{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## The draining cup problem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook we will be modelling a draining cup. We assume the cup is shaped like a [conical frustrum](http://mathworld.wolfram.com/ConicalFrustum.html) or truncated cone:\n", "\n", "![](../../assets/cup.png)\n", "\n", "$D$ and $d$ are the top and bottom diameters of the cup, $H$ is the side length between the diameters, $d_h$ is the hole diameter. We also define $H'$ as the vertical height of the cup and $h$ as the vertical height (or level) of the liquid in the cup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Volume-height relationship" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's work out the relationship between the volume of water and the level in the cup by integrating the area:\n", "\n", "$$V = \\int_0^h A(h) dh$$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sympy\n", "sympy.init_printing()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "D, d, H, h = sympy.symbols('D, d, H, h', real=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "R = D/2\n", "r = d/2\n", "Hprime = sympy.sqrt(H**2 - (R - r)**2) # Pythagoras" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The radius changes linearly from the small one to the large one:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "radius = r + h/Hprime*(R - r)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now it is easy to calculate the area:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "A = sympy.pi*radius**2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And from there, the volume:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "V = sympy.integrate(A, (h, 0, h))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$$\\frac{\\pi d^{2} h}{4} - \\frac{h^{3} \\left(\\pi D^{2} - 2 \\pi D d + \\pi d^{2}\\right)}{3 D^{2} - 6 D d - 12 H^{2} + 3 d^{2}} - \\frac{h^{2} \\left(- \\pi D d + \\pi d^{2}\\right)}{2 \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}}$$" ], "text/plain": [ " 2 3 ⎛ 2 2⎞ 2 ⎛ 2⎞ \n", "π⋅d ⋅h h ⋅⎝π⋅D - 2⋅π⋅D⋅d + π⋅d ⎠ h ⋅⎝-π⋅D⋅d + π⋅d ⎠ \n", "────── - ─────────────────────────── - ───────────────────────────────\n", " 4 2 2 2 __________________________\n", " 3⋅D - 6⋅D⋅d - 12⋅H + 3⋅d ╱ 2 2 2 \n", " 2⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "V" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pi*d**2*h/4 - h**3*(pi*D**2 - 2*pi*D*d + pi*d**2)/(3*D**2 - 6*D*d - 12*H**2 + 3*d**2) - h**2*(-pi*D*d + pi*d**2)/(2*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2))\n" ] } ], "source": [ "print(V)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dynamic model\n", "\n", "The basic model of the cup we will be working with looks something like this:\n", "\n", "\\begin{align}\n", "\\frac{dV}{dt} &= -F_{\\text{out}} & \\text{Mass Balance simplified to volume balance} \\\\\n", "F_{\\text{out}} &= f(h) & \\text{Hydraulics} \\\\\n", "h &= f(V) & \\text{Geometry} \\\\\n", "\\end{align}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above geometric description allows us to find the $V(h)$, but we actually want $h(V)$." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "Vsymb = sympy.symbols('V', real=True)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "hV = sympy.solve(Vsymb - V, h)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/latex": [ "$$\\left [ - \\frac{- \\frac{3 \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{4 D^{2} - 8 D d + 4 d^{2}} + \\frac{\\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{2}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{2}}}{3 \\sqrt[3]{\\frac{\\sqrt{- 4 \\left(- \\frac{3 \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{4 D^{2} - 8 D d + 4 d^{2}} + \\frac{\\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{2}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{2}}\\right)^{3} + \\left(\\frac{27 \\left(3 D^{2} V - 6 D V d - 12 H^{2} V + 3 V d^{2}\\right)}{\\pi D^{2} - 2 \\pi D d + \\pi d^{2}} - \\frac{9 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right) \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right) \\left(4 D^{2} - 8 D d + 4 d^{2}\\right)} + \\frac{2 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{3}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{3}}\\right)^{2}}}{2} + \\frac{27 \\left(3 D^{2} V - 6 D V d - 12 H^{2} V + 3 V d^{2}\\right)}{2 \\left(\\pi D^{2} - 2 \\pi D d + \\pi d^{2}\\right)} - \\frac{9 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right) \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{2 \\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right) \\left(4 D^{2} - 8 D d + 4 d^{2}\\right)} + \\frac{\\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{3}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{3}}}} - \\frac{\\sqrt[3]{\\frac{\\sqrt{- 4 \\left(- \\frac{3 \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{4 D^{2} - 8 D d + 4 d^{2}} + \\frac{\\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{2}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{2}}\\right)^{3} + \\left(\\frac{27 \\left(3 D^{2} V - 6 D V d - 12 H^{2} V + 3 V d^{2}\\right)}{\\pi D^{2} - 2 \\pi D d + \\pi d^{2}} - \\frac{9 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right) \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right) \\left(4 D^{2} - 8 D d + 4 d^{2}\\right)} + \\frac{2 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{3}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{3}}\\right)^{2}}}{2} + \\frac{27 \\left(3 D^{2} V - 6 D V d - 12 H^{2} V + 3 V d^{2}\\right)}{2 \\left(\\pi D^{2} - 2 \\pi D d + \\pi d^{2}\\right)} - \\frac{9 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right) \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{2 \\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right) \\left(4 D^{2} - 8 D d + 4 d^{2}\\right)} + \\frac{\\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{3}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{3}}}}{3} - \\frac{3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}}{3 \\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)}, \\quad - \\frac{- \\frac{3 \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{4 D^{2} - 8 D d + 4 d^{2}} + \\frac{\\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{2}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{2}}}{3 \\left(- \\frac{1}{2} - \\frac{\\sqrt{3} i}{2}\\right) \\sqrt[3]{\\frac{\\sqrt{- 4 \\left(- \\frac{3 \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{4 D^{2} - 8 D d + 4 d^{2}} + \\frac{\\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{2}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{2}}\\right)^{3} + \\left(\\frac{27 \\left(3 D^{2} V - 6 D V d - 12 H^{2} V + 3 V d^{2}\\right)}{\\pi D^{2} - 2 \\pi D d + \\pi d^{2}} - \\frac{9 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right) \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right) \\left(4 D^{2} - 8 D d + 4 d^{2}\\right)} + \\frac{2 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{3}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{3}}\\right)^{2}}}{2} + \\frac{27 \\left(3 D^{2} V - 6 D V d - 12 H^{2} V + 3 V d^{2}\\right)}{2 \\left(\\pi D^{2} - 2 \\pi D d + \\pi d^{2}\\right)} - \\frac{9 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right) \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{2 \\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right) \\left(4 D^{2} - 8 D d + 4 d^{2}\\right)} + \\frac{\\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{3}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{3}}}} - \\frac{\\left(- \\frac{1}{2} - \\frac{\\sqrt{3} i}{2}\\right) \\sqrt[3]{\\frac{\\sqrt{- 4 \\left(- \\frac{3 \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{4 D^{2} - 8 D d + 4 d^{2}} + \\frac{\\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{2}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{2}}\\right)^{3} + \\left(\\frac{27 \\left(3 D^{2} V - 6 D V d - 12 H^{2} V + 3 V d^{2}\\right)}{\\pi D^{2} - 2 \\pi D d + \\pi d^{2}} - \\frac{9 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right) \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right) \\left(4 D^{2} - 8 D d + 4 d^{2}\\right)} + \\frac{2 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{3}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{3}}\\right)^{2}}}{2} + \\frac{27 \\left(3 D^{2} V - 6 D V d - 12 H^{2} V + 3 V d^{2}\\right)}{2 \\left(\\pi D^{2} - 2 \\pi D d + \\pi d^{2}\\right)} - \\frac{9 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right) \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{2 \\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right) \\left(4 D^{2} - 8 D d + 4 d^{2}\\right)} + \\frac{\\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{3}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{3}}}}{3} - \\frac{3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}}{3 \\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)}, \\quad - \\frac{- \\frac{3 \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{4 D^{2} - 8 D d + 4 d^{2}} + \\frac{\\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{2}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{2}}}{3 \\left(- \\frac{1}{2} + \\frac{\\sqrt{3} i}{2}\\right) \\sqrt[3]{\\frac{\\sqrt{- 4 \\left(- \\frac{3 \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{4 D^{2} - 8 D d + 4 d^{2}} + \\frac{\\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{2}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{2}}\\right)^{3} + \\left(\\frac{27 \\left(3 D^{2} V - 6 D V d - 12 H^{2} V + 3 V d^{2}\\right)}{\\pi D^{2} - 2 \\pi D d + \\pi d^{2}} - \\frac{9 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right) \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right) \\left(4 D^{2} - 8 D d + 4 d^{2}\\right)} + \\frac{2 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{3}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{3}}\\right)^{2}}}{2} + \\frac{27 \\left(3 D^{2} V - 6 D V d - 12 H^{2} V + 3 V d^{2}\\right)}{2 \\left(\\pi D^{2} - 2 \\pi D d + \\pi d^{2}\\right)} - \\frac{9 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right) \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{2 \\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right) \\left(4 D^{2} - 8 D d + 4 d^{2}\\right)} + \\frac{\\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{3}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{3}}}} - \\frac{\\left(- \\frac{1}{2} + \\frac{\\sqrt{3} i}{2}\\right) \\sqrt[3]{\\frac{\\sqrt{- 4 \\left(- \\frac{3 \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{4 D^{2} - 8 D d + 4 d^{2}} + \\frac{\\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{2}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{2}}\\right)^{3} + \\left(\\frac{27 \\left(3 D^{2} V - 6 D V d - 12 H^{2} V + 3 V d^{2}\\right)}{\\pi D^{2} - 2 \\pi D d + \\pi d^{2}} - \\frac{9 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right) \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right) \\left(4 D^{2} - 8 D d + 4 d^{2}\\right)} + \\frac{2 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{3}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{3}}\\right)^{2}}}{2} + \\frac{27 \\left(3 D^{2} V - 6 D V d - 12 H^{2} V + 3 V d^{2}\\right)}{2 \\left(\\pi D^{2} - 2 \\pi D d + \\pi d^{2}\\right)} - \\frac{9 \\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right) \\left(- 3 D^{2} d^{2} + 6 D d^{3} + 12 H^{2} d^{2} - 3 d^{4}\\right)}{2 \\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right) \\left(4 D^{2} - 8 D d + 4 d^{2}\\right)} + \\frac{\\left(3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}\\right)^{3}}{\\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)^{3}}}}{3} - \\frac{3 D^{2} d - 6 D d^{2} - 12 H^{2} d + 3 d^{3}}{3 \\left(- 2 D \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}} + 2 d \\sqrt{- D^{2} + 2 D d + 4 H^{2} - d^{2}}\\right)}\\right ]$$" ], "text/plain": [ "⎡ \n", "⎢ \n", "⎢ \n", "⎢ \n", "⎢ \n", "⎢ \n", "⎢ \n", "⎢ \n", "⎢ \n", "⎢ \n", "⎢ \n", "⎢ \n", "⎢ \n", "⎢ \n", "⎢ \n", "⎢ \n", "⎢- ───────────────────────────────────────────────────────────────────────────\n", "⎢ _________________________________________________________\n", "⎢ ╱ ______________________________________________\n", "⎢ ╱ ╱ \n", "⎢ ╱ ╱ ⎛ \n", "⎢ ╱ ╱ ⎜ ⎛ 2 2 3 2 2 \n", "⎢ ╱ ╱ ⎜ 3⋅⎝- 3⋅D ⋅d + 6⋅D⋅d + 12⋅H ⋅d - 3⋅d\n", "⎢ ╱ ╱ - 4⋅⎜- ──────────────────────────────────────\n", "⎢ ╱ ╱ ⎜ 2 2 \n", "⎢ ╱ ╱ ⎜ 4⋅D - 8⋅D⋅d + 4⋅d \n", "⎢ ╱ ╱ ⎜ \n", "⎢ ╱ ╲╱ ⎝ \n", "⎢ 3⋅ ╱ ────────────────────────────────────────────────────────\n", "⎢ ╱ \n", "⎢ ╱ \n", "⎢ 3 ╱ \n", "⎣ ╲╱ \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " \n", " 2 \n", "4⎞ ⎛ 2 2 2 3⎞ \n", " ⎠ ⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠ \n", "── + ─────────────────────────────────────────────────────────────────────────\n", " \n", " ⎛ __________________________ __________________________⎞\n", " ⎜ ╱ 2 2 2 ╱ 2 2 2 ⎟\n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠\n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " 3 \n", " ⎞ ⎛ \n", " ⎟ ⎜ ⎛ 2 2 2⎞ ⎛ 2 2\n", " ⎟ ⎜27⋅⎝3⋅D ⋅V - 6⋅D⋅V⋅d - 12⋅H ⋅V + 3⋅V⋅d ⎠ 9⋅⎝3⋅D ⋅d - 6⋅D⋅d \n", "─⎟ + ⎜──────────────────────────────────────── - ────────────────────────────\n", "2⎟ ⎜ 2 2 ⎛ __________________\n", " ⎟ ⎜ π⋅D - 2⋅π⋅D⋅d + π⋅d ⎜ ╱ 2 \n", " ⎟ ⎜ ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅\n", " ⎠ ⎝ \n", "──────────────────────────────────────────────────────────────────────────────\n", " 2 \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " ⎛ 2 2 3 2 2 4⎞ ⎛ 2\n", " 3⋅⎝- 3⋅D ⋅d + 6⋅D⋅d + 12⋅H ⋅d - 3⋅d ⎠ ⎝3⋅D \n", " - ──────────────────────────────────────── + ────────────────────────\n", " 2 2 \n", " 4⋅D - 8⋅D⋅d + 4⋅d ⎛ ______________\n", " ⎜ ╱ 2 \n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " \n", " \n", " 2 3⎞ ⎛ 2 2 3 2 2 4⎞ \n", " - 12⋅H ⋅d + 3⋅d ⎠⋅⎝- 3⋅D ⋅d + 6⋅D⋅d + 12⋅H ⋅d - 3⋅d ⎠ \n", "─────────────────────────────────────────────────────────────────── + ────────\n", "________ __________________________⎞ \n", " 2 2 ╱ 2 2 2 ⎟ ⎛ 2 2⎞ ⎛ \n", "H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠⋅⎝4⋅D - 8⋅D⋅d + 4⋅d ⎠ ⎜ \n", " ⎝- 2⋅D⋅╲\n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2 \n", " 2 2 3⎞ \n", "⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠ \n", "────────────────────────────────────────────────── \n", " 2 \n", "____________ __________________________⎞ \n", " 2 2 ╱ 2 2 2 ⎟ \n", "+ 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠ \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", "_____________________________________________________________________ \n", " 2 \n", " 3 ⎞ \n", " ⎛ 2 2 2 3⎞ ⎟ \n", " 2⋅⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠ ⎟ \n", "──────────────────────────────────────────────────────────────────⎟ \n", " 3⎟ \n", " __________________________ __________________________⎞ ⎟ \n", " ╱ 2 2 2 ╱ 2 2 2 ⎟ ⎟ ⎛ \n", "╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠ ⎠ 27⋅⎝3⋅\n", "───────────────────────────────────────────────────────────────────── + ──────\n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2 2 2⎞ ⎛ 2 2 2 \n", "D ⋅V - 6⋅D⋅V⋅d - 12⋅H ⋅V + 3⋅V⋅d ⎠ 9⋅⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d +\n", "────────────────────────────────── - ─────────────────────────────────────────\n", " ⎛ 2 2⎞ ⎛ __________________________ \n", " 2⋅⎝π⋅D - 2⋅π⋅D⋅d + π⋅d ⎠ ⎜ ╱ 2 2 2 \n", " 2⋅⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 3⎞ ⎛ 2 2 3 2 2 4⎞ \n", " 3⋅d ⎠⋅⎝- 3⋅D ⋅d + 6⋅D⋅d + 12⋅H ⋅d - 3⋅d ⎠ \n", "──────────────────────────────────────────────────────── + ───────────────────\n", " __________________________⎞ \n", " ╱ 2 2 2 ⎟ ⎛ 2 2⎞ ⎛ _________\n", "2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠⋅⎝4⋅D - 8⋅D⋅d + 4⋅d ⎠ ⎜ ╱ 2 \n", " ⎝- 2⋅D⋅╲╱ - D + 2\n", "\n", " ___\n", " ╱ \n", " ╱ \n", " ╱ \n", " ╱ \n", " ╱ \n", " ╱ \n", " ╱ \n", " ╱ \n", " ╱ \n", " ╱ ╲╱\n", " ╱ ──\n", " ╱ \n", " ╱ \n", " 3 ╱ \n", " ╲╱ \n", "──────────────────────────────────────────────────────── - ───────────────────\n", "________________________________________________________ \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 3 \n", "⎛ 2 2 2 3⎞ \n", "⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠ \n", "─────────────────────────────────────────────────────── \n", " 3 \n", "_________________ __________________________⎞ \n", " 2 2 ╱ 2 2 2 ⎟ \n", "⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠ \n", "\n", "______________________________________________________________________________\n", " ______________________________________________________________________\n", " ╱ \n", " ╱ ⎛ \n", " ╱ ⎜ ⎛ 2 2 3 2 2 4⎞ \n", " ╱ ⎜ 3⋅⎝- 3⋅D ⋅d + 6⋅D⋅d + 12⋅H ⋅d - 3⋅d ⎠ \n", " ╱ - 4⋅⎜- ──────────────────────────────────────── + ───────────────────\n", " ╱ ⎜ 2 2 \n", " ╱ ⎜ 4⋅D - 8⋅D⋅d + 4⋅d ⎛ _________\n", "╱ ⎜ ⎜ ╱ 2 \n", " ⎝ ⎝- 2⋅D⋅╲╱ - D + 2\n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " 3 \n", " 2 ⎞ ⎛ \n", "⎛ 2 2 2 3⎞ ⎟ ⎜ ⎛ 2 \n", "⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠ ⎟ ⎜27⋅⎝3⋅D ⋅V - 6⋅D⋅\n", "───────────────────────────────────────────────────────⎟ + ⎜─────────────────\n", " 2⎟ ⎜ 2 \n", "_________________ __________________________⎞ ⎟ ⎜ π⋅D - 2\n", " 2 2 ╱ 2 2 2 ⎟ ⎟ ⎜ \n", "⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠ ⎠ ⎝ \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " \n", " \n", " 2 2⎞ ⎛ 2 2 2 3⎞ ⎛ \n", "V⋅d - 12⋅H ⋅V + 3⋅V⋅d ⎠ 9⋅⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠⋅⎝- 3⋅\n", "─────────────────────── - ────────────────────────────────────────────────────\n", " 2 ⎛ __________________________ ______\n", "⋅π⋅D⋅d + π⋅d ⎜ ╱ 2 2 2 ╱ 2 \n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " 2 \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " \n", " \n", " 2 2 3 2 2 4⎞ ⎛ 2 \n", "D ⋅d + 6⋅D⋅d + 12⋅H ⋅d - 3⋅d ⎠ 2⋅⎝3⋅D ⋅d - 6⋅\n", "─────────────────────────────────────────── + ────────────────────────────────\n", "____________________⎞ \n", " 2 2 ⎟ ⎛ 2 2⎞ ⎛ ______________________\n", "+ 2⋅D⋅d + 4⋅H - d ⎠⋅⎝4⋅D - 8⋅D⋅d + 4⋅d ⎠ ⎜ ╱ 2 2 \n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H -\n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " 3 \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "______________________________________________________________________________\n", "_____________________________________________ \n", " 2 \n", " 3 ⎞ \n", " 2 2 3⎞ ⎟ \n", "D⋅d - 12⋅H ⋅d + 3⋅d ⎠ ⎟ \n", "──────────────────────────────────────────⎟ \n", " 3⎟ \n", "____ __________________________⎞ ⎟ \n", " 2 ╱ 2 2 2 ⎟ ⎟ ⎛ 2 2 \n", " d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠ ⎠ 27⋅⎝3⋅D ⋅V - 6⋅D⋅V⋅d - 12⋅H ⋅V\n", "───────────────────────────────────────────── + ──────────────────────────────\n", " ⎛ 2 \n", " 2⋅⎝π⋅D - 2⋅π⋅D⋅d + π⋅d\n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "______________________________________________________________________________\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2⎞ ⎛ 2 2 2 3⎞ ⎛ 2 2 \n", " + 3⋅V⋅d ⎠ 9⋅⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠⋅⎝- 3⋅D ⋅d + 6⋅D⋅\n", "────────── - ─────────────────────────────────────────────────────────────────\n", "2⎞ ⎛ __________________________ _________________\n", " ⎠ ⎜ ╱ 2 2 2 ╱ 2 \n", " 2⋅⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4\n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "______________________________________________________________________________\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 3 2 2 4⎞ ⎛ 2 2 2\n", "d + 12⋅H ⋅d - 3⋅d ⎠ ⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H \n", "──────────────────────────────── + ───────────────────────────────────────────\n", "_________⎞ \n", " 2 2 ⎟ ⎛ 2 2⎞ ⎛ __________________________ \n", "⋅H - d ⎠⋅⎝4⋅D - 8⋅D⋅d + 4⋅d ⎠ ⎜ ╱ 2 2 2 \n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅\n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "________________________________ \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 3 \n", " 3⎞ \n", "⋅d + 3⋅d ⎠ \n", "─────────────────────────────── \n", " 3 \n", " __________________________⎞ \n", " ╱ 2 2 2 ⎟ 2 2 \n", "╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠ 3⋅D ⋅d - 6⋅D⋅d - 12⋅\n", "──────────────────────────────── - ───────────────────────────────────────────\n", " ⎛ __________________________ \n", " ⎜ ╱ 2 2 2 \n", " 3⋅⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2 3 \n", "H ⋅d + 3⋅d \n", "────────────────────────────────, - ──────────────────────────────────────────\n", " __________________________⎞ ___________\n", " ╱ 2 2 2 ⎟ ╱ \n", "d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠ ╱ ╱\n", " ╱ ╱ \n", " ╱ ╱ \n", " ╱ ╱ \n", " ╱ ╱ \n", " ╱ ╱ \n", " ╱ ╱ \n", " ╱ ╱ \n", " ⎛ 1 √3⋅ⅈ⎞ ╱ ╲╱ \n", " 3⋅⎜- ─ - ────⎟⋅ ╱ ──────────\n", " ⎝ 2 2 ⎠ ╱ \n", " ╱ \n", " 3 ╱ \n", " ╲╱ \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " \n", " ⎛ \n", " ⎜ ⎛ 2 2 3 2 2 4⎞ ⎛ 2 \n", " ⎜ 3⋅⎝- 3⋅D ⋅d + 6⋅D⋅d + 12⋅H ⋅d - 3⋅d ⎠ ⎝3⋅D ⋅d \n", " - 4⋅⎜- ──────────────────────────────────────── + ───────────────────────────\n", " ⎜ 2 2 \n", " ⎜ 4⋅D - 8⋅D⋅d + 4⋅d ⎛ _________________\n", " ⎜ ⎜ ╱ 2 \n", " ⎝ ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4\n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " 3 \n", " 2 ⎞ ⎛ \n", " 2 2 3⎞ ⎟ ⎜ ⎛ 2 \n", "- 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠ ⎟ ⎜27⋅⎝3⋅D ⋅V - 6⋅D⋅V⋅d - 12\n", "───────────────────────────────────────────────⎟ + ⎜─────────────────────────\n", " 2⎟ ⎜ 2 \n", "_________ __________________________⎞ ⎟ ⎜ π⋅D - 2⋅π⋅D⋅d +\n", " 2 2 ╱ 2 2 2 ⎟ ⎟ ⎜ \n", "⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠ ⎠ ⎝ \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " ⎛ 2 2 3 \n", " 3⋅⎝- 3⋅D ⋅d + 6⋅D⋅d + 12⋅H\n", " - ────────────────────────────\n", " 2 \n", " 4⋅D - 8⋅D⋅d + 4⋅d\n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " \n", " \n", " 2 2⎞ ⎛ 2 2 2 3⎞ ⎛ 2 2 \n", "⋅H ⋅V + 3⋅V⋅d ⎠ 9⋅⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠⋅⎝- 3⋅D ⋅d + \n", "─────────────── - ────────────────────────────────────────────────────────────\n", " 2 ⎛ __________________________ ______________\n", " π⋅d ⎜ ╱ 2 2 2 ╱ 2 \n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " 2 \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2 \n", "2 2 4⎞ ⎛ 2 2 2 3⎞ \n", " ⋅d - 3⋅d ⎠ ⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠ \n", "──────────── + ───────────────────────────────────────────────────────────────\n", "2 \n", " ⎛ __________________________ _________________\n", " ⎜ ╱ 2 2 2 ╱ 2 \n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4\n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " \n", " \n", " 3 2 2 4⎞ ⎛ 2 2 \n", "6⋅D⋅d + 12⋅H ⋅d - 3⋅d ⎠ 2⋅⎝3⋅D ⋅d - 6⋅D⋅d - 1\n", "─────────────────────────────────── + ────────────────────────────────────────\n", "____________⎞ \n", " 2 2 ⎟ ⎛ 2 2⎞ ⎛ __________________________ \n", "+ 4⋅H - d ⎠⋅⎝4⋅D - 8⋅D⋅d + 4⋅d ⎠ ⎜ ╱ 2 2 2 \n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2\n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "─────────── \n", " 2 \n", "_________⎞ \n", " 2 2 ⎟ \n", "⋅H - d ⎠ \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", "_____________________________________ \n", " 2 \n", " 3 ⎞ \n", " 2 3⎞ ⎟ \n", "2⋅H ⋅d + 3⋅d ⎠ ⎟ \n", "──────────────────────────────────⎟ \n", " 3⎟ \n", " __________________________⎞ ⎟ \n", " ╱ 2 2 2 ⎟ ⎟ ⎛ 2 2 \n", "⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠ ⎠ 27⋅⎝3⋅D ⋅V - 6⋅D⋅V⋅d - 12⋅H ⋅V + 3⋅V⋅d\n", "───────────────────────────────────── + ──────────────────────────────────────\n", " ⎛ 2 2⎞ \n", " 2⋅⎝π⋅D - 2⋅π⋅D⋅d + π⋅d ⎠ \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "2⎞ ⎛ 2 2 2 3⎞ ⎛ 2 2 3 \n", " ⎠ 9⋅⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠⋅⎝- 3⋅D ⋅d + 6⋅D⋅d + 12⋅\n", "── - ─────────────────────────────────────────────────────────────────────────\n", " ⎛ __________________________ _________________________\n", " ⎜ ╱ 2 2 2 ╱ 2 2 2\n", " 2⋅⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2 2 4⎞ ⎛ 2 2 2 \n", "H ⋅d - 3⋅d ⎠ ⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d\n", "──────────────────────── + ───────────────────────────────────────────────────\n", "_⎞ \n", " ⎟ ⎛ 2 2⎞ ⎛ __________________________ _____\n", " ⎠⋅⎝4⋅D - 8⋅D⋅d + 4⋅d ⎠ ⎜ ╱ 2 2 2 ╱ 2\n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D \n", "\n", " ______________________\n", " ╱ ___________\n", " ╱ ╱ \n", " ╱ ╱ ⎛ \n", " ╱ ╱ ⎜ ⎛\n", " ╱ ╱ ⎜ 3⋅⎝\n", " ╱ ╱ - 4⋅⎜- ───\n", " ╱ ╱ ⎜ \n", " ╱ ╱ ⎜ \n", " ╱ ╱ ⎜ \n", " ⎛ 1 √3⋅ⅈ⎞ ╱ ╲╱ ⎝ \n", " ⎜- ─ - ────⎟⋅ ╱ ─────────────────────\n", " ⎝ 2 2 ⎠ ╱ \n", " ╱ \n", " 3 ╱ \n", " ╲╱ \n", "──────────────────────── - ───────────────────────────────────────────────────\n", "________________________ \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 3 \n", "3⎞ \n", " ⎠ \n", "─────────────────────── \n", " 3 \n", "_____________________⎞ \n", " 2 2 ⎟ \n", " + 2⋅D⋅d + 4⋅H - d ⎠ \n", "\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " \n", " \n", " 2 2 3 2 2 4⎞ ⎛ 2 2 \n", "- 3⋅D ⋅d + 6⋅D⋅d + 12⋅H ⋅d - 3⋅d ⎠ ⎝3⋅D ⋅d - 6⋅D⋅d - \n", "───────────────────────────────────── + ──────────────────────────────────────\n", " 2 2 \n", " 4⋅D - 8⋅D⋅d + 4⋅d ⎛ __________________________ \n", " ⎜ ╱ 2 2 2 \n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d +\n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " 3 \n", " 2 ⎞ ⎛ \n", " 2 3⎞ ⎟ ⎜ ⎛ 2 2 \n", "12⋅H ⋅d + 3⋅d ⎠ ⎟ ⎜27⋅⎝3⋅D ⋅V - 6⋅D⋅V⋅d - 12⋅H ⋅V + 3⋅V\n", "────────────────────────────────────⎟ + ⎜────────────────────────────────────\n", " 2⎟ ⎜ 2 2 \n", " __________________________⎞ ⎟ ⎜ π⋅D - 2⋅π⋅D⋅d + π⋅d \n", " ╱ 2 2 2 ⎟ ⎟ ⎜ \n", " 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠ ⎠ ⎝ \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " \n", " \n", " 2⎞ ⎛ 2 2 2 3⎞ ⎛ 2 2 3 \n", "⋅d ⎠ 9⋅⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠⋅⎝- 3⋅D ⋅d + 6⋅D⋅d + 12\n", "──── - ───────────────────────────────────────────────────────────────────────\n", " ⎛ __________________________ _________________________\n", " ⎜ ╱ 2 2 2 ╱ 2 2 2\n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " 2 \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " \n", " \n", " 2 2 4⎞ ⎛ 2 2 2 \n", "⋅H ⋅d - 3⋅d ⎠ 2⋅⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅\n", "──────────────────────── + ───────────────────────────────────────────────────\n", "_⎞ \n", " ⎟ ⎛ 2 2⎞ ⎛ __________________________ _____\n", " ⎠⋅⎝4⋅D - 8⋅D⋅d + 4⋅d ⎠ ⎜ ╱ 2 2 2 ╱ 2\n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " 3 \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "______________________________________________________________________________\n", "__________________________ \n", " 2 \n", " 3 ⎞ \n", " 3⎞ ⎟ \n", "d ⎠ ⎟ \n", "───────────────────────⎟ \n", " 3⎟ \n", "_____________________⎞ ⎟ \n", " 2 2 ⎟ ⎟ ⎛ 2 2 2⎞ \n", " + 2⋅D⋅d + 4⋅H - d ⎠ ⎠ 27⋅⎝3⋅D ⋅V - 6⋅D⋅V⋅d - 12⋅H ⋅V + 3⋅V⋅d ⎠ \n", "────────────────────────── + ──────────────────────────────────────── - ──────\n", " ⎛ 2 2⎞ ⎛ \n", " 2⋅⎝π⋅D - 2⋅π⋅D⋅d + π⋅d ⎠ ⎜ \n", " 2⋅⎝- 2\n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "______________________________________________________________________________\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " ⎛ 2 2 2 3⎞ ⎛ 2 2 3 2 2 \n", " 9⋅⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠⋅⎝- 3⋅D ⋅d + 6⋅D⋅d + 12⋅H ⋅d - 3⋅d\n", "──────────────────────────────────────────────────────────────────────────────\n", " __________________________ __________________________⎞ \n", " ╱ 2 2 2 ╱ 2 2 2 ⎟ ⎛ 2 \n", "⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠⋅⎝4⋅D - \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "______________________________________________________________________________\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 3 \n", "4⎞ ⎛ 2 2 2 3⎞ \n", " ⎠ ⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠ \n", "───────────── + ──────────────────────────────────────────────────────────────\n", " \n", " 2⎞ ⎛ __________________________ ________________\n", "8⋅D⋅d + 4⋅d ⎠ ⎜ ╱ 2 2 2 ╱ 2 \n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "_____________ \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "──────────── \n", " 3 \n", "__________⎞ \n", " 2 2 ⎟ 2 2 2 3 \n", "4⋅H - d ⎠ 3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d \n", "───────────── - ──────────────────────────────────────────────────────────────\n", " ⎛ __________________________ ______________\n", " ⎜ ╱ 2 2 2 ╱ 2 \n", " 3⋅⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "─────────────, - ─────────────────────────────────────────────────────────────\n", "____________⎞ ______________________________\n", " 2 2 ⎟ ╱ ___________________\n", "+ 4⋅H - d ⎠ ╱ ╱ \n", " ╱ ╱ ⎛ \n", " ╱ ╱ ⎜ ⎛ 2 \n", " ╱ ╱ ⎜ 3⋅⎝- 3⋅D ⋅d\n", " ╱ ╱ - 4⋅⎜- ───────────\n", " ╱ ╱ ⎜ \n", " ╱ ╱ ⎜ 4\n", " ╱ ╱ ⎜ \n", " ⎛ 1 √3⋅ⅈ⎞ ╱ ╲╱ ⎝ \n", " 3⋅⎜- ─ + ────⎟⋅ ╱ ─────────────────────────────\n", " ⎝ 2 2 ⎠ ╱ \n", " ╱ \n", " 3 ╱ \n", " ╲╱ \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " \n", " \n", "2 3 2 2 4⎞ ⎛ 2 2 2 \n", " + 6⋅D⋅d + 12⋅H ⋅d - 3⋅d ⎠ ⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d \n", "───────────────────────────── + ──────────────────────────────────────────────\n", " 2 2 \n", "⋅D - 8⋅D⋅d + 4⋅d ⎛ __________________________ \n", " ⎜ ╱ 2 2 2 ╱\n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " 3 \n", " 2 ⎞ ⎛ \n", " 3⎞ ⎟ ⎜ ⎛ 2 2 2⎞ \n", "+ 3⋅d ⎠ ⎟ ⎜27⋅⎝3⋅D ⋅V - 6⋅D⋅V⋅d - 12⋅H ⋅V + 3⋅V⋅d ⎠ \n", "────────────────────────────⎟ + ⎜──────────────────────────────────────── - ─\n", " 2⎟ ⎜ 2 2 ⎛\n", "__________________________⎞ ⎟ ⎜ π⋅D - 2⋅π⋅D⋅d + π⋅d ⎜\n", " 2 2 2 ⎟ ⎟ ⎜ ⎝\n", " - D + 2⋅D⋅d + 4⋅H - d ⎠ ⎠ ⎝ \n", "──────────────────────────────────────────────────────────────────────────────\n", " 2 \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " ⎛ 2 2 3 2 2 4⎞ \n", " 3⋅⎝- 3⋅D ⋅d + 6⋅D⋅d + 12⋅H ⋅d - 3⋅d ⎠ \n", " - ──────────────────────────────────────── + ────\n", " 2 2 \n", " 4⋅D - 8⋅D⋅d + 4⋅d ⎛ \n", " ⎜ \n", " ⎝- 2\n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " \n", " \n", " ⎛ 2 2 2 3⎞ ⎛ 2 2 3 2 2 \n", " 9⋅⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠⋅⎝- 3⋅D ⋅d + 6⋅D⋅d + 12⋅H ⋅d -\n", "──────────────────────────────────────────────────────────────────────────────\n", " __________________________ __________________________⎞ \n", " ╱ 2 2 2 ╱ 2 2 2 ⎟ ⎛ 2\n", "- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠⋅⎝4⋅D \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2 \n", " ⎛ 2 2 2 3⎞ \n", " ⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠ \n", "────────────────────────────────────────────────────────────────────── \n", " 2 \n", " __________________________ __________________________⎞ \n", " ╱ 2 2 2 ╱ 2 2 2 ⎟ \n", "⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠ \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " \n", " 3 \n", " 4⎞ ⎛ 2 2 2 3⎞ \n", " 3⋅d ⎠ 2⋅⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠ \n", "──────────────── + ───────────────────────────────────────────────────────────\n", " \n", " 2⎞ ⎛ __________________________ _____________\n", " - 8⋅D⋅d + 4⋅d ⎠ ⎜ ╱ 2 2 2 ╱ 2 \n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d\n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", "__________________ \n", " 2 \n", " ⎞ \n", " ⎟ \n", " ⎟ \n", "───────────────⎟ \n", " 3⎟ \n", "_____________⎞ ⎟ \n", " 2 2 ⎟ ⎟ ⎛ 2 2 2⎞ ⎛\n", " + 4⋅H - d ⎠ ⎠ 27⋅⎝3⋅D ⋅V - 6⋅D⋅V⋅d - 12⋅H ⋅V + 3⋅V⋅d ⎠ 9⋅⎝\n", "────────────────── + ──────────────────────────────────────── - ──────────────\n", " ⎛ 2 2⎞ ⎛ __\n", " 2⋅⎝π⋅D - 2⋅π⋅D⋅d + π⋅d ⎠ ⎜ ╱ \n", " 2⋅⎝- 2⋅D⋅╲╱ -\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2 2 2 3⎞ ⎛ 2 2 3 2 2 4⎞ \n", "3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠⋅⎝- 3⋅D ⋅d + 6⋅D⋅d + 12⋅H ⋅d - 3⋅d ⎠ \n", "──────────────────────────────────────────────────────────────────────────────\n", "________________________ __________________________⎞ \n", " 2 2 2 ╱ 2 2 2 ⎟ ⎛ 2 \n", " D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠⋅⎝4⋅D - 8⋅D⋅d + \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", "______________________________________________________________________________\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 3 \n", " ⎛ 2 2 2 3⎞ \n", " ⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠ \n", "───── + ──────────────────────────────────────────────────────────────────────\n", " \n", " 2⎞ ⎛ __________________________ ________________________\n", "4⋅d ⎠ ⎜ ╱ 2 2 2 ╱ 2 2 \n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d\n", "\n", " _________________________________________\n", " ╱ ______________________________\n", " ╱ ╱ \n", " ╱ ╱ ⎛ \n", " ╱ ╱ ⎜ ⎛ 2 2 3 \n", " ╱ ╱ ⎜ 3⋅⎝- 3⋅D ⋅d + 6⋅D⋅d \n", " ╱ ╱ - 4⋅⎜- ──────────────────────\n", " ╱ ╱ ⎜ 2 \n", " ╱ ╱ ⎜ 4⋅D - 8⋅D⋅d\n", " ╱ ╱ ⎜ \n", " ⎛ 1 √3⋅ⅈ⎞ ╱ ╲╱ ⎝ \n", " ⎜- ─ + ────⎟⋅ ╱ ────────────────────────────────────────\n", " ⎝ 2 2 ⎠ ╱ \n", " ╱ \n", " 3 ╱ \n", " ╲╱ \n", "───── - ──────────────────────────────────────────────────────────────────────\n", "_____ \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "──── \n", " 3 \n", "__⎞ \n", "2 ⎟ \n", " ⎠ \n", "\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " \n", " 2 \n", " 2 2 4⎞ ⎛ 2 2 2 3⎞ \n", "+ 12⋅H ⋅d - 3⋅d ⎠ ⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠ \n", "────────────────── + ─────────────────────────────────────────────────────────\n", " 2 \n", " + 4⋅d ⎛ __________________________ ___________\n", " ⎜ ╱ 2 2 2 ╱ 2 \n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D\n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " 3 \n", " ⎞ ⎛ \n", " ⎟ ⎜ ⎛ 2 2 2⎞ \n", " ⎟ ⎜27⋅⎝3⋅D ⋅V - 6⋅D⋅V⋅d - 12⋅H ⋅V + 3⋅V⋅d ⎠ 9⋅\n", "─────────────────⎟ + ⎜──────────────────────────────────────── - ────────────\n", " 2⎟ ⎜ 2 2 ⎛ __\n", "_______________⎞ ⎟ ⎜ π⋅D - 2⋅π⋅D⋅d + π⋅d ⎜ ╱ \n", " 2 2 ⎟ ⎟ ⎜ ⎝- 2⋅D⋅╲╱ -\n", "⋅d + 4⋅H - d ⎠ ⎠ ⎝ \n", "──────────────────────────────────────────────────────────────────────────────\n", " 2 \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " \n", " \n", "⎛ 2 2 2 3⎞ ⎛ 2 2 3 2 2 4⎞ \n", "⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠⋅⎝- 3⋅D ⋅d + 6⋅D⋅d + 12⋅H ⋅d - 3⋅d ⎠ \n", "──────────────────────────────────────────────────────────────────────────────\n", "________________________ __________________________⎞ \n", " 2 2 2 ╱ 2 2 2 ⎟ ⎛ 2 \n", " D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠⋅⎝4⋅D - 8⋅D⋅d + \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "______________________________________________________________________________\n", "______________________________________________________________________________\n", " \n", " 3 \n", " ⎛ 2 2 2 3⎞ \n", " 2⋅⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠ \n", "───── + ──────────────────────────────────────────────────────────────────────\n", " \n", " 2⎞ ⎛ __________________________ ________________________\n", "4⋅d ⎠ ⎜ ╱ 2 2 2 ╱ 2 2 \n", " ⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d\n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", "3 \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "______________________________________________________________________________\n", "_______ \n", " 2 \n", " ⎞ \n", " ⎟ \n", " ⎟ \n", "────⎟ \n", " 3⎟ \n", "__⎞ ⎟ \n", "2 ⎟ ⎟ ⎛ 2 2 2⎞ ⎛ 2 \n", " ⎠ ⎠ 27⋅⎝3⋅D ⋅V - 6⋅D⋅V⋅d - 12⋅H ⋅V + 3⋅V⋅d ⎠ 9⋅⎝3⋅D ⋅d - 6⋅\n", "─────── + ──────────────────────────────────────── - ─────────────────────────\n", " ⎛ 2 2⎞ ⎛ _____________\n", " 2⋅⎝π⋅D - 2⋅π⋅D⋅d + π⋅d ⎠ ⎜ ╱ 2 \n", " 2⋅⎝- 2⋅D⋅╲╱ - D + 2⋅D⋅d\n", " \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "______________________________________________________________________________\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 2 2 3⎞ ⎛ 2 2 3 2 2 4⎞ \n", "D⋅d - 12⋅H ⋅d + 3⋅d ⎠⋅⎝- 3⋅D ⋅d + 6⋅D⋅d + 12⋅H ⋅d - 3⋅d ⎠ \n", "──────────────────────────────────────────────────────────────────────── + ───\n", "_____________ __________________________⎞ \n", " 2 2 ╱ 2 2 2 ⎟ ⎛ 2 2⎞ ⎛ \n", " + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠⋅⎝4⋅D - 8⋅D⋅d + 4⋅d ⎠ ⎜ \n", " ⎝- \n", "──────────────────────────────────────────────────────────────────────────────\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "________________________________________________________________________ \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 3 \n", " ⎛ 2 2 2 3⎞ \n", " ⎝3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎠ \n", "─────────────────────────────────────────────────────────────────────── \n", " 3 \n", " __________________________ __________________________⎞ \n", " ╱ 2 2 2 ╱ 2 2 2 ⎟ \n", "2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠ \n", "──────────────────────────────────────────────────────────────────────── - ───\n", " ⎛\n", " ⎜\n", " 3⋅⎝\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " ⎤\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " 2 2 2 3 ⎥\n", " 3⋅D ⋅d - 6⋅D⋅d - 12⋅H ⋅d + 3⋅d ⎥\n", "────────────────────────────────────────────────────────────────────────⎥\n", " __________________________ __________________________⎞⎥\n", " ╱ 2 2 2 ╱ 2 2 2 ⎟⎥\n", "- 2⋅D⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d + 2⋅d⋅╲╱ - D + 2⋅D⋅d + 4⋅H - d ⎠⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎥\n", " ⎦" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hV" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-(-3*(-3*D**2*d**2 + 6*D*d**3 + 12*H**2*d**2 - 3*d**4)/(4*D**2 - 8*D*d + 4*d**2) + (3*D**2*d - 6*D*d**2 - 12*H**2*d + 3*d**3)**2/(-2*D*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2) + 2*d*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2))**2)/(3*(sqrt(-4*(-3*(-3*D**2*d**2 + 6*D*d**3 + 12*H**2*d**2 - 3*d**4)/(4*D**2 - 8*D*d + 4*d**2) + (3*D**2*d - 6*D*d**2 - 12*H**2*d + 3*d**3)**2/(-2*D*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2) + 2*d*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2))**2)**3 + (27*(3*D**2*V - 6*D*V*d - 12*H**2*V + 3*V*d**2)/(pi*D**2 - 2*pi*D*d + pi*d**2) - 9*(3*D**2*d - 6*D*d**2 - 12*H**2*d + 3*d**3)*(-3*D**2*d**2 + 6*D*d**3 + 12*H**2*d**2 - 3*d**4)/((-2*D*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2) + 2*d*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2))*(4*D**2 - 8*D*d + 4*d**2)) + 2*(3*D**2*d - 6*D*d**2 - 12*H**2*d + 3*d**3)**3/(-2*D*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2) + 2*d*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2))**3)**2)/2 + 27*(3*D**2*V - 6*D*V*d - 12*H**2*V + 3*V*d**2)/(2*(pi*D**2 - 2*pi*D*d + pi*d**2)) - 9*(3*D**2*d - 6*D*d**2 - 12*H**2*d + 3*d**3)*(-3*D**2*d**2 + 6*D*d**3 + 12*H**2*d**2 - 3*d**4)/(2*(-2*D*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2) + 2*d*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2))*(4*D**2 - 8*D*d + 4*d**2)) + (3*D**2*d - 6*D*d**2 - 12*H**2*d + 3*d**3)**3/(-2*D*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2) + 2*d*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2))**3)**(1/3)) - (sqrt(-4*(-3*(-3*D**2*d**2 + 6*D*d**3 + 12*H**2*d**2 - 3*d**4)/(4*D**2 - 8*D*d + 4*d**2) + (3*D**2*d - 6*D*d**2 - 12*H**2*d + 3*d**3)**2/(-2*D*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2) + 2*d*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2))**2)**3 + (27*(3*D**2*V - 6*D*V*d - 12*H**2*V + 3*V*d**2)/(pi*D**2 - 2*pi*D*d + pi*d**2) - 9*(3*D**2*d - 6*D*d**2 - 12*H**2*d + 3*d**3)*(-3*D**2*d**2 + 6*D*d**3 + 12*H**2*d**2 - 3*d**4)/((-2*D*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2) + 2*d*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2))*(4*D**2 - 8*D*d + 4*d**2)) + 2*(3*D**2*d - 6*D*d**2 - 12*H**2*d + 3*d**3)**3/(-2*D*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2) + 2*d*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2))**3)**2)/2 + 27*(3*D**2*V - 6*D*V*d - 12*H**2*V + 3*V*d**2)/(2*(pi*D**2 - 2*pi*D*d + pi*d**2)) - 9*(3*D**2*d - 6*D*d**2 - 12*H**2*d + 3*d**3)*(-3*D**2*d**2 + 6*D*d**3 + 12*H**2*d**2 - 3*d**4)/(2*(-2*D*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2) + 2*d*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2))*(4*D**2 - 8*D*d + 4*d**2)) + (3*D**2*d - 6*D*d**2 - 12*H**2*d + 3*d**3)**3/(-2*D*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2) + 2*d*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2))**3)**(1/3)/3 - (3*D**2*d - 6*D*d**2 - 12*H**2*d + 3*d**3)/(3*(-2*D*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2) + 2*d*sqrt(-D**2 + 2*D*d + 4*H**2 - d**2)))\n" ] } ], "source": [ "#print(hV)" ] }, { "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }