longitud del elemento diferencial como $h_d=2y$. Por el lado de la izquierda, el borde superior sigue la ecuación,\n",
    "\n",
    "\\begin{equation}\n",
    "y_i = \\frac{L}{\\sqrt{2}} + x.\n",
    "\\end{equation}\n",
    "\n",
    "Y similarmente calculamos su altura como $h_d=2y$. Así que el momento de inercia se puede calcular como:\n",
    "\n",
    "\\begin{equation}\n",
    "I_3 = \\int_{-\\frac{L}{\\sqrt{2}}}^{0} \\sigma x^2 h_i \\, dx + \\int_{0}^{\\frac{L}{\\sqrt{2}}} \\sigma x^2h_d \\, dx = \\int_{-\\frac{L}{\\sqrt{2}}}^{0} \\sigma x^2 2(\\frac{L}{\\sqrt{2}} + x) \\, dx + \\int_{0}^{\\frac{L}{\\sqrt{2}}} \\sigma x^2 2(\\frac{L}{\\sqrt{2}} - x) \\, dx.\n",
    "\\end{equation}\n",
    "\n",
    "Hacemos ahora las cuentas en SymPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "A641SICY42Fk"
   },
   "outputs": [],
   "source": [
    "import sympy as smp\n",
    "from sympy import latex # para mostrar los resultados\n",
    "from IPython.display import Math # para mostrar los resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "QtsCq70n43bT"
   },
   "outputs": [],
   "source": [
    "x, y, z = smp.symbols('x y z') # cartesianas\n",
    "\n",
    "M, L, sigma = smp.symbols('M L sigma') # Masa, lado y densidad superficial de la placa cuadrada.\n",
    "h, d = smp.symbols('h, d') #dimensiones del molino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "id": "tp7yJSZgDi0k",
    "outputId": "165ddcb1-ee8c-45b4-b6e7-fd874bd07971"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{L^{2} M}{12}$"
      ],
      "text/plain": [
       "L**2*M/12"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D = M/L**2\n",
    "\n",
    "Iv_cdm = smp.integrate(D*(x**2)*2*((L/smp.sqrt(2) + x)), (x, -L/smp.sqrt(2), 0)) + smp.integrate(D*(x**2)*2*((L/smp.sqrt(2) - x)), (x, 0, L/smp.sqrt(2)))\n",
    "Iv_cdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cT3bkqvFEgPS"
   },
   "source": [
    "### Diagonal horizontal\n",
    "\n",
    "Lo llamamos, por ejemplo, $I_h$ en esta dirección. Por simetría, $I_h$ = $I_v$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NvmL1ty9Eu4S"
   },
   "outputs": [],
   "source": [
    "Ih_cdm = Iv_cdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ewkAMZl5E9m5"
   },
   "source": [
    "### Dirección perpendicular\n",
    "\n",
    "Lo llamamos, de momento $I_p$. Como es un cuerpo plano, $I_p = I_h + I_v$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VOwFC1_kFJi5"
   },
   "outputs": [],
   "source": [
    "Ip_cdm = Ih_cdm + Iv_cdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zI6MpMlfFQRg"
   },
   "source": [
    "### Matriz de inercia $I^*$ para cada placa\n",
    "\n",
    "Construímos la matriz, teniendo en cuenta que para cada placa habrá que orientar los ejes $I_1$ e $I_2$ correctamente. Numeramos nuestras placas como A, B, C y D, y tenemos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "id": "winNFLkGFpmg",
    "outputId": "e1b5205d-493e-4a6f-fa4f-e971fd5cd81a"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\frac{L^{2} M}{6} & 0 & 0\\\\0 & \\frac{L^{2} M}{12} & 0\\\\0 & 0 & \\frac{L^{2} M}{12}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[L**2*M/6,         0,         0],\n",
       "[       0, L**2*M/12,         0],\n",
       "[       0,         0, L**2*M/12]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IA_cdm = smp.Matrix([[Ip_cdm, 0, 0], [0, Ih_cdm, 0], [0, 0, Iv_cdm]])\n",
    "IA_cdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "id": "4lC12EUPG26G",
    "outputId": "de7dfcfc-ee3b-418f-88f1-a09ca19941f3"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\frac{L^{2} M}{12} & 0 & 0\\\\0 & \\frac{L^{2} M}{6} & 0\\\\0 & 0 & \\frac{L^{2} M}{12}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[L**2*M/12,        0,         0],\n",
       "[        0, L**2*M/6,         0],\n",
       "[        0,        0, L**2*M/12]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IB_cdm = smp.Matrix([[Ih_cdm, 0, 0], [0, Ip_cdm, 0], [0, 0, Iv_cdm]])\n",
    "IB_cdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "id": "JYUbFGR6G2rG",
    "outputId": "480af757-3617-4362-c0de-c795fd26c058"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\frac{L^{2} M}{6} & 0 & 0\\\\0 & \\frac{L^{2} M}{12} & 0\\\\0 & 0 & \\frac{L^{2} M}{12}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[L**2*M/6,         0,         0],\n",
       "[       0, L**2*M/12,         0],\n",
       "[       0,         0, L**2*M/12]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IC_cdm = smp.Matrix([[Ip_cdm, 0, 0], [0, Ih_cdm, 0], [0, 0, Iv_cdm]])\n",
    "IC_cdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "id": "yqH4mlTUG2cG",
    "outputId": "1277b928-3d50-41d3-c707-bbb217f432a0"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\frac{L^{2} M}{12} & 0 & 0\\\\0 & \\frac{L^{2} M}{6} & 0\\\\0 & 0 & \\frac{L^{2} M}{12}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[L**2*M/12,        0,         0],\n",
       "[        0, L**2*M/6,         0],\n",
       "[        0,        0, L**2*M/12]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ID_cdm = smp.Matrix([[Ih_cdm, 0, 0], [0, Ip_cdm, 0], [0, 0, Iv_cdm]])\n",
    "ID_cdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7BWtaFhyHQam"
   },
   "source": [
    "Nótese que $I_3$ es igual para todas las matrices. Si el movimiento se da con su velocidad angular contenida exclusivamente en el eje $e_3$, ésta sería la única dirección que nos importaría."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "af-QYZunkiza"
   },
   "source": [
    "## Steiner\n",
    "\n",
    "Trasladamos los momentos de cada placa al punto de apoyo, utilizando Steiner:\n",
    "\n",
    "$\\vec{\\mathbb{I}}_O = \\vec{\\mathbb{I}}_R + \\vec{\\mathbb{I}}^*$\n",
    "\n",
    "Para calcular \\vec{\\mathbb{I}}_R necesitamos la posición del centro de masas desde $O$, que habrá que calcularlo para cada placa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "id": "UcgeaEiDlrGP",
    "outputId": "7c6ea33d-f9e4-4a28-97a8-8ed455212fea"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle  \\mathbf{I_O} = \\left[\\begin{matrix}\\frac{L^{2} M}{2} + 2 M h^{2} + 2 M \\left(d^{2} + h^{2}\\right) & 0 & 0\\\\0 & \\frac{L^{2} M}{2} + 2 M h^{2} + 2 M \\left(d^{2} + h^{2}\\right) & 0\\\\0 & 0 & \\frac{L^{2} M}{3} + 4 M d^{2}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R_A = smp.Matrix([0, d, h])\n",
    "R_B = smp.Matrix([-d, 0, h])\n",
    "R_C = smp.Matrix([0, -d, h])\n",
    "R_D = smp.Matrix([d, 0, h])\n",
    "\n",
    "IR_A = M * smp.Matrix([[R_A[1]**2 + R_A[2]**2, -R_A[0]*R_A[1], -R_A[0]*R_A[2]], [-R_A[1]*R_A[0], R_A[0]**2 + R_A[2]**2, -R_A[1]*R_A[2]], [-R_A[2]*R_A[0], -R_A[2]*R_A[1], R_A[0]**2 + R_A[1]**2]])\n",
    "IR_B = M * smp.Matrix([[R_B[1]**2 + R_B[2]**2, -R_B[0]*R_B[1], -R_B[0]*R_B[2]], [-R_B[1]*R_B[0], R_B[0]**2 + R_B[2]**2, -R_B[1]*R_B[2]], [-R_B[2]*R_B[0], -R_B[2]*R_B[1], R_B[0]**2 + R_B[1]**2]])\n",
    "IR_C = M * smp.Matrix([[R_C[1]**2 + R_C[2]**2, -R_C[0]*R_C[1], -R_C[0]*R_C[2]], [-R_C[1]*R_C[0], R_C[0]**2 + R_C[2]**2, -R_C[1]*R_C[2]], [-R_C[2]*R_C[0], -R_C[2]*R_C[1], R_C[0]**2 + R_C[1]**2]])\n",
    "IR_D = M * smp.Matrix([[R_D[1]**2 + R_D[2]**2, -R_D[0]*R_D[1], -R_D[0]*R_D[2]], [-R_D[1]*R_D[0], R_D[0]**2 + R_D[2]**2, -R_D[1]*R_D[2]], [-R_D[2]*R_D[0], -R_D[2]*R_D[1], R_D[0]**2 + R_D[1]**2]])\n",
    "\n",
    "IO_A = IR_A + IA_cdm\n",
    "IO_B = IR_B + IB_cdm\n",
    "IO_C = IR_C + IC_cdm\n",
    "IO_D = IR_D + ID_cdm\n",
    "\n",
    "IO = IO_A + IO_B + IO_C + IO_D\n",
    "display(Math(r\" \\mathbf{I_O} = \" + smp.latex(IO)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2g5wUzEGzcrU"
   },
   "source": [
    "## b) Sustituimos las dimensiones del cuerpo\n",
    "\n",
    "- $𝐿 = 16$ mm\n",
    "- $𝑑 = 15$ mm\n",
    "- $ℎ = 5$ mm\n",
    "- $𝜎 = 2.8 g/cm^2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 78
    },
    "id": "_XEaj-Tfzh6s",
    "outputId": "3d09d1d1-76d1-4412-c007-e959f7b148d0"
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle  \\mathbf{I_O} = \\left[\\begin{matrix}4.859904 \\cdot 10^{-6} & 0 & 0\\\\0 & 4.859904 \\cdot 10^{-6} & 0\\\\0 & 0 & 7.06286933333333 \\cdot 10^{-6}\\end{matrix}\\right]kg·m^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Sustituimos los valores numéricos\n",
    "masa = 28*(16e-3)**2 # en kg\n",
    "valores = {L: 16e-3, d: 15e-3, h: 5e-3, M: masa}  # en kg y metros\n",
    "\n",
    "IO_real = IO.subs(valores)\n",
    "display(Math(r\" \\mathbf{I_O} = \" + smp.latex(IO_real) + \"kg·m^2\"))"
   ]
  }
 ],
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