{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Isolated Wing with Propeller BET Model\n", "\n", "This notebook shows how to set up a **BET disk** in front of an isolated wing using the Flow360 Python API. We load the geometry, define mesh refinements (including an axisymmetric region for the rotor), build a BET disk model from an XROTOR file, and submit the case through the project interface.\n", "\n", "The geometry consists of a BET disk in front of an isolated wing:\n", "\n", "![isolated wing](figures/isolated_wing.png)\n", "\n", "**Note:** The settings in this example are by no means a validation setup; they are crafted to showcase the capabilities of Flow360 and we have intentionally reduced node count and example FC cost. For rigorous validation, modify the settings as needed." ], "id": "7ecaae49" }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Create project from geometry\n", "\n", "We first import the Flow360 Python API, load the BET example files, and create a project from the isolated-wing geometry. This sets up the geometry object we will use for meshing, modeling, and post-processing." ], "id": "489308f1" }, { "cell_type": "code", "metadata": {}, "source": [ "import flow360 as fl\n", "from flow360.examples import TutorialBETDisk\n", "\n", "TutorialBETDisk.get_files()\n", "\n", "project = fl.Project.from_geometry(\n", " TutorialBETDisk.geometry, name=\"Isolated Wing with Propeller BET Model\"\n", ")\n", "geometry = project.geometry\n", "\n", "# show face and edge groupings\n", "geometry.group_faces_by_tag(\"faceName\")\n", "geometry.group_edges_by_tag(\"edgeName\")" ], "execution_count": 1, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a54f88e22f3e4bd58f71e32bd96f4b33", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/html": [ "
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     "metadata": {}
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[12:12:01] INFO: Geometry successfully submitted:                                                                  \n",
       "                   type        = Geometry                                                                          \n",
       "                   name        = Tutorial BETDisk from Python                                                      \n",
       "                   id          = geo-25e23e0d-fd3a-4772-90d6-47b912d35eff                                          \n",
       "                   status      = uploaded                                                                          \n",
       "                   project id  = prj-82a38889-787a-4db3-8b46-a6865fda2a58                                          \n",
       "                                                                                                                   \n",
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       "\u001b[2;36m[12:12:01]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Geometry successfully submitted:                                                                  \n",
       "\u001b[2;36m           \u001b[0m        type        = Geometry                                                                          \n",
       "\u001b[2;36m           \u001b[0m        name        = Tutorial BETDisk from Python                                                      \n",
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       "\u001b[2;36m           \u001b[0m        status      = uploaded                                                                          \n",
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           INFO: Waiting for geometry to be processed.                                                             \n",
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     "metadata": {}
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   ],
   "id": "881341eb"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Define BET disk refinements\n",
    "\n",
    "Here we define an axisymmetric refinement region around the rotor using a cylindrical volume and `AxisymmetricRefinement`. The cylinder approximates where the BET disk acts, and the refinement spacings control the local mesh resolution in axial, radial, and circumferential directions. Choosing a cylinder slightly larger than the rotor helps ensure the rotor-affected region is well resolved."
   ],
   "id": "cc17cacc"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "with fl.SI_unit_system:\n",
    "    cylinder1 = fl.Cylinder(\n",
    "        name=\"cylinder1\",\n",
    "        axis=[1, 0, 0],\n",
    "        center=[-2.0, 5.0, 0],\n",
    "        outer_radius=4.0,\n",
    "        height=0.6,\n",
    "    )\n",
    "\n",
    "    bet_disk_refinement = fl.AxisymmetricRefinement(\n",
    "        name=\"BET_Disk\",\n",
    "        spacing_axial=0.02,\n",
    "        spacing_radial=0.03,\n",
    "        spacing_circumferential=0.06,\n",
    "        entities=cylinder1,\n",
    "    )"
   ],
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
[12:12:22] INFO: using: SI unit system for unit inference.                                                         \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[12:12:22]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "768e09f4"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Define meshing parameters\n",
    "\n",
    "Next we define the global meshing parameters, combining an automatically generated farfield with additional local refinements. A second cylinder provides a region of uniform refinement around the wing and rotor, while the BET disk refinement from the previous step is included to resolve the rotor region. These settings control the overall mesh size, boundary layer resolution, and key refinement zones for the simulation."
   ],
   "id": "8206b22c"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "with fl.SI_unit_system:\n",
    "    cylinder2 = fl.Cylinder(\n",
    "        name=\"cylinder2\",\n",
    "        axis=[1, 0, 0],\n",
    "        center=[0, 5, 0],\n",
    "        outer_radius=4.1,\n",
    "        height=5,\n",
    "    )\n",
    "    farfield = fl.AutomatedFarfield()\n",
    "    meshing_params = fl.MeshingParams(\n",
    "        defaults=fl.MeshingDefaults(\n",
    "            surface_max_edge_length=0.5,\n",
    "            boundary_layer_growth_rate=1.15,\n",
    "            boundary_layer_first_layer_thickness=1e-06,\n",
    "        ),\n",
    "        volume_zones=[farfield],\n",
    "        refinements=[\n",
    "            bet_disk_refinement,\n",
    "            fl.UniformRefinement(name=\"cylinder_refinement\", spacing=0.1, entities=[cylinder2]),\n",
    "        ],\n",
    "    )"
   ],
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
           INFO: using: SI unit system for unit inference.                                                         \n",
       "
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       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "f79d204f"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Define BET disk model\n",
    "The BET disk model is defined from an xrotor file. This file contains a 2D database of drag, lift and moment coefficients of the airfoils used in the blades depending on Reynolds and Mach numbers.\n",
    "\n",
    "[Download XROTOR file](https://simcloud-public-1.s3.amazonaws.com/betTutorial/xv15_rotor_26deg.xrotor) to get the file used in this tutorial so you can inspect its structure. A detailed explanation of the xrotor translation tool is written in [BET translators](../../../knowledge_base/PreProcessing/bet_disk/bet_disk.rst).\n",
    "\n",
    "In the next code cell we will:\n",
    "- Define a cylindrical volume that approximates the physical rotor disk where the BET forces will be applied.\n",
    "- Load the XROTOR file using a dedicated `fl.XROTORFile` helper so Flow360 can interpret the aerodynamic tables.\n",
    "- Build a `fl.BETDisk` model from the XROTOR data and specify the rotor operating conditions (rotation direction, rotational speed, reference chord, number of loading nodes, and units).\n",
    "\n",
    "This BET model will later be attached to the simulation as one of the physical models in the `models` list."
   ],
   "id": "a9d2e216"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "with fl.SI_unit_system:\n",
    "    bet_cylinder = fl.Cylinder(\n",
    "        name=\"bet_cylinder\",\n",
    "        axis=[-1, 0, 0],\n",
    "        center=[-2.0, 5.0, 0.0],\n",
    "        outer_radius=3.81,\n",
    "        height=0.4572,\n",
    "    )\n",
    "\n",
    "    xrotor_file = fl.XROTORFile(file_path=TutorialBETDisk.extra[\"xrotor\"])\n",
    "    bet_disk_model = fl.BETDisk.from_xrotor(\n",
    "        file=xrotor_file,\n",
    "        rotation_direction_rule=\"rightHand\",\n",
    "        omega=460 * fl.u.rpm,\n",
    "        chord_ref=0.3556,\n",
    "        n_loading_nodes=20,\n",
    "        entities=bet_cylinder,\n",
    "        angle_unit=fl.u.deg,\n",
    "        length_unit=fl.u.m,\n",
    "    )"
   ],
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
           INFO: using: SI unit system for unit inference.                                                         \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "7c693ae9"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Define `SimulationParams`\n",
    "\n",
    "We now collect all inputs into a single `fl.SimulationParams` object. This includes the meshing setup, reference geometry, operating condition, and steady-state time-stepping. We also specify the physical and boundary models (walls, farfield, symmetry, fluid solver, and the BET disk) and choose which volume and surface quantities to output for later analysis."
   ],
   "id": "53735fa8"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "with fl.SI_unit_system:\n",
    "    params = fl.SimulationParams(\n",
    "        meshing=meshing_params,\n",
    "        reference_geometry=fl.ReferenceGeometry(\n",
    "            moment_center=[0.375, 0, 0],\n",
    "            moment_length=[1.26666666, 1.26666666, 1.26666666],\n",
    "            area=12.5,\n",
    "        ),\n",
    "        operating_condition=fl.AerospaceCondition.from_mach(\n",
    "            mach=0.182,\n",
    "            alpha=5 * fl.u.deg,\n",
    "            reference_mach=0.54,\n",
    "        ),\n",
    "        time_stepping=fl.Steady(\n",
    "            max_steps=10000, CFL=fl.RampCFL(initial=1, final=100, ramp_steps=2000)\n",
    "        ),\n",
    "        models=[\n",
    "            fl.Wall(\n",
    "                surfaces=[\n",
    "                    geometry[\"wing\"],\n",
    "                    geometry[\"tip\"],\n",
    "                ],\n",
    "            ),\n",
    "            fl.Freestream(surfaces=farfield.farfield),\n",
    "            fl.SlipWall(surfaces=farfield.symmetry_planes),\n",
    "            fl.Fluid(\n",
    "                navier_stokes_solver=fl.NavierStokesSolver(\n",
    "                    absolute_tolerance=1e-12,\n",
    "                ),\n",
    "                turbulence_model_solver=fl.SpalartAllmaras(\n",
    "                    absolute_tolerance=1e-10,\n",
    "                    update_jacobian_frequency=1,\n",
    "                    equation_evaluation_frequency=1,\n",
    "                ),\n",
    "            ),\n",
    "            bet_disk_model,\n",
    "        ],\n",
    "        outputs=[\n",
    "            fl.VolumeOutput(\n",
    "                output_fields=[\n",
    "                    \"primitiveVars\",\n",
    "                    \"betMetrics\",\n",
    "                    \"qcriterion\",\n",
    "                ],\n",
    "            ),\n",
    "            fl.SurfaceOutput(\n",
    "                surfaces=geometry[\"*\"],\n",
    "                output_fields=[\n",
    "                    \"primitiveVars\",\n",
    "                    \"Cp\",\n",
    "                    \"Cf\",\n",
    "                    \"CfVec\",\n",
    "                ],\n",
    "            ),\n",
    "        ],\n",
    "    )"
   ],
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
           INFO: using: SI unit system for unit inference.                                                         \n",
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       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
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     "metadata": {}
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   ],
   "id": "3e1f68fa"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Run case\n",
    "\n",
    "We now launch the simulation case from the project using the `SimulationParams` we assembled. The returned `case` object will be used to monitor the run and access results for post-processing."
   ],
   "id": "c7949afb"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "case = project.run_case(params=params, name=\"Steady BET disk\", use_beta_mesher=True)"
   ],
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "display_data",
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           INFO: using: SI unit system for unit inference.                                                         \n",
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       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
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[12:12:24] INFO: Selecting beta/in-house mesher for possible meshing tasks.                                        \n",
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     "metadata": {}
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[12:12:26] INFO: Successfully submitted:                                                                           \n",
       "                   type        = Case                                                                              \n",
       "                   name        = Case of tutorial BETDisk from Python                                              \n",
       "                   id          = case-806e077b-b493-47ce-b776-9a4f5ef46290                                         \n",
       "                   status      = pending                                                                           \n",
       "                   project id  = prj-82a38889-787a-4db3-8b46-a6865fda2a58                                          \n",
       "                                                                                                                   \n",
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       "\u001b[2;36m[12:12:26]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Successfully submitted:                                                                           \n",
       "\u001b[2;36m           \u001b[0m        type        = Case                                                                              \n",
       "\u001b[2;36m           \u001b[0m        name        = Case of tutorial BETDisk from Python                                              \n",
       "\u001b[2;36m           \u001b[0m        id          = case-\u001b[93m806e077b-b493-47ce-b776-9a4f5ef46290\u001b[0m                                         \n",
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      ]
     },
     "metadata": {}
    }
   ],
   "id": "7e021935"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Post-processing\n",
    "\n",
    "Finally, we wait for the case to finish, read the results, and make a few simple plots. We look at the evolution of integrated lift and drag coefficients and the nonlinear residuals to assess convergence and overall solution quality."
   ],
   "id": "0b64c3f7"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "case.wait()\n",
    "results = case.results\n",
    "\n",
    "total_forces = results.total_forces.as_dataframe()\n",
    "total_forces.plot(\"pseudo_step\", [\"CL\", \"CD\"])\n",
    "\n",
    "non_linear = results.nonlinear_residuals.as_dataframe()\n",
    "non_linear.plot(\n",
    "    \"pseudo_step\",\n",
    "    [\"0_cont\", \"1_momx\", \"2_momy\", \"3_momz\", \"4_energ\", \"5_nuHat\"],\n",
    "    logy=True,\n",
    ")\n",
    "plt.show()"
   ],
   "execution_count": 7,
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nU5nPjRq9VXu4l62IiIgGi8FLFvLRbVSQ4EWf5lsd8qEyoC0bdXHZiIiISgSDlyzks2C3P6oiGh+eSbeH9U6jqqAXFYHCFAwTERENFoOXLOSjVbrMb27sPVyzXuRWBNUhS/AyzK3aREREg8XgJRsy85LDnRl9Hhf8Hu2PYbiWjuQO1tUhL8r9XDYiIqLSwuAlC95oJ9b4rsFpH/5PTs873HUv1poXmXnp5LIRERGVCAYvWZi1/8+Y7NqNhXvuy+l5h7tdWnYbVbPmhYiIShCDlywIkctqF9NwZ146+swBdRXsNiIiohLD4CULUZc/L+ctG+bgRb5PecCDygIMySMiIhoKBi9ZiCo+8xs1nrPzyp2lhyuA6NW3IijzeSyZFwYvRERUGhi8ZCGqeM1vIt05O+9w7yzdE9YCr5DPbal54bIRERGVBgYvWVAVt/lNOHfBy3AvG8nMS7nfYwQvPZE44mrutz8gIiLKNQYvWVBUS3CRw8xLmU8LivoiuVuKSsfIvPg9RtYHGN4tCoiIiAaLwUsWFGEJLnKYeQn6ZPZjmJaNjJoXN3xuF9z60L3+6PAET0REREPB4CULLpHfzEvvMGVeei2ZF0VREPQOb+aHiIhoKBi8ZCFfwUtIBi/h/AcPkZiKiL4BZLme8Ql4hzd4IiIiGgoGL1lwqflZNgoN47KRNbsS1IMmGTz1cdmIiIhKAIOXLCi2zEtXzs5b5h++ZZtuPUDyuV3w6RtCymUj1rwQEVEpYPCSBZe1YDfSk7PzmgW7+Q8eevWOopDfbPsODHO3ExER0VAweMmCreYll3NejOAh/8tGMkAq85kt0kGv9p9BLzMvRERUAhi8ZMHWKp3Dgl1ZezKcmZcyS+ZF1tz0M/NCREQlgMFLFtzWIXXhHNa86MFD7zAMiZMBUsiWeWHBLhERlQ4GL1lQ8t0qHY1DiPyO6O9xyLywVZqIiEoJg5csuJCfgt2QvreREEB/VM3ZeZ3Idmxr5oWt0kREVEoYvGTBNuel92DOziuXbQBz08R8kYPwyv2WZSMfW6WJiKh0MHjJgq3bqHNvzs7rdikIyI6fPC/dmJmX5GUjtkoTEVEpYPCSBRcswUt3KxDPXZbEKNrNcwDRaxTsmsFLkDUvRERUQhi8ZMFtbZUWKtDTlrNzm+3S+V02ktkV61JViMtGRERUQhi8ZME25wXI6dKR2S6d3wBCBigBh8wLC3aJiKgUMHjJglskZEW69uTs3HJcf74LdvtjWjdTwMPtAYiIqDQxeMmCKynzksPgxTc8dScyQAlYl4285pwZIiKiYsfgJQuy2yjm8msP5DR4GZ6C3XBMBi/mH73RKs3MCxERlQAGL1mQmZee4Bjtgc7dOTu3mXnJ87JRNLlgN8CaFyIiKiHDErysWrUKLS0tCAQCmD9/PjZs2JDy2F/84hf4yEc+gpqaGtTU1GDRokVpjx9OMnjpLh+vPXBoe87OPVyZFxmgBBy6jdgqTUREpSDvwcvDDz+MZcuW4YYbbsArr7yCmTNnYvHixWhrc24zXrduHc477zw888wzWL9+PZqbm/GJT3wCu3fnLssxWG59e4DO8qO1Bw69n7Nzh4apVVpuP+C3LhsZQ+qyf+/fbdiJU3/8DP7x7oHcXCAREdEA8h683H777bjkkktw0UUXYfr06bjnnnsQCoXwq1/9yvH4Bx54AJdddhlmzZqFqVOn4pe//CVUVcXatWvzfakDkjUvXeUTtAf6DgF9h3Ny7jKZ/RimVmmnZaNwLPt9lX7x3PvYeagXX/rfF9Ha0Z+biyQiIkojr8FLJBLBxo0bsWjRIvMNXS4sWrQI69evz+gcvb29iEajqK2tdXw+HA6js7PT9pUvckhdzFsBlDdpD+Yo+yI3Z8z30o3MvFiXjfwe7T+DmCoQi2cXwMRUcxfsV3fmJpAjIiJKJ6/By4EDBxCPx9HY2Gh7vLGxEa2trRmd4zvf+Q7GjBljC4CsVqxYgaqqKuOrubl5yNeditxVWrg8QO1E7cEc1b0Md8FuwCHzAmSffenoixr32y33iYiI8qWou41uvfVWPPTQQ3j00UcRCAQcj7nuuuvQ0dFhfO3atStv12NsD+ByA6P04OXgtpyce7gKdp2WjWTmxfp8JsKxuD146WXwQkRE+efJ58nr6urgdruxb98+2+P79u1DU1NT2tfedtttuPXWW/H3v/8dM2bMSHmc3++H3+/PyfUORE7YFS4vUDdFe7Dt7ZycezgyL9G4aizzWOe8uFwKfG4XInHVmMCbiQPdEdv37X2RFEcSERHlTl4zLz6fD3PmzLEV28ri2wULFqR83Y9//GP84Ac/wOrVqzF37tx8XmJW5LIRXB6g6XjtfusbOTn3cLQrW7Mq1qUiwOw+CmeRednfFbZ938HMCxERDYO8Zl4AYNmyZbjwwgsxd+5czJs3D3fccQd6enpw0UUXAQAuuOACjB07FitWrAAA/OhHP8Ly5cvx4IMPoqWlxaiNKS8vR3l5eb4vNy1jewCXB2jSs0GH3gf6O4FA5ZDOPRzLRrJYF7AvFWnfu9GFmO2YgSQGL1w2IiKi4ZD34GXJkiXYv38/li9fjtbWVsyaNQurV682inh37twJl8v8RXr33XcjEongnHPOsZ3nhhtuwI033pjvy03LA33ZSPEAZaOAyrHalN19bwHjU2eSMmHMeQnnb9nILNZ1QVEU23NyGUluH5CJpOCFy0ZERDQM8h68AMDSpUuxdOlSx+fWrVtn+37Hjh35v6BBcgk9K+HSP7am47XgZe9rQw5eyvRW6Xzu7OzUaSTJxwaTeTmqJogPD/cx80JERMOiqLuNio1bz7zArQcvY/V6nJ2ZzaxJxzphVwgxwNGDY8x48SQHL3IZKZvMy4FuLXiZ1KAt53WwVZqIiIYBg5csyFZpoejBi8y27FwPDDHgkMGLKgY36TYT/XpgIneRthpM5qVbX+JqrgkBYM0LERENDwYvWbB1GwHA2DmA2wd07xvypF1ZsAvkr2hXLhslFutaH8sm8yLrc8ZUBwFomz5mMyeGiIhoMBi8ZMEjMy9y2cgb1AIYANj+3JDO7XYpRgCRr6JdWU+TruYlnEXmRQZZjZV+uPT6304uHRERUZ4xeMmCkXlRLHXOR5+h3b771JDPbxTt5il7IQfQBR2DF5d+TBaZF32gXrnfg6qgFwC3CCAiovxj8JIpIeDVC3YVlyV4OeYT2u3764Do0HZVlkFFvjIv1lbpRH5P9pkXmckJ+TzGslc+W72JiIgABi+ZE+YvdWPZCNCG1VWMAaK9WgAzBGV+LYDIV7t0+lZpl+2YTMjMS8jvNoqAsyn4JSIiGgwGL5lSzYyC4vKajysKMP2z2v03/zCktwjK7EUBgheZeclm2ag3rB1b5vMMKvghIiIaDAYvmYqbtRzCnfDL//hztdstfwXC3YN+i7I8b85ozHlxCl6MvY0yz5wYmRef25gdw+CFiIjyjcFLpiyZF1vBLgCMnQ2MmqQtHb3+0KDfIt+bM6areQlkmXmJq8IIhkI+tzknJovMDRER0WAweMmUddnI7bU/pyjAiZdo91/8n0EPrMt30WtfumWjLDMv1o6oMr9nUEPuiIiIBoPBS6b04CUuFCguh49t1n8AvgrgwDvAe08P6i3yX7CbplXayLxkFnz06gGWS9EG3MlsTj73ZiIiIgIYvGROD15icMOVsCMzACBQCZxwvnb/xXsG9RZBb34LdsPpWqWzLLjtsbRJK4qS12WjjR8cQntv8o7V4VgckTxtpUBERMWLwUum9ILdONxwCF00874GQNEG1rW+kfVbmJmX4V82kpmXTPdV6rUU62rnlMFPboOJ36zfgX+/ez1u+MtbtsdVVeDTP/sHTlv5DAMYIqIjDIOXTKnaL/4Y3EgZvYw6Gjju89r9Z3+c9VsEjZ2lCzHnJbtuIVlULKcCB43tBXJ77d/7sxa0/HnTHtvjfdE43m3rxt6Ofryzryun70lERMWNwUum3F685ZqC19SJzstG0qnXAFCAzX8B9r2d1VuU6QW7+a55cZ7zIjdmzCyLIYuKzcxL7luldx7sNe7Xlfttz1mzLR8e7svZexIRUfFj8JKpmvG4ouzH+HL0+tTLRgDQMM0cWvdcdtkXM/OS52Ujh12lA1lmTsytAezBSy73Zdp12AxeasvsHV6RuBm87DzUk7P3JCKi4sfgJQuyA1pJl3kBgNO+rd2+9RjQtiXj88vMi5xcm2tpJ+wOoWAXMDM3uax5sV5LYkbImnl5fz+DFyKiIwmDlywIPXpxDRC7oPFYYNpnAAjguZUZnz+kF+z2RvOTeZEBgMzwWMngI9PiV1mwK4uMzb2Nchd4WQOWxPkz1szL+wcYvBARHUkYvGRBNTIvGRx8qp59efOPwP53Mjp/SM+I5D3z4kkOXnwyeIlnWvNiz7xkOycmE+FY3PE+wMwLEdGRjMFLFgS06GXAZSMAGD0DmHIWAAH882cZnV927uRre4C+NHNefO7BTdiVXUZGwW4Or926BJVu2aizPwoiIjpyMHjJglHzkukLTr5Cu33jD0Df4QEPz3fBbvqaF71gN8PMi8yEyOUmY85LDofUhdPUvEQt1xnN8JqJiGhkYPCShYwLdqVxJwGNxwGxPuC1hwc83NoqLQa5P1IqQoi0rdIy8xKJqRm9t8zQyELfYB5apa0BS1wViFmCFGvmRQjteSIiOjIweMlCxgW7kqIAsy/Q7r/x+wEPl5mXmCoyrj3JlDUQcFw2srRPR+MZBC/6+fx6rYs/DxszJmZbbAW88dSZGCIiGtkYvGRB/kpXMl84Ao79HKC4gd0bgYPvpT00ZOkCynXRrjUjkm5IHZBcHOtEZj58CctGuZzzkpjFsQYviV1RDF6IiI4cDF6yoApZsJvFi8obgImnafc3/yXtoV63ywgGenM8Zl9mRDwuBV536oJdILN26eSal+yWjdp7IzjrZ8/jrqffTfMeiZkX89yJ1xjLIFtEREQjA4OXLIhsWqWtppyp3b7z1ICHyuxLbzi3RbvpNmUEAJdLgdet/WCZLFklZl7MvY0yy4A88vKHeGtPJ257KnUbeWIGyHruxEyL9fu9HX147NXdzMYQEY1QnkJfQCkx5rxks2wEAJM/od3uelHrOgrWpDy0zOdBe2805+3S/WnapCWf24VoPJ5h5sVe8yKDokhcRVwVcA9QGKRaioI7+6OoDHiTjkkMhNIuG1kKdj9z5ws40B1GW1c/vnbq0QP+LEREVFqYecmKXrCb7adWMx6omwKIOLD9ubSH5qtdOl2btOTLYsquzIok1rxY3yv96833eK+t2/GYxIF3tmWjxMyL5dgD3WEAwJq39w14HUREVHoYvGRBDDbzAgATPqLdfrA+7WFlvvxM2U3XJi35sthZOmJkXvTgxTK1N5PgZX9X2Lj/XooJuYmbREbSZF5iavI1R1gHQ0Q0IjF4ycKgCnalcQu0253/THuYHLef+4LdgZeN5BJQJsFLOCF4sdbMZPJ6mR0BgG0pMi/pWqUTMy+RWHKgEs3gOoiIqPQweMmC/PWY8ZwXq/Ena7etbwD9HSkPy1fBbn/COH8n2SwbJRbsAvZBdwOxZ15SBS+JrdJpuo0cMi8s2CUiGpkYvGRBVc1JL1mrHANUjQOECux9LeVhoTztb5RYYOvECD4y+KXvdL5sNne0Zl46+pz3JkoceGct4M1kzguDFyKikYnBSxaGlHkBgDEztdt0wYvcWTrHBbsya5G220jWvGRUcGuf82J9fbaZl74UgVraZaOk4MVh2Yg1L0REIxKDl2xku7dRoiYZvLye8pCQXwYv+cm8WJd5EvmzyJwkFuxazz1QzUtvJIYey8+XKlCTAVJlwGP7Hkg958W6L1Out1ggIqLiwOAlC0bB7mBPMDqDzIsvT8FLNINlo6xapQdf83KgK2L7PmXmRb/myqDX9p5AcmAiJ+xasy1cNiIiGpkYvGTBXDYaZPgyeoZ2e+AdIOLcHiy7jXpyXLDrtMyTyD+Igl17zYs5qC6d9j578JKqs8rMvOjBi6XmJTG7I9/TurcSu42IiEYmBi9ZGFKrNABUNAHBWgACOLjN8RBjzkuOW6WdlnkSZVpwG4uriOnFy4Opeenq1wKzgbJMZuYledko1d5G1hkzfdG4bRmJiIhGBgYvWRj03kZWdcdotwecNyQ05rzkPPMycM2LXPYZaH8ia3BjPZ8/w2Wjrn6tu6ixMmAcH1eTgwx5zUbmJYNdpa1LUKqArbaGiIhGBgYvWTAapYcSvdTL4MV5Q8J8F+ymq3nxZ7jsYw0cHDMv8fTXLjMvDRV+47HEot24KozrcKp5SVWw25eQsepM0YZNRESli8FLFsRQC3YBS+YlRfCSr4LdDGpeMu0Wks+7FMDjtmReslw2qqvwG1msxKDDeg6z5iX13kZyGSspeOln8EJENNIweMmCXDYadMEukPmyUa7nvMhuowzmvAwUfDgV62bzehm8VAa8xsTfxI4ja32LWfOSvGwk/yhk5qU/4TwdvQxeiIhGGgYvWTCXjYZwklGTtNuD2wCHkfZ5y7zEM2+VThzLn3SuhB2lk1+fWc1LRcCT8ueV5/C4FJT5Ugcv8jnZIp2YeZHf7+vsx2krn8GqZ5wLpYmIqHQweMnCkOe8AED1OEBxAbF+oKct6em8tUpHsxhSN0Dw0W/MjEkIXjLcXqBb/9kq/B4EUwQvsmvI53EZGz5azyt3jJbBT6qaF/mz3Pvc+/jgYC9W/m1r2msjIqLix+AlC2KoE3YBwO0FKsdq99t3Jj1dphfsJv4SHqpsal4GXDaKOy9BZbtsVBHwIOTVgrXEZSNra7esq4lZgxf95ynX94KKOXQbWa9VZcs0EdGIweAlQ9Z5IUNaNgKA6vHa7eEPkp6Sv8yjcZHRsLhM5XJjRiOL4x5c8NJpLBt5LZkXe6ZJXoPP44JH30wqFk8e/S+7syIOc14AMyMTsOym7dSWTUREpYPBS4as/3AfUsEuoC0dAUB7cvAif5kDqcfmD0YmQ+rkc5nOeRlqwW65peYl1XKP121mXqKWoEM+L5fZYgMsG1kDrfZe+4RfIiIqLQxeMmT9t/pQEy+o0TMvDsGLtcajJ4cdR5ltzJjZnJdw1Llg159h5iaTgl1ZgGv9PKzLRvL5ssSal0jitgF6RsZShHygm8ELEVEpY/CSITWny0Yy85Jc8wJY26Vzl3nJZc1LOEUWJ9PXy4LdyoAXwRQ/qzVj4nHpNS+WzEs0IfOSqttInsdaAH2wO5z2+oiIqLgxeMmQddloSAW7QNqaF8DaLp3DzIsx52Xou0pHUmRxBlewK+e8JNa8mNkdj1PmRW8zDyZkXlLVvHT3m+c/0MPMCxFRKWPwkqGcZl4qx2i3XXvtUZFOBi894VxmXjLYmFHubTTQslGqzEsGr4/FVSPLUm5plU7OmGifi9dtWTayZF5k0a0ccieLeVN1LXVbPktmXoiIShuDl0EYcsFuRZN2G+sH+tuTni7T23/7ornLvESy2R5ggDbtaDxV5kWvmUmTeem2LN9UBLwpa16MbiPLspFcGhJCGPcTMy+Jex45LRsdYPBCRFTSGLxkyLZsNNSTeYNAsEa739Wa9LTMJuQj85LRkLoBMi8yQJBBhZTJstFhfVx/yOeGz+NKuT2ArGnxOiwbWTudZQu07ESyBj3Wa7UWPx9kwS4RUUlj8JKhnC4bAUDFaO22a2/SU0bmJUcFu0KIzOa8ZFizIrMe3kHMeZFtyjUhHwCkXjayBCHyfeSykTW7IoMfGezI5SM5/yVsLBtZghdLzcuOAz3453sHUl4vEREVHwYvGbJWpgx52Qgwl446k4MX+Qs9V63S1kxKLjZmlBkQWYtivD6DVul2PfNSHfLq16MHGQmzZcylKcUYUicfs9a+BL0ysFFtt7IQ2Mi8WIIXWdQrhMDpt63Df/ziRby3vzvtz0xERMWDwUuGcj5evsJStJugLMebM1qDkYyG1A2YeTEHyDm9Pv2ykT3zEtBf0x9L3SptZF70rErcMmlXBnpynoux55GevTIKdi3dRjKj9cHBXuOx9/f3pLxmIiIqLgxeMmRvlc7BCWXmxaHmxZzzkpvMizUYSRzpb+XPoOAWMOtLPImZl4yWjbTMS9UAmZeIJUAyal70rErUsht3wOg2Um23cuktGlehqgI9lkBQBoXr3z9oeUz7rLe1deHkFWvxm385t7ETEVHhMXjJVC63BwAswUty5iXXrdLWYt10M2p8GRbsxhKKYrN5vVnzogUvA2ZePMndRrJN2uNSkrIyMitU5jOnBfcm1NPIZaP175nBy2G9DmbZ71/Dno5+fO+xNwEAv16/A5++83ns72KHEhFRsWDwkiFbwW4uTjiMBbuy9TndkhFgBiNxVdgGwiWSQURS5sWdebeRXDaSmZf+FDUvtjkvCe3QbkvwEjGe05eNfOaykbXeBTCLg99tM+tcDunByxu7O2zHPvjiTry5uxPPvbM/5c9ERETDi8FLhnJesFveoN32JHe6GK3SOV42StdpBNjbqNNlTwZqlU5XMyNrXqoTal7CKTIvfk/yxowxS7dT4pKSUbBrqYXpTghe5LKR3GMJAA71RqCqImlmYJuecdnd3qd/35/TycdERJQ9Bi8ZynmrdGiUdusQvJT5nWefDFYmO0onPp8ue5J6SJ1zIGLV0ad3GwX1ZaOU3UaWCbsue+ZFdhu5XYo5z0WfyCtv5WcYicVTZl6sQc2hnkhSx1F/NG5kZHYf7sOB7jAW3vo0Pv/zf6b8+YiIKP8YvGQop3sbAUBZvXYb7QEivban5GaFuc+8pP/j9rhd0OOEtMGLzHzIFmYps2UjvealTBbsOgc81jodmXlRBaCqwsiueN2WNuqEYl7rho3yXFV6wBSJqYirwthjCdCCl+0H7B1H1mDmw/ZevLDtAKJxgS2tXbasDRERDS9PoS+gVAh94SgnWRcA8FcAbj8QDwO9BwDfOOOpXLdKy8Ag3XRdyedxoT+qpl36iQzUKh1XIYRwDPIO98g5L3LZaOCaF2ttTVRVLcGTC16PfZJuUsFuTDWyOtUhr5H5OdQTMQp/5ffyOWnz3i7j/u7DfUYWBtDqZXxuF3629l18+5NTMKmhIulnHQohtA6pjr4oOnqjaO+LoLMvhp5wDL3ROHrDMfREzNu+iP59JIaesHYr/wxdiqLVaSlavZZLUeD3uhDyehDwuRH0apOOgz43gl4Pgj75vQchnxshnxsBr9u4H/Rqjwf1r5DXbQSYRETDgcFLhmTmJVexCxQFKKsDOndrS0fVZvBitkrnqmB34B2lJZ9bC17S1byYNSfOrdJCaEs7ic8DyRN2A0bBbrpuI/M8sbiwLRt5XfZuo5jDnBcZvFUGvMZ52rr6be93qCeaFLy8vafTuL+nvd+Widm8txPffVTrSIrGVYwfVYZn39mPO5bMQnXIi3uefQ8nTRyFMdVBPLRhF86b14x39nWjNxKD26XgQHcYVUEvXvuwA16Xgv6oiraufrR1hdHeG0VPJOa0Z2fR8rldevDjNgIbv8cFn0eb0+Nzm/e9+n2fWzHum49pBdoet/bn7nIpcCsKPG4FLkWB26V/KQrcbv3WZX65FC0b5/SYS1GMf3y4XFpApyj24E7eV/Rbl6JFfIrtObPuTXtagUsxX6MoOcrOElFKDF4yJH+R5KRYV7IGLxbGZoXh4V02AuTmirGkGhQrc9nGueYF0IKGxOcjMdWYtyJrXuQ1xfQOJ6M412jHVmyFwbG4sE349Xrs03dlrUyZZcNG+fMHvW4EvFpwltj6fLg3YixpSVtazeAlElfx4vuHjO9vf+od4/4zW/cD0LqRPrvqBePx323YZdz/4ysfYjB8bhcqg15Uh7yoDHhQHvCizOdGSGZF/G6U6ffL/DJT4kGZz20syQmhLbkJoeUPVX27iP5IHL2ROPqicfRH4+iLxNGr35r3Y+iL6sdZju/TszwyeRWJq4j0qUkB4JEsMeBREv/pk/7bpCxv4uuTn7c+p6R8LrP3zvy9Mnm/5L82GdyVupZRIfzhGycX7P0ZvGQo58tGgFn30mNvw5XFponzSQYrEs+sVdp6TLrMS8RolU4IXtz24KXMb39de58WHCgKUJlQsAtoQZYnoW5G+1d5wrKRJfOSOANGBjEyexW2ZF78XhdCPg/6oxGji6iu3I8D3WHEVYFdh/ps17t5b6fte2trtXV/pEyNKvMhLgTicYGPTm1AXzSOcbUh1Jb5UBHwoKHCj/oKP2rL/CjzuVEe8CDodRftv+Llnll9ekBjBjja0lYkpiIaV223kbhweMx6nDCeU4WWZYurQrsf127j+mNxIRBXgbiq6sdogbWqwvK8+aUKrZtMQDsWlvsysMtltksIIG47YQml0ogGUBksbPgwLO++atUqrFy5Eq2trZg5cybuvPNOzJs3z/HYt956C8uXL8fGjRvxwQcf4L//+79x1VVXDcdlpqUay0Y5/EUSqtNue+2ZF1mw25urIXXRzDMvmYz4T7W3kSz4VYVz8GNM1w164daXgqzXFLYEPNa6GkVfGpC/hKyt0om7R5sTdi2ZF8vPL9vQZeZlVJkP/dE4usMxvH/A3m0kZ9JkasHEUcbU3pvPPg4/XfsuPnVcE5Z+dBLWbmnDmceNRkXAg6iqDti2XgoURUHAq9XD1BT6YnJI6EGOagloZLeh/fHUQZCqRUnG/cSgKDGMEQ5R00CBVPI5xQDPO/+s6a8r6RWDeI/EYxjEjQSJmfXhlvfg5eGHH8ayZctwzz33YP78+bjjjjuwePFibN26FQ0NDUnH9/b2YuLEiTj33HPxrW99K9+XlzH5P3luMy968JKYebFMh43Gk5dfspXpnBcgsxH/qfY2kq/vj6qOr29PGFAHaLUHPrcLkbhqq3uxZl4ArbMprmr/apfLVh63Ys55MTIvCUPq4mb9jt+jLRsBQFunVvNSEfCgpsyL7nAM2/X9jWQ2xvozyeup8HsQF8KoR1r+6en47b8+gM/jwk++MBNrN+8DFAVfOmk8vnTSeOMc580za5r8rtIPXEYyRa+NcXFpg6ho5T10uv3223HJJZfgoosuwvTp03HPPfcgFArhV7/6lePxJ554IlauXIkvfvGL8Pv9jscUglGwm5dlo4O2h+Vmg0BuinaNZZMMu42sr3ESTdEqDZhLR07dSuaAOq/tcVmbYQ1eEgMk6zYAMlBxu1y2CbtCCKNV2si8WLqNfB6XEdTs14OTioAHtXq6R9bjTGoos13fjLFVxv1JjeVoqDD/u7xoYQue/s/TsfqqUzGmOogvL2jBly1BCxER5V5eg5dIJIKNGzdi0aJF5hu6XFi0aBHWr1+fz7fOObPbKJfLRvqguoRlI5/b7LDJxTRXY0idN0fLRrJg1yEY8qXZ3FF2GsliXfM99UF1ltfIbImRebFM0jXe36XYlq60AEa7b828WIM3uWzU1qkFL+UBL2oTgqlpoytt388eby6KTKgrw5ITtSxKy6hQ0dajEBGNZHldNjpw4ADi8TgaGxttjzc2NmLLli05eY9wOIxw2Ezxd3Z2pjl68OQ6rUOyYfCC+i/Fvnbbw4qiIORzo7M/lqPMi/NGik4y2VxRTrH1upLPl67gN3FfIynglHnR30Nes7Uw19YqbfmZrBOJyyxD6vosezsF9KyWLNitCHiSPpfpicHLuGrj/tH15fj6qRNRW+bFyUfXJf2MRESUfyU/WWrFihWoqqoyvpqbm/PyPkbBbi7/pW0EL4eTngrlsGjXqHnJcM6L9TVOopYJt0mvT5O5kTUv1QnBi9+T/J6JmRevpbbFaW8jwL7EFvKbP6vcndvv1QaqAeaclwq/B7Vl9szLsWPMZSKf24UpTWYwM6GuDC6XgiUnjkNzbSjpZyQiovzLa/BSV1cHt9uNffv22R7ft28fmpqacvIe1113HTo6OoyvXbt2DfyiQchLwa4RvBxKekr+8s3FslGmu0oD2RXsOk1VTbdFgDmgzh4sOA2qk6+XmRUZpCS2SluzP7bgxVI31KmP8vd7XEY9kZzoa615kSbW22texlQHjD/3CXX254iIaPjlNXjx+XyYM2cO1q5dazymqirWrl2LBQsW5OQ9/H4/KisrbV/5YLZK55A186Laf9mHcrhFQDZD6pzqTxKlmrALWJedkq87VcGuGbw4ZF5kwa5lkq61VdulT1HVXq+9p0sxtx0AgG59DyO/x2WbKwMA5QmZFzkK3/hZ9bbmixdOwJnHN+GYxtxuA0BERNnLe6v0smXLcOGFF2Lu3LmYN28e7rjjDvT09OCiiy4CAFxwwQUYO3YsVqxYAUAr8n377beN+7t378amTZtQXl6OSZMm5ftyU2qo9OO2c2c6/sIetGC1ditUINIFBMzlilxuERAZ5lbpVK8/POCyUXK3kU+foGu2RJuZF1kHI9uo5Wflcbvgcmkj4WOqMHaP9nvcRsGuVBXyotxvBi+NlQHb8zJo/a9PT0/6eYiIqDDyHrwsWbIE+/fvx/Lly9Ha2opZs2Zh9erVRhHvzp074bKk/vfs2YMTTjjB+P62227DbbfdhtNOOw3r1q3L9+WmVBnw4pw5R+X2pN4g4AkCsT4t+2IJXuSsl1zsLB1OmJmSTmbBS+rMi1P9itSRsmBXz/ZYMy9GkbH2nFGwq5qZF7f+/j63C+GYaiyx+Szt1TE1bgYvXpdtOQkAasv8KLfUx5x2jNa+LofttYxiXQsRUbEZlgm7S5cuxdKlSx2fSwxIWlpaHKdNjljBGqBLD15qWoyHzYLdXAQvWdS8uFMv+0ixQWZe5L43VUmt0qkzL3LvIq9D5sXrsmdl+ozMi2JcS180jq5+M6hJXLIaVeazBTSfmK4F1b//+gKs/NtW3PCZY50/BCIiKhjubVRowRqga09Sx5FR85KD/Y3Cg5jzkm5jxmiKvY0Aa/CT/PoePRArD9j/s0useVFVYbyH0SptbANgbZW2D7Azlo30x+XPIoMmv9eFkN++LFRb5kNtmQ+NlX64FAUnTqgFAMxtqcXDX89NXRYREeUWg5dCS9Eube4sncuC3YFrXgbamNE6xTabVmkhBLr1ZZ1yf2LwYp/zErUUL8tBeHJoX0xVk/ZWMoIX/fU+/XHZWWQELx43asvsS1a1ZT4EvG78fdlpECj8fh1ERDQwBi+FJot2E4MXf+4Kdgc1pC5FzUtcNTeZcxpSl+r1vZG48brE4CWxw8n6Wp/T9gCyYNdtX1Lq04MjmaWRxblx/Xi/x2UryC33e4ysT0XAvpxERETFi//MLLRUmRdvHua8ZLBsNFDwIgMHALYBcVKq7QVk0axLMTMtxmsSMi/Wlml5PtkOHY2rRqu20W2UsGzkTci8mOdy2/YlyqQGiIiIig//9i40Gbz05i/zklWr9AATdq3LSY4FuylqXmTwUu73JE0pDiRkXvotQ/XksUbBrmouW8mlJHkdchsAb0LmRfJ7XSizZH2sgRgREZUOBi+FJtujwx22h8t8Ocy8ZDGkzthYMUXNi8x6ANl1G8lBcYlLRkBy5kVer3VYnMcYUqciLneVTlo2sncbJQUvCT+/yuCFiKgkMXgpNL8+EbjfvqGkXPLoyeneRgP/cctAIJoyeNEedynmUo6VL8Wcl1SdRoCZeelPyLxYl5eM7QEsNS/eFN1GRuYlYdkocc5NVHX+GYmIqLgxeCm0gB68hLtsD8tdkXPTKi27cDKveUkVvETS7GukvYdz5qZLD17K0mRewkbmRS4bmcGHDEjiqkBMLhvpAY1cPpKZFxnUJGdeErcGYJEuEVEpYvBSaH59r5ywPfNitkrncNkoi12lUxbsJsxfSXp9imWjnnDqZaPEzIucMWPLvMiCXdVasGsOowPMJTY52C65YFc7btV/zEZ9hR93/ccJICKi0sNW6ULzO2declWwK4SwFOxmsmwkC26d60HMHaWd93hKWfOSLngxtgfQu41icdvj2vuZrdKJQ/JkEJM4pC5V8HLWjNE4a8Zox+snIqLix8xLocnMS3+KzMsQC3atyzeZFeymz7xEE9qUM319uuBFXpdZ86JnXmzLRub2APEBu41SFOxmkHkiIqLix+Cl0FLUvISMjRmHlnmxFs5m0irtdaeveTF2e06RefGnapXuT13zkpR5cZhLY92YMaral41SFuwO0G1ERESliX+bF5pfb5WO9gBxM8siC3YjMXMc/mBY9yhyGuefaKCCXbNYdnA1LxUO3UZGwa6seXGYS2N0QcXMVmmPMX03sVU6edlIUcxgh4iIShuDl0KTy0YAEDGzL9ZfvEPpOLLuKJ04HM7JQAW7ctkoVSCUKnhJ121kFOwmZF6sBbvyvDFrt5Gxq/TAy0ZVQW9GPz8RERU/Bi+F5vEBHn2/HUvdi9/jMuao9A1h6SibAXWA2akz0LJRqg0MjQm98Sy6jRIyL7LmxalVOhJXkwp2ZbBidBs5FOzWl5vbAhARUWlj8FIMjHZpM/OiKIqxv1HPENqlI1m0SQOZt0qnDF5SLhtpAZhzwa498xKOJWdejFqcmGpstJhY8yKDHqcJu/UVDF6IiEYKBi/FwGiXTug48suOo6FnXjIZUAfYMxxOIhm3StuvuSuDzEt/NA4hhNlt5E2uedEyL4lD6uw/m9OEXQYvREQjB4OXYuCQeQGAkG/os16y2VEasBbsOs95yTjzkmLZyHHCrp55UYVW02LdmDH5uqyZF33ZyGMPpOTPasu8cNmIiGjEYPBSDGS7dIpZLz1DmPXi1LmTjs8yhj/usHGhLJZNWbAra16izq3S6bqNAC37EnYYUmdkhGIOrdIJmRdZAMzMCxHRyMTgpRikWDaS7dLDW7BrHudUtCtrWVINqZMBR39Ch1T6zIt5rnBMddwewGeZPxNLWDZKzALJoIU1L0REIxODl2KQIngxd5bOQcFuhsGLtTbGqe7F2NE5xbKRUb9iKdgVQqA7krrmRVEUc8puNG5sD2DrNkqzbJRYfxPwJNe81HHZiIhoxGDwUgxS1LyU6QW7fTmY8+LLNPNiCQScOo7MVmnnZSOZ7YjEVKh6kNEbiUPoK1BOwQtgzdioloJda+ZFMc6bWLCbWIwsz5U454WIiEYGBi/FIEXNS9Cr/aKXbcaDkW3Ni6Io5jRbh8xLdICCXWudinxvua+R26XYAhL76+Ssl/Q1L9G4amSEZECWlHlxCF4aKwOO70tERKWHu0oXgxQ7SxuZl6EU7GbZbQRomYxoPI5oLLlgd6Bdpa0BR180jqDPbQQvZT53yim35qwXdYAhdcKoifEZQ+oSMy8yqHHhrv84Ab2ROJqqGLwQEY0UDF6KgbFslNhtpGdehlCwK7MU2WxK6PW4gEgckXjy+8bi9sAhkdulZW6icbPl2ew0Sr10Y828OG3MaG2VTvyZEpewrAP5Pj1jTMr3JCKi0sRlo2IwwM7SvUPKvGQfvPgsbcmJzNH8qfcJSuw4MjuNUi9dySxLOKoay00Bp8xLTDVqcXyeFJmXDJfIiIioNDF4KQZy2ai/w/awGbwMX80LkH7KrrFslKJVGrAX3wLpp+uar7F0GzltzGiteYkl1rw4LxsREdHIxL/li0GKmhdj2WhIBbvJ02oHYl2iSSRbpdN1L8ngoS8p85IueNEzL7EUNS/6FN1wTDWvQQ9aUnUbERHRyMTgpRikqHkxW6VzMWE3+2WjqEOrtDmkLs2ykbEEpNe8hFNP15Wsc17CDpkXmQ3qtsy8kQFUZcJ5GbwQEY1sDF6KQYqaF9nq2z2EzEu2u0oDliyHY+ZFznlJ/Z+OHA4nh82Z3UYDZ156I3FzoJ0lKHHa7doIXhJmuAQZvBARjWgMXoqBzLzEI0C033hYLrMMpVU6Eo0BEBnvKg1YZqo4DamLyTkvA2de5PKP7DYqT5N5kZ1Ie9r7jIF21sFyTstU8meqTOhiYs0LEdHIxr/li4Gvwrxvyb4MuWC3bQu+896X8azvW2jp3JD55aQr2M0g8yJbnOWeTD0ZFOxWh7QAZMfBXgBa9sRpzov1GuXMmMogl42IiI4kDF6KgctlBjCWuheZeRlU8BKPAr/9dzRGP8R4Vxs++sqVwKHtGb00bcGu0SqdQbeRvmzUlUHBbrWeZfngYA+A5HH+iZkeayYmMSjKpr6HiIhKD/+WLxZG3YsZvMjajUFtzLjt70Dnh+hXAnhXHQuP2g888Z8ZvdQs2E09YTfdslEwoVU6k8yLDFY+OKRlXmQmRkrKvFgClMRAKtUUXyIiGhkYvBQLWffSn5x5CcfMnZQztukBAMCa4Jm4JLoMquLVAprtzw34UhkoOBXsDrS3EWCf2QIAnX0DdxvJYEUW5CZmXhJrdphdISI6cvE3QLFwmPUia16ALKfsqnHgvWcAAE/7TsMOMRq7j/6C9tzfvw+jIjYFrydNwW48g1bphAm7XeEogOSuIKuqoC/he/uxLpdie89Md8kmIqKRh78BioXDspHf44Jb/4WdVd1L29tApBvwVWCrGA8A2DPjCsAbAna/DGz5a9qXW6fZJpKt0umH1NmDF5l5SZzHYpUYrCQuGwH2bE9iJoYrRURERw4GL8XCGFRnZl4URUFoMHUvu/TOoqPmoC+u/VZ3VTYCJ31De3zN94BYOOXLffqcl4hj5kUv2M1me4B+PfOSZmPGxGClOuRLOsZaZ5MYPGXTCk5ERKWNf+MXC4fgBQBC/kG0S8vgpXm+OaTO4wJO+RZQ3gQceh/4589SvtybJvOSScGudXsAIQQ6M9hVOjF4SczEAPaAJSl44TISEdERg3/jFwufc/Aip9JmFby0vq7djp1j7G3k87i0AOkTN2vPPfcToH2n86WkKdiNZVKw6zGXjXojcaPYOHEei1XQ67ZlT5yCl3TLRizgJSI6cvBv/GIxQOalJ9OC3XgMOPCudr9+KsKJmxwefw4wfiEQ6wNecM6+mAW7qVulPWkzL+ayUZeedXG7lLRj+xVFsRX0OtW8pM28cNmIiOiIwb/xi0Wq4MWrZ14y3d/o8A5AjWrFuVXNyRszKgpw6jXa/dd+Z2vNltIV7JrLRun2NtIzN7E4Oo16F8+A81esDUwDZV4SMy1ONTJERDQyMXgpFjJ4iXTbHjZrXjLMvOzfot3WTYZQFGPEv+2X/cTTgbop2nu9/eekU8isRrqC3Uz2NuqLxI1i3XT1LtKEujLj/oyjqpOety0bJQQvP/nCTDTXBvGTc2cO+D5ERFTaGLwUC3+5djvUmpcDW7Xb+qlG1gVI2FVaUYDjz9Xub30i6RQyMHHeHmDgzEtAn0/TF42bbdJp6l2kqxYdgwsWjMcL137MuWDX2m2U8P7TRlfi+W9/DP8+56gB34eIiEobg5di4U+e8wKYg+oyrnnZ/452W3eMLXhJqgmZ8int9r1ngEiv7an0GzPqmRc1DLx8H9D6ZtIxMvDo6Isay0YV/oEzLwuOHoWbPnscxlYHHZ9Pl3khIqIjB38DFItUNS9yZ+mMa170zRdHHW10GrkUh2WexmOBqnFa4e4HL9ie8qZdNtIeG/vP7wGPXwXcsxB4/RHbMXKTxY7eqNEmnUnmZSDpCnaJiOjIwd8AxSJlt1GWy0aHd2i3NS22TqOkYllFASaeqt3f8bztqXRzXmJxgWOUXajY8nvzwTXfs2VvZPFsVziGwz0RAJnVvAzE3iqdunOJiIhGNgYvxcIIXuwFu2W+LAp2I71A9z7tfk2LkXnxe1P8Mbd8RLvd8Q/7pXhSLxtF4irOcv8LCgQwaZGWvenaC7z+kHGMdRuADw/36o/lOHhh5oWI6IjF3wDFwqcX7Mb6gHjUeDikF+z2ZJJ5kUPn/FVAsMYYz59ygNv4hdrtnk22jI+ReXGY8xKLq5in6EXBUz8NzPt/2v03/2Qc43G7jB2kdxzQghenAtxsyW0LtPv8T5eI6EjF3wDFQmZeAMedpXsz2duo/QPttkbbjFEW7AZSDYerbgaqxwMiDux80Xg4VcFuXBXwiChOcOlD8MYvBKafrd3/4AWgu804tkZfOtrcqhUgN1b6B75+J12tQKQHgGXQHjhRl4joSMbfAMXC7QU8epeNNXjJpubFqHfRgxd9V+e0v+iNpSOz7iVVwW40ruI4ZTsCShRqqA6om6y91+hZgFCB9542jpUTcuWE3aaqwMDXn6htM/DTmcDvLwAATLTMgeFEXSKiIxd/AxQTh0F1WdW8yGWjanvmxZqxSNKiLx1Z6l5SzXmJqQLTXVp2R4w+QSv6BYAJegC0c71xbOIy0aCClxd+BsT6gW1/B3oPYUZztfFUc+erwP2fBh7+EiCSl7eIiGjkYvBSTBwG1WVV89K5W7ut0ga1GQW76TIvRt3Lq0axcKqC3WhMxdHKHgCA0jDVfGLcAu1257+MhxLH9TdVZhm8qCqw/Vnz++3PYsbYKuPbU1+/RssWbf4/rWCYiIiOGAxeiolDu3SZP4ual65W7baiSTvNQDUvgLbsUz1Oq3vZpQUfZsFuQvCiqpisfAgAcNVPMZ9onq/d7t8C9B7STmvZWNHvcTkX7B7YZtSzJD/3jhmMAcB7z6CmTAuI/IggFDloPtexG0REdORg8FJMHKbsGgW70QwyLzIDUTEaANCfSc0LAIw/RbvVl46MvY3i9uWYaFxgkkvLvKDeknkpqwNqj9bu73kVgDmoDgBGVwWS58ysuxW4aw7wy0VAtC/5mg69Z/9+31sAgCs+NgkT/QmbSXZ+mO6nIyKiEYbBSzFxyLzIZaMBJ+wKYcm8aMGLUfOSas6L1GIPXmTmJRKzv2e8tx1NymHtm/pj7OdoOk671YOMKsuyUX1FQqfR648A61Zo99veBp5bmXxNB/XgRa/fkfNrrv7EFDz+laPtx1ozL2qcmRgiohGOwUsxkbNewtaCXS14icRVx3H9hr7DWnErYC4bRTMo2AXM4EWve/EZE3btmRfloNYi3YYaIFBlew6Nx2u3+7S9jo631KeMse5VtPVJbVsBABg1Sbt12NnayLzImpyuVqMw193daj+2w5J5eeEO4L+nA5t+l3xOIiIaERi8FBOHzEvQZwYefemKdmXWJVgLeLRMhyzYDQyUeakZD1Q1A2oM2PWisWyU2G2k6N1Mu9GUfI7GY7VbfaPGeRNqcfsXZmL2uGp8YW6z9tw7TwEPna91U038KPDVv2mPH9wG9Bywn+/Q+9rtuJO0WzVq1NMkFehal43W3qTdPnaploUhIqIRh8FLMXEIXnwel9G63BtNU7SbUO8CwDJhN4N9gCxLR3LZKKYKqKqZfXF37gIAtLoakl8vl40ObAVi2n5Gn599FP502UIsnFSnTQ3+y1KtMPj4LwDnP6LVysjamV0b7Oc7qAcvDdO0gAwAZMZFBmq1E7VbuUwUT/h8LHNniIho5GDwUkyM4MVekGq0S6ere5G/0CvN4CWjVmnJFryYxbXWdmlPp5Z52edqTH59VbO2LYEa0wKYRFuf1OpWyhqAz67ShvIBQPM87XaX2WaNaJ+ZTamdaAZkMkDr1IuGx87Vv9eDl8T31YuHiYhoZGHwUkxkt5FlSB2Q4aC6Lv0XeoW5pGMOqcvgj7lZX55pfR0+lxmwWIMXb5cWULR5HIIXRTGXjvSiXZtND2i3J5wPeCwzYMbM1t/3DfMxOSnYXwWERgEV+vt16ZtOykBt7BzttrtNy7rs2WR/z/2WYKZtC7DjBefOJiIiKikMXoqJw5A6wNwiIKPMi2XZyCjYTTfnRaqdqBUMx/rhtbQpW2e9+Lq0ZaMDbofgBTCXjqyBCKAFDO+v0+4f/wX7cw3Ttdu2LeZjstNo1EQtKJI/k1w2kreNxwKKC4AAeg9onUuA2aEkMzFtm4G7TwbuPxN46D/s79+936yvISKiksDgpZg41LwA5qyXvrQ1L/YBdQDQn82ykcsFNGrBh2vfG5YtAvSaF1WFv0dbnjnodSjYBYzXJ2VePvin1glVMUarYbGSw+669gB97dp9GUzImpZymXnRf0ZZ3FsxGiirN5+TXUeTP6HdHtimTep99bdarQ2g1cFs1/dxevk+4L+PBe6aZ04Y/uMlwP+cCrzxB+2YDb8AfvM5c9NJVdW+iIioYBi8FJMBgpf0mRdZsDvGeCirzAsAjJ6h3ba+bpn1ov+i7mmDW40gLhS0exwKdgFL8PKmfb8hWTg76WPmfkhSsNq8ZrnMIzM/cvCdDMi6WoFov1kTVFZnBjbdbWbw0rIQcPuAWB9weDvwxiP6efT3WXerVjez+logHtY6mdYsB176JfDG74G9rwF/u14LYJ74T+36n/kh8PrvgZUTgfvP0jqfNj0IvPaQFvQc3qEHaRHtGnsOcs8lIqI88RT6AsgiRfAiZ72krXnplMGLteYli8wLADTpwcve1+B1nwYgbta8HNY2ZNyLUXB7fM6vb5wOKG6gZ79WRKvvsYRta7Xboz/m/LqGqVrmZf9mYNz85MyL/Jm692nLQwDg8mqzZozgZZ8ZvNS0AKMmA21vARvv154LVAMXPQGsmgd88A/gdj0DVDFGO+f257QvqXsf8MeLze833qd9AcDOfwI/npDwQygABOAJaC3aatTskopHtCxRuEur9/EEAW9AO9bl0V6rKNoSmPGV+H3iY+4BnncBrsRjEs850POJ53C6Jrf5nPw5APN76/1Ut2mPsXy+ac8zwDHGewwkk2MyPyzz82V6wiPtfFS03H6gblLB3p7BSzHxpci8DFTzosaNCbS2mpdsCnYBW+bFl7iztD7j5UNRD487xV883qBW97L3NeDDl7TgpVMPSqBos12c1E/Tshuy7kW2SY/SMy/lMvOyVwuMAG25SFHM4KVjl1kLUzVO62Jqewv458+0x47+KFA7AZh9IfDSL7THXB7gC78G3noU+Ncq/bwNwOwLgOdv074PVGmFwTJ7VFYP9B4EhKpld3zlQN8hAEL7n1kOCgT0x3X6gD8iohFh1GTgipcL9vYMXoqJNfMihPGvk5BX1rykCF56Dmg1HYrLrAFBhhszWtVP0zIa/R0Y5zuI/agwl43adwDQghe5pOToqBP14OVl4NjPmb/0x84GQrXOr5E7VO/fDPR3mG3ScgKvtduoWwYvdfbndr+i3XqC2vuMX2hmSgDg6DO0249/X8uEbH0CWLwCaD5RC5I2/5/W5r3kN0DT8VrdzjtPAqdeA8z5CvD7C7Sg5cuPaX8+258FJp6uZVf2btKyROVNwJ5XtKCofgqw60Ut4+MNAu27gLJRWqAZ7dOCnGif9ucmBACh3QrV8iW/jzs85/ClpjsunnDOxNclvmfi6xyeVxOekz8DYFkyk49Zbo3nM30Og3xd4nMDyXCZL+PVwEzPl+kJj7TzUVFL9ff5MBmW4GXVqlVYuXIlWltbMXPmTNx5552YN29eyuMfeeQRfO9738OOHTswefJk/OhHP8KZZ545HJdaWDJ4EfovOF8IABDyy5qXFMtGst6lvBFwm3+kGW/MKHl8WiDR+gamKduxETOSMi+71HrbHJgkR52o1Y7IoXNbn9RuJy1K/Rqj42iz2alUNc78n0NmXuJhbRovYAZpMvOye6P+uqO0oG/8yfb3kEtWvjLg334G4Gfmc6FaYOkGLXPi0j+rLz6o7WxdP0U735cfNQPKUC1Qc4H5ejkjBwCOmmven3i6ed+6CzcREQ1J3gt2H374YSxbtgw33HADXnnlFcycOROLFy9GW1ub4/H//Oc/cd555+Hiiy/Gq6++irPPPhtnn3023nzzzXxfauH5ymCsGVuWjsyalxSZl67kehfAumyUYeYFAJpmAgCmYAcAS8GuXvPyoaiHx5XmPxs5zn/3RuDQduDdNdr30/4t9WvkL/bufcD7z+rXcbz5vDegZTAAM7gxghe9eFgu0cg6m6qxwOhZWhbkrNu179PxBs3ABdDuN0y1r81znZ6IqCjkPXi5/fbbcckll+Ciiy7C9OnTcc899yAUCuFXv/qV4/E//elP8clPfhLXXHMNpk2bhh/84AeYPXs27rrrrnxfauEpiuOguowzL5Z6F8BSsDvQ3kZWet3LMapWdxJJzLyIenjTZXJqWrSgQcSBR76iZUtGTTYH2DnxV2gTegGt28dyHQb5s+2TwUud/XFplKWA7Ct/Ba56EzjxYhAR0ciR1+AlEolg48aNWLTIXDJwuVxYtGgR1q9f7/ia9evX244HgMWLF6c8PhwOo7Oz0/ZV0uTSUX+78ZCseelNVfPiMOMFMFulA9lkXvR25wnx7QD0OS9q3Ojk2S3q4HUNkIE4/lztdu8m7XbWfwyctZB7HMnputbMC2DWtiRmXpqO1zt2dNZlG3+5bbsEIiIaGfIavBw4cADxeByNjfaJrI2NjWhtbXV8TWtra1bHr1ixAlVVVcZXc3Nzbi6+UAJV2m1/h/GQ7DbqHTDzMsb2sFHzkk3mRc+QNKj7UYkebdmoYxegRhFXvNiLUekLdgFtCwBZx9JwLLBg6cDvO/Us874naK8jAcy6F0kGL74ye6Azdi6IiGhkK/khdddddx06OjqMr127dhX6koYmWK3dWoIXWfPSk6rmxWHGCzCIVmn5/voSzlRlp1awq4/rbw8cBRWu1K3SxjlqgEueAc69H7jw/+x7GaUy8zzz/rz/ZwZxUmLNSt0x5v3QKPO+bK8mIqIRK6/dRnV1dXC73di3b5/t8X379qGpyXnEfFNTU1bH+/1++P3+3FxwMZC/tOWofJg1LymH1DnsaySEGFzBLqAtHXXswjTXTq3mRR8adyigBTUDZl4Arcj22M9l/p7egFajsvVJ4LRrk5+XGzgCWku4tYbmo98F3nsGmPlFFtUSER0B8pp58fl8mDNnDtauXWs8pqoq1q5diwULFji+ZsGCBbbjAWDNmjUpjx9xZFeNddlI1rxk0W1k3Q06kM2yEWAswxzv2q4tG+mZl4N+GbzkKUBoOQVYfIu5QaVVs6W1vrzJaCMHoM2Q+dZbWlcRERGNeHmf87Js2TJceOGFmDt3LubNm4c77rgDPT09uOiiiwAAF1xwAcaOHYsVK1YAAK688kqcdtpp+MlPfoKzzjoLDz30EF5++WXce++9+b7U4mDUvLQbD5UZNS8OwUssbI7MrzRrXvqjZvCSdeZFL3o9QXkXz8VVY6+h/T5t6caTSeYl18ot+ymp0eTnWZhLRHTEyHvwsmTJEuzfvx/Lly9Ha2srZs2ahdWrVxtFuTt37oTLMl/j5JNPxoMPPoj/+q//wvXXX4/Jkyfjsccew3HHHZfvSy0ODjUvxsaMTstGclsAt0+rNdHJNmlFGUSm5KgTAQBHu/bi+b7DxmC4Nm8Wy0b5cMwngXdWAydfUZj3JyKiojAsE3aXLl2KpUudO07WrVuX9Ni5556Lc889N89XVaQcal6MzEskDiEEFGtdh7VN2vK4saO0x2U/PhOhWhzwHYW6yIcY2/asUfOyx98CoGvgVul8+fwvgB3/AI5ZXJj3JyKiolDy3UYjjkPNS1DPvMRVYatlAZBmQN0gi3V1u8q1IXELtt+pPdBwLNqhBVZph9TlU6ASmHqmttMxEREdsRi8FBuHmpeQZWPFpLqXTufgRc54ybpYV7d1lDYosDx6UHtgwqmIqVpA5ClU5oWIiAgMXoqPQ82Lx+0yZrUk1b3kKfOye9QCtAqzhgYTTzM2afQVKvNCREQEBi/Fx6HmBbDXvdik2hpA7ms0yEDD6/Xi5uiXsD00Azj9emDSx7WtAoD0GzMSERHl2bAU7FIWjJqXdkAIowg3mGrWy0CZl0EuG/k8LjyuLoC/5Vz85HRtp2mZeRlwwi4REVEe8Z/QxUZmXtQYEO01Hi6TU3YT9zeSwUvCnJNBbcpo4dPboa0FwjE98+IrVKs0ERERGLwUH18Z4PJq9/sOGw+HUu1v5LA1AGBZNhpk5kW+LmzZyZqZFyIiKgYMXoqNogChWu1+7yHj4TKn/Y3C3UC4U7ufWPMSHVrBrlPmRQYvBRtSR0REBAYvxUnuktx70Hgo6HUo2JXTdX3lgL/CdoqhFuzKjqJIzLJspGrLRnnb24iIiCgDDF6KkUPwIjMvPdaal8492m1F8r4+smA34B1c5kUGPWFL8CIDGWZeiIiokPhbqBg5LBvJmhdb5iVFmzRgDqkbbOZFLjc5ZV7YKk1ERIXE30LFyCnz4rQ5Y4o2acA6pC6Hy0ZGzQuXjYiIqHAYvBQjh+BF7izdl2HmxZzzMsiCXY9Twa6seeF/NkREVDj8LVSMnIIXfcJuj3Vvoy695qVyTNIpZItzYNDLRmyVJiKi4sTgpRilWTaytUqnrXnJR+ZF39uImRciIiog/hYqRmkKdm1D6tLWvAyxVdqd3G0kJ+x6GLwQEVEB8bdQMUpb86JnXoTIrOZliAW7tlZpFuwSEVERYPBSjIIy83JAC1LgUPPSdxiI9Wv303YbDXbOi9kqLfRrMIfU8T8bIiIqHP4WKkZl9dptPGKM/0+qeZFZl2At4PEnncKY8zKEXaWlaFxAVQXixpwXZl6IiKhwGLwUI18I8Fdq97u0LQCSal7S1LsAuci8mP9pROIqoqq5fOQd5FIUERFRLvC3ULEqb9Ruu2XwkjDnxQhekutdgKHvKm3tKApH40axLgB4OWGXiIgKiL+FilVi8OI3J+wKIczgpTJF5iU6tIJdl0sxCnMjcdVokwZYsEtERIXF4KVYVdiDlzJ92UgIfYaL0WnkHLz0G63Sg1s2AszsSySmGtN1AcDNmhciIiogBi/FKiHzErQMm+uJxNK2SQNAX0TLlMjlpsGwtktbB9QpCoMXIiIqHAYvxUoGL3rBrsulGAFMXyQOdOpbA6TIvMh5MEMJXqzt0kOdG0NERJQr/E1UrBIyLwBQZql7SbdsJIRAn94qHRzk9gCAPfNitl4P/nxERES54Cn0BVAKFcnBi9YuHUFPX8R83CF4icRV6CNZEMjBslEkphp1Lsy8EBFRoTF4KVblei2LXB6CuQQU69wDiDjg8gDlDUkv7Y+YnUFDybwYO0vH4pA1uoNtvSYiIsoV/iYqVtXN2m1/OxDuAmCpX2n/ULutHAO4koOT3qhW7+J1K0Ma5W/NvPTrNS+BIXQvERER5QKDl2LlrwCCNdr99l0AgDJ9fyOla7f2eOVRji+Vg+wCQ6xPMVql4yrCQ9xugIiIKFf4m6iYVenZl/adAMzMi0cGL1UpgpccFOsCZnFuOMrMCxERFQ8GL8Wsepx226FnXvRBdb4efbpu1VjHl8nMS3AIxboAENSzLH3RODMvRERUNFiwW8xk8NL+AQAzGAn06m3SlSmClxxlXuTrZZs0wG4jIiIqPAYvxSxh2UjWvJT177U/nyBnmZfEzSAx9DoaIiKioWLwUsxk5uWwlnnRal4EqsJ6+3R1iuAlZ5kX7T+P3mgcLs55ISKiIsHgpZjVT9Fu928F1DjKfB7UoROheBcABag92vFlRuZlqMGLz2Wcz6sHL8y8EBFRofGf0cWsdiLgCQCxPuDwDgR9bhyt6FmXmvGAN+D4MiPzMuSCXXPZiHsbERFRseBvomLmcpvZl31voczvxtEuPXipOybly3K2bKR3N/VFrcELMy9ERFRYDF6KXcOx2m3b2wj5PGbmJU3w0p+zVmk98xKNGx1HAbZKExFRgfE3UbFrnK7d7n0dZT4PJin6gLq6ySlf0puHmhdmXoiIqFgweCl2407Wbrc/h3JXGHNc72jfN81I+ZKc17xYMi8cUkdERIXG30TFbswJQFk9EOnCUW/djXKlHwdRBYyelfIlOa95sWReuD0AEREVGoOXYudyAZMXAwDqXrkTAPC8OlN7PIX+PGRewjFmXoiIqDjwN1EpOPGrtm8fi50EVRUpD+/N0a7S1lbp/ihrXoiIqDgweCkFY+cAn/051KpxuC56Mdaps4ylISe5G1LHzAsRERUf/iYqFSecD+Wq1/GQegYAoCcSS3mofK7cP7QByta9jczMC/+TISKiwuJvohKiKArK9CLa3nDqzEuP/lzZUIMXPXMTiavoDWsBEbcHICKiQmPwUmJCejakN5I6eOnWA40y/9ACjZCl4Le9LwqAmRciIio8/iYqMTKb0ptu2Sicm2Uja6AigyUW7BIRUaExeCkxcimnJ0XmJa4KI9AY6rKRoihJRb/cHoCIiAqNv4lKTHlAC0i6+qOOz1sLeYeaeQHsS0cAMy9ERFR4DF5KTHXQCwBo700RvOhLRh6XkpP6lMQC3YrA0AMiIiKioWDwUmJqQj4AQEdf+uClzO+BoihDfj/rlN7KgIfdRkREVHAMXkpMdUhmXiKOz3frbdK5WDICgFo9WAKAugp/Ts5JREQ0FAxeSkyVHrwcHmDZaKht0tKY6oBxv76cwQsRERUeg5cSUx3UMiGpal66+s1lo1wYXR007jPzQkRExYDBS4mRy0Ydfc7LRrma8SKNqWLmhYiIiguDlxJTPdCyUY72NZLGWDIv9cy8EBFREWDwUmIGWjbqDud42ajKErww80JEREWAwUuJsS4bCSGSns/5spGlYJczXoiIqBgweCkxMniJxoXjFgHmjtK56Taq0ofiAfaZL0RERIXCf0qXmKDXDZ/bhUhcRXtvJCnDkutuI0VR8N0zp+HNPR34yOT6nJyTiIhoKBi8lBhFUVAd8qKtK4z23iiOqrE/3x3WamEqchS8AMAlp07M2bmIiIiGistGJahOL5xt6+pPek4W8lZbJuMSERGNJAxeSlCTPnultSOc9JwZvHiTniMiIhoJGLyUoMZKLXjZ1+mQedGH18mWaiIiopEmb8HLoUOHcP7556OyshLV1dW4+OKL0d3dnfY19957L04//XRUVlZCURS0t7fn6/JKWmOltmzkGLww80JERCNc3oKX888/H2+99RbWrFmDxx9/HM899xy+9rWvpX1Nb28vPvnJT+L666/P12WNCE165qU1IXjpj8YRjqkAGLwQEdHIlZduo82bN2P16tV46aWXMHfuXADAnXfeiTPPPBO33XYbxowZ4/i6q666CgCwbt26fFzWiNFo1LzYgxeZdfG4lJwNqSMiIio2ecm8rF+/HtXV1UbgAgCLFi2Cy+XCiy++mNP3CofD6OzstH2NdE0pal4O9+r1LiEvFEUZ9usiIiIaDnkJXlpbW9HQ0GB7zOPxoLa2Fq2trTl9rxUrVqCqqsr4am5uzun5i5EMXg73RtEfNafsysyLdSouERHRSJNV8HLttddCUZS0X1u2bMnXtTq67rrr0NHRYXzt2rVrWN+/EKpDXgS82h/dXsvSUYfsNOKMFyIiGsGyKoy4+uqr8ZWvfCXtMRMnTkRTUxPa2tpsj8diMRw6dAhNTU1ZX2Q6fr8ffv+RtduxoigYVxvCO/u68cHBHkyoKwNgZl5qWKxLREQjWFbBS319PerrB97fZsGCBWhvb8fGjRsxZ84cAMDTTz8NVVUxf/78wV0p2bSMKtODl17jscPGshEzL0RENHLlpeZl2rRp+OQnP4lLLrkEGzZswAsvvIClS5fii1/8otFptHv3bkydOhUbNmwwXtfa2opNmzZh27ZtAIA33ngDmzZtwqFDh/JxmSWtRc+2bD/QYzxmDKhj5oWIiEawvM15eeCBBzB16lScccYZOPPMM3HKKafg3nvvNZ6PRqPYunUrenvNzME999yDE044AZdccgkA4NRTT8UJJ5yAv/zlL/m6zJLVMkoLXj44aAYv+zu17QJGlTPzQkREI1fehoHU1tbiwQcfTPl8S0sLhBC2x2688UbceOON+bqkEaVlVAgAbMtGcmjdmKpgQa6JiIhoOHBvoxI1Xl822nmoF7G4NlVXDq2Tex8RERGNRAxeStToygB8HhdiqsCe9n4IIYy26dFVDF6IiGjkYvBSolwuBeNrtaWj7Qd70NkfQ58+sK6JwQsREY1gDF5KmOw4+uBgj7FkpA2wcxfysoiIiPKKwUsJk0W7Ow70Ym9HHwBz6wAiIqKRisFLCZOZlx2WzAvrXYiIaKRj8FLC5KyXHQd6jGF1R9WECnlJREREecfgpYRNbiwHoBXsPv/uAQDA8WOrCnlJREREecfgpYQ1VAQwoa4MQgBv7+0EAMxoZvBCREQjG4OXEjevpda4H/S6Mam+vIBXQ0RElH8MXkrc/Ilm8HL8UVXwuPlHSkREI1ve9jai4bH42CZ8YnorVAFc8bFJhb4cIiKivGPwUuLK/B7ce8HcQl8GERHRsOEaAxEREZUUBi9ERERUUhi8EBERUUlh8EJEREQlhcELERERlRQGL0RERFRSGLwQERFRSWHwQkRERCWFwQsRERGVFAYvREREVFIYvBAREVFJYfBCREREJYXBCxEREZUUBi9ERERUUjyFvoBcE0IAADo7Owt8JURERJQp+Xtb/h5PZ8QFL11dXQCA5ubmAl8JERERZaurqwtVVVVpj1FEJiFOCVFVFXv27EFFRQUURcnpuTs7O9Hc3Ixdu3ahsrIyp+cmEz/n4cHPeXjwcx4+/KyHR74+ZyEEurq6MGbMGLhc6ataRlzmxeVy4aijjsrre1RWVvJ/jGHAz3l48HMeHvychw8/6+GRj895oIyLxIJdIiIiKikMXoiIiKikMHjJgt/vxw033AC/31/oSxnR+DkPD37Ow4Of8/DhZz08iuFzHnEFu0RERDSyMfNCREREJYXBCxEREZUUBi9ERERUUhi8ZGjVqlVoaWlBIBDA/PnzsWHDhkJfUlF77rnn8JnPfAZjxoyBoih47LHHbM8LIbB8+XKMHj0awWAQixYtwrvvvms75tChQzj//PNRWVmJ6upqXHzxxeju7rYd8/rrr+MjH/kIAoEAmpub8eMf/zjfP1pRWbFiBU488URUVFSgoaEBZ599NrZu3Wo7pr+/H5dffjlGjRqF8vJy/Pu//zv27dtnO2bnzp0466yzEAqF0NDQgGuuuQaxWMx2zLp16zB79mz4/X5MmjQJ999/f75/vKJx9913Y8aMGcZciwULFuDJJ580nudnnB+33norFEXBVVddZTzGz3robrzxRiiKYvuaOnWq8XxJfMaCBvTQQw8Jn88nfvWrX4m33npLXHLJJaK6ulrs27ev0JdWtJ544gnx3e9+V/zpT38SAMSjjz5qe/7WW28VVVVV4rHHHhOvvfaa+Ld/+zcxYcIE0dfXZxzzyU9+UsycOVP861//Es8//7yYNGmSOO+884znOzo6RGNjozj//PPFm2++KX73u9+JYDAo/ud//me4fsyCW7x4sbjvvvvEm2++KTZt2iTOPPNMMW7cONHd3W0cc+mll4rm5maxdu1a8fLLL4uTTjpJnHzyycbzsVhMHHfccWLRokXi1VdfFU888YSoq6sT1113nXHM+++/L0KhkFi2bJl4++23xZ133incbrdYvXr1sP68hfKXv/xF/PWvfxXvvPOO2Lp1q7j++uuF1+sVb775phCCn3E+bNiwQbS0tIgZM2aIK6+80nicn/XQ3XDDDeLYY48Ve/fuNb72799vPF8KnzGDlwzMmzdPXH755cb38XhcjBkzRqxYsaKAV1U6EoMXVVVFU1OTWLlypfFYe3u78Pv94ne/+50QQoi3335bABAvvfSSccyTTz4pFEURu3fvFkII8fOf/1zU1NSIcDhsHPOd73xHTJkyJc8/UfFqa2sTAMSzzz4rhNA+V6/XKx555BHjmM2bNwsAYv369UIILdB0uVyitbXVOObuu+8WlZWVxmf77W9/Wxx77LG291qyZIlYvHhxvn+kolVTUyN++ctf8jPOg66uLjF58mSxZs0acdpppxnBCz/r3LjhhhvEzJkzHZ8rlc+Yy0YDiEQi2LhxIxYtWmQ85nK5sGjRIqxfv76AV1a6tm/fjtbWVttnWlVVhfnz5xuf6fr161FdXY25c+caxyxatAgulwsvvviiccypp54Kn89nHLN48WJs3boVhw8fHqafprh0dHQAAGprawEAGzduRDQatX3WU6dOxbhx42yf9fHHH4/GxkbjmMWLF6OzsxNvvfWWcYz1HPKYI/H/gXg8joceegg9PT1YsGABP+M8uPzyy3HWWWclfR78rHPn3XffxZgxYzBx4kScf/752LlzJ4DS+YwZvAzgwIEDiMfjtj8kAGhsbERra2uBrqq0yc8t3Wfa2tqKhoYG2/Mejwe1tbW2Y5zOYX2PI4mqqrjqqquwcOFCHHfccQC0z8Hn86G6utp2bOJnPdDnmOqYzs5O9PX15ePHKTpvvPEGysvL4ff7cemll+LRRx/F9OnT+Rnn2EMPPYRXXnkFK1asSHqOn3VuzJ8/H/fffz9Wr16Nu+++G9u3b8dHPvIRdHV1lcxnPOI2ZiQ6Ul1++eV488038Y9//KPQlzIiTZkyBZs2bUJHRwf+8Ic/4MILL8Szzz5b6MsaUXbt2oUrr7wSa9asQSAQKPTljFif+tSnjPszZszA/PnzMX78ePz+979HMBgs4JVljpmXAdTV1cHtdidVWu/btw9NTU0FuqrSJj+3dJ9pU1MT2trabM/HYjEcOnTIdozTOazvcaRYunQpHn/8cTzzzDO2XdWbmpoQiUTQ3t5uOz7xsx7oc0x1TGVlZcn8ZTdUPp8PkyZNwpw5c7BixQrMnDkTP/3pT/kZ59DGjRvR1taG2bNnw+PxwOPx4Nlnn8XPfvYzeDweNDY28rPOg+rqahxzzDHYtm1byfz3zOBlAD6fD3PmzMHatWuNx1RVxdq1a7FgwYICXlnpmjBhApqammyfaWdnJ1588UXjM12wYAHa29uxceNG45inn34aqqpi/vz5xjHPPfccotGoccyaNWswZcoU1NTUDNNPU1hCCCxduhSPPvoonn76aUyYMMH2/Jw5c+D1em2f9datW7Fz507bZ/3GG2/YgsU1a9agsrIS06dPN46xnkMecyT/P6CqKsLhMD/jHDrjjDPwxhtvYNOmTcbX3Llzcf755xv3+VnnXnd3N9577z2MHj26dP57zknZ7wj30EMPCb/fL+6//37x9ttvi6997WuiurraVmlNdl1dXeLVV18Vr776qgAgbr/9dvHqq6+KDz74QAihtUpXV1eLP//5z+L1118Xn/3sZx1bpU844QTx4osvin/84x9i8uTJtlbp9vZ20djYKL785S+LN998Uzz00EMiFAodUa3S3/jGN0RVVZVYt26dre2xt7fXOObSSy8V48aNE08//bR4+eWXxYIFC8SCBQuM52Xb4yc+8QmxadMmsXr1alFfX+/Y9njNNdeIzZs3i1WrVh1RraXXXnutePbZZ8X27dvF66+/Lq699lqhKIp46qmnhBD8jPPJ2m0kBD/rXLj66qvFunXrxPbt28ULL7wgFi1aJOrq6kRbW5sQojQ+YwYvGbrzzjvFuHHjhM/nE/PmzRP/+te/Cn1JRe2ZZ54RAJK+LrzwQiGE1i79ve99TzQ2Ngq/3y/OOOMMsXXrVts5Dh48KM477zxRXl4uKisrxUUXXSS6urpsx7z22mvilFNOEX6/X4wdO1bceuutw/UjFgWnzxiAuO+++4xj+vr6xGWXXSZqampEKBQSn/vc58TevXtt59mxY4f41Kc+JYLBoKirqxNXX321iEajtmOeeeYZMWvWLOHz+cTEiRNt7zHSffWrXxXjx48XPp9P1NfXizPOOMMIXITgZ5xPicELP+uhW7JkiRg9erTw+Xxi7NixYsmSJWLbtm3G86XwGXNXaSIiIioprHkhIiKiksLghYiIiEoKgxciIiIqKQxeiIiIqKQweCEiIqKSwuCFiIiISgqDFyIiIiopDF6IiIiopDB4IaKideONN2LWrFmFvgwiKjIMXoiIACiKgscee6zQl0FEGWDwQkRERCWFwQsRAQBOP/10LF26FEuXLkVVVRXq6urwve99D3L7s5///OeYPHkyAoEAGhsbcc455xivVVUVK1aswIQJExAMBjFz5kz84Q9/MJ6///77UV1dbXu/xx57DIqi2B679dZb0djYiIqKClx88cXo7++3Pa+qKm666SYcddRR8Pv9mDVrFlavXp3RzxeJRLB06VKMHj0agUAA48ePx4oVKwAALS0tAIDPfe5zUBTF+B4A/vznP2P27NkIBAKYOHEivv/97yMWixnPK4qCu+++G5/61KcQDAYxceJE289ORHmQsy0eiaiknXbaaaK8vFxceeWVYsuWLeK3v/2tCIVC4t577xUvvfSScLvd4sEHHxQ7duwQr7zyivjpT39qvPbmm28WU6dOFatXrxbvvfeeuO+++4Tf7xfr1q0TQghx3333iaqqKtv7Pfroo8L6V9DDDz8s/H6/+OUvfym2bNkivvvd74qKigoxc+ZM45jbb79dVFZWit/97ndiy5Yt4tvf/rbwer3inXfeGfDnW7lypWhubhbPPfec2LFjh3j++efFgw8+KIQQoq2tzdiNe+/evaKtrU0IIcRzzz0nKisrxf333y/ee+898dRTT4mWlhZx4403GucFIEaNGiV+8YtfiK1bt4r/+q//Em63W7z99ttZ/xkQUWYYvBCREEILXqZNmyZUVTUe+853viOmTZsm/vjHP4rKykrR2dmZ9Lr+/n4RCoXEP//5T9vjF198sTjvvPOEEJkFLwsWLBCXXXaZ7Zj58+fbgpcxY8aIW265xXbMiSeemPQ6J1dccYX42Mc+Zvv5rACIRx991PbYGWecIX74wx/aHvvNb34jRo8ebXvdpZdemnTd3/jGNwa8JiIaHC4bEZHhpJNOsi3lLFiwAO+++y7OOOMMjB8/HhMnTsSXv/xlPPDAA+jt7QUAbNu2Db29vfj4xz+O8vJy4+vXv/413nvvvYzfe/PmzZg/f77tsQULFhj3Ozs7sWfPHixcuNB2zMKFC7F58+YBz/+Vr3wFmzZtwpQpU/DNb34TTz311ICvee2113DTTTfZfq5LLrkEe/fuNX7+xOuU32dyTUQ0OJ5CXwARFb/y8nK88sorWLduHZ566iksX74cN954I1566SV0d3cDAP76179i7Nixttf5/X4AgMvlMmpnpGg0OjwXr5s9eza2b9+OJ598En//+9/xhS98AYsWLUpbn9Ld3Y3vf//7+PznP5/0XCAQyOflElEazLwQkeHFF1+0ff+vf/0LkydPhtvthsfjwaJFi/DjH/8Yr7/+Onbs2IGnn34a06dPh9/vx86dOzFp0iTbV3NzMwCgvr4eXV1d6OnpMc69adMm23tNmzbN8f2lyspKjBkzBi+88ILtmBdeeAHTp0/P6OerrKzEkiVL8Itf/AIPP/ww/vjHP+LQoUMAAK/Xi3g8bjt+9uzZ2Lp1a9LPNWnSJLhc5l+f1uuU30+bNi2jayKi7DHzQkSGnTt3YtmyZfj617+OV155BXfeeSd+8pOf4PHHH8f777+PU089FTU1NXjiiSegqiqmTJmCiooK/Od//ie+9a1vQVVVnHLKKejo6MALL7yAyspKXHjhhZg/fz5CoRCuv/56fPOb38SLL76I+++/3/beV155Jb7yla9g7ty5WLhwIR544AG89dZbmDhxonHMNddcgxtuuAFHH300Zs2ahfvuuw+bNm3CAw88MODPdvvtt2P06NE44YQT4HK58Mgjj6CpqcnogmppacHatWuxcOFC+P1+1NTUYPny5fj0pz+NcePG4ZxzzoHL5cJrr72GN998EzfffLNx7kceeQRz587FKaecggceeAAbNmzA//7v/+bkz4SIHBS66IaIisNpp50mLrvsMnHppZeKyspKUVNTI66//nqhqqp4/vnnxWmnnSZqampEMBgUM2bMEA8//LDxWlVVxR133CGmTJkivF6vqK+vF4sXLxbPPvusccyjjz4qJk2aJILBoPj0pz8t7r33XpH4V9Att9wi6urqRHl5ubjwwgvFt7/9bVvBbjweFzfeeKMYO3as8Hq9YubMmeLJJ5/M6Oe79957xaxZs0RZWZmorKwUZ5xxhnjllVeM5//yl7+ISZMmCY/HI8aPH288vnr1anHyySeLYDAoKisrxbx588S9995rPA9ArFq1Snz84x8Xfr9ftLS02D4bIso9RYiEhWgiOiKdfvrpmDVrFu64445CX0pJURQFjz76KM4+++xCXwrREYM1L0RERFRSGLwQ0Yjwwx/+0NbSbP361Kc+VejLI6Ic4rIREY0Ihw4dMjqHEgWDwaQ2biIqXQxeiIiIqKRw2YiIiIhKCoMXIiIiKikMXoiIiKikMHghIiKiksLghYiIiEoKgxciIiIqKQxeiIiIqKQweCEiIqKS8v8BYcY0Y7axLOQAAAAASUVORK5CYII=",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
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     "metadata": {}
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   "id": "f0069d6b"
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 ],
 "metadata": {
  "kernelspec": {
   "display_name": "flow360",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}