{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparison of equations of state for model fluids\n", "\n", "In this notebook, we take a look at various model fluid equations of state and compare them.\n", "These EoS are:\n", "\n", "- The PeTS equation of state (from `feos.pets`),\n", "- the uv-theory equation of state (from `feos.uvtheory`), \n", "- the uv-B3-theory equation of state (from `feos.uvtheory` using `VirialOrder.Third`), and\n", "- the Thol equation of state (implemented in this notebook as Python class).\n", "\n", "The PeTS equation of state is for a Lennard-Jones fluid with cut off distance of $r_c = 2.5 \\sigma$ and a potential shift while the uv-theory and Thol equation of state model the full Lennard-Jones potential. Note that the uv-theory was developed to model Mie potentials and not primarily adjusted to the Lennard-Jones fluid. Thus, we don't expect similar results when comparing uv-theory and Thol to PeTS." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/bauer/opt/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.2\n", " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" ] } ], "source": [ "import numpy as np\n", "from feos.si import *\n", "from feos.uvtheory import UVParameters, Perturbation, VirialOrder\n", "from feos.pets import PetsParameters\n", "from feos.eos import State, PhaseEquilibrium, PhaseDiagram, EquationOfState, Contributions\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "sns.set_context('talk')\n", "sns.set_palette(\"hls\", 8)\n", "sns.set_style('ticks')\n", "colors = sns.palettes.color_palette('Dark2', 8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implement the Thol equation of state as Python class" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Parameters for Thol EoS:\n", "A = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0,\n", " 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0])\n", "N = np.array([0.005208073, 2.186252000, -2.161016000, 1.452700000, -2.041792000, 0.186952860, -0.090988445, \n", " -0.497456100, 0.109014310, -0.800559220, -0.568839000, -0.620862500, -1.466717700, 1.891469000, \n", " -0.138370100, -0.386964500, 0.126570200, 0.605781000, 1.179189000, -0.477326790, -9.921857500, -0.574793200, 0.003772923])\n", "T = np.array([1.000, 0.320, 0.505, 0.672, 0.843, 0.898, 1.294, 2.590, 1.786, 2.770, 1.786,\n", " 1.205, 2.830, 2.548, 4.650, 1.385, 1.460, 1.351, 0.660, 1.496, 1.830, 1.616, 4.970])\n", "D = np.array([4.0, 1.0, 1.0, 2.0, 2.0, 3.0, 5.0, 2.0, 2.0, 3.0, 1.0,\n", " 1.0, 1.0, 1.0, 2.0, 3.0, 3.0, 2.0, 1.0, 2.0, 3.0, 1.0, 1.0])\n", "L = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 1.0, 2.0, 2.0,\n", " 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])\n", "ETA = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.067, 1.522,\n", " 8.82, 1.722, 0.679, 1.883, 3.925, 2.461, 28.2, 0.753, 0.82])\n", "BETA = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.625,\n", " 0.638, 3.91, 0.156, 0.157, 0.153, 1.16, 1.73, 383, 0.112, 0.119])\n", "GAMMA = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.71,\n", " 0.86, 1.94, 1.48, 1.49, 1.945, 3.02, 1.11, 1.17, 1.33, 0.24])\n", "EPSILON = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2053,\n", " 0.409, 0.6, 1.203, 1.829, 1.397, 1.39, 0.539, 0.934, 2.369, 2.43])\n", "\n", "class Thol:\n", " def __init__(self):\n", " self.sigma = 3.7039 * ANGSTROM\n", " self.eps_k = 150.03 * KELVIN\n", " self.tc = 1.32 * self.eps_k / KELVIN\n", " self.rhoc = 0.31 / self.sigma**3 * ANGSTROM**3\n", " \n", " def components(self): \n", " return 1\n", " \n", " def subset(self, components):\n", " return self\n", " \n", " def molar_weight(self):\n", " return np.array([1.0])\n", " \n", " def max_density(self, moles):\n", " return 0.04\n", " \n", " def helmholtz_energy(self, state):\n", " \"\"\"\n", " state (StateHD):\n", " temperature in Kelvin als Float, Dual oder HD oder HD3, HDD, HDD3,\n", " partial_density in # / Angstrom^3\n", " volume in Angstrom^3\n", " moles in mol\n", " \"\"\"\n", " tau = self.tc / state.temperature\n", " delta = np.sum(state.partial_density) / self.rhoc\n", " a = 0.0\n", " \n", " for i in range(6):\n", " a = a + N[i] * delta**D[i] * tau**T[i]\n", " for i in range(6, 12):\n", " a = a + N[i] * delta**D[i] * tau**T[i] * np.exp(-1.0 * delta**L[i])\n", " for i in range(12, 23):\n", " a = a + N[i] * delta**D[i] * tau**T[i] * np.exp(- 1.0 * ETA[i] * (delta - EPSILON[i])**2.0 - 1.0 * BETA[i] * (tau - 1.0 * GAMMA[i])**2.0)\n", " return a * np.sum(state.moles)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Instantiate all equations of state" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```rust\n", "let omega = if t_x.re() < 175.0 {\n", " (-t_x * CU_WCA[5] * (reduced_density - CU_WCA[6]).powi(2)).exp()\n", " * ((t_x * CU_WCA[7]).tanh().recip() - 1.0).powi(2)\n", " } else {\n", " (-t_x * CU_WCA[5] * (reduced_density - CU_WCA[6]).powi(2)).exp() * 0.0\n", " };\n", "\n", " -(-(reduced_density.powi(2) * ((rep_x + CU_WCA[4]).recip() * CU_WCA[3] + CU_WCA[2]) + omega))\n", " .exp()\n", " + 1.0\n", "```" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "sigma = 3.7039\n", "sigma_a = sigma * ANGSTROM\n", "eps_k = 150.03\n", "eps_k_k = eps_k * KELVIN\n", "rep = 12.0\n", "parameters = UVParameters.new_simple(rep, 6.0, sigma, eps_k)\n", "CU_WCA = [26.454, 1.8045, 1.7997, 161.96, 11.605, 12., 0.4, 2.0]\n", "\n", "uvtheory_b3 = EquationOfState.uvtheory(parameters, virial_order=VirialOrder.Third)\n", "\n", "def ufraction(state):\n", " t_x = state.temperature / eps_k\n", " reduced_density = state.density * sigma**3\n", " rep_x = rep\n", " omega = 0.0\n", " if t_x.value < 175:\n", " omega = (-t_x * CU_WCA[5] * (reduced_density - CU_WCA[6])**2).exp() \\\n", " * (1.0 / (t_x * CU_WCA[7]).tanh() - 1.0)**2\n", " ufrac = -(-(reduced_density**2 * (1.0 / (rep_x + CU_WCA[4]) * CU_WCA[3] + CU_WCA[2]) + omega)).exp() + 1.0\n", " return ufrac\n", "\n", "uvtheory_b3_uf = EquationOfState.uvtheory(parameters, virial_order=VirialOrder.Third, ufraction=ufraction)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare Helmholtz energies at single state point" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "u-fraction: 0.9996284573272382 + [-0.00011029124189623964]ε1 + [-0.000026521524493183303]ε1²\n", "u-fraction: 0.9988379797559929 + [-0.00027300538747495245]ε1 + [-0.00004990266174618351]ε1²\n", "u-fraction: 0.9966746965481094 + [-0.0006062062111358156]ε1 + [-0.00008146147063767258]ε1²\n", "u-fraction: 0.9912931419552962 + [-0.001203092119270068]ε1 + [-0.00011367301308275238]ε1²\n", "u-fraction: 0.9791404839344673 + [-0.0021238832321700614]ε1 + [-0.00013243123583561224]ε1²\n", "u-fraction: 0.9542743833113834 + [-0.0033138426175314743]ε1 + [-0.00012339313039826935]ε1²\n", "u-fraction: 0.9082877185095404 + [-0.004529474333716721]ε1 + [-0.00008324992172326038]ε1²\n", "u-fraction: 0.8316907324621025 + [-0.005354527151690742]ε1 + [-0.000026953387161395334]ε1²\n", "u-fraction: 0.7173811027783104 + [-0.005369248730264456]ε1 + [0.000019078372866783005]ε1²\n", "u-fraction: 0.5657828152983877 + [-0.004424758231726879]ε1 + [0.00003598597556005315]ε1²\n", "u-fraction: 0.38958466059040253 + [-0.0028312484896880603]ε1 + [0.000026773517218010987]ε1²\n", "u-fraction: 0.21484307935875258 + [-0.0012491170649092847]ε1 + [0.000010336886840306822]ε1²\n", "u-fraction: 0.07594022435560499 + [-0.00027430427708941533]ε1 + [0.000001465064860887849]ε1²\n", "u-fraction: 0.00492400656989278 + [-0.00000461539514104226]ε1 + [0.00000000648383857547817]ε1²\n", "u-fraction: 0.000012350254504078784 + [-0.0000000005797770223328869]ε1 + [0.00000000000004085836761109884]ε1²\n", "u-fraction: 0.000013087009891443735 + [-0.0000000006324654622769026]ε1 + [0.00000000000004588267534449147]ε1²\n", "u-fraction: 0.00001308704432334551 + [-0.0000000006324679601653612]ε1 + [0.000000000000045882916959367675]ε1²\n", "u-fraction: 0.000013044906825188107 + [-0.0000000006294135239265427]ε1 + [0.000000000000045587706390672894]ε1²\n", "u-fraction: 0.000013087044213766497 + [-0.0000000006324679522144209]ε1 + [0.000000000000045882916190291433]ε1²\n", "u-fraction: 0.00001308704432334551 + [-0.0000000006324679601653662]ε1 + [0.00000000000004588291695936817]ε1²\n", "u-fraction: 0.00001308704432334551 + [-0.0000000006324679601653662]ε\n", "u-fraction: 0.00001308704432334551 + [-0.0000000006324679601653665]ε\n" ] } ], "source": [ "s1 = State(uvtheory_b3, temperature=300*KELVIN, pressure=1*BAR)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "u-fraction: 0.9996284573272382 + [-0.00011029124189623964]ε1 + [-0.000026521524493183303]ε1²\n", "u-fraction: 0.9988379797559929 + [-0.00027300538747495245]ε1 + [-0.00004990266174618351]ε1²\n", "u-fraction: 0.9966746965481094 + [-0.0006062062111358156]ε1 + [-0.00008146147063767258]ε1²\n", "u-fraction: 0.9912931419552962 + [-0.001203092119270068]ε1 + [-0.00011367301308275238]ε1²\n", "u-fraction: 0.9791404839344673 + [-0.0021238832321700614]ε1 + [-0.00013243123583561224]ε1²\n", "u-fraction: 0.9542743833113834 + [-0.0033138426175314743]ε1 + [-0.00012339313039826935]ε1²\n", "u-fraction: 0.9082877185095404 + [-0.004529474333716721]ε1 + [-0.00008324992172326038]ε1²\n", "u-fraction: 0.8316907324621025 + [-0.005354527151690742]ε1 + [-0.000026953387161395334]ε1²\n", "u-fraction: 0.7173811027783104 + [-0.005369248730264456]ε1 + [0.000019078372866783005]ε1²\n", "u-fraction: 0.5657828152983877 + [-0.004424758231726879]ε1 + [0.00003598597556005315]ε1²\n", "u-fraction: 0.38958466059040253 + [-0.0028312484896880603]ε1 + [0.000026773517218010987]ε1²\n", "u-fraction: 0.21484307935875258 + [-0.0012491170649092847]ε1 + [0.000010336886840306822]ε1²\n", "u-fraction: 0.07594022435560499 + [-0.00027430427708941533]ε1 + [0.000001465064860887849]ε1²\n", "u-fraction: 0.00492400656989278 + [-0.00000461539514104226]ε1 + [0.00000000648383857547817]ε1²\n", "u-fraction: 0.000012350254504078784 + [-0.0000000005797770223328869]ε1 + [0.00000000000004085836761109884]ε1²\n", "u-fraction: 0.000013087009891443735 + [-0.0000000006324654622769026]ε1 + [0.00000000000004588267534449147]ε1²\n", "u-fraction: 0.00001308704432334551 + [-0.0000000006324679601653612]ε1 + [0.000000000000045882916959367675]ε1²\n", "u-fraction: 0.000013044906825188107 + [-0.0000000006294135239265427]ε1 + [0.000000000000045587706390672894]ε1²\n", "u-fraction: 0.000013087044213766497 + [-0.0000000006324679522144209]ε1 + [0.000000000000045882916190291433]ε1²\n", "u-fraction: 0.00001308704432334551 + [-0.0000000006324679601653662]ε1 + [0.00000000000004588291695936817]ε1²\n", "u-fraction: 0.00001308704432334551 + [-0.0000000006324679601653662]ε\n", "u-fraction: 0.00001308704432334551 + [-0.0000000006324679601653665]ε\n" ] } ], "source": [ "s2 = State(uvtheory_b3_uf, temperature=300*KELVIN, pressure=1*BAR)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "u-fraction: 1.9996000799840032 + [0.006665333599946677]ε\n" ] }, { "data": { "text/latex": [ "$96.684\\,\\mathrm{\\frac{ J}{molK}}$" ], "text/plain": [ "96.68386253040592 J/mol/K" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s2.molar_entropy()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('Ideal gas (default)', -4.8170398860180577e-20 J),\n", " ('Hard Sphere', 9.709460360314697e-24 J),\n", " ('Reference Perturbation', 6.640075859812054e-25 J),\n", " ('Attractive Perturbation', -1.7058012153921695e-23 J)]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1.helmholtz_energy_contributions()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[('Ideal gas (default)', -4.8698028896541795e-20 J),\n", " ('Hard Sphere', 8.547391383441759e-24 J),\n", " ('Reference Perturbation', 5.844972740477965e-25 J),\n", " ('Attractive Perturbation', 5.475676104748454e-22 J)]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s2.helmholtz_energy_contributions()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.0016213224956115704" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1.molar_helmholtz_energy(Contributions.ResidualNvt) / (RGAS * 300 * KELVIN)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.0016138652202999679" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s2.molar_helmholtz_energy(Contributions.ResidualNvt) / (RGAS * 300 * KELVIN)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.0016144150962601586" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s3.molar_helmholtz_energy(Contributions.ResidualNvt) / (RGAS * 300 * KELVIN)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.000940704010881386" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s4.molar_helmholtz_energy(Contributions.ResidualNvt) / (RGAS * 300 * KELVIN)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare critical points\n", "- Reference data for the Lennard-Jones from Potoff and Panagiotopoulos (MC): $T_c^*=1.3120$, $\\rho_c^*=0.316$, $p_c^*=0.1279$" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "uvtheory_b3:\n", "Tc*=1.31329, Error: 0.099%\n", "pc*=0.1286, Error: 0.548%\n", "rhoc*=0.31425, Error: 0.553%\n", "\n", "uvtheory_wca:\n", "Tc*=1.30977, Error: 0.17%\n", "pc*=0.12448, Error: 2.674%\n", "rhoc*=0.30233, Error: 4.327%\n", "\n", "uvtheory_bh:\n", "Tc*=1.32083, Error: 0.673%\n", "pc*=0.13486, Error: 5.442%\n", "rhoc*=0.31865, Error: 0.838%\n", "\n", "thol:\n", "Tc*=1.32, Error: 0.61%\n", "pc*=0.13006, Error: 1.689%\n", "rhoc*=0.3132, Error: 0.886%\n", "\n" ] } ], "source": [ "def evaluate_critical_points():\n", " names = ['uvtheory_b3', 'uvtheory_wca', 'uvtheory_bh', 'thol']\n", " print('')\n", " i = 0\n", " for eos in [uvtheory_b3, uvtheory_wca, uvtheory_bh, thol]:\n", " rep = 12\n", " att = 6.0\n", " sigma = 3.7039 # in Angstrom\n", " eps_k = 150.03 # eps / kB in K\n", " print('{}:'.format(names[i]))\n", " Tc = State.critical_point(eos).temperature / eps_k_k\n", " print('Tc*={}, Error: {}%'.format(np.round(Tc, 5), np.round(np.abs(Tc - 1.3120 ) / 1.3120 * 100 ,3)))\n", " pc = State.critical_point(eos).pressure() * (sigma * ANGSTROM)**3 / KB / eps_k_k\n", " print('pc*={}, Error: {}%'.format(np.round(pc, 5), np.round(np.abs(pc - 0.1279 ) / 0.1279 * 100 ,3)))\n", " rhoc = State.critical_point(eos).density * (sigma * ANGSTROM)**3 * NAV\n", " print('rhoc*={}, Error: {}%'.format(np.round(rhoc, 5), np.round(np.abs(rhoc - 0.316) / 0.316 * 100 ,3)))\n", " i += 1\n", " print('')\n", " return\n", "\n", "evaluate_critical_points()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare Phase diagrams" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 20.3 ms, sys: 667 µs, total: 21 ms\n", "Wall time: 20.9 ms\n" ] } ], "source": [ "%%time\n", "vle_uv = PhaseDiagram.pure(uvtheory_wca, 150*KELVIN, 500)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 16.8 ms, sys: 0 ns, total: 16.8 ms\n", "Wall time: 16.7 ms\n" ] } ], "source": [ "%%time\n", "vle_uv_bh = PhaseDiagram.pure(uvtheory_bh, 150*KELVIN, 500)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 24.8 ms, sys: 0 ns, total: 24.8 ms\n", "Wall time: 24.8 ms\n" ] } ], "source": [ "%%time\n", "vle_uv_b3 = PhaseDiagram.pure(uvtheory_b3, 150*KELVIN, 500)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.79 s, sys: 40.7 ms, total: 1.83 s\n", "Wall time: 1.83 s\n" ] } ], "source": [ "%%time\n", "vle_thol = PhaseDiagram.pure(thol, 150*KELVIN, 500)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 7.01 ms, sys: 4.16 ms, total: 11.2 ms\n", "Wall time: 11.1 ms\n" ] } ], "source": [ "%%time\n", "vle_pets = PhaseDiagram.pure(pets, 150*KELVIN, 500)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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trhovrholp
00.700.0020120.8432370.001381
100.710.0022840.8391060.001586
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\n", "
" ], "text/plain": [ " t rhov rhol p\n", "0 0.70 0.002012 0.843237 0.001381\n", "10 0.71 0.002284 0.839106 0.001586\n", "20 0.72 0.002584 0.834868 0.001815\n", "30 0.73 0.002912 0.830543 0.002069\n", "40 0.74 0.003271 0.826147 0.002350" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# import reference data from the NIST database\n", "nist_data = pd.read_csv(\"data/lennard_jones_nist.csv\", delim_whitespace=True)\n", "nist_data = nist_data[::10]\n", "nist_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the Phase-Diagrams" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "s3 = NAV * sigma_a**3 # factor to calculate dimensionless density \n", "colors = sns.color_palette(\"Dark2\", n_colors = 6)\n", "plt.figure(figsize=(12, 6))\n", "\n", "plt.plot(nist_data.rhov, nist_data.t, linestyle=\"\", color=\"k\", marker=\".\", label='MC', alpha=0.5)\n", "plt.plot(nist_data.rhol, nist_data.t, linestyle=\"\", color=\"k\", marker=\".\", alpha=0.5)\n", "\n", "plt.plot(vle_uv.vapor.density * s3 , vle_uv.vapor.temperature / eps_k_k, color=colors[0], label='uv (WCA)')\n", "plt.plot(vle_uv.liquid.density * s3, vle_uv.vapor.temperature / eps_k_k, color=colors[0])\n", "\n", "plt.plot(vle_uv_bh.vapor.density * s3 , vle_uv_bh.vapor.temperature / eps_k_k, color=colors[1], label='uv (BH)')\n", "plt.plot(vle_uv_bh.liquid.density * s3, vle_uv_bh.vapor.temperature / eps_k_k, color=colors[1])\n", "\n", "plt.plot(vle_uv_b3.vapor.density * s3 , vle_uv_b3.vapor.temperature / eps_k_k, color=colors[2], label='uv (B3)')\n", "plt.plot(vle_uv_b3.liquid.density * s3, vle_uv_b3.vapor.temperature / eps_k_k, color=colors[2])\n", "\n", "plt.plot(vle_thol.vapor.density * s3 , vle_thol.vapor.temperature / eps_k_k, color=colors[3], label='Thol')\n", "plt.plot(vle_thol.liquid.density * s3, vle_thol.vapor.temperature / eps_k_k, color=colors[3])\n", "\n", "plt.ylabel(r\"$T*$\")\n", "plt.xlabel(r\"$\\rho*$\")\n", "plt.xlim(0., 0.8)\n", "plt.ylim(1.0, 1.35)\n", "plt.legend(frameon=False)\n", "sns.despine(offset=10);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Calculate Errors compared to NIST data (MC)**\n", "- dimensionless vapor density $\\rho^{v*}$\n", "- dimensionless liquid density $\\rho^{l*}$\n", "- dimensionless vapor pressure $p^{v*}$" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "def plot_vle_errors():\n", " nist_data_subset = nist_data.loc[nist_data['t']< 1.30977] #use only data with temperatures where all EoS are subcritical\n", "\n", " fig, axs = plt.subplots(3, 1, sharex=True, sharey=True)\n", "\n", " for i, row in nist_data_subset.iterrows():\n", " temp = row['t']\n", " rhov = row['rhov']\n", " rhol = row['rhol']\n", " psat = row['p']\n", "\n", " vle_uvb3 = PhaseEquilibrium.pure(uvtheory_b3, temp * eps_k_k)\n", " rhov_uvb3 = vle_uvb3.vapor.density * NAV * (sigma * ANGSTROM)**3\n", " rhol_uvb3 = vle_uvb3.liquid.density * NAV * (sigma * ANGSTROM)**3\n", " ps_uvb3 = vle_uvb3.vapor.pressure() / (KB * eps_k_k ) * (sigma * ANGSTROM)**3\n", " err_uvb3_rhov = (rhov - rhov_uvb3) / rhov * 100\n", " err_uvb3_rhol = (rhol - rhol_uvb3) / rhol * 100\n", " err_uvb3_ps = (psat - ps_uvb3) / psat * 100\n", "\n", " vle_uvwca = PhaseEquilibrium.pure(uvtheory_wca, temp * eps_k_k)\n", " rhov_uvwca = vle_uvwca.vapor.density * NAV * (sigma * ANGSTROM)**3\n", " rhol_uvwca = vle_uvwca.liquid.density * NAV * (sigma * ANGSTROM)**3\n", " ps_uvwca = vle_uvwca.vapor.pressure() / (KB * eps_k_k ) * (sigma * ANGSTROM)**3\n", " err_uvwca_rhov = (rhov - rhov_uvwca) / rhov * 100\n", " err_uvwca_rhol = (rhol - rhol_uvwca) / rhol * 100\n", " err_uvwca_ps = (psat - ps_uvwca) / psat * 100\n", "\n", "\n", " vle_uvbh = PhaseEquilibrium.pure(uvtheory_bh, temp * eps_k_k)\n", " rhov_uvbh = vle_uvbh.vapor.density * NAV * (sigma * ANGSTROM)**3\n", " rhol_uvbh = vle_uvbh.liquid.density * NAV * (sigma * ANGSTROM)**3\n", " ps_uvbh = vle_uvbh.vapor.pressure() / (KB * eps_k_k ) * (sigma * ANGSTROM)**3\n", " err_uvbh_rhov = (rhov - rhov_uvbh) / rhov * 100\n", " err_uvbh_rhol = (rhol - rhol_uvbh) / rhol * 100\n", " err_uvbh_ps = (psat - ps_uvbh) / psat * 100\n", "\n", "\n", " axs[0].plot(temp, err_uvb3_rhov, 'r.')\n", " axs[0].plot(temp, err_uvwca_rhov, 'g.')\n", " axs[0].plot(temp, err_uvbh_rhov, 'b.')\n", "\n", " axs[1].plot(temp, err_uvb3_rhol, 'r.')\n", " axs[1].plot(temp, err_uvwca_rhol, 'g.')\n", " axs[1].plot(temp, err_uvbh_rhol, 'b.')\n", " \n", " axs[2].plot(temp, err_uvb3_ps, 'r.')\n", " axs[2].plot(temp, err_uvwca_ps, 'g.')\n", " axs[2].plot(temp, err_uvbh_ps, 'b.')\n", " \n", " \n", " \n", " \n", " axs[0].plot(temp, err_uvb3_rhov, 'r.', label='uv-B3')\n", " axs[0].plot(temp, err_uvwca_rhov, 'g.', label='uv-WCA')\n", " axs[0].plot(temp, err_uvbh_rhov, 'b.', label='uv-BH')\n", "\n", "\n", " axs[0].text(0.7, 2.1, '$Y=\\\\rho^{v*}$')\n", " axs[1].text(0.7, 2, '$Y=\\\\rho^{l*}$')\n", " axs[2].text(0.7, 2, '$Y=p^{v*}$')\n", "\n", " axs[0].legend(frameon=False, bbox_to_anchor=(1.01, 1.05))\n", " axs[1].set_ylabel('100$(Y^{MC}-Y^{EoS}) / Y^{EoS}$')\n", "\n", " plt.xlabel('$T^*$') \n", " return\n", "\n", "plot_vle_errors() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare Third Virial Coefficients" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { "image/png": 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hybRtNQFjUMwl7wdVVXEcOkDhxvUUb/8fuFy+dcHdexIxYgyWPn1RDPK1ILQnR6FoUjwumzeISn7GYf0ZR/FhrKVnfAFkNYI1CIpDLxxCOvREGFsSZWpFpLElEcaWhBmaEWpoRpihGSZdSJ1fH3KrLorKssh1nuCMfR+nS34ix3Uat+KhwAQFWNnrTiXiSCodlbb0bD6ZiOgBKMqFB1N1F1sp3rqFwi9TffMpgXdA1rAhw4gYPhpTq9Z1+lmEqC0JJ9FouV0lv7SIin/GVnyE/LIMiozeECoygDXce03ofEy6YJqZ2tDM1JZoUxyRxlZEmVoRZohBp1z0PFyd0isGokytiTK1plNoMsSCy+PkpD2dw4WbOWpLowQ7hSb4geOk5S2gbWYoSTE30yJ2RIWw9Did2NJ+wLrla0p2pfnGvAPv1BURI0YTmjwIndncoJ9RiOqScBKNgrusmNLinykt9raGCksOk+fJwWrAG0YGKIk4f2tIh54oU2tfEMWYvT/DDDF+PQacQWciwdKHBEsfVFXltD2dXdkrOOLci1tROWoq5ljhW7TN+TdJMTcTkdsC63dfU7Jjq2+8OwAlKIjQAQOJGD5a5lMSjcIlh1N2tvfiaWysdCsVdU9VPbgcWZSWHKe0+Cj2kp/JdfxMvlrgbQkZvWFUFnn+1wjWhRNrTiDG3I4Yc1tiTO2IMrVGrzTu/4spikJcSA/i2vXA6bGzK/sTdhatw66Uccxk5XjhP0jIhs67wGAHdDpCel1G2BVDsFzeT1pJolG54L/Wt99+m6FDh9KpUyfOnDnDfffdx+7du1EUhZ49e7Jw4UJatpS7xEXNeNzOs73kjuEsPkphyWFyyk5QpHNSdPa0XIkJ1PN8pyroiDa2JuacIIo1tSPEENmgn6OheZxOytL30vYHG5G7gjh2WRmHk8Bhhp9bw5l7oG9+Zy7rNhujdAMXjZSiqmdvbKjCkCFDWLNmDRaLhfvuu4+oqCgeeughAF599VWys7N5/fXXG6xYf9W/f38AduzYoXEl/sntslFmO4XTdgqn/RQO2wnyHMfJV/N+aQ0ZLtzV2qyEEGtuR4y5/dmf7YgyxWFQjA33QTTkKijAtmcXtrQfKPkpDdXhqLBel9CWn8e62dvsFJ6zZylbuiyMbTOXcEsHDSoW4sIu9r15wZZTcXExwWfvddi5cycbNmzAfPbUwOOPP86wYcPqslbRiHlcdsocWZQ5MilzZOFyZGG3nSDfeQqrWkSJHorPBlGxCTznbQ0pRBqaExPUwXtqztSWGHM7LPoov742VNc8TieOQ/ux/bQL2+5dvinPfRSFoC5dCe07AEvf/hhjm9MB6GM7zIZTL5Gh5JNhKOHfJ+cyKvJmOrQYp8nnEKKmLhhO7du3Z/v27SQnJxMREUFeXh6tWrUCID8/P6C+LAKZqnpwlxWeHbYnH/fZ4XvKHFnYHRlYyzIpVkuw/ermVXswcIEBDkxKEDGmtsQGdfCeljO1pZmpDQZd4M0HpLpcOH4+jGP/Puz707Hv3/vLEEJnKWYzwd16Etq3P5Y+/dCHh1d6nWYhHbmh02J2ZX7EN9ZPcehVPi/6kMuLf2RQ+8fR6Rr3dTcROC54pM6aNYs//vGP3HvvvVx//fXMmDGDqVOnAvDee+9x/fXXN0iRou6pHhfusiLcLituZxHuMitu19mfZUW4nYU4nXnYynKwuYtw6DzY9VR6OKsxhY8Bo/eeIXOcryUUa2pHqKFZwP4Hx11STOnPR7Af3I9j/14chw+ilpVV2s6c0IGQnr0I6XkZQZ26VOsGWUVRuKzl72gVehlfnJ5Pkc7JD+oeMg/cw7Xt52M2yxQXwv9d8JoTwFdffcVrr73Gnj17KN+0devWTJkyhbvuuitgv1zOpfU1J9Xjwu0qxl1mxVNm9QZO2dmHy4qnrBhXWREOVyGl7iIc7mJK1VLvWHA6KFO8P516KNWdfegvfH/QrynoiDDEEmmKO3vjaivfPUOBdkru1zx2O6XHfsbx8xFKj3ofZZkZVW5rbNmK4C5dCe7ek5DuvapsHV0Kp6uYNcee5qh6EoBmLjMT2/2NkOC4Wr2uELV1se/Ni4ZTObvdTlFRERaLhVCZ8bKC+gon1eOirDTHOwCpI5NS+xnsrnwcrkIcLiulnmJKPXacOHGdDZmysz9dul/+XKbzDkZKLfLBoBgJM8QQZmhOuDHG+2djLOEG758thmh0FxmpoKlTPR7KsrMoO32S0lMncZ466Q2ijDO+AVUrUBRM8W0JTuxGcGJXgjp3xRAZWfd1qSpbT77BttKvAIh0GRgf/zQRoZ3q/L2EqK5adYg4V3BwsK9zhKh7qqpiz95HxqkvyXTup1CfR4nJiV0PNoO3ZeNRAP3ZRy1vWVFUBZMSRJAulCBjOEH6MCz6SEIMkYToIwjRR2I5588mXXBAt37KqaqKp6SYsqwsyrIyKcvKxHn6JM7Tpyg7c6rKU3PljC1bYU7oQFD7Dpjbd8TcNgFdUFC916woCgPb3EtQRjhfWf9LgcHFipNPMqHVo0RHXFbv7y9ETdTo6uitt97KNddcw+9+97u6rifgZO5fy/6c1ZwOzSMvGFxh1X+uzg3GUjA4wWg/+7CBwQHGcx4GB5js5/xuB4NTRcEO2EHJQRccgs5iQR9iQRca6v1pseC2hGKzWHCEWNBbQr3bWCzoQs4+goNRdE2nxeRxOHDl5+EqyMedn4+rIA9Xfj6uvFzKsrNwZWdWGHmhKorBiLFVa0xxcZjbJmBO6IA5oQP6kPqd/uJi+rS8FbM+jA0FH1Fs8LAy43kmqHOIieynaV1CVKVG4bRjxw4iIyOZPHkyp06dwmKx0KKFzI55qQoyfmKl8g5lv9p1ejeE242EOi2EuaOJ0LXEYoj+ZeRrQyhmfSh6jw50LlSjB1XnhiAXnpBSPCUluEuK8ehLcCsleDzFuMtK8JT/2V0C/DLWGqqKx1aCx1aCi0ukKOiCgtGFhKCzhKIPCTkbXCHoQkLQW0JRzEHoTCYUsxmdOcj702TyLjebUUwmdCYT6A0oej2KXg96/SW11FSPB7WsDNVZisfpRD378DhLUcucqKVO3LYSPCXFuEtKftlHJcW4i70/XYWFqI4LB8+5dBYLxtgW3iBqHYcprg2m1nEYm7fw28DuFjsekz6UNblvYdOrfJbxMtcb/kJYqAxpJPxLjfuVbt++nQEDBlB29jRGTEwM11xzDdOmTaN58+Z1VmBTZrBEoctXMKgqLW3hJJj70rbNGJqFJlx0pOnaUFUVtbT07Jdzie+n74vbVv4FXv57ifcL3FaCx2areP1EVfHYbXjsNsjNqdtCFQVFbwC9DkWnB1RvpxyPCh4Pqurx1lL+qOP31oeFY4iKQh8ZjSEqGmPz5hhjm2OIbYExNha9pXFee+0YPZJrFAP/zXkDq8HDZyeeZnKHVzCbZUgy4T+q3SHiXF27dkVRFNq2bUtycjJOp5Ndu3Zx5MgRwsLCeO2117jiiivqo16/VJsOEaqqoqI2ms4EqsfjDSObzRtaZ396bDZfkPnWlZTgsZfgcZSiljq8LZrSUjyljgqjZNc7RfG2zkLOnpK0hFY8RWkJRW+xoI+IxBAZhT4yCkNEZJOf12hX1nK+LFoBQBunhes6v4bBaNG4KhEo6qxDxK8lJSXxz3/+E8M5/4B37drF3LlzmTlzJqtWraJdu3Y1ffmAoSgKSm260TUwRadDf/bLvTZUl+tsWDnwlJ499eb2gMeN6naD2/vT92ePG1C8p8sUBXQKKLpfflcU76lC0y+nCRWT9/QhBoN05qhC7+Y3UFSWxQ/2rzlhKmHToUcZmfgKugC8CVr4nxqFk06nY8yYMRWCCaB3794sW7aM8ePHs2TJEp577rk6KVI0PYrBgN5gAI07CQS6wa3vxXo8h4Nle9lrzCHs4DMkd/mLhLnQXI3OJUVERFBQUHDedZMnT+abb76pTV1CiAagKApj2s6llc47LNkO3SGOn/qPxlUJUcNw6t27N8uXL6e4uLjK9ZGRkecNLyGEf9ErBq5r+1csqhmPAhutqygu2q91WSLA1Sicpk2bRlZWFjfffDM//vhjhXVOp5P//ve/fj0ZYUlJCX/961+58sor6d27N5MmTWLDhg1alyWEZoIMFsa2ehhU78jxXx5/HrerROuyRACrUTglJSXx7LPPcvz4caZMmcJvfvMbHnjgAebMmcPo0aPZtWsX48b57xD9s2bN4rPPPuOBBx7gzTffpFOnTsyaNYvNmzdrXZoQmokL7U3/sNEAHAmy8+PB56lBZ14h6kSNe+tNnDiR/v378+6777Jp0ybWrl0LgNls5rbbbuO+++6rsyLr0ubNm/nuu+9YtGgRo0d7/yEOHDiQEydO8Pzzz8scVSKgDWzxe47bdpHlyWSb7iCtTq2kVfxkrcsSAahWN9e0adOGefPmsXHjRrZv386mTZvYsWMHf/rTn9DrLzCtqYbWr19PWFgYI0eO9C1TFIWJEydy5MgRDh06pGF1QmhLp+i5On4uRlWPUw9fFi7HXiT/JkTDq3Y4DRs2jL/85S9s2bIFdxU3UIaFhdGqVSuMRv+eNvvgwYN06tQJ3a+Gl0lMTATgwIEDWpQlhN+IMDVnePO7AMg2w7ZjL6N6LnlgKyFqpdqn9UaMGEFqaioffPAB4eHhDBs2jDFjxnDllVc2qtHKCwoKSEhIqLQ8IiLCt/7Xyu9kPh+r1UpY2CWM2CqEn+sacRVHrP/jkCONdHM+nU+uoHXbm7QuSwSQarecnnrqKTZv3sx//vMfbrrpJnbv3s19993HwIEDueeee1i5ciX5+fn1WWududANhnLzoRBeV7WcgREDZTrYVrAap/2M1iWJAHLJHSJ69+5N7969efjhhzl8+DCpqamkpqYyd+5cdDodffv2ZfTo0YwcOZK4OP+bbfN892AVFhYCv7SgznWxMfMu1rISojEKMUQyMPomvs77gOPBHg4cWkiPns/Kf+BEg6hVh4iOHTty9913s3z5cr788ksef/xx9Ho9L7zwAqNGjWLixIl89dVXdVVrnejUqROHDx/G4/FUWF5+ralLly5alCWEX7os6hqi9DGgwA/6IxRlyu0WomHU2VDYLVq04NZbb+Xdd9/lu+++47nnniMuLo6DBw/W1VvUidGjR1NUVMTGjRsrLE9JSaF9+/Z06iRTVwtRTqfoGd7yXgAKTJB2+h3cziKNqxKBoF7mBAgPD2fChAlMmDChPl6+VoYNG0ZycjJz586loKCA+Ph4UlJS+P7771m8eLHW5Qnhd+KDu9MppD+HbDvYaymlw8/v0CbxAa3LEk1cnbScXC4Xb7zxBo8//jh79uwhMzOTO+64gxEjRvDUU09RWlpaF29TJxRFYfHixfz2t79lwYIF3HXXXezfv59FixYxYsQIrcsTwi8NaX4HBgw49bDTvgVb/k9alySauBpNNvhrzz33HHv37kVRFA4cOMCNN95I165dcbvdvPHGGwwfPpw5c+bURb1+qTaTDQrRWOzIS+G7vH+jqDDCFk/33i9K5whRY/U22eC51qxZQ0pKCqqqMmjQIMaPH0+HDh0A6Ny5M7NmzWrS4SREIOgT9Vt2F66nyJ3LHt1J2uVsJTR2oNZliSaqTk7rFRcXExUVRXR0NCEhIb5gAu/IC7m5uXXxNkIIDRkUI1fE/A6AjGD4+cQHqGrl0WKEqAt1Ek5hYWE4HA4AZsyYUWGd1Wr1+yGNhBDV0zn0CiL03ulw9hmzsUrXclFP6iSchg8fzqlTpwCYPn16hXXr16+nR48edfE2QgiN6RQdSc1uAOBMEBw78TEej1PjqkRTVCcdIi7EZrOhKEqjGn/vUkmHCBFIPKqb944+QJE7h9Y2GBl9G1Hxv9W6LNHIXOx785JaTna7nfT09PNOz16VkJCQJh1MQgQanaInqdn1AJwOhhOnV+Jx2TSuSjQ11Q6n3bt3c9VVVzF58mSGDBlCSkoKAEVFRSxbtoz58+fz7rvvcvr06fqqVQjhJxLDriRM3wwUOBhUQv6pz7UuSTQx1e5KvnDhQoqKihg+fDjZ2dk88cQTJCQkMGfOHE6dOuWbznnBggX8+c9/9svRIYQQdUOvGBgQPYmN2W9xOhhOn/mMiFZjMJgqD5wsRE1Uu+W0Z88epkyZwuLFi1m+fDlXX301Dz/8MAUFBfzxj3/kzTff5IknniA2NpYnnniCffv21WfdQgiNdQsfRqg+GlWBQyFO8k+u1rok0YRUO5zy8vIq9Lq77bbbOHXqFNOnT+eOO+5g2LBh3HrrraxatYrIyEj+9a9/1UvBQgj/oFcM9I+eAMDJYMjI2oBbrj2JOlLtcPJ4PJjNZt/vrVu3Bn6Z3rxcWFgYkydPZuvWrXVUohDCX3UPH45FH4WqwJHgUooyNl78SUJUQ43vc9Lr9QCYTKZK69q2bUtOTk7NqxJCNAoGxUjfqGsBb+sp9/QXMmqEqBOXFE5ff/01W7ZsuWhXcqPRSFlZWa0KE0I0Dt3ChmFQjLh1cEyfR3HONq1LEk3AJQ38+umnn/LZZ5+hKApt2rRBURQ2b96MXq+nc+fOREVF1VedQgg/FaQPpUvoYNKtX3I8BDqf+pyw2Cu0Lks0ctUOpx07dpCenk56ejq7d+8mPT0dRVF49913fZ0fmjVrJjPJChGAekWMJt36JVYjZBQeIrboAMHhXbQuSzRi1Q6n0NBQkpKSSEpK8i2z2Wzs3buXPXv2kJ6ezp49e9i+fTtut1vmeREigLQI6khzcweySo9w3AJtT30h4SRqpVbzOYWEhNCvXz/69evnW1ZaWuoLLCFE4OgVMZoNWW+SEQS5Wf+jmSMLY1BzrcsSjVSdjEp+LrPZTJ8+fbjlllvq+qWFEH6sS+ggzLoQPIq3517h6bValyQasToPJyFEYDLqzHQLGwbA8RAozNggA8KKGpNwEkLUmZ4RowCwGyDT4KAo80ttCxKNloSTEKLORJviiA/2DnN2PAQKTv0fqurRuCrRGEk4CSHqVK+I0QBkmcHqysaW/6PGFYnGSMJJ+K01a9aQmJhIampqpXUjRowgMTGR3bt3V1judDrp1asXs2fPrrC8qKiIRYsWMWHCBC6//HJ69uzJ6NGjeeKJJ0hPT6/y/efNm0diYiKPPfZY3X2oANDB0h+LPgoUb+vJmrlZ65JEIyThJPxWUlISiqKwbVvF4XBOnTrFqVOnCAkJqbRu586dOJ1OkpOTfcuOHj3KhAkTeOeddxgyZAivvvoqb7/9NnfddRdHjx7l9ttvr/TedrudL774AoC1a9de0uzPgU6vGOgRPhyAE8Fgzd2Bu0z2n7g0Ek7Cb0VHR9OpU6dKAbR161bCw8O5+uqrK63bvn07gC+c3G439913H1arleXLl/Pwww8zdOhQkpKSuPHGG1m2bBnPPvtspfcuD6Thw4djs9n4v//7v3r6lE1Tj4iRKCg49ZBlclGc8z+tSxKNjISTxlSXi7LsLM0fqstVo/ofe+wxRowYUWn5woULfdOpjB8/nttuu63SNg6Hg759+/LUU0+d9/WTk5PZv38/hYWFvmXbtm2jX79+JCUlsWPHDjyeXy64b926lebNm5OQkABAamoqBw4c4O6776ZDhw5VvseYMWMqLVuxYgUWi4X58+cTGRnJihUrzlujqCzM0MzXMeJ0sJzaE5euViNEiNpRXS6O/+lhyrIytS4FY/MWtP3byyiGuj8kJk6cyPPPP8/JkyeJj4/3LV+3bh0lJSVMmjTpvM9NSkpi2bJlbN++nVGjvN2Ut23bxq233kpSUhJWq5X09HR69uyJ0+nkxx9/rBA23377LUCVAXo+J06cYPv27UyePJmIiAiuvfZali1bxuHDh+nYseOlfvyA1SVsECfsu8kyQ3HWQZy205hCWmtdlmgkpOUk6t24ceMwGAykpKRUWL5q1So6duzIZZdddt7nDhgwoMJ1p/LrTQMGDKB169a0bt3at+7HH3+ktLS0wvWm06dPA1QIxYv55JNPUFXVF5rlP6X1dGk6WpLQocetO9tzL+trrUsSjYi0nDSkGAy0/dvLuPLztC4FQ1R0vbSawHvtaMiQIaSkpDBz5kwURSEjI4P//e9/vl51Ho+nwuk5RVHQ6/VER0fTuXNnXwBt3boVi8VC9+7dAW94bdu2jTvvvNO3zbnhdKk8Hg+rV68mISHBN2Zkjx496Nq1K6tXr2b27NkY6mk/NTVB+lDahlzGUdsPnAmGtllfE93uBhRF/k8sLk7+lWlMMRgwxjb9wTEnTZrErFmz2L59O0lJSaSkpKAoCuPHjwfg9ddfZ9GiRb7t4+Li2LjRO+V3cnIyH3zwAYWFhb7rTeUzMQ8YMID58+fj8XjYtm0brVq1om3btr7Xad3aexrp1KlTtG/f/qJ1fvPNN5w5c4YZM2ZQVFTkW3711VezYMECNm/ezMiRI2u/QwJEl7BBHLX9QLYZ7AU52AvTCYnsqXVZohGQcBK1YjKZcDqdlZbn5+dX+P2qq64iKiqKVatW+cLpyiuvpHlzbzDfeOONXHXVVRVet1xSUhLvv/8+27dvZ9u2bUyZMsW3rn///litVn788UfS0tIYO3ZshfcdPHgwH3/8MZs2bapWOJWfuluyZAlLliypcr2EU/V1sPTHoJhw4SQzCKIzv5JwEtUi4SRqJT4+ntzcXHJycoiJiQG8N8J+8803FbYzGo1ce+21rFixguuuu46ff/6ZBx980Le+RYsWtGjRosr3KL/ulJKSwqlTpyrMKda+fXtiY2NZunQpDoejwjqAUaNG0blzZ958801GjBjh68V3rvXr1zN69GgKCgrYuHEjSUlJzJo1q9J2S5cuZfPmzRU+q7gwky6IBEtfDhX/j9NB0CZnK7Gd7kSnD9K6NOHn5OSvqJWrr74avV7PQw89xObNm1m3bh3Tpk3D7XZX2nby5MnYbDYef/xxIiMjq92DLioqii5dupCamkpISAg9evSosL5fv35s2LABqHy9Sa/Xs2jRIiwWCzfccAMLFizg66+/Zvv27SxfvpypU6fypz/9CYBPP/0Up9PJ7bffTnJycqXH73//e1wuF6tXr67JrgpYXUIHAZBrBgelFOdsu8gzhJBwErXUpk0bXn/9daxWK/fffz8vvPACY8eOZcKECZW27datG127diUjI4Pf/va3FU7dXUxycjKqqtKnTx+MRmOFdUlJSaiqSlxcXJW98hISEkhJSeH222/nyy+/5P777+eOO+7gH//4BwkJCSxbtgyAlStXEhsbW+H04rkGDRpEmzZtpNfeJUoI6YNRCUZVICNI7nkS1aOoqqpqXURj179/fwB27NihcSVC+Kd1ma+zz/o10aUwME+h3YCFGIPk1Gggu9j3prSchBD1rvzUXp4J7DpV7nkSFyXhJISod21CehGkCwUFMoLBmrUZOWkjLkTCSQhR7/SKgU6h3s4qp4OgzJ6Bs+SYxlUJfybhJIRoEJ3PntorNEGJHkpy5RqtOD8JJyFEg4gL7uadhBA4EwTFEk7iAiSchBANQqfo6Bw6EIAzweAsOUqZI1vjqoS/CrhwWrlyJYmJiVU+SktLtS5PiCatc+gVAFiN5af2vte4IuGvAnb4ovnz51cayuZSbgoVQly6lkGdCNFHYHMXkmWGmLwdRMaNvfgTRcAJ2HBKTEykW7duWpchREBRFB3tQvqw17qZ7CCw56XjLitGbwzVujThZwLutJ4QQlsJlssB7w25LsWDLT9N24KEXwrYcPrDH/5At27dSE5O5qGHHuLYMbnnQoiG0Da4Nwo6PArkmqRLuahawJ3Wi4mJYcaMGfTp0weLxUJ6ejpvvvkmN954I5988glt2rSp9JzyMaDOx2q1EhYWVl8lC9GkmPUhtA5K5JRjL1lB0DI/DdVThqIzXvzJImA06nDaunUrU6dOrda2W7ZsITo6mqFDhzJ06FDf8qSkJAYNGsSkSZNYsmQJzz77bH2VK+rAunXruO+++yosCwsLo2PHjsyaNYshQ4ZoVJm4FAmWvpxy7CXbDJ5CB7aCPVii+2hdlvAjjTqcOnTowHPPPVetbUNDz3/BtUuXLvTs2ZO0tLQq119stPGLtaxE3dmzZw8Ab7/9NhaLBY/Hw4kTJ3jllVeYOXMmqampvtl1hf9KCOnDt7kf4NCD1eA9tSfhJM7VqMMpNjaWSZMm1clreTwedLqAvQTXaKSnpxMbG8vgwYN9y/r27Utubi7z58/n0KFDEk6NQLQpnjBDDFZXDllmiMr7HlW9E0WRf4PCS44E4MCBA6Snp3PZZZdpXUqj9Nhjj1U5q+3ChQtJTEwEvFOhJyYmsm1b5VlQFy9eTPfu3cnMzLzoe+3du7fKWwAyMjLQ6XRVTsMu/I+iKCRY+gKQHQRuZz6lxUc0rkr4k0bdcqqJO+64g4EDB9KpUydCQkLYu3cvb731FmFhYcyYMaPB61E9LlzOvAZ/318zmKJRdPV3OFx11VVERUWxatUqkpKSKqxbvXo1gwYNokWLFhd8jaysLLKzsxk/fjwulwuA/Px81q9fz4cffsjdd99N69at6+0ziLqVENKHnwrXkW8Ep+I9tRcU1knrsoSfCLhw6ty5M59++ilnzpyhtLSU2NhYRowYwcyZMxv8i031uDj+/cOUOS7eYqhvxqAWtO33cr0FlNFo5Nprr2XFihXMmzePkJAQAL7//nuOHj3KAw88cNHXKL/etHTpUpYuXepbbjabmTt3LjfffLNv2bx589i0aRM2m424uDhmz57N8OHD6/hTidqID+6BXjHipowcM4Tm7qBZwhStyxJ+IuDC6U9/+pPWJQSsyZMn8/7777N27VomTpwIQEpKChEREYwaNQoAVVVxu90VnmcweA/T8nD6xz/+QXR0NAB5eXm88847PPvss3Tu3NnXOeX3v/898+bNw2QysWvXLu688042btxIeHh4g3xWcXFGnZn44B4cs6WRFQStC07itGdgCm6pdWnCDwRcOPkTRWegbb+XA+K0HkC3bt3o2rUrK1euZOLEiTgcDv7v//6P6667zjeu4bZt2yrdHrBhwwbi4+NJT08nLCyMYcOGVVjfo0cPBg8ezIoVK3zh1LFjx18+m8GA0+kkMzNTwsnPJIRczjFbGtlmBRWVktwdmOKv1bos4QcknDSm6AwYgxp37zKTyYTT6ay0PD8/v9KyiRMn8vzzz3Py5EnS0tKwWq2+VhR4g+aTTz6p8Jzy3nfp6el07dq10muazWYAHA5HheVPP/00K1eupLS0lKuuuopOneR6hr9JsFzO5px3KNOpFBghOHcHURJOAumtJ+pAfHw8ubm55OTk+JY5nU6++eabStuOGzcOg8FASkoKq1atonPnzvTu3du3PjQ0lF69elV4mEwm8vPzOXPmTJXhtGbNGqDy/WZPP/00O3fu5J133mHw4MEoilJXH1nUkQhjc6KMcQBkm8FRtB+3s0jjqoQ/kHAStXb11Vej1+t56KGH2Lx5M+vWrWPatGmVrh0BREdHM2TIEP7973+zZcuWCq2mCym/3hQUFERaWhppaWls3ryZF154gb/85S/069ePG264odLz9Ho9gwYN4ttvv+Xrr7+u3QcV9aL92YFgs4IUQKVEBoIVSDiJOtCmTRtef/11rFYr999/Py+88AJjx45lwoQJVW4/efJksrOzURSFcePGVes90tPTAXjrrbe46aabuOmmm3jwwQfZtm0bs2fP5t13373gfFxut5vjx49f8mcT9a9diDeciowqDh3YC9M1rkj4A0VVVVXrIhq78tNJFxvmSDSMkpISUlNTGTVqFGazmfXr1/PHP/6R5cuXV3laUGjLrbr4x5G7KFPt9CqA9p4Y2g1YKKdhm7iLfW9KhwjR5CiKwooVK/jLX/6Cqqq0a9eOV155RYLJT+kVA21DenG4ZBtZQdAmPweXIwtj8IVvyhZNm4STaHJCQkJ47733tC5DXIL2lr4cLtlGjgk8gK1wDxESTgFNrjkJITTXLqQPAG6dd4Zce8FubQsSmpNwEkJozmKIJMaUAJSH0x7kcnhgk3ASQviF+GDvaPO5ZnCXFVJmO6VxRUJLEk5CCL8QdzacCo3gBmyFcmovkEk4CSH8Quuz4eRRIP/sqT0RuCSchBB+IVgfRjNTW+DsdafCdFTVo3FVQisSTkIIvxEf3B3whpPHVYKz5JjGFQmtSDgJIfxG+XWnAtPZ605yai9gSTgJIfzGudedCuS6U0CTcBJC+I0QfTjRpnjg7HWnor2oHpfGVQktyPBFosYSExOrtd2GDRvYtm0bjz/+OCkpKXTr1q1O3n/EiBEkJSXx/PPP18nrCf8QH9ydPOdJ8kygFjtwFB8hOLyL1mWJBibhJGrs448/rvD7Sy+9xNGjR1m0aFGF5eUz2QpRHXHB3dlVuI78s9ed7AV7JJwCkISTqLE+ffpU+D08PByTyVRpuRCXIi7ol+tOhUYILdgNbas3KaVoOuSak2hQxcXFPPnkkyQnJ5OcnMysWbPIzMyssI3L5WLx4sWMHj2anj17cuWVVzJv3jzy8/M1qlo0pBBDhG/q9lwzOIoO4PE4Na5KNDRpOWnMrbooceVpXQYWQzR6pf4Ph8cff5zhw4fz8ssvc+bMGV588UUeeeSRClNcPPHEE6SkpHDHHXcwePBgjhw5wt///nfS0tJYvnw5QUFB9V6n0FZ8cDfyy06dve5UhqPoICGRPbQuSzQgCScNuVUXy44/TGFZ5sU3rmcRxhbc2vbleg+oq666irlz5/p+Lyws5MUXXyQrK4vmzZtz6NAhVq1axZ133smjjz4KwJVXXknr1q2ZOXMmKSkpTJkypV5rFNqLC+7OT0WpFJgUPKjYC3ZLOAUYOa0nGtTIkSMr/F7e4+/06dMAbN26FYBx48ZV2G7UqFGEhYX51oumLe7sSBFuRaXACPZCud8p0EjLSUN6xcCtbV8OqNN6kZGRFX43mUwAlJaWAt6WFEBMTEyl58bExFBQUFCv9Qn/YDFEEmVsTX7ZafJMEG09jMdlR2cI1ro00UAknDSmVwyEG6Wrdbny8MrJySE2NrbCupycnDq7R0r4v7jgbt5wMgMlbuxF+7BEX651WaKByGk94VeSk5MB+PTTTyss37hxI1arlYEDB2pRltBA+am9fJOCB+8o5SJwSMtJ+JWOHTty/fXX88477wDezhBHjhzh1VdfpWvXrkyYMEHbAkWDKR8E1q2oFBohWMbZCygSTsLvPPPMM7Rp04YVK1bw/vvvExERwTXXXMPs2bMxm81alycaSKghmkhjSwrKMsgzQVTxz3LdKYAoqqqqWhfR2PXv3x+AHTt2aFyJEE3Lhqx/sKdoIzEOSMqH1r3mSZfyJuJi35tyzUkI4bd88zuZvdedSq2HtC1INBgJJyGE3yofZ8+lqBQZwSHhFDAknIQQfivMGEO4wXurRa5JwimQSDgJIfxa/Nku5XkmcDvzcZXmalyRaAgSTkIIv/bL/U6gIq2nQCHhJITwa62CvRMNunRQbJBwChQSTkIIvxZhaEGQLgyAAukUETAknIQQfk1RFFoEdQS84VRqPYKqejSuStQ3CSchhN9rGdQJgEITqJ5SnLaTGlck6puEkxDC77Uwe1tOVgO4kVN7gUDCSQjh91qcbTmpChQaZaSIQNAkwungwYM89dRTXH/99fTq1YvExEROnjx/s/+zzz5j3Lhx9OrVi6FDh/LSSy/5JrsTQvifYH0YEcYWABSYwFEk4dTUNYlw2r17N5s2bSImJoa+fftecNvVq1czZ84c+vbty1tvvcXdd9/NBx98wGOPPdZA1QohaqKl+ex1JyM4bSfwuB0aVyTqU5MIp/Hjx/PVV1+xZMkShg8fft7t3G43L774IiNGjODpp59m4MCB3HLLLTz++ON88cUX/Pjjjw1YtRDiUpzbYw9USq1HNK1H1K8mEU46XfU+RlpaGtnZ2UycOLHC8uuuuw6j0cjatWvrozwhRB1oEdQZALsBSnXSKaKpC6jJBg8ePAhA586dKywPDg6mTZs2vvW/Vj7vyPlYrdZqbSeEqJ1Sjw2At1XQsQed/hmNKxI1ZbVaURTlvOsDKpwKCgoAiIiIqLQuIiLCt74mzreTy4MrLCysxq8diGS/1UxT329mXUi9vG5T32/1pTb7TVGUC5718rtw2rp1K1OnTq3Wtlu2bCE6OvqS3+N8QXK+5bWZ4VZmya0Z2W81I/utZmS/1Ux97je/C6cOHTrw3HPPVWvb0NDQS3rtyMhIwNuCioqKqrCusLCQ+Pj4S3o9IYQQ9cPvwik2NpZJkybVy2t36uTtinrw4EHat2/vW2632zlx4sQFe/oJIYRoOE2it1519enTh9jYWFavXl1h+X//+1/KysoYM2aMRpUJIYQ4l9+1nGrCbrezefNmAPbv3w/AV199RXR0NNHR0SQlJQFgMBh4+OGHeeyxx3jmmWf4zW9+w+HDh3nppZf4zW9+Q58+fbT6CEIIIc7RJMIpNzeXBx54oMKyP//5zwAkJSXx/vvv+5ZPnDgRnU7H0qVL+c9//kNUVBRTpkzh/vvvb9CahRBCnF+TCKf4+Hhfi6k6xo8fz/jx4+uxIiGEELURUNechBBCNA6Kqqqq1kUIIYQQ55KWkxBCCL8j4SSEEMLvSDgJIYTwO02it54WSkpKWLBgAWvWrKGoqIhOnToxc+ZMRo4cedHnHj9+nOeff56tW7fi8Xjo378/jz76qG8Ei6aspvtt4cKFLFq0qNLymJgYvv322/oq129kZGSwdOlS9uzZw759+7DZbLz33nskJydX6/mBeszVZr8F6jG3ZcsWVq9ezc6dO8nIyCAiIoLevXtz3333kZiYeNHn19WxJuFUQ7NmzSI9PZ05c+YQHx/PqlWrmDVrFkuWLGHYsGHnfV5ubi6/+93vaNasGfPnz0ev1/PGG29w6623kpKSQsuWLRvwUzS8mu63cu+88w4hIb+MTG00GuuzXL9x7NgxPv/8c7p3787AgQPZuHFjtZ8byMdcbfZbuUA75j766CMKCgr4/e9/T8eOHcnJyWHp0qVcf/31vP/++xccrKBOjzVVXLIvv/xS7dKli7pu3TrfMo/Ho06ZMkUdO3bsBZ87f/58tVevXmpGRoZvWV5ennr55ZerTz75ZL3V7A9qs99ee+01tUuXLmphYWF9l+mX3G6378/r169Xu3Tpov7vf/+r1nMD+ZirzX4L1GMuJyen0rLCwkK1f//+6qxZsy743Lo81uSaUw2sX7+esLCwCqeiFEVh4sSJHDlyhEOHzj9DZ2pqKoMGDaJFixa+ZVFRUQwfPpz169fXa91aq81+C3TVne25KoF8zNVmvwWqZs2aVVoWHh5Ou3btyMjIuOBz6/JYk7+5Gjh48CCdOnWqdOCXn489cOBAlc9zOBwcP36cLl26VFqXmJhIbm4uubm5dV+wn6jpfjvXNddcQ7du3bjyyit54oknmvT+qguBfszVBTnmIC8vj4MHD1aaRfxcdX2syTWnGigoKCAhIaHS8vIZds83o25hYSGqqlY5E++5c01V9T+XpqCm+w2gTZs2zJ49m27dumE0Gvnhhx9YunQpW7ZsYeXKlVXuUyHHXG3IMeelqirz5s3D4/Ewbdq0825X18eahFMNnW/W3Iutq876pqym+23ChAkVfr/iiivo06cPd955Jx988AH33ntvXZXYJAXyMVdTcsx5vfDCC6SmpvLcc8/RsWPHi25fV8eanNargcjIyCr/l19YWAhw3v9RRUREoChKlc8tX1b+P4ymqKb77XwGDx5MbGwsaWlpdVBd0xTox1xdC7RjbsGCBbz99tvMnTv3opPA1vWxJuFUA506deLw4cN4PJ4Ky8uvmVR1zhUgKCiINm3aVHlt5cCBA0RHRzfp0ys13W8XoqqqXPS+gEA/5upDoBxzr776KkuWLOGRRx5h6tSpF92+ro+1pr+H68Ho0aMpKiqqdM9ESkoK7du3v+DNZqNGjeK7774jOzvbt6ygoIBNmzYxevToeqvZH9Rmv1Xlm2++IScnh8suu6wuy2xyAvmYq2uBcswtWrSIxYsX88ADD/CHP/yh2s+ry2NNrjnVwLBhw0hOTmbu3LkUFBQQHx9PSkoK33//PYsXL/Ztd9ttt7Ft27YKc01NmzaNTz/9lOnTpzNz5kwMBgNvvPEGBoOBGTNmaPFxGkxt9tuECROYMGEC7du3x2AwsHPnTv75z3/Srl07brnlFi0+ToNbs2YNAD/99BMA27dvJz8/n+DgYN8NzHLMVVbT/Raox9zbb7/NwoULGT58OIMGDapwCtNkMtG9e3eg/o81mTKjhoqLi3nllVdYu3ZthWF4Ro0a5dumqr88gKNHjzJ//ny2bt2Kqqr069ePRx999ILdNJuKmu632bNns3v3brKysnC5XLRs2ZIRI0Zw7733Bsw1k/MNHRMXF+drjcoxV1lN91ugHnPl+6IqDXmsSTgJIYTwO3LNSQghhN+RcBJCCOF3JJyEEEL4HQknIYQQfkfCSQghhN+RcBJCCOF3JJyEEEL4HQknIYQQfkfCSYhG6MCBA3Tv3p1vv/220rpJkyZx3XXXNWg9qamp9OzZk6NHjzbo+4qmS8bWE8LP/fnPfyY1NZWvvvrKN1fO888/T9++fRk8eHCFbV0uFwcPHmTs2LG1es+SkhL69+9faQT589m2bRtdunThpZdeYtGiRbV6byFAwkkIv6aqKhs2bGDkyJG+YNq5cyfffvstr7/+eqXtDx06hNPppFu3brV6X7fbzfPPP19h2UcffcTOnTt59NFHK0x9YDKZiIiIYOrUqTz66KMXnc5biOqQcBLCj/30009kZmZWGBj3ww8/JDIy0jei9rn27dsHUOtwCg8PZ/z48RWW/etf/8JsNjN16lQMhspfHaNHj+bpp5/m3//+N/PmzavV+wsh15yE0ICqqnzyySdMmTKF5ORkevXqxfDhw7n77rspKyvzbbd+/XrCwsJITk4GvKftUlNTGTx4MEajsdLrpqenAxXDqaioiHvvvZdevXrx8ccf16jesrIyDh48SGJiYpXBBGCxWOjXr59vigohakNaTkJo4KmnnuLjjz/mN7/5DePGjUOv13P69GlOnjxZIXTWr1/PsGHDfMv27NmDzWajd+/eVb7u3r17adWqlW9Kh927d/PAAw+gqiofffQRPXv2rFG91T1dePnll/PNN99w+PBhOnbsWKP3EgIknIRocFarleXLl3PTTTfxzDPPnHe7w4cP8/PPP/Pggw/6lh06dAiANm3aVPmcffv20a9fP8B7+u+5557jiiuu4IUXXqjVHETlLbIePXpccLvyug4dOiThJGpFwkmIBmYwGAgNDWX37t3s2rWLuLg4TCYTYWFhFbZLTU3FZDIxZMgQ37K8vDwAIiIiKr3uiRMnKCoqIiEhgYcffpgvvviCmTNnMnPmTF9nipqq6nRhVcoDMDc3t1bvJ4SEkxANLDg4mDfeeIMHH3yQG264AYCxY8fy6quvVthu/fr1DB48GIvF4lt2oZAp7wyxbNkyFEVh6dKllbqal3O73fTt2xdFUVAUBY/HQ4cOHXjyySe5/PLLK22fnp6OwWA476yyv1bbMBRCOkQI0cDWrl3L9OnTGTRoEAsWLOCdd95h9uzZFbbJyMhg9+7djBw5ssLy6OhoAAoKCiq9bnnr5oYbbsDpdHLgwIHz1nD48GEcDgdff/01O3fuZNu2bcTFxTF//vxK23o8Hvbt20eHDh0wm80X/GzldZXXKURNSctJiAZUWFjIY489xsSJE3nyySfPu11qaiqKolQKp/L7h44dO1bpOXv37iUqKoqnnnoKh8PBCy+8QFxcHGPGjKm07Z49e2jZsqXvVKLZbKZHjx6kpqZW2vbo0aPYbLaLXm8COH78eIU6hagpaTkJ0YAOHDiAzWYjISHhgtutX7+efv36VWqBdO/endDQUH788cdKz9m7dy/du3cH4JlnniEpKYlHHnmkym337NnjCxBVVUlPT2f58uXcfvvtlbat7vUmgLS0NGJiYujQocNFtxXiQqTlJEQDSkhIICQkhAULFnDixAk6d+6M0+nk+PHj5OTk8Morr1BYWMiOHTt45JFHKj1fr9czZswYNmzYgNPpxGQyAZCfn09GRgbXXnstAEajkYULFzJlyhTuuecePv744wo9/Pbs2cPu3bvp378/ZWVllJaWMmPGDMaNG1fpPavbU6+kpITvv/+eyZMn13j/CFFOWk5CNKDY2FiWLl1K3759Wb16NU8//TSLFi1i9+7dvl55mzZtwuVyVTqlV+7mm2+msLCQTZs2+Zbt3bsXwNdyAu8oD2+++SYA06dPp7CwEPjlGtLLL7/Mjh07SEtL46OPPuKtt97ihx9+qPR+e/fuRVEUunbtesHPtm7dOux2OzfddNMl7BEhqqaoqqpqXYQQ4hczZ87k5MmTrF69+rzbTJs2DbvdzocffnjJr3/o0CF++9vfsnnzZlq2bOlbPmDAAObOncuECRNqUjaTJk2idevWMvCrqBPSchLCz/Tp06dS771fe+yxx0hLS+Obb7655Nffs2cPzZs39wVTSUkJr732Gh6Ph0GDBtWo5tTUVA4cOMCcOXNq9Hwhfk1aTkIEmL/97W+8//77BAUFoSgKoaGh9O3bl5kzZ0ovO+E3JJyEEEL4HTmtJ4QQwu9IOAkhhPA7Ek5CCCH8joSTEEIIvyPhJIQQwu9IOAkhhPA7Ek5CCCH8joSTEEIIv/P/6gcfmOB9eEMAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_third_virial_coeff():\n", " tvec = np.linspace(0.5, 20, 800)\n", " \n", " b3_uv_wca = []\n", " b3_uv_b3 = []\n", " b3_thol = []\n", " for temp in tvec:\n", " b3_uv_wca.append(uvtheory_wca.third_virial_coefficient(temp * eps_k_k) / (sigma * ANGSTROM)**6 / NAV**2)\n", " b3_uv_b3.append(uvtheory_b3.third_virial_coefficient(temp * eps_k_k) / (sigma * ANGSTROM)**6 / NAV**2)\n", " b3_thol.append(thol.third_virial_coefficient(temp * eps_k_k) / (sigma * ANGSTROM)**6 / NAV**2)\n", "\n", " plt.plot(1/tvec, b3_uv_wca, '-', label='uv-WCA')\n", " plt.plot(1/tvec, b3_uv_b3, label='uv-$B_3$')\n", " plt.plot(1/tvec, b3_thol, '-', label='Thol')\n", " plt.ylim(-10, 10)\n", " plt.ylabel('$B_3 / \\\\sigma^6$')\n", " plt.xlabel('$\\\\varepsilon/(k_BT)$')\n", " plt.legend(frameon=False)\n", " return\n", "plot_third_virial_coeff()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare isochoric heat capacity isotherms" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_heat_capcities(temp):\n", " dimensionless_density = np.linspace(0.01, 1.0, 100)\n", " cv_thol = []\n", " cv_uv_b3 = []\n", " cv_uv_wca = []\n", " cv_uv_bh = []\n", " for rho in dimensionless_density:\n", " density = rho / (sigma * ANGSTROM)**3 / NAV\n", " temperature = temp * eps_k_k\n", " s_thol = State(thol, density = density, temperature = temperature)\n", " cv_thol.append(s_thol.c_v(Contributions.ResidualNvt) / (RGAS) + 3.0 / 2.0)\n", " s_uv_b3 = State(uvtheory_b3, density = density, temperature = temperature)\n", " cv_uv_b3.append(s_uv_b3.c_v(Contributions.ResidualNvt) / (RGAS) + 3.0 / 2.0)\n", " s_uv_wca = State(uvtheory_wca, density = density, temperature = temperature)\n", " cv_uv_wca.append(s_uv_wca.c_v(Contributions.ResidualNvt) / (RGAS) + 3.0 / 2.0)\n", " s_uv_bh = State(uvtheory_bh, density = density, temperature = temperature)\n", " cv_uv_bh.append(s_uv_bh.c_v(Contributions.ResidualNvt) / (RGAS) + 3.0 / 2.0)\n", " \n", " \n", " plt.plot(dimensionless_density, cv_thol, '-', label='Thol')\n", " plt.plot(dimensionless_density, cv_uv_b3, '-', label='uv-$B_3$')\n", " plt.plot(dimensionless_density, cv_uv_wca, '-', label='uv-WCA')\n", " plt.plot(dimensionless_density, cv_uv_bh, '-', label='uv-BH')\n", " \n", " plt.xlabel('$\\\\rho^*$')\n", " plt.ylabel('$C_v/Nk_B$')\n", " plt.title('$T^*={}$'.format(temp))\n", " plt.legend(frameon=False)\n", " return\n", "plot_heat_capcities(1.5)\n", " " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 4 }