One_Stage.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "681b7434-d9db-40a6-a1a9-b797ff5fd206",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "from ipynb.fs.full.functions import i_star, i_c, i_s, properties, rotor_inlet, delta_star, lc_star, lc_OD, compressor_solver\n",
    "from ipynb.fs.full.functions import delta_OD, rotor_outlet, stator_outlet, efficiency, checker, stage_solver, stage_solver_star"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a14d81bd-b085-4c6b-9300-4911d4961c11",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6042210f-216a-4d85-b481-10961e000f35",
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha1 = 43                #degree\n",
    "N_design = 6000            #RPM\n",
    "N_stage  = 1\n",
    "P01 = 100/101.325          #bar\n",
    "T01 = 288                  #k\n",
    "t_max_c = 0.12             #t_max/c\n",
    "\n",
    "r_h1 = 0.2                 #meter\n",
    "r_t1 = 0.3                 #meter\n",
    "H1 = r_t1-r_h1\n",
    "\n",
    "r_h2 = 0.205                #meter\n",
    "r_t2 = 0.295                #meter\n",
    "H2 = r_t2-r_h2\n",
    "\n",
    "r_h3 = 0.21                #meter\n",
    "r_t3 = 0.29                #meter\n",
    "H3 = r_t3-r_h3\n",
    "\n",
    "r_m1 = (r_h1+r_t1)/2\n",
    "r_m2 = (r_h2+r_t2)/2\n",
    "r_m3 = (r_h3+r_t3)/2\n",
    "\n",
    "#for compressor:\n",
    "C_p = 1.004                #kJ/kg-k\n",
    "gama = 1.4  \n",
    "R = 0.287                  #kJ/kg-k"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "49753a0e-d179-44f2-aa6c-d4b7f84a5d8b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Rotor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "28e0eb17-b642-4b8a-a0d6-fe36a0939ba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "beta_prime1 = 50         #degree\n",
    "beta_prime2 = 30         #degree\n",
    "sigma_r = 1              #rotor solidity at r_m\n",
    "c_r = 0.04               #rotor chord[meter]\n",
    "s_r = c_r/sigma_r"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "02b06f7d-24ef-4c41-b2dd-9a1ac83a9824",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Stator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0981a7a4-6f6e-491b-8802-8c6efb37dbe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha_prime2 = 57.6      #degree\n",
    "alpha_prime3 = 32.6      #degree\n",
    "sigma_s = 0.91           #stator solidity at r_m\n",
    "c_s = 0.05               #stator chord[meter]\n",
    "s_s = c_s/sigma_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b56f5d3-379e-42ef-b92e-e32ec9cb2a6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "blade_type = ['C_Series','circular']\n",
    "a_c = 0.5"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4b53a006-d262-4e33-861c-9741ee90c168",
   "metadata": {},
   "source": [
    "# Operating Curves"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b793338b-6682-4e12-a915-2d392d98d8c8",
   "metadata": {},
   "source": [
    "## Operating Range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6c2860e7-b165-4e5d-b8a9-e79dbff77a61",
   "metadata": {},
   "outputs": [],
   "source": [
    "stage1_data = (alpha1, P01, T01, beta_prime1, beta_prime2, a_c, t_max_c, blade_type, sigma_r, sigma_s, alpha_prime2,\n",
    "               alpha_prime3, r_m1, r_m2, r_m3, H1, H2, H3, r_h1, r_h2, r_t1, r_t2, s_s, s_r)\n",
    "compressor_data = [stage1_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2bb40849-d0b1-4b3e-bbc5-d4fc4a26c5a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "i_star_r, delta_star_r, beta2_star_r, i_star_s, delta_star_s, beta2_star_s, M_W1_star, M_C2_star, i_s_r, i_c_r, i_s_s, i_c_s, loss_coefficient_star_r, loss_coefficient_star_s = stage_solver_star(\n",
    "    alpha1, N_design, P01, T01, beta_prime1, beta_prime2, a_c, t_max_c, blade_type, sigma_r, sigma_s, \n",
    "    alpha_prime2, alpha_prime3, r_m1, r_m2, r_m3, H1, H2, H3, gama, R, r_h1, r_h2, r_t1, r_t2, s_s, s_r, N_stage\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6cc0eeca-04e5-4b5d-bd51-9182080b0366",
   "metadata": {},
   "outputs": [],
   "source": [
    "i_or = np.linspace(i_c_r, i_s_r, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "974b639a-2650-4247-b6d3-6995073f202e",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_or = N_design*np.array([0.5, 0.7, 0.8, 0.9, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1e7e1ed5-d069-489a-9da2-bd6b326cf3d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "etta_or = np.zeros((len(N_or), len(i_or)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d1e7df14-9555-4306-bdc9-65a3d6423112",
   "metadata": {},
   "outputs": [],
   "source": [
    "P02_P01_or = np.zeros((len(N_or), len(i_or)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92daf05b-5c3a-4a4c-a799-b6a847e028e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_dot_or =  np.zeros((len(N_or), len(i_or)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a980949-9ddf-45ca-895d-9b612f379ce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "m, n = 0, 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c6fa0a20-acf8-4260-ae4c-7714beac987a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for N = 3000.0 and i = -9.053541839608163, We have:\n",
      "in stage 1, h is not True\n",
      "in stage 1, P is not True\n",
      "for N = 3000.0 and i = -7.79908315576556, We have:\n",
      "in stage 1, h is not True\n",
      "in stage 1, P is not True\n",
      "for N = 3000.0 and i = -6.544624471922956, We have:\n",
      "in stage 1, h is not True\n",
      "in stage 1, P is not True\n",
      "for N = 3000.0 and i = -5.290165788080351, We have:\n",
      "in stage 1, P is not True\n",
      "for N = 3000.0 and i = -4.035707104237748, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 3000.0 and i = -2.781248420395144, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 3000.0 and i = -1.5267897365525398, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 3000.0 and i = -0.2723310527099354, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 3000.0 and i = 0.9821276311326681, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 3000.0 and i = 2.2365863149752716, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 3000.0 and i = 3.491044998817875, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 3000.0 and i = 4.74550368266048, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 3000.0 and i = 5.999962366503084, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 3000.0 and i = 7.254421050345689, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 3000.0 and i = 8.508879734188293, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4200.0 and i = -9.053541839608163, We have:\n",
      "in stage 1, h is not True\n",
      "in stage 1, P is not True\n",
      "for N = 4200.0 and i = -7.79908315576556, We have:\n",
      "in stage 1, h is not True\n",
      "in stage 1, P is not True\n",
      "for N = 4200.0 and i = -6.544624471922956, We have:\n",
      "in stage 1, h is not True\n",
      "in stage 1, P is not True\n",
      "for N = 4200.0 and i = -5.290165788080351, We have:\n",
      "in stage 1, P is not True\n",
      "for N = 4200.0 and i = -4.035707104237748, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4200.0 and i = -2.781248420395144, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4200.0 and i = -1.5267897365525398, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4200.0 and i = -0.2723310527099354, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4200.0 and i = 0.9821276311326681, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4200.0 and i = 2.2365863149752716, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4200.0 and i = 3.491044998817875, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4200.0 and i = 4.74550368266048, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4200.0 and i = 5.999962366503084, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4200.0 and i = 7.254421050345689, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4200.0 and i = 8.508879734188293, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4800.0 and i = -9.053541839608163, We have:\n",
      "in stage 1, h is not True\n",
      "in stage 1, P is not True\n",
      "for N = 4800.0 and i = -7.79908315576556, We have:\n",
      "in stage 1, h is not True\n",
      "in stage 1, P is not True\n",
      "for N = 4800.0 and i = -6.544624471922956, We have:\n",
      "in stage 1, h is not True\n",
      "in stage 1, P is not True\n",
      "for N = 4800.0 and i = -5.290165788080351, We have:\n",
      "in stage 1, P is not True\n",
      "for N = 4800.0 and i = -4.035707104237748, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4800.0 and i = -2.781248420395144, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4800.0 and i = -1.5267897365525398, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4800.0 and i = -0.2723310527099354, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4800.0 and i = 0.9821276311326681, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4800.0 and i = 2.2365863149752716, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4800.0 and i = 3.491044998817875, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4800.0 and i = 4.74550368266048, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4800.0 and i = 5.999962366503084, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4800.0 and i = 7.254421050345689, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 4800.0 and i = 8.508879734188293, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 5400.0 and i = -9.053541839608163, We have:\n",
      "in stage 1, h is not True\n",
      "in stage 1, P is not True\n",
      "for N = 5400.0 and i = -7.79908315576556, We have:\n",
      "in stage 1, h is not True\n",
      "in stage 1, P is not True\n",
      "for N = 5400.0 and i = -6.544624471922956, We have:\n",
      "in stage 1, h is not True\n",
      "in stage 1, P is not True\n",
      "for N = 5400.0 and i = -5.290165788080351, We have:\n",
      "in stage 1, P is not True\n",
      "for N = 5400.0 and i = -4.035707104237748, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 5400.0 and i = -2.781248420395144, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 5400.0 and i = -1.5267897365525398, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 5400.0 and i = -0.2723310527099354, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 5400.0 and i = 0.9821276311326681, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 5400.0 and i = 2.2365863149752716, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 5400.0 and i = 3.491044998817875, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 5400.0 and i = 4.74550368266048, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 5400.0 and i = 5.999962366503084, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 5400.0 and i = 7.254421050345689, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 5400.0 and i = 8.508879734188293, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 6000.0 and i = -9.053541839608163, We have:\n",
      "in stage 1, h is not True\n",
      "in stage 1, P is not True\n",
      "for N = 6000.0 and i = -7.79908315576556, We have:\n",
      "in stage 1, h is not True\n",
      "in stage 1, P is not True\n",
      "for N = 6000.0 and i = -6.544624471922956, We have:\n",
      "in stage 1, h is not True\n",
      "in stage 1, P is not True\n",
      "for N = 6000.0 and i = -5.290165788080351, We have:\n",
      "in stage 1, P is not True\n",
      "for N = 6000.0 and i = -4.035707104237748, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 6000.0 and i = -2.781248420395144, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 6000.0 and i = -1.5267897365525398, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 6000.0 and i = -0.2723310527099354, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 6000.0 and i = 0.9821276311326681, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 6000.0 and i = 2.2365863149752716, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 6000.0 and i = 3.491044998817875, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 6000.0 and i = 4.74550368266048, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 6000.0 and i = 5.999962366503084, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 6000.0 and i = 7.254421050345689, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n",
      "for N = 6000.0 and i = 8.508879734188293, We have:\n",
      "everything in stage 1 is alright\n",
      "everything in compressor is ok\n"
     ]
    }
   ],
   "source": [
    "for N in N_or:\n",
    "    for i in i_or:\n",
    "        \n",
    "        print(f'for N = {N} and i = {i}, We have:')\n",
    "        P02_P01_or[m][n], etta_or[m][n], m_dot_or[m][n], flag = compressor_solver(compressor_data, gama, i, N, R, N_design, N_stage)\n",
    "                \n",
    "        if flag == 0:\n",
    "            P02_P01_or[m][n], etta_or[m][n], m_dot_or[m][n] = 0, 0, 0\n",
    "            \n",
    "        n = n+1\n",
    "    \n",
    "    m = m+1\n",
    "    n = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "76b53c25-eea3-4c5e-9a57-7aebf0d72cb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x960 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(12, 12), dpi=80)\n",
    "plt.subplot(2, 1, 1)\n",
    "\n",
    "for i in range(0, len(N_or)):\n",
    "    \n",
    "    m_dot_corrected = np.array([m for m in m_dot_or[i] if m!=0])\n",
    "    etta_corrected = np.array([m for m in etta_or[i] if m!=0])\n",
    "    \n",
    "    plt.plot(m_dot_corrected, etta_corrected, marker = \"o\", linestyle = \"solid\", label=f'N = {N_or[i]/N_design*100}%')\n",
    "    plt.xlabel('Mass flow rate[kg/s]')\n",
    "    plt.ylabel('Efficiency[%]')\n",
    "    plt.legend()\n",
    "    \n",
    "    \n",
    "plt.subplot(2, 1, 2)\n",
    "\n",
    "\n",
    "for i in range(0, len(N_or)):\n",
    "    \n",
    "    m_dot_corrected = np.array([m for m in m_dot_or[i] if m!=0])\n",
    "    P02_P01_corrected = np.array([m for m in P02_P01_or[i] if m!=0])\n",
    "    \n",
    "    plt.plot(m_dot_corrected, P02_P01_corrected, marker = \"o\", linestyle = \"solid\", label=f'N = {N_or[i]/N_design*100}%')\n",
    "    plt.xlabel('Mass flow rate[kg/s]')\n",
    "    plt.ylabel('Pressure ratio')\n",
    "    plt.legend()\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}