{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMEeoBF+ccDyxxTrh7uQrJf"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# PyroMat\n","link: http://pyromat.org/"],"metadata":{"id":"HhauKinCtngj"}},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qXqrfxHgtWb_","executionInfo":{"status":"ok","timestamp":1692114948286,"user_tz":240,"elapsed":6106,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"bd50c14d-95f2-41b1-8abe-d1711e99205d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: pyromat in /usr/local/lib/python3.10/dist-packages (2.2.4)\n","Requirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.10/dist-packages (from pyromat) (1.23.5)\n"]}],"source":["%pip install pyromat\n","import pyromat as pm\n","from numpy import *\n","import matplotlib.pyplot as plt"]},{"cell_type":"markdown","source":["### Verificar las unidades de medida"],"metadata":{"id":"gMX3jkdIrBe2"}},{"cell_type":"code","source":["pm.config"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kUw--zupqeq8","executionInfo":{"status":"ok","timestamp":1692110723609,"user_tz":240,"elapsed":5,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"f81584de-6adb-4273-8d29-e1306f8e319c"},"execution_count":2,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" config_file : ['/usr/local/lib/python3.10/dist-packages/pyromat/...\n"," config_verbose : False\n"," dat_dir : ['/usr/local/lib/python3.10/dist-packages/pyromat/data']\n"," dat_exist_fatal : False\n"," dat_overwrite : True\n"," dat_recursive : True\n"," dat_verbose : False\n"," def_T : 298.15\n"," def_oob : nan\n"," def_p : 1.01325\n"," error_verbose : True\n"," install_dir : '/usr/local/lib/python3.10/dist-packages/pyromat'\n"," reg_dir : ['/usr/local/lib/python3.10/dist-packages/pyromat/...\n"," reg_exist_fatal : False\n"," reg_overwrite : True\n"," reg_verbose : False\n"," unit_energy : 'kJ'\n"," unit_force : 'N'\n"," unit_length : 'm'\n"," unit_mass : 'kg'\n"," unit_matter : 'kg'\n"," unit_molar : 'kmol'\n"," unit_pressure : 'bar'\n","unit_temperature : 'K'\n"," unit_time : 's'\n"," unit_volume : 'm3'\n"," version : '2.2.4'\n"," warning_verbose : True"]},"metadata":{},"execution_count":2}]},{"cell_type":"markdown","source":["### También se pueden observar todas las unidades disponibles para cada propiedad."],"metadata":{"id":"igf-4VkJqzwl"}},{"cell_type":"code","source":["pm.units.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KTQcbgZlsWmn","executionInfo":{"status":"ok","timestamp":1692110725918,"user_tz":240,"elapsed":5,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"2cfd0b1f-065c-4009-c98d-f295fdc2eb02"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":[" length : km m cm mm um nm A in nmi ft yd mile mi \n"," time : ns us ms s min hr day year \n"," mass : kg g mg lbm lb oz slug u amu \n"," force : N kN lb kgf lbf oz \n"," molar : kmol mol lbmol n Nm3 Ncum NL Ncc scf sci \n"," temperature : K C F R eV \n"," energy : J kJ cal kcal eV BTU \n"," volume : m3 mm3 cm3 in3 ft3 L mL uL cum cc cumm cuin cuft gal USgal UKgal qt pt \n"," pressure : Pa kPa MPa GPa bar atm Torr mmHg mmH2O psi psf ksi inHg inH2O \n","See also...\n"," abs_to_gauge() Absolute to gauge pressure\n"," gauge_to_abs() Gauge to absolute pressure\n"," matter() Moles and mass conversions\n"," temperature_scale() Correct handling of non-absolute temperatures\n"]}]},{"cell_type":"markdown","source":["### Cambiemos la unidad de Presión de bar a kPa"],"metadata":{"id":"3M0HMHNItju9"}},{"cell_type":"code","source":["pm.config['unit_pressure'] = 'kPa'"],"metadata":{"id":"JY4N-ENgtpIg","executionInfo":{"status":"ok","timestamp":1692110727902,"user_tz":240,"elapsed":263,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}}},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":["## Verifiquemos que la unidad cambió."],"metadata":{"id":"ZCYppjtnt2eD"}},{"cell_type":"code","source":["pm.config"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ar5NrjWwt5vb","executionInfo":{"status":"ok","timestamp":1692110729932,"user_tz":240,"elapsed":269,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"05fd293c-ece8-44ba-951f-c8230ebf938e"},"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" config_file : ['/usr/local/lib/python3.10/dist-packages/pyromat/...\n"," config_verbose : False\n"," dat_dir : ['/usr/local/lib/python3.10/dist-packages/pyromat/data']\n"," dat_exist_fatal : False\n"," dat_overwrite : True\n"," dat_recursive : True\n"," dat_verbose : False\n"," def_T : 298.15\n"," def_oob : nan\n"," def_p : 1.01325\n"," error_verbose : True\n"," install_dir : '/usr/local/lib/python3.10/dist-packages/pyromat'\n"," reg_dir : ['/usr/local/lib/python3.10/dist-packages/pyromat/...\n"," reg_exist_fatal : False\n"," reg_overwrite : True\n"," reg_verbose : False\n"," unit_energy : 'kJ'\n"," unit_force : 'N'\n"," unit_length : 'm'\n"," unit_mass : 'kg'\n"," unit_matter : 'kg'\n"," unit_molar : 'kmol'\n"," unit_pressure : 'kPa'\n","unit_temperature : 'K'\n"," unit_time : 's'\n"," unit_volume : 'm3'\n"," version : '2.2.4'\n"," warning_verbose : True"]},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","source":["## Ejemplo 1\n","\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 presión 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} $"],"metadata":{"id":"GKgI4pPJqUYT"}},{"cell_type":"markdown","source":["### 1) Definir la sustancia, en este caso es agua"],"metadata":{"id":"CYqdhQn2un5-"}},{"cell_type":"code","source":["H2O = pm.get('mp.H2O') # 'mp' significa multifase, en caso de tratar con un gas ideal sería 'ig'\n","\n","pm.info(name= 'water') # Se pueden ver todas las propiedades disponibles, la Energía Interna es 'e'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mGzU40hjur9v","executionInfo":{"status":"ok","timestamp":1692113848621,"user_tz":240,"elapsed":9,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"1d4428cc-563d-47b4-c7da-4257d974ce26"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":[" PYroMat\n","Thermodynamic computational tools for Python\n","version: 2.2.4\n","------------------------------------------------------------------------\n"," ID : class : name : properties\n","------------------------------------------------------------------------\n"," ig.BrH : ig : Hydrogen bromide : T p d v cp cv gam e h s mw R \n"," ig.CHN : ig2 : Hydrogen cyanide : T p d v cp cv gam e h s mw R \n"," ig.ClH : ig : Hydrogen chloride : T p d v cp cv gam e h s mw R \n"," ig.DHO : ig : Water-d : T p d v cp cv gam e h s mw R \n"," ig.H2O : ig2 : Water : T p d v cp cv gam e h s mw R \n"," ig.H2S : ig2 : Hydrogen sulfide : T p d v cp cv gam e h s mw R \n"," mp.H2O : mp1 : Water : T p d v cp cv gam e h s mw R \n"]}]},{"cell_type":"markdown","source":["### 2) Desarrollar por puntos"],"metadata":{"id":"xMiiPTlyvxcS"}},{"cell_type":"code","source":["# Punto 1)\n","\n","P_1 = 101325 + (40*9.81)/(pi*0.1**2/4) # Pa\n","P_1 = P_1/1000 # kPa\n","x_1 = 0.25\n","\n","u_1 = H2O.e(p= P_1, x= x_1)\n","v_1 = H2O.v(p= P_1, x= x_1)\n","\n","\n","# Punto 2)\n","\n","P_2 = P_1\n","v_2 = 4.5 * v_1\n","\n","u_2 = H2O.e(p= P_2, v= v_2)\n","v_2 = H2O.v(p= P_2, v= v_2)\n","\n","# Punto 3)\n","\n","P_3 = 300 # kPa\n","v_3 = v_2\n","\n","u_3 = H2O.e(p= P_3, v= v_3)\n","v_3 = H2O.v(p= P_3, v= v_3)"],"metadata":{"id":"uMXRhZ3Zv0tI","executionInfo":{"status":"ok","timestamp":1692110735836,"user_tz":240,"elapsed":779,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}}},"execution_count":7,"outputs":[]},{"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":"sIKf-zwGxsxm"}},{"cell_type":"code","source":["q_13 = u_3 - u_1 + P_1*(v_2-v_1)\n","print(q_13)\n","\n","masa = (pi*0.1**2/4*0.045)/v_2\n","Q_13 = masa*q_13\n","print(Q_13)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rKX-z8V6xzTx","executionInfo":{"status":"ok","timestamp":1692110738081,"user_tz":240,"elapsed":4,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"3a62f033-9d07-436e-ea19-297f0ec0f55c"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["[2424.15793249]\n","[0.66031045]\n"]}]},{"cell_type":"markdown","source":["### Gráfico del proceso.\n"],"metadata":{"id":"NhMCKkAYiSFw"}},{"cell_type":"code","source":["import numpy as np\n","\n","clear_plots = False\n","\n","f2 = plt.figure(2)\n","if clear_plots:\n"," plt.clf()\n","ax2 = f2.add_subplot(111)\n","ax2.set_ylabel('Pressure, p (kPa)') # Titulo eje y\n","ax2.set_xlabel('Volume, v (m$^3$/kg)') # Titulo eje x\n","ax2.set_title('Diagrama P-v del ejemplo 1') # Titulo del gráfico\n","\n","# Esta parte gráfica la campana\n","Tt,pt = H2O.triple()\n","Tc,pc = H2O.critical()\n","T = np.arange(Tt,Tc,2.5)\n","p = H2O.ps(T)\n","dL,dV = H2O.ds(T=T)\n","\n","ax2.plot(1./dL,p,'k')\n","ax2.plot(1./dV,p,'k')\n","\n","plt.xscale('log') # Escala logaritmica eje x\n","plt.yscale('log') # Escala logaritmica eje y\n","\n","# Graficar Puntos\n","\n","plt.scatter(v_1,P_1,marker =\"o\", color= 'red', s= 6) # punto 1\n","plt.scatter(v_2, P_2, marker= 'o', color= 'red', s= 6) # punto 2\n","plt.scatter(v_3, P_3, marker= 'o', color= 'red', s= 6) # punto 3\n","\n","# Graficar líneas entre los puntos\n","\n","p12 = np.array([P_1,P_2])\n","v12 = np.array([v_1, v_2])\n","plt.plot(v12, p12, linewidth= 1.5, color= 'red')\n","\n","p23 = np.array([P_2,P_3])\n","v23 = np.array([v_2, v_3])\n","plt.plot(v23, p23, linewidth= 1.5, color= 'red')\n","\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":502},"id":"U1aaSj2CiW-n","executionInfo":{"status":"ok","timestamp":1692110741941,"user_tz":240,"elapsed":1279,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"d9df028f-faac-4b7d-afd5-ae1528f26fca"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[]"]},"metadata":{},"execution_count":9},{"output_type":"display_data","data":{"text/plain":["
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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":"J8sw2rOa8rx-"}},{"cell_type":"code","source":["agua = pm.get('mp.H2O')\n","\n","pm.config['unit_pressure'] = 'bar' # como el enunciado está en 'bar' mejor usar 'bar'\n","\n","# Punto 1\n","\n","P_1 = 7\n","x_1 = 0\n","\n","v_1 = agua.v(p = P_1, x = x_1)\n","h_1 = agua.h(p = P_1, x = x_1)\n","u_1 = agua.e(p = P_1, x = x_1)\n","\n","# Punto 2\n","\n","P_2 = 1\n","v_2 = v_1\n","\n","h_2 = agua.h(p = P_2, v = v_2)\n","u_2 = agua.e(p = P_2, v = v_2)\n","\n","# Punto 3\n","\n","P_3 = P_2\n","v_3 = 0.3292\n","\n","h_3 = agua.h(p = P_3, v = v_3)\n","u_3 = agua.e(p = P_3, v = v_3)\n","\n","# Punto 4\n","\n","P_4 = P_1\n","v_4 = v_3\n","\n","h_4 = agua.h(p = P_4, v = v_4)\n","u_4 = agua.e(p = P_4, v = v_4)\n"],"metadata":{"id":"indd5Jj48ukE","executionInfo":{"status":"ok","timestamp":1692110751036,"user_tz":240,"elapsed":241,"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)\n","print(W*100) # El trabajo está en J, y la presión en bar = 10^5"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hKX2eYk0HwGC","executionInfo":{"status":"ok","timestamp":1692117737573,"user_tz":240,"elapsed":273,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"dd3287e6-8b6c-4bc9-9d20-46807feadd96"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["[-1968.54947686]\n","[-1968.55225028]\n"]}]},{"cell_type":"markdown","source":["### Diagrama P-v del proceso"],"metadata":{"id":"YtNYeOCjp4by"}},{"cell_type":"code","source":["import numpy as np\n","\n","clear_plots = True\n","\n","f2 = plt.figure(2)\n","if clear_plots:\n"," plt.clf()\n","ax2 = f2.add_subplot(111)\n","ax2.set_ylabel('Pressure, p (bar)') # Titulo eje y\n","ax2.set_xlabel('Volume, v (m$^3$/kg)') # Titulo eje x\n","ax2.set_title('Diagrama P-v del ejemplo 2') # Titulo del gráfico\n","\n","# Esta parte gráfica la campana\n","Tt,pt = H2O.triple()\n","Tc,pc = H2O.critical()\n","T = np.arange(Tt,Tc,2.5)\n","p = H2O.ps(T)\n","dL,dV = H2O.ds(T=T)\n","\n","ax2.plot(1./dL,p,'k')\n","ax2.plot(1./dV,p,'k')\n","\n","plt.xscale('log') # Escala logaritmica eje x\n","plt.yscale('log') # Escala logaritmica eje y\n","\n","# Graficar Puntos\n","\n","plt.scatter(v_1,P_1,marker =\"o\", color= 'red', s= 6) # punto 1\n","plt.scatter(v_2, P_2, marker= 'o', color= 'red', s= 6) # punto 2\n","plt.scatter(v_3, P_3, marker= 'o', color= 'red', s= 6) # punto 3\n","plt.scatter(v_4, P_4, marker= 'o', color= 'red', s= 6) # punto 4\n","\n","# Graficar líneas entre los puntos\n","\n","p12 = np.array([P_1,P_2])\n","v12 = np.array([v_1, v_2])\n","plt.plot(v12, p12, linewidth= 1.5, color= 'red')\n","\n","p23 = np.array([P_2,P_3])\n","v23 = np.array([v_2, v_3])\n","plt.plot(v23, p23, linewidth= 1.5, color= 'red')\n","\n","p34 = np.array([P_3,P_4])\n","v34 = np.array([v_3, v_4])\n","plt.plot(v34, p34, linewidth= 1.5, color= 'red')\n","\n","p41 = np.array([P_4,P_1])\n","v41 = np.array([v_4, v_1])\n","plt.plot(v41, p41, linewidth= 1.5, color= 'red')\n","\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":575},"id":"RnWFeATUp88Z","executionInfo":{"status":"ok","timestamp":1692115157919,"user_tz":240,"elapsed":1845,"user":{"displayName":"Joaquin Oyarzun","userId":"09611244636883761613"}},"outputId":"115c732b-0601-482d-f33a-984ca05f859a"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stderr","text":[":40: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n"," v23 = np.array([v_2, v_3])\n",":48: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n"," v41 = np.array([v_4, v_1])\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["### Links donde encontrar ejemplos de graficos\n","\n","http://pyromat.org/doc_howto.html#cycle_brayton\n","\n","http://pyromat.org/src/rankine.py"],"metadata":{"id":"vILrkBk5qTeb"}}]}