{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNZcamo0acyx77fMCJUUJEK"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# CoolProp\n","link: http://www.coolprop.org/\n"],"metadata":{"id":"NavOTSx4cXtT"}},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EkaxYEcdYSoD","executionInfo":{"status":"ok","timestamp":1692110841738,"user_tz":240,"elapsed":11943,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"3cb9469b-cf05-421d-814f-eb36fe804882"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting CoolProp\n","  Downloading CoolProp-6.5.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.7 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.7/6.7 MB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: CoolProp\n","Successfully installed CoolProp-6.5.0.post1\n"]}],"source":["%pip install CoolProp"]},{"cell_type":"code","source":["from numpy import *\n","import matplotlib.pyplot as plt\n","import CoolProp.CoolProp as cp"],"metadata":{"id":"QjIrpg1Ka1MH","executionInfo":{"status":"ok","timestamp":1692110868352,"user_tz":240,"elapsed":405,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}}},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":["# Cómo calcular propiedades\n","  Para esto se usa la función PropSI()\n"],"metadata":{"id":"wHR829rCdq5-"}},{"cell_type":"code","source":["densidad = cp.PropsSI('D', 'T', 300, 'P', 100e3, 'water')\n","print(densidad)\n","\n","# A continuacion dejo una tabla con los parametros y sus unidades\n","\n","\"\"\"\n","Simbolo|         Propiedad        |\n","-------|--------------------------|\n","   D   | Densidad (kg/m^3)        |\n","   H   | Entalpia (J/kg)          |\n","   P   | Presion (Pa)             |\n","   Q   | Calidad/ Titulo (-)      |\n","   S   | Entropia (J/(kg K))      |\n","   T   | Temperatura (K)          |\n","   U   | Energia Interna (J/kg)   |\n","\"\"\""],"metadata":{"id":"mQa_6Jv0bA4k","colab":{"base_uri":"https://localhost:8080/","height":71},"executionInfo":{"status":"ok","timestamp":1692115293874,"user_tz":240,"elapsed":433,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"52d771e8-626c-4682-fb65-72ee7ad8a422"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["996.5563403888981\n"]},{"output_type":"execute_result","data":{"text/plain":["'\\nSimbolo|         Propiedad        |\\n-------|--------------------------|\\n   D   | Densidad (kg/m^3)        |\\n   H   | Entalpia (J/kg)          |\\n   P   | Presion (Pa)             |\\n   Q   | Calidad/ Titulo (-)      |\\n   S   | Entropia (J/(kg K))      |\\n   T   | Temperatura (K)          |\\n   U   | Energia Interna (J/kg)   |\\n'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":13}]},{"cell_type":"markdown","source":["## Argumentos de la función PropsSI()\n","\n","1. Que es lo que queremos.\n","2. Primer parámetro que conocemos, su valor.\n","3. Segundo parámetro que conocemos, su valor.\n","4. Sustancia con la que estamos trabajando.\n","\n","Es importante colocar todas las unidades de medida en SI.\n","\n","## Cuales sustancias podemos estudiar con esta libreria.\n","Para visualizar todas las sustancias disponibles se usa cp.FluidList()."],"metadata":{"id":"NSv8UPB0eITH"}},{"cell_type":"code","source":["sustancias = cp.FluidsList()\n","print(sustancias[:20])        # Esto es para no mostrar todas las sustancias."],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vRXTHBh2zvRe","executionInfo":{"status":"ok","timestamp":1692118046944,"user_tz":240,"elapsed":393,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"fdd24b1e-ccbf-481a-c1c0-b7044caf5ab4"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["['Krypton', 'Neopentane', 'Fluorine', 'R142b', 'ParaHydrogen', 'CycloPropane', 'Ethylene', 'n-Heptane', 'HydrogenChloride', 'EthylBenzene', 'R410A', 'R134a', 'EthyleneOxide', '1-Butene', 'R41', 'R507A', 'DimethylEther', 'R141b', 'DiethylEther', 'n-Dodecane']\n"]}]},{"cell_type":"markdown","source":["## Ejemplo\n","Considere un sistema cilindro-pistón que contiene agua con un título de 0.25.\n","Se agrega calor hasta que el pistón sube a 4.5 cm y la presión final es de 3 bar. La posición inicial del pistón es a 1cm de la base.\n","La masa de pistón es de 40 kg y el diámetro del cilindro es de 10 cm.\n","La presion exterior es de 1 atm.\n","\n","Resumen\n","1. Expansión a presion constante desde 1 cm a 4.5 (1 ---> 2)\n","2. Aumento en la presión hasta 3 bar (2 ---> 3)\n","\n","$P = P_{atm} + \\frac{mg}{A}$ para calcular la presión en el punto 1.\n","\n","$ A = \\pi \\cdot \\frac{D^2}{4}$\n","\n","Finalmente se utiliza la fórmula\n","\n","$Q = U + W$ para cada etapa, donde $W = P\\cdot \\Delta V$\n","\n","$Q_{13} = Q_{12} + Q_{23} $\n","\n","##Punto 1"],"metadata":{"id":"s3AKWPhn5nCU"}},{"cell_type":"code","source":["P_1 = 101325 + (40*9.81)/(pi*0.1**2/4)\n","x_1 = 0.25\n","\n","U_1 = cp.PropsSI('U', 'P', P_1, 'Q', x_1, 'water')\n","d_1 = cp.PropsSI('D', 'P', P_1, 'Q', x_1, 'water')\n","v_1 = 1/cp.PropsSI('D', 'P', P_1, 'Q', x_1, 'water')\n","\n","print(U_1/1000)\n","print(v_1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0NOGFCZE7P2X","executionInfo":{"status":"ok","timestamp":1692110876227,"user_tz":240,"elapsed":408,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"e1f20358-f0cb-451f-93eb-5aa0a79d6025"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["980.9164814074014\n","0.28830400778279114\n"]}]},{"cell_type":"markdown","source":["##Punto 2"],"metadata":{"id":"DDYmP9W_9rfC"}},{"cell_type":"code","source":["P_2 = P_1\n","v_2 = 4.5 * v_1\n","d_2 = 1/v_2\n","\n","# notar que la funcion PropsSi no recibe volumen específico,\n","# pero sí la densidad y d = 1/v\n","\n","U_2 = cp.PropsSI('U', 'P', P_2, 'D', d_2, 'water')\n","v_2 = 1/cp.PropsSI('D', 'P', P_2, 'D', d_2, 'water')\n","\n","print(U_2/1000)\n","print(v_2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8v8kl8L_9tHQ","executionInfo":{"status":"ok","timestamp":1692110878505,"user_tz":240,"elapsed":390,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"ab2168bf-7ede-4a76-8e24-59748e335249"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["2591.087222149354\n","1.29736803502256\n"]}]},{"cell_type":"markdown","source":["##Punto 3"],"metadata":{"id":"Li0PdFT8-VZJ"}},{"cell_type":"code","source":["P_3 = 300000\n","v_3 = v_2\n","d_3 = 1/v_3\n","\n","U_3 = cp.PropsSI('U', 'P', P_3, 'D', d_3, 'water')\n","v_3 = 1/cp.PropsSI('D', 'P', P_3, 'D', d_3, 'water')\n","\n","print(U_3/1000)\n","print(v_3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bwlaN8RU-Xjk","executionInfo":{"status":"ok","timestamp":1692110880668,"user_tz":240,"elapsed":3,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"e9e2f8dd-6959-4d03-f5b0-8f1ab5aff4dd"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["3252.2181597891877\n","1.29736803502256\n"]}]},{"cell_type":"markdown","source":["## Cálculo Final\n","\n","$q_{13} = u_2 - u_1 + u_3 - u_2 + w_{12} + w_{23}$ pero como en el último proceso el pistón no se mueve el trabajo 23 es 0.\n","\n","Por lo tanto el cálculo final queda:\n","\n","$q_{13} = u_3 - u_1 + P_1\\cdot(v_2-v_1)$"],"metadata":{"id":"SkEdDEqv-9Dj"}},{"cell_type":"code","source":["q_13 = U_3 - U_1 + P_1*(v_2-v_1)\n","print(q_13/1000)                     # para pasar de J a kJ\n","\n","masa = (pi*0.1**2/4*0.045)/v_2\n","Q_13 = masa*q_13\n","print(Q_13/1000)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9AwkzOeu_6DM","executionInfo":{"status":"ok","timestamp":1692110884319,"user_tz":240,"elapsed":3,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"e8ef81b9-3c4f-4ca2-81a6-b61ec6cdb4d0"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["2423.9598668786966\n","0.6603354709622904\n"]}]},{"cell_type":"markdown","source":["## Gráficos\n","Es importante saber mostrar bien los resultados para poder interpretarlos."],"metadata":{"id":"nCPhaz6TM8oh"}},{"cell_type":"code","source":["from os import cpu_count\n","from CoolProp.Plots import PropertyPlot                                          # importar función que grafica\n","\n","\n","plot = PropertyPlot('HEOS::Water', 'PD', unit_system='EUR', tp_limits='ORC')     # tp_limits puede ser 'ORC' o 'ACHP'\n","\n","plot.title('Diagrama P-D')\n","\n","plot.calc_isolines(cp.iQ, num=2)                                                 # línea de saturación, n es 2 por que un lado es 0 (líquido) y el otro es 1 (vapor)\n","\n","plot.calc_isolines(cp.iT, iso_range= [100,200], num=10, rounding=True)           # isolíneas de temperatura\n","\n","plot.props[cp.iT]['color'] = 'green'                                             # cambiar el color de las isolíneas de temperatura\n","\n","plot.draw()\n","\n","plot.isolines.clear()\n","\n","plot.show()\n","\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":594},"id":"JKKoNn_NNEqk","executionInfo":{"status":"ok","timestamp":1692110891053,"user_tz":240,"elapsed":3046,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"bf034e51-6b13-4259-ff51-6fb985863db3"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/CoolProp/Plots/Common.py:608: UserWarning: Please use \"calc_sat_range\" to calculate saturation and isoquality lines. Input ranges are discarded.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/CoolProp/Plots/Common.py:608: UserWarning: Please use \"calc_sat_range\" to calculate saturation and isoquality lines. Input ranges are discarded.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/CoolProp/Plots/Plots.py:217: UserWarning: Detected an incomplete phase envelope, fixing it numerically.\n","  warnings.warn(\"Detected an incomplete phase envelope, fixing it numerically.\")\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## Ejemplo 2\n","\n","Considere un sistema de cilindro-pistón que continene en su interior 10 kg de agua líquida saturada a 7 bar y realiza el siguiente ciclo termodinámico.\n","\n","1. La presión disminuye a volumen constante hasta 1 bar.\n","2. El volumen aumenta a presión constante hasta 0.3292 m3/kg.\n","3. La presión aumenta a volumen constante hasta tener vapor a 7 bar.\n","4. Se vuelve al punto inicial a presión contante.\n","\n","Dibujar el ciclo en un diagrama P-v. Calcular el calor transferido y el trabajo realizado en cada etapa. Calcular el calor neto y el trabajo neto del ciclo.\n","\n","Recordar que:\n","1. Proceso a volumen constante: $q = Δu$\n","2. Proceso a presión constante: $q = Δh$"],"metadata":{"id":"A_NiQ-KJ69G2"}},{"cell_type":"code","source":["# Punto 1\n","\n","P_1 = 7e05\n","x_1 = 0\n","\n","u_1 = cp.PropsSI('U', 'P', P_1, 'Q', x_1, 'water')\n","d_1 = cp.PropsSI('D', 'P', P_1, 'Q', x_1, 'water')\n","v_1 = 1/cp.PropsSI('D', 'P', P_1, 'Q', x_1, 'water')\n","h_1 = cp.PropsSI('H', 'P', P_1, 'Q', x_1, 'water')\n","\n","\n","# Punto 2\n","\n","P_2 = 1e05\n","v_2 = v_1                  # disminuyó la presión a volumen constante\n","d_2 = 1/v_2\n","\n","u_2 = cp.PropsSI('U', 'P', P_2, 'D', d_2, 'water')\n","h_2 = cp.PropsSI('H', 'P', P_2, 'D', d_2, 'water')\n","\n","# Punto 3\n","\n","P_3 = P_2                 # aumenta el volumen a presión constante\n","v_3 = 0.3292\n","d_3 = 1/v_3\n","\n","u_3 = cp.PropsSI('U', 'P', P_3, 'D', d_3, 'water')\n","h_3 = cp.PropsSI('H', 'P', P_3, 'D', d_3, 'water')\n","\n","\n","# Punto 4\n","\n","P_4 = P_1\n","v_4 = v_3\n","d_4 = 1/v_4\n","\n","u_4 = cp.PropsSI('U', 'P', P_4, 'D', d_4, 'water')\n","h_4 = cp.PropsSI('H', 'P', P_4, 'D', d_4, 'water')\n"],"metadata":{"id":"hWrZGuHM8wi-","executionInfo":{"status":"ok","timestamp":1692110894378,"user_tz":240,"elapsed":388,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["# Cálculo por etapas\n","\n","# Etapa 1-2\n","\n","q12 = u_2 - u_1\n","w12 = 0                 # no cambia el volumen\n","\n","# Etapa 2-3\n","\n","q23= h_3-h_2\n","w23= P_2*(v_3 - v_2)\n","\n","# Etapa 3-4\n","\n","q34= u_4 - u_3\n","w34 = 0                 # no cambia el volumen\n","\n","# Etapa 4-1\n","\n","q41= h_1 - h_4\n","w41 = P_4*(v_1-v_4)\n","\n","# Cálculo neto\n","masa = 10      # kg\n","Q = masa*(q12 + q23 + q34 + q41)\n","W = masa*(w12 + w23 + w34 + w41)\n","\n","print(Q/1000)\n","print(W/1000)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SkUppXIN_nE0","executionInfo":{"status":"ok","timestamp":1692110898126,"user_tz":240,"elapsed":399,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"53736137-1881-404d-e382-3ac811c400b9"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["-1968.5522408644204\n","-1968.5522408644392\n"]}]},{"cell_type":"markdown","source":["### Links con ejemplos de graficos\n","\n","http://www.coolprop.org/coolprop/python-cycles.html"],"metadata":{"id":"V0fqMabUrMRU"}}]}