{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Notebook per l'esercitazione legata alla lezione 13.\n", "\n", "Calcolo delle traiettorie P/T adiabatiche\n", "\n", "Il programma è una modifica di *gibbs_tp* già usato nelle esercitazioni precedenti, a cui è stata aggiunta una funzione (*adiabat*) che fa i calcoli rilevanti in questo caso. \n", "\n", "La nuova funzione è riportata nella cella sottostante per chi volesse studiarla. La strategia che segue nel fare i calcoli è descritta nelle ultime slide delle lezioni *termodinamica_13*.\n", "\n", "Si tratta di una funzione *apparentemente* complessa perchè deve gestire un input *flessibile* che consenta di trattare sia fasi singole, sia sistemi che includono una reazione e un output diversificato.\n", "\n", "Come parametri *obbligatori*, la funzione richiede quelli per la definizione della griglia P/T sulla quale effettuare il calcolo; tutti gli altri argomenti di input sono in forma di keyword e prevedono dei valori di default. Vedremo il loro uso negli esempi sottostanti." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "def adiabat(tini,tfin,nt,pini,pfin,npp,prod=['py',1], \\\n", " rea=['ens',1.5,'cor', 1], phase='', env=0.,nsamp=0,\\\n", " grd=False, ret=False):\n", " \"\"\"\n", " Computes adiabats on a P/T grid\n", " \n", " Input:\n", " tini, tfin, nt: minimum, maximum and number of points \n", " along the temperature axis\n", " pini, pfin, npp: minimum, maximum and number of points\n", " along the pressure axis\n", " env: if env > 0., value of the entropy correspondent to wanted\n", " P/T path (default = 0.) \n", " nsamp: if > 0, restricts the number of entries of the printed\n", " P/T/V list to nsamp values (default = 0.). Relevant if\n", " env > 0.\n", " grd: if True, plots the grid of sampling P/T points \n", " (default = False)\n", " prod: list of products of the reaction in the form \n", " [name_1, c_name_1, name_2, c_name_2, ...]\n", " where name_i is the name of the i^th mineral, as stored\n", " in the database, and c_name_i is the corresponding\n", " stoichiometric coefficient\n", " rea: list of reactants; same syntax as the \"prod\" list.\n", " phase: if phase is not '', the computation is done for a single\n", " (specified) phase (default = '')\n", " ret: if True, T/P values of the adiabat are returned \n", " \n", " \"\"\"\n", " \n", " phase_flag=False\n", " if phase !='':\n", " phase_flag=True\n", " \n", " if not phase_flag: \n", " lprod=len(prod)\n", " lrea=len(rea)\n", " prod_spec=prod[0:lprod:2]\n", " prod_coef=prod[1:lprod:2]\n", " rea_spec=rea[0:lrea:2]\n", " rea_coef=rea[1:lrea:2]\n", " \n", " lastr=rea_spec[-1]\n", " lastp=prod_spec[-1]\n", " \n", " prod_string=''\n", " for pri in prod_spec:\n", " prod_string=prod_string + pri\n", " if pri != lastp:\n", " prod_string=prod_string+' + '\n", " \n", " rea_string=''\n", " for ri in rea_spec:\n", " rea_string = rea_string + ri\n", " if ri != lastr:\n", " rea_string=rea_string+' + '\n", " \n", " \n", " t_list=np.linspace(tini,tfin,nt)\n", " p_list=np.linspace(pini,pfin,npp)\n", " tg, pg=np.meshgrid(t_list,p_list)\n", " \n", " ntp=nt*npp\n", " \n", " tgl=tg.reshape(ntp)\n", " pgl=pg.reshape(ntp)\n", " \n", " ent=np.array([])\n", " \n", " if not phase_flag:\n", " index=0\n", " for it in tgl:\n", " ip=pgl[index]\n", " gprod=0.\n", " for pri, pci in zip(prod_spec, prod_coef):\n", " gprod=gprod+(eval(pri+'.g_tp(it,ip)'))*pci\n", " \n", " grea=0.\n", " for ri,rci in zip(rea_spec, rea_coef):\n", " grea=grea+(eval(ri+'.g_tp(it,ip)'))*rci \n", " \n", " ient=0.\n", " if gprod < grea:\n", " for pri, pci in zip(prod_spec, prod_coef):\n", " ient=ient+(eval(pri+'.s_tp(it,ip)'))*pci\n", " else:\n", " for ri,rci in zip(rea_spec, rea_coef):\n", " ient=ient+(eval(ri+'.s_tp(it,ip)'))*rci \n", " \n", " ent=np.append(ent,ient)\n", " index=index+1 \n", " else:\n", " index=0\n", " for it in tgl:\n", " ip=pgl[index]\n", " ient=eval(phase+'.s_tp(it,ip)')\n", " ent=np.append(ent,ient)\n", " index=index+1\n", " \n", " ent=ent.reshape(npp,nt)\n", " \n", " if grd:\n", " plt.figure()\n", " plt.scatter(tg,pg,s=20,color='k')\n", " plt.xlabel(\"T (K)\")\n", " plt.ylabel(\"P GPa\")\n", " plt.title(\"Grid\")\n", " plt.show()\n", " \n", " plt.figure()\n", " if env > 0.:\n", " con=plt.contour(tg,pg,ent, [env])\n", " p1=con.collections[0].get_paths()[0]\n", " path=p1.vertices\n", " else:\n", " con=plt.contour(tg,pg,ent)\n", " \n", " if env > 0.:\n", " plt.close()\n", " else:\n", " plt.clabel(con, inline=1, fontsize=10)\n", " plt.xlabel(\"T (K)\")\n", " plt.ylabel(\"P (GPa)\")\n", " plt.title(\"Entropy (J/K mol)\")\n", " plt.show()\n", " \n", " if env > 0.:\n", " t_val=path[:,0]\n", " p_val=path[:,1]\n", " plt.figure()\n", " plt.plot(p_val,t_val)\n", " plt.ylabel(\"T (K)\")\n", " plt.xlabel(\"P (GPa)\")\n", " title=\"P/T adiabat for an entropy of \" + str(env) \\\n", " + \" J/(K mol)\"\n", " plt.title(title)\n", " plt.show()\n", " \n", " ipos=p_val.argsort()\n", " t_val=t_val[ipos]\n", " p_val=p_val[ipos]\n", " \n", " ism=1\n", " if nsamp > 0:\n", " lt=len(t_val)\n", " if lt > nsamp:\n", " ism=int(lt/nsamp)\n", " \n", " t_ret=t_val\n", " p_ret=p_val\n", " \n", " t_val=t_val[0:-1:ism]\n", " p_val=p_val[0:-1:ism]\n", " v_val=np.array([])\n", " \n", " if not phase_flag:\n", " index=0\n", " for it in t_val:\n", " ip=p_val[index]\n", " gprod=0.\n", " ivp=0.\n", " ivr=0.\n", " for pri, pci in zip(prod_spec, prod_coef):\n", " gprod=gprod+(eval(pri+'.g_tp(it,ip)'))*pci\n", " ivp=ivp+(eval(pri+'.volume_p(it,ip)'))*pci\n", " \n", " grea=0.\n", " for ri,rci in zip(rea_spec, rea_coef):\n", " grea=grea+(eval(ri+'.g_tp(it,ip)'))*rci \n", " ivr=ivr+(eval(ri+'.volume_p(it,ip)'))*rci\n", " \n", " index=index+1\n", " \n", " if gprod < grea:\n", " v_val=np.append(v_val,ivp)\n", " else:\n", " v_val=np.append(v_val,ivr)\n", " else:\n", " index=0\n", " for it in t_val:\n", " ip=p_val[index]\n", " iv=eval(phase+'.volume_p(it,ip)')\n", " v_val=np.append(v_val,iv)\n", " index=index+1\n", " \n", " serie=(p_val.round(2),t_val.round(1),v_val.round(3))\n", " pd.set_option('colheader_justify', 'center')\n", " df=pd.DataFrame(serie, index=['P (GPa)','T (K)','Vol (J/bar)'])\n", " df=df.T\n", " print(\"\")\n", " print(df.to_string(index=False))\n", " \n", " if env > 0.:\n", " if ret:\n", " return t_ret, p_ret\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lanciamo il programma" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "%run gibbs_tp_adiabat\n", "import ipywidgets as wd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Per una descrizione dell'input accettato da *adiabat*, si usi il comando *help*" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function adiabat in module __main__:\n", "\n", "adiabat(tini, tfin, nt, pini, pfin, npp, prod=['py', 1], rea=['ens', 1.5, 'cor', 1], phase='', env=0.0, nsamp=0, grd=False, ret=False)\n", " Computes adiabats on a P/T grid\n", " \n", " Input:\n", " tini, tfin, nt: minimum, maximum and number of points \n", " along the temperature axis\n", " pini, pfin, npp: minimum, maximum and number of points\n", " along the pressure axis\n", " env: if env > 0., value of the entropy correspondent to the wanted\n", " P/T path (default = 0.) \n", " nsamp: if > 0, restricts the number of entries of the printed\n", " P/T/V list to nsamp values (default = 0.). Relevant if\n", " env > 0.\n", " grd: if True, plots the grid of sampling P/T points \n", " (default = False)\n", " prod: list of products of the reaction in the form \n", " [name_1, c_name_1, name_2, c_name_2, ...]\n", " where name_i is the name of the i^th mineral, as stored\n", " in the database, and c_name_i is the corresponding\n", " stoichiometric coefficient\n", " rea: list of reactants; same syntax as the \"prod\" list.\n", " phase: if phase is not '', the computation is done for a single\n", " (specified) phase (default = '')\n", " ret: if True, T/P values of the adiabat are returned\n", "\n" ] } ], "source": [ "help(adiabat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cominciamo col dare un'occhiata alle traiettorie P/T adiabatiche dell'enstatite, nell'intervallo di temperatura tra 300 e 1000K e nell'intervallo di pressione tra 0 e 10 GPa; usiamo una risoluzione di 20 punti lungo l'asse delle T e 20 punti lungo l'asse delle P; visualizziamo anche la griglia." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "adiabat(300,1000,20,0,10,20,phase='ens',grd=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I valori di entropia sono scritti direttamente sulle curve in colore. Possiamo selezionare un valore di entropia compreso tra 160 e 360 J/K mole, volendo i dati relativi a una adiabatica specifica che parta, per, esempio da un certo valore di temperatura. Se poniamo S=310 J/K mole, saremmo in corrispondenza di 700 K a P=0GPa." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " P (GPa) T (K) Vol (J/bar)\n", " 0.00 717.1 6.335 \n", " 1.05 723.4 6.271 \n", " 2.11 729.4 6.210 \n", " 2.84 733.3 6.170 \n", " 3.68 737.8 6.125 \n", " 4.74 743.2 6.071 \n", " 5.79 748.3 6.019 \n", " 6.84 753.1 5.969 \n", " 7.40 755.6 5.943 \n", " 8.42 760.0 5.898 \n", " 9.47 764.4 5.853 \n" ] } ], "source": [ "adiabat(600,800,10,0,10,20,env=310,phase='ens',nsamp=10)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "85877d7b76b947899d33f16ee025163c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(FloatSlider(value=400.0, description='tini', max=500.0, min=300.0, step=10.0), FloatSlid…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "w=wd.interact_manual(adiabat,tini=(300., 500., 10.), tfin=(500., 1000., 10.), nt=(10,50,5), pini=(0,10,1), pfin=(0,5,1),npp=(10,50, 5), env=(160., 360., 10.,), \n", " nsamp=(5,20,5), phase=(['ens','py']),prod=wd.fixed([]),rea=wd.fixed([]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notate la diminuzione significativa del volume molare (che è dato in J/bar) all'aumentare della pressione, e il significativo aumento della temperatura (di circa 50 K). La keyword nsamp=10 ha generato una tabella P/T/V di 10 entries. Volendo ottenere in uscita una lista dei valori T/P della traiettoria calcolata (per poi utilizzarla in altra applicazioni), specificare la keyword ret=True: " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " P (GPa) T (K) Vol (J/bar)\n", " 0.00 717.0 6.335 \n", " 1.05 723.3 6.271 \n", " 1.58 726.4 6.240 \n", " 2.63 732.2 6.181 \n", " 3.50 736.8 6.134 \n", " 4.21 740.5 6.097 \n", " 5.26 745.7 6.044 \n", " 5.79 748.2 6.019 \n", " 6.84 753.1 5.969 \n", " 7.89 757.7 5.921 \n", " 8.42 760.0 5.898 \n", " 9.47 764.3 5.853 \n" ] } ], "source": [ "t,p=adiabat(600,800,20,0,10,20,env=310,phase='ens',nsamp=10,ret=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Per esempio i valori di T sono adesso salvati nella variabile *t*:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[717.01765763 720.21818027 723.32884084 726.31578947 726.35385182\n", " 729.32788264 732.22187011 735.03903214 736.84210526 737.7924614\n", " 740.49344292 743.12541783 745.69098664 747.36842105 748.20128889\n", " 750.66694341 753.07251126 755.42012301 757.71180409 757.89473684\n", " 759.97073432 762.17877866 764.33580895 766.44350969]\n" ] } ], "source": [ "print(t)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consideriamo adesso l'esempio che trovate nelle slide in *termodinamica_13*: la reazione ens+cor <--> py. \n", "\n", "In tal caso dobbiamo specificare la liste dei prodotti (py) e dei reagenti (ens, cor) insieme ai rispettivi coefficienti stechiometrici (1 per py; 1.5 per ens e 1 per cor). La sintassi è \n", "\n", "```\n", "prod=['py',1], rea=['ens',1.5,'cor',1]\n", "\n", "```\n", "\n", "e quindi:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "adiabat(300,1500,20,0,5,20,prod=['py',1],rea=['ens',1.5,'cor',1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notate le discontinuità sulle curve a entropia costante, dovute alla reazione (a basse pressioni abbiamo ens+cor; ad alte pressioni abbiamo py). Questa reazione è confermata da:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " T (K) P (GPa) DH(J/mol) DS (J/mol K) DV (J/bar) Slope (bar/K)\n", " 300.0 3.67 5304.500 17.682 -0.527 -33.54 \n", " 409.1 3.31 7325.018 17.906 -0.539 -33.20 \n", " 518.2 2.95 9215.754 17.785 -0.552 -32.20 \n", " 627.3 2.61 10811.291 17.235 -0.566 -30.45 \n", " 736.4 2.29 12066.310 16.386 -0.580 -28.26 \n", " 845.5 1.99 12975.214 15.347 -0.594 -25.85 \n", " 954.5 1.72 13547.235 14.192 -0.607 -23.36 \n", " 1063.6 1.48 13797.264 12.972 -0.621 -20.89 \n", " 1172.7 1.27 13742.094 11.718 -0.634 -18.48 \n", " 1281.8 1.08 13398.789 10.453 -0.647 -16.15 \n", " 1390.9 0.91 12783.963 9.191 -0.660 -13.93 \n", " 1500.0 0.77 11913.469 7.942 -0.672 -11.82 \n", "\n", "\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Average Clapeyron Slope (from Delta S/Delta V): -24.01 bar/K\n", "Clapeyron slope (from a linear fit of the P/T curve): -24.32 bar/K\n" ] } ], "source": [ "equilib(300,1500,12,prod=['py',1],rea=['ens',1.5,'cor',1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Concentriamoci nell'adiabatica a S=700 J/K mole:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " P (GPa) T (K) Vol (J/bar)\n", " 1.00 881.1 12.041 \n", " 1.21 882.5 12.020 \n", " 1.41 883.8 11.999 \n", " 1.56 884.8 11.984 \n", " 1.77 886.2 11.964 \n", " 1.90 873.7 11.947 \n", " 2.03 860.5 11.336 \n", " 2.23 861.6 11.322 \n", " 2.44 862.7 11.309 \n", " 2.59 863.5 11.299 \n", " 2.79 864.6 11.285 \n" ] } ], "source": [ "adiabat(800,1000,20,1,3,40,prod=['py',1],rea=['ens',1.5,'cor',1],env=700,nsamp=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Come spiegato nelle slide, il salto *negativo* di temperatura (da circa 886 K prima della reazione, a circa 860 K dopo la reazione) è dovuto al fatto che il $\\Delta H$ di reazione è positivo (reazione *endotermica*) che assorbe calore e quindi, in una adiabatica, la temperatura diminuisce. Parimenti, il salto *negativo* di volume (da circa 11.95 a 11.34 J/bar) in prossimità della zona di reazione (circa 2 GPa a 860 K) è dovuto al fatto che il volume molare del piropo è più basso di quello della miscela 1.5 enstatite + corindone.\n", "\n", "Esaminiamo ancora il caso della transizione quarzo/coesite. Preliminarmente, usiamo *equilib* per determinare un campo P/T conveniente per studiare la transizione:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " T (K) P (GPa) DH(J/mol) DS (J/mol K) DV (J/bar) Slope (bar/K)\n", " 300.0 2.89 -303.957 -1.013 -0.181 5.61 \n", " 363.6 2.93 -285.275 -0.785 -0.180 4.37 \n", " 427.3 2.95 -253.669 -0.594 -0.178 3.33 \n", " 490.9 2.97 -221.092 -0.450 -0.177 2.54 \n", " 554.5 2.98 -192.284 -0.347 -0.176 1.97 \n", " 618.2 2.99 -168.734 -0.273 -0.175 1.56 \n", " 681.8 3.00 -150.380 -0.221 -0.174 1.27 \n", " 745.5 3.01 -136.402 -0.183 -0.173 1.06 \n", " 809.1 3.02 -125.617 -0.155 -0.172 0.90 \n", " 872.7 3.02 -116.679 -0.134 -0.170 0.78 \n", " 936.4 3.03 -108.192 -0.116 -0.169 0.68 \n", " 1000.0 3.03 -98.762 -0.099 -0.168 0.59 \n", "\n", "\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Average Clapeyron Slope (from Delta S/Delta V): 2.06 bar/K\n", "Clapeyron slope (from a linear fit of the P/T curve): 1.78 bar/K\n" ] } ], "source": [ "equilib(300,1000,12,prod=['coe',1],rea=['q',1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Eseguiamo quindi un calcolo di adiabatica:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "adiabat(300,800,20,2.8,3.1,20,prod=['coe',1],rea=['q',1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "e focalizziamoci sull'adiabatica a S=56 J/K mole. " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " P (GPa) T (K) Vol (J/bar)\n", " 2.80 403.0 2.191 \n", " 2.83 403.0 2.190 \n", " 2.86 403.0 2.189 \n", " 2.89 403.0 2.188 \n", " 2.93 403.0 2.188 \n", " 2.96 407.9 2.008 \n", " 2.97 408.0 2.008 \n", " 3.01 408.0 2.007 \n", " 3.04 408.1 2.007 \n", " 3.07 408.1 2.006 \n" ] } ], "source": [ "adiabat(350,450,20,2.8,3.1,20,prod=['coe',1],rea=['q',1],env=56,nsamp=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Poichè si tratta di una transizione esotermica ($\\Delta H$ è negativo come si vede dall'output di *equilib*), alla pressione di transizione abbiamo un aumento di temperatura." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Come considerazione finale, discutiamo il modo attraverso il quale l'entropia viene determinata a ogni valore di pressione e temperatura. Come detto nelle slide, *S* è calcolata dalla derivata parziale di *G* rispetto a *T*, a pressione costante:\n", "\n", "$$S=-\\left(\\frac{\\partial G}{\\partial T}\\right)_P$$\n", "\n", "In effetti, mentre a pressione ambiente, l'entropia può essere ottenuta direttamente per integrazione su $T$ di $C_P(T)/T$, dove $C_P$ è il calore specifico a pressione (ambiente) costante, a più alte pressioni questa via non può essere seguita (appunto perchè non disponiano del $C_P$ misurato in alta pressione). \n", "\n", "La funzione *s_tp* della classe *mineral* calcola l'entropia a qualunque $T$ e $P$ appunto usando la derivata di $G$ rispetto a $T$:\n", "\n", "```\n", " def s_tp(self,tt,pp):\n", " gtp=lambda tf: self.g_tp(tf,pp)\n", " t_list=np.linspace(tt-5, tt+5, 5)\n", " g_list=np.array([])\n", " for ti in t_list:\n", " gi=gtp(ti)\n", " g_list=np.append(g_list,gi)\n", " fit=np.polyfit(t_list,g_list,2)\n", " fitder=np.polyder(fit,1)\n", " return -1*np.polyval(fitder,tt)\n", "\n", "```\n", "\n", "La funzione *s_tp*:\n", "\n", "- definisce preventivamente una funzione *lambda* (si tratta di una funzione *anonima*) assegnata alla variabile *gtp* (che diventa una funzione a tutti gli effetti, che accetta come argomento un valore di temperatura); *gtp* calcola l'energia libera $G$ alle condizioni T/P specificate come argomenti di *s_tp*;\n", "- costruisce una lista *t_list* di 5 valori di temperatura centrati sul valore *tt* voluto;\n", "- si calcola le energie libere corrispondenti ai diversi valori di temperatura in *t_list*, alla pressione *pp* specificata, e le pone in una lista *g_list*;\n", "- esegue un fit polinomiale di secondo grado sulle liste *t_list* e *g_list* $[g(t)=at^2+bt+c]$ e pone i coefficienti ottimizzati ($a,b,c$) nella lista *fit*, usando la funzione *polyfit* di Numpy (*np.polyfit*);\n", "- esegue la derivata del polinomio quadratico, usando la funzione *polyder* di Numpy (specificando la *derivata prima*) e pone i coefficienti nella lista *fitder*;\n", "- restituisce il valore delle derivata (cambiata di segno) valutata alla temperatura voluta (*tt*), usando la funzione *polyval* di Numpy, che corrisponde all'entropia.\n", "\n", "Proviamo a fare a mano il calcolo per il piropo; per esempio, a T=500 K, e P=3 GPa, dovremmo ottenere:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "453.427" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "py.s_tp(500,3).round(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vediamo di ottenere *manualmente* questo valore, lavorando come *s_tp*:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "t_list=np.linspace(495,505,5) # lista dei valori di temperatura\n", "g_list=np.array([]) # inizializziamo la lista g_list\n", "for it in t_list: # ciclo sui valori di temperatura\n", " ig=py.g_tp(it,3) # calcolo della G ad ogni T in t_list e P=3GPa\n", " g_list=np.append(g_list,ig) # aggiungiamo i valori di G alla lista g_list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vediamo le due liste:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[495. 497.5 500. 502.5 505. ]\n", "[-5668080.99748005 -5669206.74777866 -5670337.72064672 -5671473.89873853\n", " -5672615.26479645]\n" ] } ], "source": [ "print(t_list)\n", "print(g_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Facciamone un plot:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(t_list,g_list,\"k*\")\n", "plt.xlabel(\"T (K)\")\n", "plt.ylabel(\"G (J/mole)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Determiniamo il polinomio quadratico $G(T)$:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-4.16419913e-01 -3.70075109e+01 -5.54772899e+06]\n" ] } ], "source": [ "fit=np.polyfit(t_list,g_list,2)\n", "print(fit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Già che ci siamo, controlliamo graficamente la qualità del fit:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x_list=np.linspace(495,505,20)\n", "y_list=np.polyval(fit,x_list)\n", "plt.figure()\n", "plt.plot(x_list,y_list,\"b-\",label=\"Fit\")\n", "plt.plot(t_list,g_list,\"k*\",label=\"valori effettivi\")\n", "plt.legend(frameon=False)\n", "plt.xlabel(\"T (K)\")\n", "plt.ylabel(\"G (J/mole)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Facciamo la derivata prima del polinomio:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "fitder=np.polyder(fit,1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calcoliamo la derivata a T=500K (cambiata di segno):" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Entropia -453.427 (J/K mole)\n" ] } ], "source": [ "S=np.polyval(fitder,500)\n", "print(\"Entropia %6.3f (J/K mole)\" % S)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Come vedete, il valore è identico a quello calcolato direttamente con la funzione *s_tp* (vuol dire che non abbiamo fatto errori...). Del resto, l'ho scritta io quella funzione perchè facesse i calcoli che abbiamo visto *manualmente*... quindi è ovvio che i risultati siano identici...\n", "\n", "Sappiate che potete dire di aver capito veramente tutto di un dato problema solo nel momento in cui siate in grado di fare i calcoli numerici per risolverlo (conoscere solo la teoria, a livello simbolico, non è garanzia di *comprensione*) e, soprattutto siate anche in grado di codificare il calcolo in un programma scritto in qualche linguaggio. \n", "\n", "Gli algoritmi che vediamo in questi programmi ed esercitazioni, sono gli stessi di quelli implementati (in linguaggio *Fortran*) nel programma [Perplex](http://www.perplex.ethz.ch/) che è usato professionalmente da chiunque nel mondo si occupi di metamorfismo o, più in generale, di modellizzazione termodinamica di crosta e mantello terrestre. Più avanti vedremo anche l'uso di Perplex: i numeri che otterrete usando quel programma coincidono all'ultima cifra decimale con quelli che calcoliamo con questi progrtammi Python (ovviamente, a patto di utilizzare gli stessi database termodinamici per $C_P$, $K$, $\\alpha$, $G_0$, $S_0$ e $V_0$ di ogni fase minerale). \n", "\n", "Conoscere gli algoritmi attraverso i quali vengono fatti i calcoli, ed essere addirittura in grado di riprodurli indipendentemente, vi dà non una, ma due marce in più di chi usi quegli stessi programmi a *scatola chiusa*, conoscendone solo approssimativamente la termodinamica implicata e ancor meno il modo con cui questa è implementata. Saper fare i calcoli vi rende consapevoli anche dei *limiti* degli stessi, che dipendono dalle *approssimazioni*, dalle *assunzioni* fatte per poterli eseguire, e dalla *qualità* dei dati termodinamici di base contenuti nei database, che potete anche giudicare in modo *critico*. " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }