RANKINE_CYCLE_PLOT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "48684255-786e-49f4-8fa8-fe677406fb12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the pressure at point 1: 7380\n",
      "enter the Temp at point 1: 313\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter the pressure at point 2: 238000\n",
      "enter the temp at point 5: 493\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Step    temperature    pressure    entropy          enthalpy    density    specfic_gravity\n",
      "------  -------------  ----------  ---------  ----------------  ---------  -----------------\n",
      "     1        313            7380    570.399  166906            992.232           0.00100783\n",
      "     2        317.789      238000    633.777  187127            990.423           0.00100967\n",
      "     3        398.948      238000   1590.07   528473            938.362           0.00106569\n",
      "     4        398.948      238000   7068.86        2.71423e+06    1.32881         0.752551\n",
      "     5        493          238000   7508.02        2.90908e+06    1.05708         0.946\n",
      "     6        313            7380   7508.02        2.33934e+06  992.232           0.00100783\n",
      "--------------------------------------------------------------\n",
      "heat input= 122977.35800000004\n",
      "mdot= 0.05714009595188522\n",
      "turbine work= 132202.6893141863\n",
      "pump work= 1650.6332886399832\n",
      "cycle work= 130552.05602554634\n",
      "cycle efficiency= 1.0615942491263008\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CompletedProcess(args=['python', 'receiver_script.py', 'Hello, receiver script!'], returncode=2)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%reset -f\n",
    "from numpy import *\n",
    "import matplotlib.pyplot as PT\n",
    "import CoolProp.CoolProp as CP\n",
    "from tabulate import tabulate\n",
    "import subprocess\n",
    "\n",
    "P1=float(input(\"enter the pressure at point 1:\"))\n",
    "T1=float(input(\"enter the Temp at point 1:\"))\n",
    "S1=CP.PropsSI('S', 'T', T1, 'P', P1, 'water')\n",
    "#print(S1)\n",
    "H1=CP.PropsSI('H', 'T', T1, 'P', P1, 'water')\n",
    "D1=CP.PropsSI('D', 'P', P1, 'T', T1, 'water')\n",
    "SV1 = 1/D1\n",
    "Q1=CP.PropsSI('Q', 'T', T1, 'S', S1, 'water')\n",
    "print(Q1)\n",
    "#print(D1)\n",
    "#print(SV1)\n",
    "#print(H1)\n",
    "\n",
    "S2=S1/.90\n",
    "P2=float(input(\"enter the pressure at point 2:\"))\n",
    "T2=CP.PropsSI('T', 'S', S2, 'P', P2, 'water')\n",
    "#print(T2)\n",
    "H2=CP.PropsSI('H', 'T', T2, 'P', P2, 'water')\n",
    "D2=CP.PropsSI('D', 'P', P2, 'T', T2, 'water')\n",
    "SV2 = 1/D2\n",
    "#print(D2)\n",
    "#print(SV2)\n",
    "#print(H2)\n",
    "\n",
    "P3=P2\n",
    "S3=CP.PropsSI('S', 'Q', 0, 'P', P3, 'water')\n",
    "#print(S3)\n",
    "T3=CP.PropsSI('T', 'S', S3, 'P', P3, 'water')\n",
    "#print(T3)\n",
    "H3=CP.PropsSI('H', 'T', T3, 'Q', 0, 'water') #note\n",
    "D3=CP.PropsSI('D', 'Q', 0, 'T', T3, 'water') #note\n",
    "SV3 = 1/D3\n",
    "#print(D3)\n",
    "#print(H3)\n",
    "\n",
    "P4=P3\n",
    "T4=T3\n",
    "S4=CP.PropsSI('S', 'T', T4, 'Q', 1, 'water')\n",
    "#print(S4)\n",
    "H4=CP.PropsSI('H', 'T', T4, 'Q', 1, 'water')\n",
    "D4=CP.PropsSI('D', 'P', P4, 'Q', 1, 'water')\n",
    "SV4 = 1/D4\n",
    "#print(D1)\n",
    "#print(SV1)\n",
    "#print(H4)\n",
    "\n",
    "P5=P4\n",
    "T5=float(input(\"enter the temp at point 5:\"))\n",
    "S5=CP.PropsSI('S', 'T', T5, 'P', P4, 'water')\n",
    "#print(S5)\n",
    "H5=CP.PropsSI('H', 'T', T5, 'P', P5, 'water')\n",
    "D5=CP.PropsSI('D', 'P', P5, 'T', T5, 'water')\n",
    "SV5 = 1/D5\n",
    "#print(H5)\n",
    "\n",
    "S6=S5\n",
    "P6=P1\n",
    "T6=T1\n",
    "H6=CP.PropsSI('H', 'S', S6, 'P', P6, 'water')\n",
    "D6=CP.PropsSI('D', 'P', P6, 'T', T6, 'water')\n",
    "SV6 = 1/D6\n",
    "#print(D6)\n",
    "#print(H6)\n",
    "\n",
    "print(tabulate([[\"1\",T1,P1,S1,H1,D1,SV1],[\"2\",T2,P2,S2,H2,D2,SV2],[\"3\",T3,P3,S3,H3,D3,SV3],[\"4\",T4,P4,S4,H4,D4,SV4],[\"5\",T5,P5,S5,H5,D5,SV5],[\"6\",T6,P6,S6,H6,D6,SV6]], headers=[\"Step\",\"temperature\",\"pressure\",\"entropy\",\"enthalpy\",\"density\",\"specfic_gravity\"]))\n",
    "\n",
    "print(\"--------------------------------------------------------------\")\n",
    "\n",
    "N_P=.70\n",
    "N_T=.85\n",
    "HS2=1280565.3\n",
    "HS1=1029591.1\n",
    "msdot=.49\n",
    "WS=msdot*(HS2-HS1)\n",
    "mdot=(WS/(H6-H2))\n",
    "Wp=mdot*(H2-H1)/N_P\n",
    "Wt=mdot*(H5-H2)*N_T\n",
    "W=Wt-Wp\n",
    "print(\"heat input=\",WS)\n",
    "print(\"mdot=\",mdot)\n",
    "print(\"turbine work=\",Wt)\n",
    "print(\"pump work=\",Wp)\n",
    "print(\"cycle work=\",W)\n",
    "n=W/WS\n",
    "print(\"cycle efficiency=\",n)\n",
    "\n",
    "x=[S1,S2,S3,S4,S5,S6,S1]\n",
    "y=[T1,T2,T3,T4,T5,T6,T1]\n",
    "PT.plot(x,y,marker='o',color='red', linestyle='dashed')\n",
    "PT.ylim(273.15,650)\n",
    "PT.xlim(0,10000)\n",
    "PT.xlabel('ENTROPY')\n",
    "PT.ylabel('TEMPERATURE')\n",
    "PT.title('TS-Diagram')\n",
    "\n",
    "\n",
    "data = \"Hello, receiver script!\"\n",
    "subprocess.run([\"python\", \"receiver_script.py\", data])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1dc6a37-e6a7-47ff-ab19-7db3757ed349",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}