{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# II - Equilibrium calculations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Equilibrium explanations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Equilibrium calculations** are usually performed to obtain the adiabatic flame temperature, the equilibrium composition, and the thermodynamic state of a specific mixture under given conditions. These are virtually performed in every simulation.
\n", "For example, Cantera will call its equilibrium solver to initialize the gas state before trying to obtain a solution to the equations for a free flame. As such, it is interesting to understand how Cantera proceeds." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are 2 different types of solver currently implemented for equilibrium calculation in Cantera that\n", "deserves our attention :\n", "- **The element potential 'ChemEquil' solver**
\n", "The class ChemEquil implements a chemical equilibrium solver for single-phase solutions. It is a\n", "\"non-stoichiometric\" solver in the terminology of Smith and Missen, meaning that every intermediate\n", "state is a valid chemical equilibrium state, but does not necessarily satisfy the element constraints.\n", "
\n", "Non-stoichiometric methods are faster when they converge, but stoichiometric ones tend to be more\n", "robust.\n", "- **The 'VCS' chemical equilibrium solver**
\n", "The other type of solver is designed to be used to set a mixture containing one or more phases to\n", "a state of chemical equilibrium. It uses a \"stoichiometric\" algorithm, in which each intermediate\n", "state satisfies the element constraints but is not a state of chemical equilibrium. \n", "
\n", "More specifically, it\n", "implements the VCS algorithm, described in Smith and Missen, \"Chemical Reaction Equilibrium\". It\n", "finds a set of component species and a complete set of formation reactions for the non-components in\n", "terms of the components." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the **ChemEquil solver is the fastest** of the Cantera equilibrium solvers for many single-\n", "phase equilibrium problems (particularly if there are only a few elements but very many species), **but\n", "can be less stable**.
\n", "Problem situations include low temperatures where only a few species have non-zero\n", "mole fractions, precisely stoichiometric compositions (we will see an example shortly). In general, if\n", "speed is important, this solver should always be tried first before falling back to another one in case of\n", "failure." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The function equilibrate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default setting in Cantera, when launching an equilibrium calculation without specifying\n", "the solver, is to try the 'element potential' before falling back to another vcs solver labelled 'gibbs' :\n", "\n", "gas.equilibrate('TP')\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The equilibrate function can be applied on a single phase or on a mixture. Here, we recall its definition:\n", "\n", "equilibrate(self, XY, solver, double rtol, int maxsteps, int maxiter, int loglevel)\n", "\n", "
\n", "Parameters:\n", "\n", "XY - A two-letter string, which must be one of the set: ['TP','TV','HP','SP','SV','UV'].\n", "solver - Specifies the equilibrium solver to use. May be one of the following :\n", " element_potential = A fast solver using the element potential method.\n", " gibbs = A slower but more robust Gibbs minimization solver.\n", " vcs = The VCS non-ideal equilibrium solver.\n", " auto = The element potential solver will be tried first, then if it fails the gibbs solver will be\n", " tried.\n", "rtol - The relative error tolerance.\n", "maxsteps - Maximum number of steps in composition to take to find a converged solution.\n", "maxiter - This specifies the number of outer iterations on T or P when some property pair other than TP is\n", " specified (only for 'gibbs').\n", "loglevel - Is currently deprecated.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Useful python imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import cantera as ct\n", "import numpy as np\n", "import csv\n", "from matplotlib import *\n", "import matplotlib.pyplot as plt\n", "import sys" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Simple homogeneous equilibrium of a gas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
Please create the gas object so that :
\n", "- the mechanism used is the gri30
\n", "- this is an air/CH4 mix
\n", "- the mix is at stoichiometry
\n", "- the temperature is 300K and the pressure 1 bar.\n", "
" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " gri30:\n", "\n", " temperature 300 K\n", " pressure 100000 Pa\n", " density 1.10784 kg/m^3\n", " mean mol. weight 27.6332 amu\n", "\n", " 1 kg 1 kmol\n", " ----------- ------------\n", " enthalpy -3.0153e+06 -8.332e+07 J\n", " internal energy -3.1056e+06 -8.582e+07 J\n", " entropy 7234 1.999e+05 J/K\n", " Gibbs function -5.1855e+06 -1.433e+08 J\n", " heat capacity c_p 1111.3 3.071e+04 J/K\n", " heat capacity c_v 810.42 2.239e+04 J/K\n", "\n", " X Y Chem. Pot. / RT\n", " ------------- ------------ ------------\n", " H2O 0.190114 0.123943 -121.334\n", " CO2 0.095057 0.151392 -185.839\n", " N2 0.714829 0.724665 -23.382\n", " [ +50 minor] 2.5076e-19 2.90375e-19\n", "\n", "None\n" ] } ], "source": [ "gas = ct.Solution('gri30.cti') # create an object representing the gas phase\n", "\n", "gas.TPX = 300, 100000,{'CH4':1, 'O2':2, 'N2':7.52} # set initial state\n", "\n", "gas.equilibrate('TP') # equilibrate using Temperature (T) and Pressure (P)\n", "\n", "print(gas())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you see, the gas has been equilibrated since it now shows only quantities for the product of the reaction (H2O and CO2). You can try to set yourself out of the perfect mixing (for example set CH4 to 0.4 and to 0.6) and see the impact on the species in the mix at the end." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Failure of a solver" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cantera has 3 different equilibrium solvers, 2 of them are worth mentionning: \n", "- The 'ChemEquil' solver uses the element potential method for homogeneous equilibrium in gas mixtures. It is fast, but sometimes doesn't converge. \n", "- The 'VCS' solver uses the VCS algorithm (Gibbs minimization), which is slower but more robust. It can also handle multiple phases.
\n", "Here we'll solve a problem for which the ChemEquil solver fails, but the VCS solver has no problem." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Properties of the gas" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "pressure = 1.0e5 # pressure\n", "temperature = 400.0 # unburned gas temperature\n", "comp = 'CH4:0.5, O2:1, N2:3.76' # premixed gas composition\n", "\n", "gas = ct.Solution('gri30.xml')\n", "gas.TPX = temperature, pressure, comp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initial state of the gas" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "******************************************************** \n", " Initial state :\n", "******************************************************** \n", "P = 1.0000e+05 Pa\n", "T = 4.0000e+02 K\n", "V = 1.2035e+00 m3/kg\n", "U = -2.6590e+05 J/kg\n", "H = -1.4554e+05 J/kg\n", "S = 7.5652e+03 J/kg/K\n", "\n", "\n" ] } ], "source": [ "print(\"******************************************************** \")\n", "print(\" Initial state :\")\n", "print(\"******************************************************** \")\n", "print(\"P = \", \"%10.4e \" % (gas.P) + \" Pa\")\n", "print(\"T = \", \"%10.4e \" % (gas.T) + \" K\")\n", "print(\"V = \", \"%10.4e \" % (gas.volume_mass) + \" m3/kg\")\n", "print(\"U = \", \"%10.4e \" % (gas.int_energy_mass) + \" J/kg\")\n", "print(\"H = \", \"%10.4e \" % (gas.enthalpy_mass) + \" J/kg\")\n", "print(\"S = \", \"%10.4e \" % (gas.entropy_mass) + \" J/kg/K\")\n", "print(\"\")\n", "print(\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Comparing chemical potentials and element potentials" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, the chemical potentials (noted mu_xx) are compared to the corresponding calculated values with the element potentials (noted lambda_xx).
\n", "For instance, mu_H2 = lambda_H x 2.
\n", "This is a good way to check whether the solver has managed to compute the results correctly. The chemical potentials are the one of the vapor phase." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Comparison between Chem potentials and element potentials:\n", "\n", "mu_H2 : -2.3501e+09 , -4.2513e+09\n", "mu_O2 : -8.8089e+07 , -4.2260e+09\n", "mu_OH : -2.3320e+09 , -4.2386e+09\n", "mu_H2O : -2.6153e+09 , -6.3643e+09\n" ] } ], "source": [ "chemeq = np.zeros(gas.n_species)\n", "chemeq = gas.chemical_potentials\n", "\n", "mu_H2 = chemeq[gas.species_index(\"H2\")]\n", "mu_OH = chemeq[gas.species_index(\"OH\")]\n", "mu_H2O = chemeq[gas.species_index(\"H2O\")]\n", "mu_O2 = chemeq[gas.species_index(\"O2\")]\n", "lambda_H = chemeq[gas.species_index(\"H\")]\n", "lambda_O = chemeq[gas.species_index(\"O\")]\n", "\n", "print()\n", "print(\"Comparison between Chem potentials and element potentials:\")\n", "print()\n", "s_mu_H2 = \"%11.4e\" % mu_H2\n", "s_lam_mu_H2 = \"%11.4e\" % (2.0 * lambda_H)\n", "print(\"mu_H2 : \", s_mu_H2, \", \", s_lam_mu_H2)\n", "\n", "s_mu_O2 = \"%11.4e\" % mu_O2\n", "s_lam_mu_O2 = \"%11.4e\" % (2.0 * lambda_O)\n", "print(\"mu_O2 : \", s_mu_O2, \", \", s_lam_mu_O2)\n", "\n", "s_mu_OH = \"%11.4e\" % mu_OH\n", "s_lam_mu_OH = \"%11.4e\" % (lambda_H + lambda_O)\n", "print(\"mu_OH : \", s_mu_OH, \", \", s_lam_mu_OH)\n", "\n", "s_mu_H2O = \"%11.4e\" % mu_H2O\n", "s_lam_mu_H2O = \"%11.4e\" % (2.0 * lambda_H + lambda_O)\n", "print(\"mu_H2O : \", s_mu_H2O, \", \", s_lam_mu_H2O)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Program equilibrate" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ChemEquil solver failed! Trying the vcs solver...\n" ] } ], "source": [ "try:\n", " # print(\"0\")\n", " gas.equilibrate('TP', solver='element_potential') # use the ChemEquil solver\n", "except:\n", " print(\"\")\n", " print(\"ChemEquil solver failed! Trying the vcs solver...\")\n", " gas.equilibrate('TP', solver='vcs', maxsteps=1500)\n", " # gas.equilibrate('TP', solver = 'gibbs') # the gibbs solver works also" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compare the results with the initial values" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "******************************************************** \n", " Final state :\n", " Tadiabatique = 400.0 K\n", "******************************************************** \n", "P = 1.0000e+05 Pa\n", "T = 4.0000e+02 K\n", "V = 1.2035e+00 m3/kg\n", "U = -3.0233e+06 J/kg\n", "H = -2.9029e+06 J/kg\n", "S = 7.5571e+03 J/kg/K\n", "\n", "\n" ] } ], "source": [ "print(\"\")\n", "print(\"******************************************************** \")\n", "print(\" Final state :\")\n", "print(\" Tadiabatique = \" + str(gas.T) + \" K\")\n", "print(\"******************************************************** \")\n", "print(\"P = \", \"%10.4e \" % (gas.P) + \" Pa\")\n", "print(\"T = \", \"%10.4e \" % (gas.T) + \" K\")\n", "print(\"V = \", \"%10.4e \" % (gas.volume_mass) + \" m3/kg\")\n", "print(\"U = \", \"%10.4e \" % (gas.int_energy_mass) + \" J/kg\")\n", "print(\"H = \", \"%10.4e \" % (gas.enthalpy_mass) + \" J/kg\")\n", "print(\"S = \", \"%10.4e \" % (gas.entropy_mass) + \" J/kg/K\")\n", "print(\"\")\n", "print(\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Comparing chemical and element equilibrium for the equilibrate mixture" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Comparison between Chem potentials and element potentials:\n", "\n", "mu_H2 : -2.1760e+08 , -2.1760e+08\n", "mu_O2 : -2.1169e+08 , -2.1169e+08\n", "mu_OH : -2.1465e+08 , -2.1465e+08\n", "mu_H2O : -3.2345e+08 , -3.2345e+08\n" ] } ], "source": [ "chemeq = gas.chemical_potentials\n", "mu_H2 = chemeq[gas.species_index(\"H2\")]\n", "mu_OH = chemeq[gas.species_index(\"OH\")]\n", "mu_H2O = chemeq[gas.species_index(\"H2O\")]\n", "mu_O2 = chemeq[gas.species_index(\"O2\")]\n", "lambda_H = chemeq[gas.species_index(\"H\")]\n", "lambda_O = chemeq[gas.species_index(\"O\")]\n", "\n", "print()\n", "print(\"Comparison between Chem potentials and element potentials:\")\n", "print()\n", "s_mu_H2 = \"%11.4e\" % mu_H2\n", "s_lam_mu_H2 = \"%11.4e\" % (2.0 * lambda_H)\n", "print(\"mu_H2 : \", s_mu_H2, \", \", s_lam_mu_H2)\n", "\n", "s_mu_O2 = \"%11.4e\" % mu_O2\n", "s_lam_mu_O2 = \"%11.4e\" % (2.0 * lambda_O)\n", "print(\"mu_O2 : \", s_mu_O2, \", \", s_lam_mu_O2)\n", "\n", "s_mu_OH = \"%11.4e\" % mu_OH\n", "s_lam_mu_OH = \"%11.4e\" % (lambda_H + lambda_O)\n", "print(\"mu_OH : \", s_mu_OH, \", \", s_lam_mu_OH)\n", "\n", "s_mu_H2O = \"%11.4e\" % mu_H2O\n", "s_lam_mu_H2O = \"%11.4e\" % (2.0 * lambda_H + lambda_O)\n", "print(\"mu_H2O : \", s_mu_H2O, \", \", s_lam_mu_H2O)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Saving the results" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Output written to 2-Output/all_mole_fractions.csv\n" ] } ], "source": [ "csv_file = '2-Output/all_mole_fractions.csv'\n", "with open(csv_file, 'w') as outfile:\n", " writer = csv.writer(outfile)\n", " writer.writerow(['phi', 'T (K)'] + gas.species_names)\n", " writer.writerow(['1', gas.T] + list(gas.X))\n", "print(('Output written to {0}'.format(csv_file)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Perform adiabatic flame calculations as a function of equivalence ratio" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set gas composition and interesting parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now perform **several constant pressure equilibrium calculations of an ethylene/air mixture, at\n", "an initial temperature of 300K and under atmospheric pressure**, in order to obtain the adiabatic flame temperature and burnt gas state for several equivalence ratios.
\n", "The goal of this exercise is to find a way to loop through several gaseous composition, in order to perform several computations in a single script; and to learn how to properly store the results in a *csv* file." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "**** WARNING ****\n", "For species C2H3O, discontinuity in cp/R detected at Tmid = 1000\n", "\tValue computed using low-temperature polynomial: 26.0651\n", "\tValue computed using high-temperature polynomial: 11.7479\n", "\n", "\n", "**** WARNING ****\n", "For species C2H3O, discontinuity in h/RT detected at Tmid = 1000\n", "\tValue computed using low-temperature polynomial: 13.2934\n", "\tValue computed using high-temperature polynomial: 9.71415\n", "\n", "\n", "**** WARNING ****\n", "For species C2H3O, discontinuity in s/R detected at Tmid = 1000\n", "\tValue computed using low-temperature polynomial: 47.9078\n", "\tValue computed using high-temperature polynomial: 43.1354\n", "\n", "\n", "**** WARNING ****\n", "For species C4H612, discontinuity in cp/R detected at Tmid = 1000\n", "\tValue computed using low-temperature polynomial: 20.128\n", "\tValue computed using high-temperature polynomial: 20.1809\n" ] } ], "source": [ "gas = ct.Solution('Mechanisms/mech.cti')\n", "\n", "T = 300.0 # Temperature\n", "P = 101325.0 # Pressure\n", "\n", "phi_min = 0.3 # Minimal equivalence ratio\n", "phi_max = 3.5 # Maximal equivalence ratio\n", "npoints = 50 # Point in-between the two preceeding values\n", "\n", "fuel_species = 'C2H4' # fuel species\n", "air_N2_O2_molar_ratio = 3.76 # ratio representing the air" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Loop of equilibrium" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "At phi = 0.3000 Tad = 1142.8447\n", "At phi = 0.3653 Tad = 1300.2150\n", "At phi = 0.4306 Tad = 1450.9493\n", "At phi = 0.4959 Tad = 1595.5516\n", "At phi = 0.5612 Tad = 1734.1502\n", "At phi = 0.6265 Tad = 1866.3165\n", "At phi = 0.6918 Tad = 1990.7250\n", "At phi = 0.7571 Tad = 2104.7920\n", "At phi = 0.8224 Tad = 2204.7841\n", "At phi = 0.8878 Tad = 2286.8442\n", "At phi = 0.9531 Tad = 2348.1575\n", "At phi = 1.0184 Tad = 2386.8246\n", "At phi = 1.0837 Tad = 2401.1649\n", "At phi = 1.1490 Tad = 2391.6957\n", "At phi = 1.2143 Tad = 2364.6794\n", "At phi = 1.2796 Tad = 2328.6936\n", "At phi = 1.3449 Tad = 2289.2479\n", "At phi = 1.4102 Tad = 2248.8202\n", "At phi = 1.4755 Tad = 2208.4207\n", "At phi = 1.5408 Tad = 2168.4587\n", "At phi = 1.6061 Tad = 2129.0955\n", "At phi = 1.6714 Tad = 2090.3866\n", "At phi = 1.7367 Tad = 2052.3409\n", "At phi = 1.8020 Tad = 2014.9472\n", "At phi = 1.8673 Tad = 1978.1865\n", "At phi = 1.9327 Tad = 1942.0375\n", "At phi = 1.9980 Tad = 1906.4788\n", "At phi = 2.0633 Tad = 1871.4899\n", "At phi = 2.1286 Tad = 1837.0513\n", "At phi = 2.1939 Tad = 1803.1444\n", "At phi = 2.2592 Tad = 1769.7514\n", "At phi = 2.3245 Tad = 1736.8549\n", "At phi = 2.3898 Tad = 1704.4378\n", "At phi = 2.4551 Tad = 1672.4833\n", "At phi = 2.5204 Tad = 1640.9742\n", "At phi = 2.5857 Tad = 1609.8935\n", "At phi = 2.6510 Tad = 1579.2239\n", "At phi = 2.7163 Tad = 1548.9479\n", "At phi = 2.7816 Tad = 1519.0480\n", "At phi = 2.8469 Tad = 1489.5082\n", "At phi = 2.9122 Tad = 1460.3206\n", "At phi = 2.9776 Tad = 1431.6157\n", "At phi = 3.0429 Tad = 1426.1343\n", "At phi = 3.1082 Tad = 1427.0861\n", "At phi = 3.1735 Tad = 1424.6955\n", "At phi = 3.2388 Tad = 1420.8402\n", "At phi = 3.3041 Tad = 1416.7761\n", "At phi = 3.3694 Tad = 1412.7752\n", "At phi = 3.4347 Tad = 1408.8672\n", "At phi = 3.5000 Tad = 1405.0538\n" ] } ], "source": [ "phi = np.zeros(npoints) # 1D array\n", "tad = np.zeros(npoints) # 1D array\n", "\n", "xeq = np.zeros((gas.n_species, npoints)) # 2D array\n", "\n", "for i in range(npoints):\n", "\n", " gas.TP = T, P\n", " \n", " phi[i] = phi_min + (phi_max - phi_min) * i / (npoints - 1)\n", " gas.set_equivalence_ratio(phi[i], {fuel_species: 1}, {'O2': 1, 'N2': air_N2_O2_molar_ratio})\n", "\n", " gas.equilibrate('HP') # Equilibrate the mixture adiabatically at constant P with the solver vcs\n", " \n", " xeq[:, i] = gas.X\n", " tad[i] = gas.T\n", " print(\"At phi = \",\"%10.4f\"% (phi[i])+ \" Tad = \",\"%10.4f\"% (tad[i]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Save files" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Output written to 2-Output/yourfile.csv\n" ] } ], "source": [ "csv_file = '2-Output/yourfile.csv'\n", "with open(csv_file, 'w') as outfile:\n", " writer = csv.writer(outfile)\n", " writer.writerow(['Phi', 'T (K)'] + gas.species_names)\n", " for i in range(npoints):\n", " writer.writerow([phi[i], tad[i]] + list(xeq[:, i]))\n", "print(\"Output written to\", \"%s\" % csv_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the results" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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STvXdIC2JyXf34f4BLZmyeDvX/mUWK3ce8jqWiIiIyBmpNIlEuHeX7OCxfyynX5vafLdLgmfHMJVFwO/j4SvaMGlMb47mF3LDX79mwlebCE6eKSIiIlI5Vd5vVyJyRlNX7ObhN5fSu1kNnrujB4FKOMJ0Khe2qMXHD1zCxa1q8cT7q7jrlYUcOJrvdSwRERGRU1JpEolQM9bs5fuvL6Jzw2qMvbPXeZ+stqLVSIln7J09+dk17Zm5di+D//wlC7fovNUiIiJS+ag0iUSgORv3cc/EhbSqU4UJIzJITYjMU66ZGaP6NuMf37uIhDgfd4ydx9frs72OJSIiIvINKk0iEWbx1gOMnjCfRjWSmTg6g2rJcV5HOm8dG1Tj7e9cSKMaSYycMJ8v1mZ5HUlERETk31SaRCLIqp2HuXP8PGqmJjBpTG9qpiZ4Hanc1K6SwOt39aF57VTGvLyA6av3eB1JREREBFBpEokY6/fmMGzcXFISAkwa05v0qoleRyp3NVMTeP2u3rSpW4V7Ji7kk5W7vY4kIiIiotIkEgm27jvG7WPnYGZMGtObRjWSvY4UNmnJ8bw6pjcdG1Tj3kmL+HDZLq8jiYiISIxTaRKp5HYdOs5tY+eQV1jMq2MyaF471etIYVctKY5XRmXQrXEa3399Ef9cvMPrSCIiIhLDVJpEKrGComJGvjSfQ8cKeGVUBm3rVvU6UoWpkhjHhJEZ9G5Wk4feXMJbC7Z5HUlERERilEqTSCU24avNrN59hD8M7ULnhmlex6lwKQkBxo/oRd+Wtfjh28t4be5WryOJiIhIDFJpEqmkdh/K5U+frWVg2zpc0aGu13E8kxTv58XhPenfpjY//sdyXpm92etIIiIiEmNUmkQqqV99lElBsePxazt4HcVziXF+nhvWg8vbp/Ozd1cy9suNXkcSERGRGKLSJFIJfbU+m/eX7uR7/VrQuGb0zpR3NhICfv52e3eu7lSPX36Yyd9mrPc6koiIiMSIgNcBROSb8guL+dm7K2hcI5nvXNrC6ziVSpzfx59v6UrAb/xu6hoKCh0PXNbK61giIiIS5VSaRCqZ8V9tYkPWUcaP6ElinN/rOJVOwO/j6aFdCfh8/PGztRQUFfODK1pjZl5HExERkSil0iRSiew8eJxnpq3j8vbpDGib7nWcSsvvM566qTPxAePZz9eTX1TMY4PbqjiJiIhIWKg0iVQiv/owk6Jix8+uae91lErP5zN+9a1OxPl9vPDFRhIDPh6+oo3XsURERCQKqTSJVBJfrsviw+W7+MHlrWlUQ5M/lIXPZ/z8ug7kFRTzzPT1VE2KY8zFzb2OJSIiIlFGpUmkEsgrLOLxd1fStGYyd12iL/1nw8z49Y2dyMkr5JcfZlIlMcDNvRp7HUtERESiiEqTSCUwbtYmNmYfZcLIXpr84Rz4fcYfb+7KkbxCHpuynCqJcVzVqZ7XsURERCRK6DxNIh7bcfA4f5m2nis7pNOvTR2v40Ss+ICP5+7oTvfG1Xlg8mJmrs3yOpKIiIhECZUmEY/98oNVOBw/1eQP5y05PsC4Eb1oVacK90xcwILN+72OJCIiIlFApUnEQzPXZvHxit18f0ArGlbX5A/loVpSHK+MzqB+tSRGTpjPyp2HvI4kIiIiEU6lScQjeYVFPPHeSprVSmHMxc28jhNVaqUmMHFMb6okBBg+bh4bs3K8jiQiIiIRTKVJxCMvfrGRTdlHeeK6DiQENPlDeWuQlsTEMb0BuGPsXHYcPO5xIhEREYlUKk0iHti2/xjPfr6ewR3rcmnr2l7HiVotaqfy8qgMjuQWMmzsXLJz8ryOJCIiIhFIpUnEA7/4YBWG8RNN/hB2HRtUY/zIXuw8dJzh4+Zx6HiB15FEREQkwqg0iVSwz1fv5V+r9vD9gS1pkJbkdZyY0KtpDZ67owfr9h5h9IT5HM8v8jqSiIiIRBCVJpEKlFtQxBPvr6R57RTG9G3udZyY0q9NHf50czcWbT3Ad15dSH5hsdeRREREJEKoNIlUoBe+2MiWfcf4+XUdiA/o16+iXd25Hv93Yydmrs3ioTeWUFTsvI4kIiIiESDgdQCRWLFt/zH++vl6ru5Uj4tbafIHr9zcqzGHjxfyq48ySU0I8JshnTAzr2OJiIhIJabSJFJBfv7+Kvw+4yfXtPM6Ssy765LmHM4t4C/T11MtOY4fX6VtIiIiIqen0iRSAb5Ym8VnmXt4dHBb6lXT5A+VwcOXt+bgsQJe+GIjNVPiuefSFl5HEhERkUpKpUmkAjw3cwN1qyYy6qJmXkeREDPjies6sP9YPv/38WpqpiZwU4+GXscSERGRSkilSSTMVu48xNcb9vHo4Laa/KGS8fuMp4d24dCxAn70zjKqJ8cxsF2617FERESkktE3OJEwG/vlJlLi/dya0djrKHIKCQE/zw3rQYf6VfnepEXM37zf60giIiJSyag0iYTRrkPHeX/pTob2akS1pDiv48hppCYEeGlELxqkJTF6wnxW7z7sdSQRERGpRFSaRMJowlebKXZOxzJFgJqpCbw8KoOkeD93jp/Htv3HvI4kIiIilYRKk0iY5OQV8tq8rQzuWI9GNZK9jiNl0KhGMq+M6s3x/CKGj59Hdk6e15FERESkElBpEgmTN+Zv40huIWMu1ihTJGlTtwrjR/Ri58HjjHxpPjl5hV5HEhEREY+pNImEQWFRMeNnbaJX0+p0a1zd6zhylno2rcHfbu/Oql2HuWfiAvIKi7yOJCIiIh5SaRIJg6krd7Pj4HHGXNzc6yhyjga2S+e3Qzrz1fp9PPzmUoqKndeRRERExCM6T5NIOXPO8eKXm2haM5nLdM6fiHZTj4bsP5rHrz9aTc2UeH5+XQfMzOtYIiIiUsFUmkTK2YItB1i67SC/uL4Dfp++YEe6uy9pQXZOPi98sZGaKQk8cFkrryOJiIhIBVNpEilnL3yxkbTkOG7q0cjrKFJOHh3Uln05+fzxs7XUTI3njj5NvI4kIiIiFUilSaQcbco+ymeZe7i3X0uS4v1ex5Fy4vMZvxnSiYPH8vnpuyuokRLPVZ3qeR1LREREKogmghApR+NmbSTO52P4hRqJiDZxfh/P3tadHo2r8+DkJXy1PtvrSCIiIlJBVJpEysmBo/m8vXA73+pWnzpVEr2OI2GQFO9n3J29aFYrhbtfWcDy7Ye8jiQiIiIVQKVJpJy8OmcLuQXFmmY8ylVLjuPlURmkJccz4qV5bMo+6nUkERERCTOVJpFykFtQxMuzt3Bp69q0Tq/idRwJs7rVEpk4OgMHDBs3lz2Hc72OJCIiImGk0iRSDt5bspPsnDzu0ihTzGheO5UJI3tx4Gg+w8fN49CxAq8jiYiISJioNImcp+DJbDfStm4VLmpZ0+s4UoE6N0zj+WE92Zidw+iX53M8v8jrSCIiIhIGKk0i52nG2izW7c3hroubY6aT2caavq1q8cebu7Jw6wHue20RBUXFXkcSERGRcqbSJHKexn65kfSqCVzbpb7XUcQj13Suz5PXd2Ta6r08+s5ynHNeRxIREZFypJPbipyHlTsP8dX6ffxoUFviA/obRCwb1qcJ+3Ly+NNn66iVGs9jV7XzOpKIiIiUE5UmkfMw7stNJMf7uS2jsddRpBJ4YGAr9uXk8/wXG6mREs89l7bwOpKIiIiUA5UmkXO0+1Au7y3dyR19mlAtOc7rOFIJmBlPXNeB/cfy+b+PV1MzNYGbejT0OpaIiIicJ5UmkXM04evNFDvH6L7NvI4ilYjfZzw9tAuHjhXwo3eWUT05joHt0r2OJSIiIudBB2GInIOjeYW8NncLgzrWpVGNZK/jSCWTEPDz3LAedKhfle9NWsT8zfu9jiQiIiLnQaVJ5By8uWAbh3MLGaOT2cpppCYEeGlELxqkJTF6wnxW7z7sdSQRERE5RypNImepsKiYcbM20aNJdbo3ru51HKnEaqYm8PKoDJLi/QwfN49t+495HUlERETOgUqTyFn6ZOUeth84zl0aZZIyaFQjmVdG9Sa3oIjh4+eRnZPndSQRERE5SypNImfBOceLX26kSc1kLm+vg/ulbNrUrcL4Eb3Ydeg4I16ax5HcAq8jiYiIyFlQaRI5Cwu3HGDJtoOM7tsMv8+8jiMRpGfTGvzt9u5k7jrCPRMXkltQ5HUkERERKaOwlSYza2Rmn5tZppmtNLMHQsufMrPVZrbMzP5hZmklHvOYma03szVmdmWJ5YNCy9ab2aPhyixyJmO/3ES1pDide0fOyYC26Tx1U2e+3rCPByYvprCo2OtIIiIiUgbhHGkqBH7gnGsH9AHuNbP2wKdAR+dcZ2At8BhA6L5bgA7AIOBvZuY3Mz/wV2Aw0B64NbSuSIXKOpLHp5l7uKVXI5LjdYozOTc3dm/Iz65pzycr9/C//1iBc87rSCIiInIGYfvm55zbBewKXT9iZplAA+fcv0qsNge4KXT9emCycy4P2GRm64GM0H3rnXMbAcxscmjdVeHKLnIq7y3dSVGx0yiTnLdRfZtx4Fg+f5m+nrSUOB4b3M7rSCIiIlIKq4i/cppZU+ALgiNMh0ssfx94wzn3qpk9C8xxzr0aum8c8HFo1UHOuTGh5cOA3s65+056jbuBuwHS09N7TJ48ObxvKorl5OSQmprqdYxK52dfHcdv8PiFSV5HOS1tu8jhnGPiqnymbytkaOs4LqmTr20XofR7F7m07SKXtl3kqmzbrn///gudcz3PtF7Y9zEys1TgHeDBkwrT/xLchW/SiUWneLjj1LsQ/lfTc869ALwA0LNnT9evX7/zCx7DZsyYgX5+35S56zBbp37JE9e2p99FzbyOc1radpHlkksdD0xezJvLdpESl8Dj1/TzOpKcA/3eRS5tu8ilbRe5InXbhbU0mVkcwcI0yTk3pcTyO4FrgIHuP0Nd24FGJR7eENgZun665SIVYsqi7QR8xnVdG3gdRaKI32c8PbQrh3MLmbAyi94rdjGoYz2vY4mIiMhJwjl7ngHjgEzn3NMllg8CfgRc55w7VuIh7wG3mFmCmTUDWgHzgPlAKzNrZmbxBCeLeC9cuUVOVlhUzD+X7KR/2zrUSIn3Oo5EmfiAj+fu6E7zaj7uf30JX63P9jqSiIiInCScs+ddBAwDBpjZktDlKuBZoArwaWjZcwDOuZXAmwQneJgK3OucK3LOFQL3AZ8AmcCboXVFKsSX67PJOpLHkO6aAELCIzk+wEM9EmlWK4W7X1nA0m0HvY4kIiIiJYRz9rxZnPo4pY9KecyvgF+dYvlHpT1OJJymLNpBWnIc/dvW9jqKRLHUeOOV0RkM+fvXjHhpHm995wJa1qnidSwREREhvCNNIhHvcG4B/1q5m2s71ych4Pc6jkS59KqJvDq6N36fj2Hj5rHj4HGvI4mIiAgqTSKl+mjZLvIKixmiczNJBWlaK4VXRmWQk1fIsHFz2ZeT53UkERGRmKfSJFKKKYt20Lx2Cl0aVvM6isSQ9vWrMu7OXuw4cJwRL83nSG6B15FERERimkqTyGls3XeMeZv3M6R7Q4KTQYpUnIxmNfjb7d1Zteswd7+ykNyCIq8jiYiIxCyVJpHTmLJ4O2ZwQzedm0m8MbBdOr//dmdmb9zH/a8vprCo2OtIIiIiMUmlSeQUnHNMWbSDC1vUpH5aktdxJIbd0K0hj1/bnn+t2sNjU5bzn/OBi4iISEUJ25TjIpFswZYDbN1/jAcGtvI6iggjL2rGgaP5PDN9PVWT4vjJ1e20y6iIiEgFUmkSOYV3Fm4nOd7PoI51vY4iAsBDl7fmcG4h42ZtolpSHPer0IuIiFQYlSaRk+QWFPHhsl0M6liXlAT9ikjlYGb87Jr2HM4t4OlP11IlMcDIi5p5HUtERCQm6BuhyEn+tWoPR/IKuam7zs0klYvPZ/xuSGdycgv5+furqJIYx006h5iIiEjYaSIIkZNMWbSd+tUS6dO8ptdRRP5LwO/jL7d1o2/LWjzy9lKmrtjldSQREZGop9IkUsLew7l8sTaLG7o3wOfTgfZSOSUE/Dw/rAddGqVx/+tL+HJdlteRREREoppKk0gJ7y7ZSbGDG7VrnlRyKQkBJozIoHntFO5+ZSELtxzwOpKIiEjUUmkSCXHO8c6lomhDAAAgAElEQVSi7XRtlEaL2qlexxE5o2rJcbwyOoP0qgmMfGkeq3Ye9jqSiIhIVFJpEglZteswq3cfYUj3Bl5HESmzOlUSeXVMb1ISAgwfP5dN2Ue9jiQiIhJ1VJpEQt5ZuIM4v3Ftl/peRxE5Kw2rJzNxdG+KHdwxdi47Dx73OpKIiEhUUWkSAQqKinlv6Q4Gtk0nLTne6zgiZ61lnVReGZXB4eMF3DFuLtk5eV5HEhERiRoqTSLAF2uzyM7JZ4jOeSMRrGODaowb0YudB48zfNw8Dh0v8DqSiIhIVFBpEgGmLNpBjZR4Lm1d2+soIuclo1kNnrujB+v2HmH0hPkczy/yOpKIiEjEU2mSmHfoWAGfrtrDdV3qEx/Qr4REvn5t6vCnm7uxaOsB7nl1IfmFxV5HEhERiWj6higx74PlO8kvKmaIzs0kUeTqzvX4zY2d+WJtFg++sZiiYud1JBERkYgV8DqAiNfeWbid1umpdGxQ1esoIuVqaK9GHM4t4JcfZpISv4zfDumMz2dexxIREYk4Kk0S0zZlH2XR1oM8OrgtZvoyKdFnzMXNOZJbyJ+nrSMlIcDj17bXZ11EROQsqTRJTJuyaDs+gxu66YS2Er0evKwVx/ILefHLTSTH+3lkUFuvI4mIiEQUlSaJWcXFjimLdnBRy1qkV030Oo5I2JgZP76qHcfyi/jbjA0kx/u5b0Arr2OJiIhEDJUmiVlzN+1nx8HjPDKojddRRMLOzPjF9R05nl/E7/+1lqT4AKP7NvM6loiISERQaZKYNWXRdlITAlzRvq7XUUQqhM9n/O6mzhzLL+IXH6wiOd7PrRmNvY4lIiJS6WnKcYlJx/IL+Wj5Lq7qVJekeL/XcUQqTMDv45lbu9GvTW1+/I/lvLtkh9eRREREKj2VJolJ/1q5h6P5RdyoczNJDIoP+Hjujh70blaDh99cyicrd3sdSUREpFJTaZKY9M6i7TRISyKjaQ2vo4h4IjHOz9g7e9GpQTW+/9piZq7N8jqSiIhIpaXSJDFn96FcvlqfzZDuDXSiT4lpqQkBXh6ZQYs6qdwzcQFzN+7zOpKIiEilpNIkMeeDZTspdvAtnZtJhGrJcUwcnUGDtCRGv7yAJdsOeh1JRESk0lFpkpgzdcVu2tWrSvPaqV5HEakUaqUmMGlMH6qnxHHn+Hlk7jrsdSQREZFKRaVJYsrew7ks3HqAwR01zbhISXWrJfLamD4kxfkZNm4uG7JyvI4kIiJSaag0SUz5ZOVunINBKk0i/6VRjWQm3dUbgNtfnMu2/cc8TiQiIlI5qDRJTPl4xW6a106hVR3tmidyKi1qpzJxdG+OFxRx+9i57D6U63UkERERz6k0SczYfzSfuZv2M7hjXcw0a57I6bSrV5WXR2WwLyeP28fOITsnz+tIIiIinlJpkpjx2ao9FBU7Bnes53UUkUqva6M0xo/oxY6Dx7lj7FwOHM33OpKIiIhnVJokZny8YhcNqyfRoX5Vr6OIRITezWsydngvNmYfZdj4uRw6XuB1JBEREU+oNElMOJxbwKz12QzqoF3zRM5G31a1eP6OHqzZfYQ7x88jJ6/Q60giIiIVTqVJYsL0zL0UFDkGd9KseSJnq3/bOjx7W3eW7zjEqJfmcyxfxUlERGKLSpPEhI9X7CK9agLdGlX3OopIRLqyQ13+fEtXFmzZz5iXF5BbUOR1JBERkQqj0iRR71h+ITPXZnFlh7r4fNo1T+RcXdO5Pn8Y2oXZG/dxz8SF5BWqOImISGxQaZKoN3NNFrkFxTqhrUg5uKFbQ/7vhk7MXJvFvZMWU1BU7HUkERGRsFNpkqj38Yrd1EiJJ6NpDa+jiESFWzIa8+T1Hfgscw8PTF5MoYqTiIhEuYDXAUTCKa+wiOmr93J1p3oE/PobgUh5GX5BU/ILi/nlh5nE+Zfy9NCu+LX7q4iIRCmVJolqs9Zlk5NXyCDNmidS7sZc3Jy8wmKe+mQNCQEfv7mxs44bFBGRqKTSJFHt4xW7qZIY4KIWtbyOIhKV7u3fkrzCYp6Zto74gI9fXN9R50ITEZGoo9IkUaugqJjPMvdwWbt04gPaNU8kXB66rBX5hcU8N3MDcX4fP7umvYqTiIhEFZUmiVpzN+7n4LECzZonEmZmxo8GtSGvsIiXvtpMQsDPjwa1UXESEZGoodIkUevjFbtIivNzSavaXkcRiXpmxs+uaf/vEafEOB8PXtba61giIiLlQqVJolJRseOTlXvo37Y2SfF+r+OIxAQz4xfXdyS/sJg/fbaOOL+Pe/u39DqWiIjIeVNpkqi0cMsBsnPyGNSxntdRRGKKz2f8ZkhnCoqCs+oFfMY9l7bwOpaIiMh5UWmSqDR1xW7iAz4GtK3jdRSRmOP3Gb//dheKHPzfx6vx+4wxFzf3OpaIiMg5U2mSqOOc45OVu7mkVS1SE/QRF/FCwO/jj0O7UFzs+OWHmfjMGNW3mdexREREzom+UUrUWbb9EDsOHufBy1p5HUUkpgX8Pv50S1eKnePJD1bh9xl3XtjU61giIiJnTSevkajz8YrdBHzG5e3TvY4iEvPi/D6eubUbV7RP5/H3VjJxzhavI4mIiJw1lSaJKs45pq7YxQUtapKWHO91HBEhWJyeva07l7Wrw0//uYLX5m71OpKIiMhZUWmSqLJ69xE27zumE9qKVDLxAR9/vb07A9rW4cf/WM4b81WcREQkcqg0SVSZumI3ZnBFe5UmkcomIeDnb7d359LWtXl0ynLeWrDN60giIiJlotIkUWXqit30alqD2lUSvI4iIqeQGOfn+WE96NuyFo+8s4wpi7Z7HUlEROSMVJokamzMymHNniMM6qBRJpHKLDHOz4vDe3Jhi5r8z1tLeXfJDq8jiYiIlEqlSaLGxyt2A+h4JpEIkBjnZ+zwXmQ0q8FDbyzhvaU7vY4kIiJyWipNEjWmrthNl0Zp1E9L8jqKiJRBUryf8SN60bNpsDh9uGyX15FEREROSaVJosK2/cdYvuMQgzXKJBJRkuMDvDSiF90bp3H/5MVMXaHiJCIilY9Kk0SFT1aGds3T8UwiESclIcBLIzPo0rAa9722+N+/zyIiIpWFSpNEhakrdtO2bhWa1krxOoqInIPUhAAvj8qgY4Nq3PfaIv6l4iQiIpWISpNEvL2Hc1m49QCDO9bzOoqInIcqiXG8MjqD9vWrce9rizTiJCIilYZKk0S8T1buxjkY3Em75olEuqqJcUwcnUGH+tW4d9Iipq5QcRIREe+FrTSZWSMz+9zMMs1spZk9EFpew8w+NbN1of9WDy03M3vGzNab2TIz617iue4Mrb/OzO4MV2aJTB+v2E3z2im0qpPqdRQRKQdVQyNOnRoGd9XT5BAiIuK1cI40FQI/cM61A/oA95pZe+BRYJpzrhUwLXQbYDDQKnS5G/g7BEsW8DjQG8gAHj9RtET2H81n7qb9DO5YFzPzOo6IlJOqiXG8MiqDzqHJIT5eruIkIiLeCVtpcs7tcs4tCl0/AmQCDYDrgZdDq70MfCt0/XrgFRc0B0gzs3rAlcCnzrn9zrkDwKfAoHDllsjy2ao9FBU7Hc8kEoWqJMbx8qgMujRK477XVZxERMQ75pwL/4uYNQW+ADoCW51zaSXuO+Ccq25mHwC/cc7NCi2fBvwI6AckOud+GVr+U+C4c+73J73G3QRHqEhPT+8xefLkcL+tqJWTk0NqamTs6vb0wlx25hTz1CVJGmkisradfJO23ekdL3T8YUEuGw8V890uCfSqG/A60jdo20UubbvIpW0XuSrbtuvfv/9C51zPM60X9n95zCwVeAd40Dl3uJQvtqe6w5Wy/JsLnHsBeAGgZ8+erl+/fueUV2DGjBlEws/vcG4BmZ9+yp0XNKV///Zex6kUImXbyX/TtivdxRcXMmL8PJ5bdpD27TtwdefKM7qsbRe5tO0il7Zd5IrUbRfW2fPMLI5gYZrknJsSWrwntNsdof/uDS3fDjQq8fCGwM5SlkuMm565l4Iip1nzRGJAakKACaMy6N44jfsnL+aDZfpnQEREKk44Z88zYByQ6Zx7usRd7wEnZsC7E3i3xPLhoVn0+gCHnHO7gE+AK8ysemgCiCtCyyTGTV2xm/SqCXRrpHlBRGJBakKAl0YGi9MDk5fw/lIVJxERqRjhHGm6CBgGDDCzJaHLVcBvgMvNbB1weeg2wEfARmA98CLwPQDn3H7gF8D80OXJ0DKJYbkFRcxcm8Xl7dPx+XQsk0isSE0IMGFkBj0aV+fBN1ScRESkYoTtmKbQhA6n+zY78BTrO+De0zzXeGB8+aWTSDdn4z6OFxQxsF2611FEpIKlJAR4aWQvRk6YzwOTF+OA67rU9zqWiIhEsbAe0yQSLtNX7yUpzs8FzWt6HUVEPJCSEOClEb3o2bQGD05ezLtLdngdSUREophKk0Qc5xzTV+/lopa1SIzzex1HRDySkhBgwshe9Gpag4feWKLiJCIiYaPSJBFn3d4cth84zsB2dbyOIiIeS44P7qqX0SxYnP65WMVJRETKn0qTRJxpmcFZ6vu3UWkSkWBxGj+iF72b1eShN5fw9sLtXkcSEZEoo9IkEWf66j10qF+VutUSvY4iIpXEieLUt2Utfvj2UibP2+p1JBERiSIqTRJRDhzNZ+GWAwxsq1EmEfmmpHg/Lw7vySWtavPolOVMnLPF60giIhIlSp1y3MwaArcAFwP1gePACuBD4GPnXHHYE4qUMHNtFsUOBmiqcRE5hcQ4Py8M78G9kxbx03+uoLComJEXNfM6loiIRLjTjjSZ2UsEz42UD/wWuJXgCWc/AwYBs8zskooIKXLCtNV7qZUaT+cG1byOIiKVVELAz99u78GVHdL5+furePGLjV5HEhGRCFfaSNMfnHMrTrF8BTDFzOKBxuGJJfLfCoqKmblmL1d2qIvPd7rzJouIQHzAx7O3defByUv41UeZFBQX871+Lb2OJSIiEaq0Y5rqne4OM/utcy7fObc+DJlETmnhlgMczi3UVOMiUiZxfh9/vqUr13etz++mruGZaeu8jiQiIhGqtJGmv5rZQ865D08sMDMfwV326oY9mchJpq/eS5zf6NuqttdRRCRCBPw+nh7aFb/PePrTtRQWFfPQ5a0x02i1iIiUXWml6QpgqpklOOemmFki8DZwCLi2QtKJlDAtcw99mtckNaHU+UtERL7B7zOeuqkLAZ/xzPT1FBQ7HrmyjYqTiIiU2Wm/fTrnNpvZZcAnZlYHGAbMdc49XGHpREI2Zx9lQ9ZR7ujTxOsoIhKB/D7jNzd2JuD38fcZGygsKubHV7VTcRIRkTI5bWkys+6hq48ArwCfAq+eWO6cWxT+eCJB01fvBWCAzs8kIufI5zN+9a2OxPmMF7/cREGR4/Fr26s4iYjIGZU6e16J68uA9BLLHDAgXKFETjZ99V5a1kmlSc0Ur6OISAQzM564rgMBv49xszZRWFzMk9d11IycIiJSqtJ2z+tfkUFETicnr5C5m/YxSieoFJFyYGb85Op2BPzG8zM3Uljk+PUNnVScRETktErbPa+vc25WKfdXBRqf5lxOIuVm1rosCoqcds0TkXJjZjw6qC1xPh/Pfr6egiLH727qjF/FSURETqG03fOGmNnvgKnAQiALSARaAv2BJsAPwp5QYt60zL1UTQzQo0l1r6OISBQxM35wRWsCfuNPn60jv6iYp4d2Ic5f2ikMRUQkFpW2e95DZlYduAn4NsGT3R4HMoHnSxuFEikvxcWOz9fspV+bOgT0RUZEypmZ8eBlrUkI+Pnt1NXkFxbxzK3dSAj4vY4mIiKVSKknvHHOHQBeDF1EKtyyHYfIzsnXrnkiElbf7deCxDgfP39/FfdMXMhzd/QgMU7FSUREgvSne6nUpmfuwWdwaevaXkcRkSg38qJm/PqGTsxcm8WoCfM5ll/odSQREakkVJqkUpu2ei89mlSnekq811FEJAbc1rsxf/h2F+Zs3MfwcfM4klvgdSQREakEVJqk0tp9KJeVOw8zoG2611FEJIbc2L0hf7m1O0u2HeSOsXM5eCzf60giIuKxM5YmM0s2s5+a2Yuh263M7JrwR5NYN331XgAGttPxTCJSsa7uXI/n7uhB5q4j3PriXPbl5HkdSUREPFSWkaaXgDzggtDt7cAvw5ZIJGT66j00rJ5EqzqpXkcRkRh0Wft0xt7Zk03ZOdz8whz2Hs71OpKIiHikLKWphXPud0ABgHPuOKCz/0lY5RYUMWt9NgPb1sFMHzcR8cYlrWszYWQGOw8eZ+jzs9lx8LjXkURExANlKU35ZpYEOAAza0Fw5EkkbGZv2EduQTED2ul4JhHxVp/mNZk4ujf7cvIZ+txstuw76nUkERGpYGUpTY8DU4FGZjYJmAY8EtZUEvOmrd5Dcryf3s1qeB1FRIQeTarz2l19OJpfyNDnZ7N+b47XkUREpAKVWposuF/UauBGYATwOtDTOTcj7MkkZjnnmJ65l74ta+nkkiJSaXRqWI3Jd/ehqNhxywuzWb37sNeRRESkgpRampxzDvinc26fc+5D59wHzrnsCsomMWrNniPsPJSrWfNEpNJpW7cqb9xzAQGfj1temMPy7Ye8jiQiIhWgLLvnzTGzXmFPIhIyLTM41Xj/NipNIlL5tKidypv3XEBKfIDbxs5h/YEiryOJiEiYlaU09Qdmm9kGM1tmZsvNbFm4g0nsmr56L50bVqNO1USvo4iInFLjmsm8+Z0LqJkSz1MLcvl6vXbCEBGJZmUpTYOBFsAA4FrgmtB/Rcrd/qP5LNp6gAFtNcokIpVbg7Qk3rznAmonGSMmzGda5h6vI4mISJiUpTS501xEyt2MNXtxDga21VTjIlL51amayKMZSbStW4V7Ji7k/aU7vY4kIiJhECjDOh8SLEkGJALNgDVAhzDmkhg1bfVe6lRJoEP9ql5HEREpk9R4Y9KY3oyesID7Jy/mWH4hN/dq7HUsEREpR2ccaXLOdXLOdQ79txWQAcwKfzSJNQVFxXyxJov+berg85nXcUREyqxKYhwvj8rg4la1+dE7yxk/a5PXkUREpByVZfe8b3DOLQI0m56Uu/mb93Mkr5ABmmpcRCJQUryfF4f3YFCHujz5wSr+Mm0dwTN3iIhIpDvj7nlm9nCJmz6gO5AVtkQSs6Zn7iXe76Nvy1peRxEROScJAT/P3taNR95exh8+XUtOfiGPDmpL8FzxIiISqcpyTFOVEtcLCR7j9E544kgsm756L31a1CQloSwfSxGRying9/H7b3chOcHP8zM3cjSvkCev66jdjkVEIlhZvp2ucs69VXKBmX0beOs064uctY1ZOWzMPsqdFzb1OoqIyHnz+YxfXN+RlIQAz8/cyLG8In53U2cC/rPeK15ERCqBsvzf+7EyLhM5Z9NX7wXQ+ZlEJGqYGY8Oasv/XNGaKYt3cN9ri8krLPI6loiInIPTjjSZ2WDgKqCBmT1T4q6qBHfTEyk301fvpXV6Ko1qJHsdRUSk3JgZ9w1oRXJ8gCc/WMVdryzk+Tt6kBTv9zqaiIichdJGmnYCC4BcYGGJy3vAleGPJrHicG4B8zbtZ4BOaCsiUWpU32b8bkhnvlyXxZ3j53Ekt8DrSCIichZOO9LknFsKLDWz15xz+r+7hM2sddkUFjsGaqpxEYliQ3s1Iinez0NvLOH2sXN5eWQG1VPivY4lIiJlUJZjmpqa2dtmtsrMNp64hD2ZxIxpmXtJS46jW6M0r6OIiITVtV3q8/ywHqzefYSbX5jNnsO5XkcSEZEyKEtpegn4O8HjmPoDrwATwxlKYkdRsWPGmr30a11bs0qJSEwY2C6dCSN6sf3Acb793Gy27jvmdSQRETmDsnxLTXLOTQPMObfFOfcEMCC8sSRWLN1+kH1H8xnQTscziUjsuLBlLV67qw+Hcwu46bmvWbP7iNeRRESkFGUpTblm5gPWmdl9ZnYDoINPpFzMWJOFz+CSVrW8jiIiUqG6NkrjzXsuAGDo87NZvPWAx4lEROR0ylKaHgSSgfuBHsAdwJ3hDCWxY+baLLo0SiMtWQdDi0jsaZ1ehbe/cyHVkuK4fexcvlqf7XUkERE5hVJLk5n5gaHOuRzn3Hbn3Ejn3BDn3JwKyidR7MDRfJZtP8ilrWt7HUVExDONaybz9ncuoFH1ZEa+NJ9PVu72OpKIiJyk1NLknCsCepiZVVAeiSFfrs/GObhEpUlEYlydqom8cU8f2tevyndfXcjbC7d7HUlEREo47XmaSlgMvGtmbwFHTyx0zk0JWyqJCV+szaJaUhxdGmqqcRGRtOR4Jo3pzT0TF/I/by3lSG4BIy9q5nUsERGhbKWpBrCPb86Y5wCVJjlnzjm+WJtF31a18Ps0kCkiApCSEGDciJ488PoSfv7+Kg4dL+CBga3QDh8iIt46Y2lyzo2siCASW1bvPsLeI3k6nklE5CQJAT/P3taNR6cs50+frePgsQJ+dk17fPoDk4iIZ844e56ZtTazaWa2InS7s5n9JPzRJJp9sTYLgEtaqTSJiJws4PfxuyGdGXVRMyZ8vZkfvr2MwqJir2OJiMSsskw5/iLwGFAA4JxbBtwSzlAS/WauzaJt3SrUrZbodRQRkUrJ5zN+ek07Hr68Ne8s2s73Ji0it6DI61giIjGpLKUp2Tk376RlheEII7HhaF4hCzYf0Kx5IiJnYGbcP7AVT1zbnn+t2sOoCfPJydM/wSIiFa0spSnbzFoQnPwBM7sJ2BXWVBLV5mzcR35RsY5nEhEpoxEXNePpoV2Yu2k/t4+dy4Gj+V5HEhGJKWUpTfcCzwNtzWwH8CDwnbCmkqj2xdoskuL89Gxa3esoIiIR48buDfn77d3J3HWYoc/PZvehXK8jiYjEjDOWJufcRufcZUBtoK1zrq9zbkv4o0m0mrk2iwta1CQh4Pc6iohIRLmiQ11eHpnBrkO5DPn712zMyvE6kohITCjL7Hk1zewZ4Etghpn92cxqhj+aRKMt+46yed8xLmlVy+soIiIR6YIWNZl8dx9yC4q46bnZLN9+yOtIIiJRryy7500GsoAhwE2h62+EM5RErxNTjV/apo7HSUREIlfHBtV4+7sXkhTn55YXZvP1hmyvI4mIRLWylKYazrlfOOc2hS6/BNLCHUyi08y12TSqkUTTmsleRxERiWjNaqXwzncvpEH1JEaMn8/UFZqjSUQkXMpSmj43s1vMzBe6DAU+DHcwiT75hcXM3pDNpa1rY6Yz24uInK+61RJ5854L6NigKt+btIjX5231OpKISFQqS2m6B3gNyA9dJgMPm9kRMzscznASXRZuOcDR/CIuaaWpxkVEyktacjyvjunNJa1r89iU5fz18/U457yOJSISVcoye14V55zPORcIXXyhZVWcc1UrIqREh5lrswj4jAtbahIIEZHylBwf4MXhPbm+a32e+mQNv/wwk+JiFScRkfISKMtKZtYZaFpyfefclDBlkij1xdosejSpTmpCmT52IiJyFuL8Pv44tCvVk+MZN2sTB47l89shnYnzl2WnEhERKc0Zv72a2XigM7ASKA4tdoBKk5TZ3iO5rNp1mEcGtfE6iohI1PL5jMevbU+NlHie/nQth44V8Oxt3UmK13nxRETOR1n+5N/HOdc+7Ekkqn25Njgdro5nEhEJLzPj/oGtqJ4Sz8/eXcHw8XMZe2cvqiXFeR1NRCRilWXMfraZqTTJeZm5NotaqQm0r6fD4EREKsKwPv/f3n2Hx1We6R+/H3XLau692xRjio1wTzCQAllKCCSUhE5MzWaTzW7qJpsN2V92s9lsaAZTlpIEQscQEsImGHCTJTsGjAFbtuXeVCxZtiWrPL8/NHKEkeTBeObMmfl+rmsuzZw5I932e409t8553zNCd1w2USs27dYl9y7WzrqGoCMBQGhFU5oeVltxet/M3jKzt83srcO9yMweNLOdZrayw7ZTzGyJma0wszIzmxzZbmZ2u5mVR37GpA6vucrM1kRuVx3JHxLBaml1vbFmlz45rq/S0lhqHADi5dyTBuvBq0/Txup9uviexdpQtTfoSAAQStGUpgclXSHpbEnnSTo38vVwHoq8pqP/lPRjdz9F0g8jjyXpHEnjIrfZkuZIkpn1lvQjSVMkTZb0IzPrFcXPRgJZuaVWNfuadPqxnJoHAPH2iXH99NuvTlVdQ5MumrNYK7fUBh0JAEInmtK00d3nuft6d9/Qfjvci9z9dUnVh26W1H5+VqGkrZH7F0h6xNsskVRkZoMkfVbSK+5e7e41kl7Rh4sYEtzrq3fJTJrJUuMAEIhThhXpqRunKSvddOncJVq0tjLoSAAQKna4C+CZ2d2SiiS9IKmxfXs0S46b2UhJL7r7hMjj4yW9LMnUVtimu/sGM3tR0s/cfUFkvz9L+rakWZJy3P22yPZ/kbTf3f+rk581W21HqTRgwIBTH3/88cPFQxfq6+uVl5d31L7fT5fsV3Or9KPpPY7a90TnjvbYIX4Yu/AK09hVN7TqF2UN2rHXNfvkbE0emNqXgAjT2OGDGLvwSrSxO+OMM5a5e/Hh9ovmX8seaitLn+mw7UiXHL9J0jfc/Wkz+5KkByR9Sm0l6lDezfYPb3SfK2muJBUXF/usWbOOIB4kaf78+Tpaf3+1+5u07k+v6KbTx2jWLJYbj7WjOXaIL8YuvMI2dmd9sknXPVyqOW/WaOCIsbpy2sigIwUmbGOHv2HswiusY3fY0uTu1xzFn3eVpK9H7j8p6f7I/c2ShnXYb6jaTt3brLajTR23zz+KeRBji8or1dLqzGcCgARRmJupX18/Rbf+9q/64fPvaNeeRn3z08fIjIV6AKArh53TZGbHmNmf21fBM7OTzOwHR/jztko6PXL/TElrIvfnSboysoreVEm17r5NbafyfcbMekUWgPhMZBtC4vU1u5SfnaFThhUFHQUAEJGTma57vmvKmoUAACAASURBVDJJlxQP0x1/Kdd3n3lbzS2th38hAKSoaE7Pu0/SP0m6V5Lc/S0z+62k27p7kZk9prajRH3NbLPaVsH7qqRfmVmGpAZF5iBJeknS5ySVS9on6ZrIz6o2s59IKo3s92/ufujiEkhQ7q7X3t+lGWP7KjM9mjVHAADxkpGepp9ddKL65WfrzlfLVVl/QHdePlE5melBRwOAhBNNacp196WHHLZvPtyL3P2yLp46tZN9XdItXXyfB9W27DlCZu2uem2tbdCtZ3JqHgAkIjPTtz57rPrlZ+tfX3hHVzxQovuvPE2FuZlBRwOAhBLNr/8rzWyMIgswmNnFkrbFNBWSwvz3d0mSPnkMS40DQCK7avpI3XHZRL25qVZfunexttc2BB0JABJKNKXpFrWdmnecmW2R9A+SboxpKiSF19dUaky/nhraKzfoKACAwzj3pMF66JrTtGX3fl00Z5HKd9YHHQkAEkY0pcnd/VOS+kk6zt1nRvk6pLCGphaVrKvS6cf0DzoKACBK08f21eOzp6qxuVUX37NIyzfWBB0JABJCNOXnaUly973uviey7anYRUIyKFlfrcbmVk7NA4CQmTCkUM/cNF2FPTJ1+X1L9Op7O4OOBACB67I0mdlxZnaRpEIz+0KH29WScuKWEKH0+updys5I09TRfYKOAgD4iIb3ydVTN07X2P55uv6RMj21bHPQkQAgUN2tnnespHMlFUk6r8P2PWpbOhzo0murd2nyqN4sXQsAIdUvP1uPz56mGx9dpm89+aZ27WnUjaeP5iK4AFJSl6XJ3Z+X9LyZTXP3xXHMhJDbsnu/ynfW69LThgUdBQDwMeRlZ+jBq0/Tt558U//xx/e0o65B/3LueKWnUZwApJbDXqeJwoSP6vXVbUuNn34M12cCgLDLykjT/1xyigYUZOu+N9ZrR12DfnnJKZxJACClRHNxW+AjeX31Lg0uzNHY/nlBRwEAHAVpaabv/914DSjI0W2/f1dV9Ut135XFXAQXQMpg6XAcVc0trVpQXqlPHtOP894BIMlc/4nRuuOyiVqxabcuvmeRtuzeH3QkAIiLw5YmMxtgZg+Y2R8ij8eb2XWxj4YwWrFpt/Y0NHNqHgAkqfNOHqyHr52s7XUNuujuRXpve13QkQAg5qI50vSQpJclDY48Xi3pH2IVCOH22updSk8zTR/L9ZkAIFlNG9NHT944TZL0xTmLtWhtZcCJACC2oilNfd39CUmtkuTuzZJaYpoKofX66l2aOKxIhT04zx0AktlxAwv0zM3TNbAwR1c/WKoX3twadCQAiJloStNeM+sjySXJzKZKqo1pKoRS9d4DemtLrT7JqXkAkBIGF/XQUzdO1ynDivS1x/6q+99YF3QkAIiJaErTNyXNkzTGzBZKekTS12KaCqH0xppdcmepcQBIJYW5mXrkusk6Z8JA3fb7d3Xbi6vU2upBxwKAoyqa6zQtN7PTJR0rySS97+5NMU+G0Hlt9S71ys3UhCGFQUcBAMRRTma67rx8kn7y4irdv2C9duxp1H998SRlZ3AtJwDJ4bClyczSJX1O0sjI/p8xM7n7f8c4G0KktdX1+upKfWJcP64UDwApKD3N9KPzxmtQYY7+3x/e0649DZp7ZbEKcpjjCiD8ojk97wVJV0vqIym/ww046N3tdaqsb2Q+EwCkMDPTDaeP0S8vOVllFTX60j2Ltb22IehYAPCxHfZIk6Sh7n5SzJMg1F5f3bbc7CfHsdQ4AKS6CycOVd+8bN346DJ94e6FeujayTpmAL9vBRBe0Rxp+oOZfSbmSRBqr63eqeMHFah/QU7QUQAACeAT4/rpdzdMU3Or66I5i7iWE4BQi6Y0LZH0rJntN7M6M9tjZlz+GwftO9Cs5Rt26xMcZQIAdDBhSKGeuXm6BhTk6KoHl+r5FVuCjgQARySa0vQLSdMk5bp7gbvnu3tBjHMhREoranSgpVUzx1KaAAAfNLRXrp6+cbomDe+lrz++QnfPL5c7S5IDCJdoStMaSSudf+HQhQVrdikrI02TR/UOOgoAIAG1X8vp/JMH6z//+L5+8NxKNbe0Bh0LAKIWzUIQ2yTNN7M/SGps38iS42i3oLxKxSN6KSeT63EAADqXnZGu/7nkFA0u6qF7Xlur7bUNuuPyicrNiuajCAAEK5ojTesl/VlSllhyHIeorG/Uu9vqNINT8wAAh5GWZvrOOcfpJ5+foFff36lL5y7Rrj2Nh38hAATssL/ecfcfxyMIwmnR2ipJYj4TACBqV0wdoYEFOfraY8v1hTkL9dA1kzWmX17QsQCgS4c90mRm/czs52b2kpn9pf0Wj3BIfAvXVKogJ0MThhQGHQUAECKfHj9Aj8+epn2NLbpoziKVVlQHHQkAuhTN6Xm/kfSepFGSfiypQlJpDDMhJNxdC8orNX1MX6WnWdBxAAAhc8qwIj1z83T1ys3Sl+8v0e/f2hZ0JADoVDSlqY+7PyCpyd1fc/drJU2NcS6EQEXVPm3ZvV8zuT4TAOAIjejTU0/fNF0nDinUrY8t1/1vrGNJcgAJJ5rS1BT5us3M/s7MJkoaGsNMCIkF5W1Xd2c+EwDg4+jdM0u/uX6Kzj5hoG77/bv68Qur1NJKcQKQOKIpTbeZWaGkf5T0LUn3S/pGTFMhFBauqdSQoh4a0Sc36CgAgJDLyUzXnZdP0rUzRumhRRW6+TfL1NDUEnQsAJAURWly9xfdvdbdV7r7Ge5+qrvPi0c4JK6WVteitZWaObavzJjPBAD4+NLTTD88b7z+5dzx+tOqHbrsviWqrGdJcgDB63LJcTO7Q1KXx8bd/e9jkgihsHJLreoamjWD+UwAgKPsupmjNKQoR19/fIW+cPci/e81p7EkOYBAdXekqUzSsm5uSGHt85mmj+kTcBIAQDI6e8IgPTZ7qvY2NusLdy9SybqqoCMBSGHdlaZPufvDkorc/eFDb/EKiMS0YE2lxg8qUN+87KCjAACS1KThvfTszTPUJy9LVzywVM+v2BJ0JAApqrvSdKqZjZB0rZn1MrPeHW/xCojEs/9Ai5ZtqGGpcQBAzA3vk6tnbpquicOL9PXHV+iOP69hSXIAcdflnCZJ90j6o6TRajsdr+Nsf49sRwoqrajWgZZWzWCpcQBAHBTlZumR6ybr20+9pV+8slqbavbppxeeqMz0aBYBBoCPr8vS5O63S7rdzOa4+01xzIQEt7C8UlnpaTptZK+gowAAUkR2Rrp+eckpGt47V7f/pVxbdzfo7q9MUkFOZtDRAKSAaJYcpzDhAxaUV2rSiCLlZnV3oBIAgKPLzPTNzxyrn198kpasq9LFcxZpc82+oGMBSAEc18ZHUr33gN7ZWqeZnJoHAAjIF4uH6eFrJ2vb7gZdePcivb25NuhIAJIcpQkfycLIUuMzx/ULOAkAIJXNGNtXT988XVnpafrSvYv1f6t2BB0JQBI7bGkys1FmltPhcQ8zGxnLUEhcC8srlZ+ToROHFAYdBQCQ4o4ZkK9nb5musf3zNPvRMj2yuCLoSACSVDRHmp6U1NrhcUtkG1KMu+uNNZWaPqaP0tPs8C8AACDG+ufn6Hc3TNWZx/XXD59/Rz95cZVaWlmSHMDRFU1pynD3A+0PIvezYhcJiWpj9T5t2b2f+UwAgISSm5Whe68o1tXTR+qBBet182+Waf+BlqBjAUgi0ZSmXWZ2fvsDM7tAUmXsIiFRLYjMZ+L6TACARJOeZvrX80/QD88drz+t2qFL5i7WzrqGoGMBSBLRlKYbJX3PzDaa2SZJ35Z0Q2xjIREtLK/U4MIcjerbM+goAAB06tqZo3TfFcUq31mvz9+1UO9uqws6EoAkEM11mta6+1RJ4yWNd/fp7l4e+2hIJC2troXlVZo5rq/MmM8EAEhcnxo/QE/cME0t7vriPYv16vs7g44EIOS6LE1m9pXI12+a2TclzZb01Q6PkULe2Vqr2v1NnJoHAAiFCUMK9dwtMzS8d66ue6iUlfUAfCzdHWlqPwcrv5NbXoxzIcG0z2eaPobSBAAIh0GFPfTkjdMOrqz34xfeYWU9AEcko6sn3P3eyN3/c/eFHZ8zsxkxTYWEs7C8UscNzFe//OygowAAELWe2W0r6/309+/qwYXrtbFqn26/bKJ6Znf5EQgAPiSahSDuiHIbklRDU4tKK2pYahwAEErpaaYfnjdeP7ngBL36/k598Z7F2la7P+hYAEKky1+zmNk0SdMl9TtkDlOBpPRYB0PiKKuo0YHmVs0YR2kCAITXFdNGaljvXN3627/q83ct1M0TWNgIQHS6O9KUpba5Sxn64HymOkkXxz4aEsUb5buUmW6aMqp30FEAAPhYZh3bX0/dNE0ZaWn6aUmDXlm1I+hIAEKguzlNr0l6zcwecvcNccyEBLOwvFKThvdSbhbnfwMAwu+4gQV69pbpuuTOVzX70TJ9/3PH67qZo7ikBoAuRfMpeJ+Z/VzSCZJy2je6+5kxS4WEUb33gN7ZWqdvfuqYoKMAAHDU9M/P0Xcm5+jZrQW67ffvqqJqr/71vBOUkR7NdG8AqSaafxl+I+k9SaMk/VhShaTSGGZCAlm8tkruYj4TACDpZKeb7v7yJN1w+mj9eslGXftwmfY0NAUdC0ACiqY09XH3ByQ1uftr7n6tpKkxzoUEsaC8UvnZGTppSGHQUQAAOOrS0kzfPed4/b8vnKhF5ZW6aM4ibareF3QsAAkmmtLU/iuXbWb2d2Y2UdLQGGZCAllYXqmpY/pwugIAIKldNnm4Hr52srbXNujzdy3Usg3VQUcCkECi+SR8m5kVSvpHSd+SdL+kb8Q0FRLCxqp92li9T5/g1DwAQAqYMbavnr1lhvJzMnTZ3BI999ctQUcCkCAOW5rc/UV3r3X3le5+hruf6u7z4hEOwVpQXimp7T8RAABSwZh+eXr25hmaNKJI//C7FfrFn95Xa6sHHQtAwA5bmsxstJm9YGaVZrbTzJ43s9HxCIdgLSyv1KDCHI3u2zPoKAAAxE2vnll65NopuqR4mO74S7lufWy59h9oCToWgABFc3rebyU9IWmgpMGSnpT0WCxDIXitra6Fays1Y2xfrlsBAEg5WRlp+tlFJ+r7nztef1i5XZfMXayddQ1BxwIQkGhKk7n7o+7eHLn9WhLHqZPcqm112r2vSTM5NQ8AkKLMTF/95GjNvaJY5TvrdcFdC7VyS23QsQAEoMvSZGa9zay3pFfN7DtmNtLMRpjZP0v6ffwiIgjt85mmj+0TcBIAAIL16fED9OSN0yRJX7xnsV5+Z3vAiQDEW3dHmpZJKpN0iaQbJL0qab6kmyRdE/NkCNSCNZU6bmC++ufnBB0FAIDAnTC4UM/fMkPHDMjTjb9epnteWyt3TrwBUkVGV0+4+6h4BkHiONDiWlpRrSumjgg6CgAACaN/QY5+d8M0/eOTb+pnf3hPa3fW66cXnqisDK5lCCS7LktTR2Y2QdJ4SQcPO7j7I7EKhWCV727VgeZW5jMBAHCInMx03XHpRI3pl6fb/7xGG6r36Z6vnKrePbOCjgYghqJZcvxHku6I3M6Q9J+Szo9xLgToncoWZaSZJo/qHXQUAAASTlqa6ZufPkb/c8kpWrFpty68e6HKd9YHHQtADEVzPPliSWdJ2u7u10g6WVJ2TFMhUKuqWjRpeC/1zI7qQCQAACnp8xOH6LGvTtXexmZdePdCvb56V9CRAMRINKVpv7u3Smo2swJJOyVxcdsktXvfAVXUtWoGp+YBAHBYp47opWdvnqEhRT109f8u1f8uXM8CEUASiqY0lZlZkaT71Lai3nJJSw/3IjN70Mx2mtnKQ7Z/zczeN7N3zOw/O2z/rpmVR577bIftZ0e2lZvZd6L+k+GILFpbJZc0cxylCQCAaAzrnaunbpquM48boB+/sErfe3almlpag44F4Cg67PlX7n5z5O49ZvZHSQXu/lYU3/shSXdKOrhghJmdIekCSSe5e6OZ9Y9sHy/pUkknSBos6f/M7JjIy+6S9GlJmyWVmtk8d18VzR8OH92C8krlpEsnDy0MOgoAAKGRl52huVecqp//6X3Nmb9W6yvrNefLp6oXC0QASaG7i9seF/k6qf0mqbekjMj9brn765KqD9l8k6SfuXtjZJ+dke0XSHrc3Rvdfb2kckmTI7dyd1/n7gckPR7ZFzGysLxSx/dJV0Y6y6cCAPBRpKWZvn32cfrvL52s5Rt264K7FmrNjj1BxwJwFFhX592a2X3u/lUze7WTp93dzzzsNzcbKelFd58QebxC0vOSzpbUIOlb7l5qZndKWuLuv47s94CkP0S+zdnufn1k+xWSprj7rZ38rNmSZkvSgAEDTn388ccPFw+H2LWvVf/0+n5dPNp17jF5QcfBEaivr1deHmMXRoxdeDF24RXLsSuvadHtf21QU6t008nZOqkfiysdTbzvwivRxu6MM85Y5u7Fh9uvu4vbfjXy9YyjmCtDUi9JUyWdJukJMxstyTqLoM6PhHXa8tx9rqS5klRcXOyzZs06GnlTyu9KN0p6WxMH54q/v3CaP38+YxdSjF14MXbhFcuxmyXpnDP26/qHy/Q/y+v0vc+N1nUzR8mss488+Kh434VXWMeuy9JkZl/o7oXu/swR/LzNkp7xtsNbS82sVVLfyPZhHfYbKmlr5H5X23GULVpbpX752Rrck3/QAQD4uIYU9dBTN07TN59Yodt+/67W7KjXTz4/QVkZnAIPhE13x4rPi3ztL2m6pL9EHp8hab6kIylNz0k6U9L8yEIPWZIqJc2T9Fsz+2+1LQQxTm0r9JmkcWY2StIWtS0WcfkR/Fwchrtr8doqTRvdR2a1QccBACAp9MzO0Jwvn6r/fmW17ny1XOsr92rOVyapTx6XvATCpMtfdbj7NZGL2bqk8e5+kbtfpLYV7g7LzB6TtFjSsWa22cyuk/SgpNGRZcgfl3SVt3lH0hOSVkn6o6Rb3L3F3Zsl3SrpZUnvSnoisi+OsnWVe7VzT6OmjekTdBQAAJJKWprpW589Vr+69BSt2Ny2QMT721kgAgiTaGYljnT3bR0e75B0TFc7t3P3y7p46itd7P9TST/tZPtLkl6KIic+hkVrqyRJ00b3UcXKdQGnAQAg+VxwyhAN752r2Y8u0xfuXqjbL5uos44fEHQsAFGI5qTa+Wb2spldbWZXqa3AdLaiHkJsydoqDS7M0Yg+uUFHAQAgaU0c3kvzbp2hUf166vpHyjT39bXqaiVjAInjsKUpsrz3PZJOlnSKpHvd/WuxDob4aW11LVlXpalj+rCqDwAAMTaosIeeuGGazpkwUP/+0nv61pNvqaGpJehYALoR1fIt7v6su3/D3b8haZeZ3RXjXIij1Tv3qGrvAU0bzXwmAADiITcrQ3deNklfP2ucnl6+WZfdt0Q76xqCjgWgC1GVJjM7xcz+w8wqJP1E0nsxTYW4Wtw+n4lFIAAAiJu0NNM3Pn2M7v7yJL23bY/Ov3Oh3tq8O+hYADrRZWkys2PM7Idm9q6kO9V2LSVz9zPc/Y64JUTMLV5bpeG9czW0F/OZAACIt8+dOEhP3TRN6WmmL96zWPPe5JKUQKLp7kjTe5LOknSeu8+MFCVOuE0yLZH5TJyaBwBAcE4YXKjnb52hk4cW6e8f+6t+/vJ7am1lgQggUXRXmi6StF3Sq2Z2n5mdpbaLzSKJvLutTnUNzZo+ltIEAECQ+uZl69fXT9Flk4fprlfXavajZdrT0BR0LADq/uK2z7r7JZKOkzRf0jckDTCzOWb2mTjlQ4wt7nB9JgAAEKysjDT9+4Un6t8uOEGvvr9LF81ZpA1Ve4OOBaS8aJYc3+vuv3H3cyUNlbRC0ndingxxsWhtpUb366n+BTlBRwEAAJLMTFdOG6lHr52snXsadcFdC7WovDLoWEBKi2r1vHbuXu3u97r7mbEKhPhpbmlVaUWNprNqHgAACWf62L56/pYZ6peXrSseXKpHFldwIVwgIB+pNCG5vL2lVvWNzZo2um/QUQAAQCdG9OmpZ26erjOO7acfPv+OvvfsSh1obg06FpByKE0pbFFkPtPU0b0DTgIAALqSn5OpuVcU65YzxuixpRv1lftLVFXfGHQsIKVQmlLYknVVOm5gvvrkZQcdBQAAdCMtzfRPnz1Ov7r0FL25ebfOv3OhVm2tCzoWkDIoTSnqQHOrSiuqNZVV8wAACI0LThmiJ2+cppZW10VzFun3b20LOhKQEihNKWrFpt1qaGrVNBaBAAAgVE4aWqR5X5uh4wfl65bfLtd/vfw+F8IFYozSlKIWr62SmTR1FKUJAICw6Z+fo8dmT9UlxcN056vl+uojZarjQrhAzFCaUtTidZU6YXCBCnMzg44CAACOQHZGun52UduFcF9bvUsX3rVQ63bVBx0LSEqUphTU0NSi5Rt2axrzmQAACLWDF8K9bopq9jXpgrsW6tX3dwYdC0g6lKYUtHxDjQ60tGr6GK7PBABAMpg2po/m3TpDw3rl6tqHSjVn/louhAscRZSmFLR4XZXS00ynjeL6TAAAJIuhvXL19E3T9XcnDtJ//PE9/f3jK7T/QEvQsYCkQGlKQYvWVunEIYXKy84IOgoAADiKemSl647LJuqfzz5WL761VRffs0iba/YFHQsIPUpTitnb2Kw3N+3WdJYaBwAgKZmZbp41Vg9edZo2Vu/T+Xcu1JJ1VUHHAkKN0pRiyjbUqLnVuT4TAABJ7ozj+uu5W2aoKDdTX7m/RI8urmCeE3CEKE0pZtHaSmWmm4pHMJ8JAIBkN6Zfnp67ZYY+eUw//cvz7+i7z7ytxmbmOQEfFaUpxSxZW6WJw3qpR1Z60FEAAEAcFORk6r4ri3XLGWP0eOkmXX5fiXbuaQg6FhAqlKYUUtfQpLe31Goqp+YBAJBS0tNM//TZ43TX5ZO0amudzrtjgf66sSboWEBoUJpSyNJ11Wp1cVFbAABS1N+dNEhP3zRdWRlpuuTeJfpd6cagIwGhQGlKIYvXVSk7I00ThxcFHQUAAARk/OACzbtlpqaM7q1vP/22fvDc2zrQ3Bp0LCChUZpSyKK1VTp1RC/lZDKfCQCAVNarZ5b+9+rTdMMnR+vXSzbqy/cvYZ4T0A1KU4qo2XtA726r49Q8AAAgScpIT9N3P3e8br9sot7eUqvz71jIPCegC5SmFFGyvu2idtPHUpoAAMDfnH/yYD1z0wxlpJsuuXeJnijdFHQkIOFQmlLEorVVys1K10lDmc8EAAA+aPzgAr1w60xNHtVb//z0W/qX51YyzwnogNKUIhavrVLxyN7KTGfIAQDAh/XqmaWHrmmb5/Tokg368v1LtGtPY9CxgITAJ+gUsGtPo9bsrNd0rs8EAAC6ceg8p/PuWKAVm3YHHQsIHKUpBSxe1zafiUUgAABANDrOc/rSPYuZ54SUR2lKAYvXVik/O0MnDC4IOgoAAAgJ5jkBf0NpSgFL1lVpyujeymA+EwAA+Ag6m+fE9ZyQivgUneS21e7X+sq9msqpeQAA4Ah0Ns9p2Qau54TUQmlKcovXRuYzsQgEAAD4GM4/ebCevXmGsjPSdencxfr1kg1y96BjAXFBaUpyi9dWqSg3U8cPZD4TAAD4eI4f1DbPacbYvvrBcyv1z0+9pYamlqBjATFHaUpyi9ZWaeqoPkpLs6CjAACAJFCYm6kHrzpNf3/mWD25bLO+dO9ibdm9P+hYQExRmpLYpup92rJ7v6aP5dQ8AABw9KSlmb75mWN135XFWr9rr867Y4EWlVcGHQuIGUpTEjs4n4lFIAAAQAx8evwAPXfrDPXpmaWvPFCiua+vZZ4TkhKlKYktWlupvnnZGts/L+goAAAgSY3pl6dnb5mhsycM1L+/9J5ufeyv2tvYHHQs4KiiNCUpd9fidVWaNqaPzJjPBAAAYicvO0N3XT5J3znnOP3h7W268O6FWl+5N+hYwFFDaUpS6yv3akddI6fmAQCAuDAz3Xj6GD1y7RTt2tOo8+9coD+/uyPoWMBRQWlKUou4PhMAAAjAzHF9Ne/WmRreO1fXPVymX76yWq2tzHNCuFGaktTidVUaVJijkX1yg44CAABSzLDeuXr6pun6wqQh+tWf1+j6R8pUu68p6FjAEaM0JSF315K1VZo2mvlMAAAgGDmZ6frFF0/WTy44Qa+v3qXz7lygVVvrgo4FHBFKUxJavaNeVXsPaCqn5gEAgACZma6YNlK/u2GqGptbdOHdC/X0ss1BxwI+MkpTElq8tu3ictMpTQAAIAGcOqK3XvzaJzRxeJH+8ck39f1n31Zjc0vQsYCoUZqS0OJ1VRrWu4eG9mI+EwAASAz98rP16+um6IZPjtZvSjbqS/cu0Zbd+4OOBUSF0pRkWltdJeurNXUUR5kAAEBiyUhP03c/d7zu+cokrd1Zr3Nvf0ML1lQGHQs4LEpTklm9c49272vSFK7PBAAAEtTZEwbp+VtnqF9+tq58sER3vVrOsuRIaJSmJLN0fbUkacqo3gEnAQAA6NqYfnl69uYZOvekwfr5y+9r9qNlqt3PsuRITJSmJFOyrlpDinpoWG/mMwEAgMTWMztDv7r0FP3ovPGa//4unc+y5EhQlKYk4u4qWV+lyRxlAgAAIWFmumbGKD0+e6oamlr0hTksS47EQ2lKIusq96qy/gCn5gEAgNApHtm2LPnJQ9uWJf/BcyxLjsRBaUoiJesi85lYBAIAAIRQv/xs/eb6tmXJf72EZcmRODKCDoCjp2R9lfrlZ2tkH+YzAQCAcGpflnzi8CJ968m3NONnf9HgwhyN6tdTo/r21Ki+earf1ayRlXs1tFcPZaRzDACxR2lKEu6uknXVmjKqt8ws6DgAAAAfy9kTBum4gQV64c2tWl+1V+t27dW8FVtV19AsSfrlsvnKSDMN75Or0X3/VqhG9e2p0f16qn9+9sHPRO6u5lZXU0urDjS36kD71+ZWeq8zVwAAFehJREFUNbX4B7Y1tbRKkgp6ZKowcsvPyVAm5SylUZqSxKbq/dpe18CpeQAAIGmM7NtTXztr3MHH7q6afU16+k9vqGjYMVpfuffg7Y01lWpsbj24b4/MdGWkWVsZammVf8zLQPXMSj9YpAp6ZKogp/1+xsFyVdgjU/3yszWgIEcDCnJUkJPBL7OTBKUpSSxZXyWJ6zMBAIDkZWbq3TNL43qla1bxsA8819rq2lbXoPW79mp9Zb0qqvap1V1ZGWnKTk9TZnqasjL+9jWr/WtGx+dM2RlpanWpbn+Tavc3Rb42q66h7XH7ts01+7Rqa5PqGppV39jcad4emekaWJij/vnZGliYo4GRMjWgIEcDC9vKVf/8HGVlcBQr0VGakkTJuuq2f0T65wUdBQAAIO7S0kxDinpoSFEPzRzXN64/u7mlVXsamrV7f5N21jVox55G7aht0Pa6ttvOugYt31ijHbWNOtDS+qHX9+mZpYGFORpUmBP52kMDC3I0qOhv93tkpcf1z4QPojQliaUVVZo8kvlMAAAA8ZaRnqZePbPUq2eWRvXt2eV+7acX7oiUqR21DdpR19hWrmr3a8vuBpVtqNHufU0fem1RbqYGFfb4W7EqaPs6uKiHBhS0Fa6e2Xy0jxX+ZpPA1t37tal6v66ZPiroKAAAAOhC++mFvXtm6fhBBV3ut/9Ai7bXNWhb7X5tr23QttoP3n9z025V7T3wodfl52RoUGHOwRI1sL1kFeQcPJJV2COTX7IfAUpTEihpn880mvlMAAAAYdcjKz2yGmDXR60amlq0s65RW2v3a0ddW5na3l6u6hq1escu7dzT+KEFMHIy0w6WqIEFORrQ4ajVgMjXfnnZLOV+CEpTEli6vloFORk6bmDXv7EAAABA8sjJTNfwPrka3s31OZtaWrVrT6O21TZ0KFb7Dxas0ooa7dzToKaWDzarNJP65mX/rUh1KFkdy1VeCp0OmDp/0iRWsq5ap43srfQ0DrUCAACgTWZ6mgYX9dDgoh5d7tPa6qred0DbI8Wqfa5V2yIWjdpQtVcl66oOXh+ro55Z6RpQmKMB+W0lqn9B9sH7AwqSa3XAmJUmM3tQ0rmSdrr7hEOe+5akn0vq5+6V1nZi5a8kfU7SPklXu/vyyL5XSfpB5KW3ufvDscocRjvrGrSucq8unTzs8DsDAAAAHaSlmfrmZatvXrYmDCnscr/2eVbt5WrHwZUB2xayKK2o1s66rlcH7F+Qo4EF2Urf36hZs2L4B4qRWB5pekjSnZIe6bjRzIZJ+rSkjR02nyNpXOQ2RdIcSVPMrLekH0kqluSSlpnZPHeviWHuUFlaUS1JmjKKi9oCAAAgNqKZZ9W+OuD22gbt2NO21Pr22kbt2BNZKXBPgxr2frhUhUHMSpO7v25mIzt56peS/lnS8x22XSDpEXd3SUvMrMjMBkmaJekVd6+WJDN7RdLZkh6LVe6wKVlXrZ5Z6TphMPOZAAAAEJyOqwOOV+efTefPnx/fUEdJXOc0mdn5kra4+5uHLHU4RNKmDo83R7Z1tb2z7z1b0mxJGjBgQGgH5KP6y8p9GlWQpgVvvH7Uvmd9fX3K/P0lG8YuvBi78GLswouxCy/GLrzCOnZxK01mlivp+5I+09nTnWzzbrZ/eKP7XElzJam4uNhnhfFkyY+oeu8BbfnjK7p8xljNmjX2qH3f+fPnKxX+/pIRYxdejF14MXbhxdiFF2MXXmEdu3guZTFG0ihJb5pZhaShkpab2UC1HUHquJLBUElbu9kOtS01LklTRnF9JgAAACBW4laa3P1td+/v7iPdfaTaCtEkd98uaZ6kK63NVEm17r5N0suSPmNmvcysl9qOUr0cr8yJrmR9lbIz0nTS0KKgowAAAABJK2alycwek7RY0rFmttnMrutm95ckrZNULuk+STdLUmQBiJ9IKo3c/q19UQi0HWmaNLxXUqx9DwAAACSqWK6ed9lhnh/Z4b5LuqWL/R6U9OBRDZcEavc3adW2On39rHFBRwEAAACSGocoQqqsolruXJ8JAAAAiDVKU0gtXV+trPQ0TRzOfCYAAAAglihNIbVkfbVOHlaonMz0oKMAAAAASY3SFEL1jc1auaWWU/MAAACAOKA0hdDyDTVqaXVN5vpMAAAAQMxRmkKoZH2V0tNMp47oFXQUAAAAIOlRmkKoZF21ThxSqJ7ZMVsxHgAAAEAEpSlkGppa9Obm3ZrCqXkAAABAXFCaQmb5xho1tbimjKY0AQAAAPFAaQqZknXVMpOKR1KaAAAAgHigNIXM0vXVGj+oQAU5mUFHAQAAAFICpSlEGptbtHxjDddnAgAAAOKI0hQib22uVWNzK/OZAAAAgDiiNIVIyboqSdJpzGcCAAAA4obSFCIl66t17IB89e6ZFXQUAAAAIGVQmkKiqaVVyzbUcGoeAAAAEGeUppBYuaVW+w60aDIXtQUAAADiitIUEkvXV0sSpQkAAACIM0pTSJSsr9bofj3VPz8n6CgAAABASqE0hUBLq6t0fbWmcJQJAAAAiDtKUwi8u61OexqbuagtAAAAEABKUwiUMJ8JAAAACAylKQRK1lVpWO8eGlzUI+goAAAAQMqhNCW41lZXaUU1p+YBAAAAAaE0Jbg1O+tVs6+JRSAAAACAgFCaElzJ+ipJ4kgTAAAAEBBKU4IrWV+tQYU5Gtab+UwAAABAEChNCczdVbKu7fpMZhZ0HAAAACAlUZoS2LrKvaqsb9RkTs0DAAAAAkNpSmBLI9dnmjKaRSAAAACAoFCaEljJuir1zcvW6L49g44CAAAApCxKU4Jyd5WsZz4TAAAAEDRKU4LaXLNf22obODUPAAAACBilKUGVROYzTeaitgAAAECgKE0JqqyiWgU5GTqmf37QUQAAAICURmlKUKUV1Soe2VtpacxnAgAAAIJEaUpAVfWNWrtrr4pH9go6CgAAAJDyKE0JaNmGGknSaSOZzwQAAAAEjdKUgMo21CgrPU0nDikMOgoAAACQ8ihNCai0olonDS1UTmZ60FEAAACAlEdpSjD7D7Ro5ZZaFXNqHgAAAJAQKE0J5s3Nu9XU4jqNRSAAAACAhEBpSjBlFW0XtT11BKUJAAAASASUpgRTWlGjYwbkqSg3K+goAAAAAERpSigtra7lG2qYzwQAAAAkEEpTAnl/+x7taWxmPhMAAACQQChNCaRsQ9t8puIRHGkCAAAAEgWlKYGUVtRoYEGOhvbqEXQUAAAAABGUpgTh7ipdX63ikb1kZkHHAQAAABBBaUoQW3bv1/a6Bp3GIhAAAABAQqE0JYiyihpJUjGLQAAAAAAJhdKUIEorqpWXnaHjBhYEHQUAAABAB5SmBFFWUaNJI3opPY35TAAAAEAioTQlgNp9TXp/xx5N5tQ8AAAAIOFQmhLAso2R6zOxCAQAAACQcChNCaC0okaZ6aaThxYFHQUAAADAIShNCaCsoloThhSqR1Z60FEAAAAAHILSFLCGpha9uamW6zMBAAAACYrSFLCVW2p1oKVVxSNYBAIAAABIRJSmgC2taFsE4lRKEwAAAJCQKE0BK6uo0Zh+PdUnLzvoKAAAAAA6QWkKUGurq6yimvlMAAAAQAKjNAVozc561TU0c30mAAAAIIFRmgJUGpnPdNpI5jMBAAAAiYrSFKCyimr1y8/W8N65QUcBAAAA0AVKU4BKK2p02sheMrOgowAAAADoAqUpIFt379eW3ftVPIL5TAAAAEAiozQFpGxDjSSxch4AAACQ4ChNASmrqFZuVrqOH5QfdBQAAAAA3aA0BaS0okaThvdSRjpDAAAAACSymH1iN7MHzWynma3ssO3nZvaemb1lZs+aWVGH575rZuVm9r6ZfbbD9rMj28rN7DuxyhtPdQ1Nem97nYpZahwAAABIeLE8zPGQpLMP2faKpAnufpKk1ZK+K0lmNl7SpZJOiLzmbjNLN7N0SXdJOkfSeEmXRfYNteUbauTOfCYAAAAgDGJWmtz9dUnVh2z7k7s3Rx4ukTQ0cv8CSY+7e6O7r5dULmly5Fbu7uvc/YCkxyP7hlpZRY3S00ynDCs6/M4AAAAAApUR4M++VtLvIveHqK1Etdsc2SZJmw7ZPqWzb2ZmsyXNlqQBAwZo/vz5RzPrUfXKiv0anmcqXbwg6Cidqq+vT+i/P3SNsQsvxi68GLvwYuzCi7ELr7COXSClycy+L6lZ0m/aN3Wym6vzI2He2fd097mS5kpScXGxz5o16+MHjYEDza2q+L+X9eUpIzRrVmKeaTh//nwl6t8fusfYhRdjF16MXXgxduHF2IVXWMcu7qXJzK6SdK6ks9y9vQBtljSsw25DJW2N3O9qeyit3FqrxuZWncYiEAAAAEAoxHW9azM7W9K3JZ3v7vs6PDVP0qVmlm1moySNk7RUUqmkcWY2ysyy1LZYxLx4Zj7ayirapnmdSmkCAAAAQiFmR5rM7DFJsyT1NbPNkn6kttXysiW9YmaStMTdb3T3d8zsCUmr1Hba3i3u3hL5PrdKellSuqQH3f2dWGWOh9KKGo3sk6v++TlBRwEAAAAQhZiVJne/rJPND3Sz/08l/bST7S9JeukoRguMu6usolpnHT8g6CgAAAAAohTX0/NS3dpde1Wzr4n5TAAAAECIUJriqH0+UzEXtQUAAABCg9IUR6UVNerdM0uj+/YMOgoAAACAKFGa4qhsQ7WKR/RSZBEMAAAAACFAaYqTnXUN2lC1T5NHcWoeAAAAECaUpjgp21AjiflMAAAAQNhQmuKktKJaOZlpOmFwQdBRAAAAAHwElKY4Kauo0cRhvZSZzl85AAAAECZ8go+D+sZmvbO1luszAQAAACFEaYqDFRt3q9WZzwQAAACEEaUpDkorqpVm0sThRUFHAQAAAPARUZrioGxDtY4fVKD8nMygowAAAAD4iChNMdbU0qq/btyt0zg1DwAAAAglSlOMrdpap30HWlTMIhAAAABAKFGaYqy0olqSVDyCI00AAABAGFGaYqysokbDevfQwMKcoKMAAAAAOAKUphhLS5Nmju0XdAwAAAAARygj6ADJ7u4vnxp0BAAAAAAfA0eaAAAAAKAblCYAAAAA6AalCQAAAAC6QWkCAAAAgG5QmgAAAACgG5QmAAAAAOgGpQkAAAAAukFpAgAAAIBuUJoAAAAAoBuUJgAAAADoBqUJAAAAALpBaQIAAACAblCaAAAAAKAblCYAAAAA6AalCQAAAAC6QWkCAAAAgG5QmgAAAACgG5QmAAAAAOgGpQkAAAAAukFpAgAAAIBuUJoAAAAAoBuUJgAAAADoBqUJAAAAALph7h50hqPOzHZJ2hB0jhDrK6ky6BA4IoxdeDF24cXYhRdjF16MXXgl2tiNcPd+h9spKUsTPh4zK3P34qBz4KNj7MKLsQsvxi68GLvwYuzCK6xjx+l5AAAAANANShMAAAAAdIPShM7MDToAjhhjF16MXXgxduHF2IUXYxdeoRw75jQBAAAAQDc40gQAAAAA3aA0AQAAAEA3KE0pzMzONrP3zazczL7TyfNXm9kuM1sRuV0fRE58kJk9aGY7zWxlF8+bmd0eGde3zGxSvDOic1GM3Swzq+3wnvthvDOic2Y2zMxeNbN3zewdM/t6J/vw3ktAUY4d770EZGY5ZrbUzN6MjN2PO9kn28x+F3nflZjZyPgnxaGiHLtQfc7MCDoAgmFm6ZLukvRpSZsllZrZPHdfdciuv3P3W+MeEN15SNKdkh7p4vlzJI2L3KZImhP5iuA9pO7HTpLecPdz4xMHH0GzpH909+Vmli9pmZm9csi/mbz3ElM0Yyfx3ktEjZLOdPd6M8uUtMDM/uDuSzrsc52kGncfa2aXSvoPSZcEERYfEM3YSSH6nMmRptQ1WVK5u69z9wOSHpd0QcCZEAV3f11SdTe7XCDpEW+zRFKRmQ2KTzp0J4qxQ4Jy923uvjxyf4+kdyUNOWQ33nsJKMqxQwKKvJfqIw8zI7dDVzC7QNLDkftPSTrLzCxOEdGFKMcuVChNqWuIpE0dHm9W5/+JXBQ5zeQpMxsWn2j4mKIdWySmaZHTGf5gZicEHQYfFjn9Z6KkkkOe4r2X4LoZO4n3XkIys3QzWyFpp6RX3L3L9527N0uqldQnvinRmSjGTgrR50xKU+rq7Lcwh/4G4AVJI939JEn/p7/9JgeJLZqxRWJaLmmEu58s6Q5JzwWcB4cwszxJT0v6B3evO/TpTl7Cey9BHGbseO8lKHdvcfdTJA2VNNnMJhyyC++7BBXF2IXqcyalKXVtltSx0Q+VtLXjDu5e5e6NkYf3STo1Ttnw8Rx2bJGY3L2u/XQGd39JUqaZ9Q04FiIi5+U/Lek37v5MJ7vw3ktQhxs73nuJz913S5ov6exDnjr4vjOzDEmF4jTohNLV2IXtcyalKXWVShpnZqPMLEvSpZLmddzhkHPxz1fbeeBIfPMkXRlZyWuqpFp33xZ0KByemQ1sPxffzCar7d/oqmBTQWpbGU/SA5Ledff/7mI33nsJKJqx472XmMysn5kVRe73kPQpSe8dsts8SVdF7l8s6S/uzpGmgEUzdmH7nMnqeSnK3ZvN7FZJL0tKl/Sgu79jZv8mqczd50n6ezM7X20rD1VLujqwwDjIzB6TNEtSXzPbLOlHaptgKXe/R9JLkj4nqVzSPknXBJMUh4pi7C6WdJOZNUvaL+lS/vNPGDMkXSHp7cg5+pL0PUnDJd57CS6aseO9l5gGSXo4suJvmqQn3P3FQz6rPCDpUTMrV9tnlUuDi4sOohm7UH3ONP5NAAAAAICucXoeAAAAAHSD0gQAAAAA3aA0AQAAAEA3KE0AAAAA0A1KEwAAAAB0g9IEAAAAAN2gNAEAkp6ZjTQzN7OKoLMAAMKH0gQASFpmlmlmZ0m6PrKpyMxuMbNJQeYCAIQLF7cFACQlM7te0k8kDexil7ckfc3dX49fKgBAGHGkCQCQdMzs55Luk5Qv6YeSzok8tUnSTEkPSDpR0p/N7MJAQgIAQoMjTQCApGJmZ0r6s6R6STPc/S0zGylpvaQN7j4yst+Vkh6WtFvSWHevCiQwACDhcaQJAJBsZke+3uXub3W1k7s/IqlEUpGki+MRDAAQTpQmAECymRD5uiCKfdv3OSFGWQAASYDSBABIVtGcf96+j8UyCAAg3ChNAIBksyrydUYU+7bvs6rbvQAAKY3SBABINvdHvt5qZl2edmdml0uaJqlO0lPxCAYACCdKEwAgqbj7nyT9Sm3LjS8xs+9KGht5Os3MJpvZPZIeldQi6Xp33xVMWgBAGLDkOAAgKZnZTZL+VVL/LnZZpbaL2/4lbqEAAKFEaQIAJC0zy5Z0euT2PUm1aitSiyUtdf4TBABEgdIEAEh6nV3cFgCAaFGaAAAAAKAbLAQBAAAAAN2gNAEAAABANyhNAAAAANANShMAAAAAdIPSBAAAAADdoDQBAAAAQDcoTQAAAADQDUoTAAAAAHSD0gQAAAAA3fj/Ukcvy7ZPEq8AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rcParams['figure.figsize'] = (14, 10)\n", "\n", "plt.plot(phi, tad, '-')\n", "\n", "plt.title(r'Tad vs. $\\Phi$ for $C_{2}H_{4}/Air$ flames')\n", "plt.xlabel(r'$\\Phi$', fontsize=20)\n", "plt.ylabel(\"Adiabatic flame temperature (K)\")\n", "\n", "plt.grid()\n", "\n", "plt.show()\n", "plt.savefig('2-Output/plot_tad.png', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "for i, cas in enumerate(gas.species_names):\n", " if cas in ['O2','CO2','CO']:\n", " plt.plot(phi,xeq[i,:], label = cas)\n", " plt.xlabel('Equivalence ratio')\n", " plt.ylabel('Mole fractions')\n", " plt.legend(loc='best')\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have just generated the skeleton of a script to perform a series of common equilibrium calculations\n", "to obtain the constant pressure equilibrium composition of a fuel/air mixture. Starting from there,\n", "you could modify your initial conditions, plot the mole/mass fractions of other species, change the\n", "solver or even try another fuel (methane, acetylene) without changing your mechanism.
\n", "Technically, adiabatic flame calculations could also be performed at constant volume: simply invoke\n", "the good equilibrate option of your equilibrate function, 'UV' (see 3.1.2), in your script." ] }, { "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": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }