diff --git a/Introduction to Bamboo.ipynb b/Introduction to Bamboo.ipynb index f522704..7e7c4db 100644 --- a/Introduction to Bamboo.ipynb +++ b/Introduction to Bamboo.ipynb @@ -352,7 +352,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -386,8 +386,8 @@ " coolant_transport, \n", " mdot_coolant, \n", " configuration = \"spiral\", \n", - " channel_shape = \"semi-circle\", \n", - " channel_width = 0.020)\n", + " channel_width = 0.02,\n", + " channel_height = 0.01)\n", "\n", "\n", "#We can see a rough representation of the cooling jacket on the geometry plot \n", @@ -412,7 +412,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -487,7 +487,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "Wall tempreature was above coolant boiling point when using the Sieder-Tate equation (h_coolant_model = '2') - coolant boiling temperature was used instead of wall temperature.\n", "Exported JSON data to 'heating_output.json'\n", "\n", "dict_keys(['x', 'q_dot', 'T_ablative_inner', 'T_wall_inner', 'T_wall_outer', 'T_coolant', 'T_gas', 'h_gas', 'h_coolant', 'R_gas', 'R_ablative', 'R_wall', 'R_coolant', 'p_coolant', 'p0_coolant', 'mu_gas', 'k_gas', 'Pr_gas', 'Pr_coolant', 'mu_coolant', 'k_coolant', 'cp_coolant', 'rho_coolant', 'v_coolant', 'boil_off_position'])\n" @@ -522,12 +521,12 @@ "output_type": "stream", "text": [ "Final x position = -0.4163781058592353\n", - "Coolant exit temperature: 440.7072479415034 K\n" + "Coolant exit temperature: 434.0353107515504 K\n" ] }, { "data": { - "image/png": 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\n", 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3MDfddBMXLlzg5Zdfrlu3adMmPvroI0aMGMHrr79OdXU1J06cYO3atQwePLjB9b664YYbeO+99ygvL6esrIw//vGPHtt9+9vf5sUXXwScewFnz55tdLulpaV06dKFyspKXnvtNZ/zqtWzZ09OnDhRV0AqKyvZuXPnRW3Kyso4c+YMY8aMYfbs2Wzbts3v/owxvrE9kDATEZYvX85jjz3GzJkzSUpKIiMjg9mzZzNixAg2bNhAbm4uIsLzzz9P586dGTdunMf1tYeQvDVo0CDuuusucnNzSU9PJy8vj5SUlEva/e53v2PKlCnMmzcPh8PBiy++2Oi9EZ5++mmGDBlCeno6ffv2pbS01Ne3BXDe2GfZsmVMmzaNM2fOUFVVxWOPPUafPn3q2pSWljJ27FjKy8tRVX7729/61ZcxxnfS1GEMEbkCmMylN5SKuu+C5OXlaVFR0UXrdu/eTe/evQPaTyRN59GUsrIykpOTOXfuHCNGjGDu3LkMGDDA7+1F4tiD8Rl70lJu6eqvWB5/Sx+7iGxW1Uu+F+DNHsi7wP8CfwaqA52YCa8pU6awa9cuysvLefDBB5tVPIwxscWbAtJaVf8p6JmYsFi8eHG4UzDGRClvTqK/LyJjgp6JMcaYqOJNAfkJziJSLiKlrkfjl+EYY4xp8bz5ImFknRE1xhgTEby6jFdE7gJq58ootAkUjTHGNHkIS0Rm4jyMtcv1+IlrnQmg5cuXIyLs2bMHuHgyxPoyMjI4efJko9t79tlnL1oeNmxYYBI1xhgXb/ZAxgD9VbUGQEQWAluB6c3tXERGA78DHMArqjqz3uvien0McA6YpKpbvIn116xZs/j6668DsSkA2rRp49UMsUuWLOGGG25g6dKlzJgxo9n9Pvvss/ziF7+oW66dQdcYYwLF26lM2rs9v/Sryn4QEQfwe+A2IBuYICLZ9ZrdBvRwPaYAL/oQ65dAFg9vt1dWVsa6deuYN29e3T1BAM6ePcu4cePIzs5m6tSp1NTUXBJ79913M3DgQPr06cPcuXMB5/Tm58+fp3///tx///0AJCcnA3DfffexcuXKuvhJkybx1ltvUV1dzRNPPMGgQYPo168ff/jDH5o1bmNMy+dNAfk3YKuILHDtfWwGnm0ixhuDgf2qekBVK4ClwNh6bcYCi9TpE6C9iHTxMjZqvPPOO4wePZqsrCw6duzIli1bANi4cSMvvPAC27dv57PPPuPtty+9i/D8+fPZvHkzRUVFzJkzh5KSEmbOnMlll13Gtm3bLpmLavz48XX3D6moqKCgoIAxY8Ywb948UlJS2LRpE5s2beLll1/m888/D/7gjTFRy5tb2i4Brsd5D/S3gaGqurTxKK90Aw67LRe71nnTxpvYqLFkyRLGjx8POH/BL1myBHBOcX7NNdfgcDiYMGECH3/88SWxc+bMITc3l+uvv57Dhw+zb1/jM+/fdtttrF69mgsXLvCnP/2JESNGcNlll/HBBx+waNEi+vfvz5AhQygpKWlyW8aY2NbYLW17qeoeEamd26LY9bOriHStPRfRDOJhXf2JuRpq402scwMiU3Ae/iI1NfWiqcUBUlJS/J7sz1uNbb+kpITVq1ezfft2RITq6mpEhPz8fGpqaupiy8vLqayspLS0FFWlrKyMjRs3smrVKj744ANat27NmDFj+Oqrr+pi6vdbuzx8+HDeeecdli1bxne/+11KS0uprKzkueee4+abb/Y6d0+qq6uD/n76qry8/JLPPRjKyspC0k+kiuXxx+rYGzuJ/jOcv3hf8PCaAjc1s+9i4Cq35TTgiJdtEr2IdSaqOheYC87JFOtPeLZ79+6gT/7X2PYXL17MxIkTLzrnMHLkSLZs2cLmzZs5efIk6enpvPvuu0yZMoW2bdsiIiQnJ1NZWUmnTp1ITU1lz549bNq0idatW9O2bVsSEhJISkoiISHhkjweeOABXnnlFYqKinjttddITEzk9ttvZ+HChdxxxx0kJCSwd+9eunXrRps2bXwaayROppiUlMR1110X9H5a+oR6TYnl8cfq2Bs8hKWqU1xPb1PVG90fOK+Kaq5NQA8RyRSRRGA8sKJemxXARHG6Hjijqke9jI0KS5YsYdy4cRetu+eee1i8eDFDhw5l+vTp5OTkkJmZeUm70aNHU1VVRb9+/fjlL3/J9ddfX/falClT6NevX91JdHe33nora9eu5eabbyYxMRGAhx9+mOzsbAYMGEBOTg4//OEPqaqqCsKIjTEthTeX8a4H6k/R6mmdT1S1SkQeAVbhvBR3vqruFJGprtdfAlbiLFb7cV7G+4PGYpuTT602bdoE/DLexnja7Z02bRrTpk1rMMb9vh9/+tOfPLZ57rnneO655+qWy8rK6p4nJCRQUlJyUfu4uDieffbZS74/YowxDWnsHEhnnCemLxOR6/jmvEM7oHUgOlfVlTiLhPu6l9yeK/AP3sYGgjff2WhKJB7GMcaYQGtsD2QUMAnn+YXfuK0vBX7hKcAYY0zsaLCAqOpCYKGI3KOqb4UwJ2OMMVGgsUNYf6eqrwIZIvKz+q+r6m88hBljjIkRjR3Cqj37mxyKRIwxxkSXxg5h/cH189ehS8cYY0y08GY69+dFpJ2IJIhIgYicFJG/C0VyseCnP/0ps2fPrlseNWoUDz/8cN3yz3/+c37zG89HCydNmsSyZcsAyM/Pp6ioKKi5GmOMO28mU7xVVc8Cd+D8ZngW8ERQswqjzp1BpHmPdu3a1j3v3Lnx/oYNG1Y31XpNTQ0nT55k585vvtKyfv16hg8fHswhG2OMX7wpILVzYYwBlqjqV0HMJ+yOHQvt9oYPH15XQHbu3ElOTg5t27bl1KlTXLhwgd27d7Nq1SoGDRpETk4OU6ZMwfn1GGOMCS9vCsh7IrIHyAMKROQKoDy4acWOrl27Eh8fz9/+9jfWr1/P0KFDGTJkCBs2bKCoqIh+/frxyCOPsGnTJnbs2MH58+d5/327o7AxJvy8mc59OjAUyFPVSuBrovjeG5Godi+ktoAMHTq0bnnYsGGsWbOGIUOG0LdvX1avXn3RIS5jjAmXJufCEpEE4AFghPMOs3wEvNRokPFJ7XmQ7du3k5OTw1VXXcULL7xAu3bteOihh3j44YcpKiriqquuYsaMGZSX2w6gMSb8vDmE9SIwEPhP12OAa50JkOHDh/P+++/TsWNHHA4HHTt25PTp02zYsIGhQ4cC0KlTJ8rKyuquujLGmHDzZjbeQaqa67a8WkQ+DVZCsahv376cPHmS73//+xetKysro1OnTkyePJm+ffuSkZHBoEGDwpipMcZ8w5sCUi0i3VX1MwARuQaoDm5a4ZOaGtgrsVJTm27jcDg4e/bsResWLFhQ9/yZZ57hmWeeuSTOvU0s3g3NGBNe3hSQJ4A1InIA55Tu6bjuy9ESffll87dh07kbY2JBkwVEVQtEpAfQE2cB2aOqF4KemTHGmIjmzVVYScCPgRtw3gv9f0XkJVW1S4GMMSaGeXMIaxHOm0j9P9fyBOC/ge8GK6lQU1VclyibFsa+tW9M8HhTQHrWuwprTUu6CispKYmSkhIuv/xyKyItjKpSUlJCUlJSuFMxpkXypoBsFZHrVfUTABEZAqwLblqhk5aWRnFxMSdOnAjYNsvLy2P2l1akjT0pKYm0tLRwp2FMi+RNARkCTBSRv7mWrwZ2i8h2QFW1X9CyC4GEhAQyMzMDus3CwkKuu+66gG4zWsTy2I2JNd4UkNFBz8IYY0zU8eYy3kOhSMQYY0x08WYurIATkY4i8qGI7HP97NBAu9Ei8n8isl9EprutnyEiX4jINtdjTOiyN8YYA94dwgqG6UCBqs50FYbpwD+5NxARB/B74Bacd0LcJCIrVHWXq8lvVXVWKJM2xsQ2VaW8vJyysjLOnTvHhQsXqK6upry8nM8++4yOHTvSvn37mLmi05svEj4CvKaqpwLY71gg3/V8IVBIvQICDAb2q+oBVx5LXXG7MMaYIKusrOTIkSMUFxdz+PBhjh8/XjdnncPhqCsSqkpmZiZvvvkmNTU1JCQkMHLkSAYMGEB8fLj+Rg8NaeqLViLyDDAe2ALMB1ZpM7+dJSKnVbW92/IpVe1Qr829wGhVfdi1/AAwRFUfEZEZwCTgLFAE/LyhAiciU4ApAKmpqQOXLl3anNS9UlZWRnJyctD7iUQ29tgcO7SM8VdVVVFeXk55eTmVlZWIiFdfRm3VqhUXLnwzw1NtcUlOTqZNmzZRv0dy4403blbVvPrrmywgAOIc/a04J1HMA94A5tXO0NtAzJ+Bzh5eehJY6EUB+S4wql4BGayqj4pIKnAS59QqTwNdVPWhpsaRl5enRUVFTTVrtsLCQvLz84PeTySyseeHO42widbxl5WVsXXrVjZv3szXX3+NqlJd7duE41lZWezdu/eS9fHx8YgIubm5DBs2jA4dPJ7ujXgi4rGAeLV/paoqIl8CXwJVQAdgmYh8qKr/2EDMzY0kc0xEuqjqURHpAhz30KwYuMptOQ044tp23YTrIvIyYDcJN8b4pLi4mLVr13LgwAFEhKqqqoD3UbvNLVu2sG3bNrp168bw4cPp3r07cXFhuYYpoLw5BzINeBDnX/yvAE+oaqWIxAH7AI8FpAkrXNuc6fr5roc2m4AeIpIJfIHzMNr3XTl1UdWjrnbjgB1+5GCMiUGHDh1i1apVnDx5ksrKypD0WVNTQ01NDYcOHeLo0aPEx8eTl5fHgAEDSElJCUkOweDNHkgn4Dv1vw+iqjUicoef/c4E3hCRvwf+hmtiRhHpCryiqmNUtcp1An8V4ADmq+pOV/zzItIf5yGsg8AP/czDGBMjTpw4wR//+EeOHDkSssLhSUVFBRUVFaxbt45169bRpUsXBg8eTK9evUhISAhbXv7w5ouEv2rktd3+dKqqJcC3Paw/AoxxW14JrPTQ7gF/+jXGxJ6KigoKCgrYsmVLUA5T+av2PEtxcTHHjx9nxYoVZGVlMWDAADIzM6PiEFfLvsbMGBPTDh06xJtvvsmFCxciqnjUV1FRAcCuXbvYv38/ANnZ2eTm5nL11VdHbDGxAmKMaXFqamooKChg48aNEV04PKktJp9++im7du1CROjduzd9+/YlPT0dh8MR5gy/YQXEGNOifP311yxevJgTJ05EXfFwp6p1xWTbtm3s2rULVeXaa6+lb9++dO/encTExLDmaAXEGNNiHD9+nEWLFnH+/HlqamrCnU5A1RaT3bt389lnn1FdXU3Xrl3JyckhKyuL9u3bhzwnKyDGmBbh0KFDLF68uO4XbUtWO8bDhw9z9OhRPvzwQ1q3bk3v3r3p2bMnV199dUgOdVkBMcZEvX379vHGG29E9SErf9WO+ezZs2zcuJGtW7dSU1NDWloavXv35tprr6VDhw5BmU7FCogxJqrFcvGoz/28ycGDBykuLubDDz8kISGBO++8k969ewe0v8i8NswYY7xgxaNxVVVVVFVVcf78eQ4fPhzw7VsBMcZEJSse4WcFxBgTdax4RAYrIMaYqGLFI3JYATHGRA0rHpHFCogxJipY8Yg8VkCMMRHPikdksgJijIloVjwilxUQY0zEsuIR2ayAGGMikhWPyGcFxBgTcax4RAcrIMaYiGLFI3pYATHGRAwrHtHFCogxJiJY8Yg+VkCMMWFnxSM6WQExxoSVFY/oFZYCIiIdReRDEdnn+tmhgXbzReS4iOzwJ94YE9mseES3cO2BTAcKVLUHUOBa9mQBMLoZ8caYCGXFI/qFq4CMBRa6ni8E7vbUSFXXAl/5G2+MiUxWPFoGUdXQdypyWlXbuy2fUtWGDmNlAO+rao6f8VOAKQCpqakDly5d6lfO27dXUFGRiEjT71e3bmV88UWyX/1EOxt7bI4dYnv8kT52VeHKKy9w1VWt/Iq/8cYbN6tqXv318c3OrAEi8megs4eXngxWn56o6lxgLkBeXp7m5+f7tZ2bVmSgKYe8ajurZhaPlz4ONQ6Iq/ato9Ppzp/tvevL7xjwL78mYurG3tz8Iv198NCXx7E3N78I+mw9csuvyfF7iAl6fv7EnHHl5+X/d4BZlXN4vPgFn2L86Qfw+3P62ZEDPPBAYA86Ba2AqOrNDb0mIsdEpIuqHhWRLsBxHzff3Hifacoh/ivdu721tomF/Fe68oND4nVMrR8gAD7F+RMD+JdfEzG1Y29ufhH/Pnjoy9PYm5tfJH22HmPc8mtq/J5igp6fnzHgW34lDmfx8Omz9aOf2jh/PqdHJgbhjIWqhvwB/Dsw3fV8OvB8I20zgB3+xrs/Bg4cqP5iBl63XbNmjc8x7v34GudPTG1coGNqx14/JhRjCvX7UD/O09ibivGnH2/jQhVTG9fU+D3F+NpXqGJ8jZu1eFaL+2wvioci9fA7NVwn0WcCt4jIPuAW1zIi0lVEVtY2EpElwAagp4gUi8jfNxZvjDEmdIJ2CKsxqloCfNvD+iPAGLflCb7EG2OMCR37Jroxxhi/WAExxhjjl7B8DyRc8vLytKioyK/YjNkZHDrj5WW8WbN4fO/jOMRBtfp2uV16ivPSPm/78jcG8Cu/pmJqx97c/CL9ffDUl6exNze/SPpsPXHPr6nxe4oJdn6h+j84p/ccXjjyQsR+tukp6Rx87KBPMe5EJLTfA2lpfHnzCwsL0QmxU5jd2dhjc+wQ2+MvLCzk4PcOhjuNkLNDWMYYY/xiBcQYY4xfrIAYY4zxixUQY4wxfrECYowxxi8xdRmviJwAfJz60i+dgJMh6CcS2dhjVyyPv6WPPV1Vr6i/MqYKSKiISJGna6ZjgY09NscOsT3+WB27HcIyxhjjFysgxhhj/GIFJDjmhjuBMLKxx65YHn9Mjt3OgRhjjPGL7YEYY4zxixWQZhKRjiLyoYjsc/3s0Ehbh4hsFZH3Q5ljMHkzfhFJEpGNIvKpiOwUkV+HI9dA83LsV4nIGhHZ7Rr7T8KRa6B5++9eROaLyHER2RHqHANNREaLyP+JyH4Rme7hdRGROa7X/yoiA8KRZyhZAWm+6UCBqvYAClzLDfkJsDskWYWON+O/ANykqrlAf2C0iFwfuhSDxpuxVwE/V9XewPXAP4hIdghzDBZv/90vAEaHKqlgEREH8HvgNiAbmODhc7wN6OF6TAFeDGmSYWAFpPnGAgtdzxcCd3tqJCJpwO3AK6FJK2SaHL86lbkWE1yPlnDyzZuxH1XVLa7npTj/gOgWqgSDyKt/96q6FvgqRDkF02Bgv6oeUNUKYCnO98DdWGCR69/7J0B7EekS6kRDyQpI86Wq6lFw/rIArmyg3WzgH4GaEOUVKl6N33X4bhtwHPhQVf8SuhSDxtvPHgARyQCuA2Ju7C1AN+Cw23Ixl/4h4E2bFsVuKOUFEfkz0NnDS096GX8HcFxVN4tIfgBTC4nmjh9AVauB/iLSHlguIjmqGvHHxQMxdtd2koG3gMdU9Wwgcgu2QI29hRAP6+rvRXvTpkWxAuIFVb25oddE5JiIdFHVo67d1eMemg0H7hKRMUAS0E5EXlXVvwtSygEVgPG7b+u0iBTiPC4e8QUkEGMXkQScxeM1VX07SKkGXCA/9xagGLjKbTkNOOJHmxbFDmE13wrgQdfzB4F36zdQ1X9W1TRVzQDGA6ujpXh4ocnxi8gVrj0PROQy4GZgT6gSDCJvxi7APGC3qv4mhLkFW5Njb2E2AT1EJFNEEnH+P15Rr80KYKLraqzrgTO1h/laLFW1RzMewOU4r0LZ5/rZ0bW+K7DSQ/t84P1w5x3K8QP9gK3AX3Hudfwq3HmHcOw34DyM8Vdgm+sxJty5h2LsruUlwFGgEudf6H8f7tybMeYxwF7gM+BJ17qpwFTXc8F5pdZnwHYgL9w5B/th30Q3xhjjFzuEZYwxxi9WQIwxxvjFCogxxhi/WAExxhjjFysgxhhj/GIFxJgQEZGpIjLR9XySiHR1e+2VQE2yKCJ3i8ivfIz5c2MzSRvjiV3Ga0wYuL6N/7iqFgVh2+uBu1T1pA8xDwJpqvqvgc7HtFy2B2JimogMct27IUlE2rju2ZFTr02GiOwRkYWutstEpLXrtW+77vGy3XXvi1au9TNFZJer/SzXuhki8riI3AvkAa+JyDYRuUxECkUkz9Vugmt7O0TkObc8ykTkX8V5X5VPRCTVw3iygAu1xUNEFojIi657khwQkZGuPHeLyAK30BXAhEC+t6blswJiYpqqbsL5y/MZ4HngVfU8yWNPYK6q9gPOAj8WkSSc97u4T1X74pxb7kci0hEYB/RxtX+mXp/LgCLgflXtr6rna19zHdZ6DrgJ571TBonI3a6X2wCfqPO+KmuByR7yHA5sqbeug2t7PwXeA34L9AH6ikh/V06ngFYicnmDb5Yx9VgBMQb+BbgF517B8w20Oayq61zPX8U5RUlP4HNV3etavxAYgbPAlAOviMh3gHM+5DIIKFTVE6paBbzm2iZABVB7N8vNQIaH+C7AiXrr3lPnsertwDFV3a6qNcDOets4jnMqEmO8YgXEGOgIJANtcc6W7En9k4WK5+m7cf3iH4xzBt67gf/xIReP23Sp1G9OWlbjeTbt81w6hguunzVuz2uX3beR5Io3xitWQIyBucAvcf61/1wDba4WkaGu5xOAj3HOKJwhIte61j8AfOS690eKqq4EHsN5KKq+UpwFq76/ACNFpJPrNqoTgI98GMtu4NomW9XjmjW4M3DQ11gTu6yAmJjmuqy2SlUXAzNxnnO4yUPT3cCDIvJXnHssL6pqOfAD4E0R2Y7zL/qXcBaG911tP8J57qG+BcBLtSfRa1eqc/rvfwbWAJ8CW1TVl6nS1wLXuQqCLwbiPL9S5WOciWF2Ga8xTXDdivZ9Vc1pqm0kEJHf4Tzv8WcfY1aoakHwMjMtje2BGNPyPAu09jFmhxUP4yvbAzHGGOMX2wMxxhjjFysgxhhj/GIFxBhjjF+sgBhjjPGLFRBjjDF+sQJijDHGL/8fJRk3CubPBKsAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -603,7 +602,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "scrolled": true }, @@ -624,7 +623,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "Wall tempreature was above coolant boiling point when using the Sieder-Tate equation (h_coolant_model = '2') - coolant boiling temperature was used instead of wall temperature.\n", "Exported JSON data to 'heating_output.json'\n" ] } @@ -644,6 +642,7 @@ " configuration = \"vertical\",\n", " channel_height = 0.001, \n", " blockage_ratio = 0.5, \n", + " number_of_ribs = 20,\n", " outer_wall = outer_wall_material)\n", "\n", "engine.plot_geometry()\n", @@ -654,7 +653,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { "scrolled": true }, @@ -669,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -698,7 +697,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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Svpxuc5q2tHeSNrPzJJ0nSXvvvXdo9QEAALRpn30gvX6TNOdvUvlWacjh0rG/l4Z9WYpyv4vGyNZPb4ikwZKSo9b9Jc0xswlKjFQPSOnbX9LqdAdx91sl3SpJ48ePb9pnaQMAALR2m9ZIM38jzQkugxt1snTgd6W9Rta9HzKWlXDt7gsk9U6um9kKSePdfb2ZPS7pXjP7nRIXNA6T9GY26gQAAGgTtm2QXvm99OatUjwmjTtXmvhDpn40gea6Fd99kg6T1NPMVkn6mbvflq6vuy80swclLZIUk/Qd7hQCAACwG+IV0py7pBeukbZ/Lu17qnTY5VL3wdmurM1qrruFnF7P9kE11n8p6ZdNWRMAAECbtnqu9OQPE68DD5aO+g3TP5oBM9YBAADakopyaeZ10szfSh17Sif+VRr11Xb5KPJsIFwDAAC0Fevekf55XmK0et9TE6PV3KO6WRGuAQAA2oKF/5Qe/Y6Uky+dfJc04oRsV9QuEa4BAABas3iF9PzV0ms3SP2/IJ1yt9Slb7ararcI1wAAAK1V+Q7pkW9Ki5+Qxn9DmjJNysnLdlXtGuEaAACgNdqxUbrvDOmDV6QjfyUdeGG2K4II1wAAAK3Pjk3S3SdIHy+QTrotcTcQtAiEawAAgNZk5zbpvtOkNfOkU++Rio/OdkVIQbgGAABoLSrKpQfPlj54TTrprwTrFohwDQAA0Fo8d5W0bLp07B+YCtJCRbJdAAAAADIw52/SGzdLB3xHGv/1bFeDWhCuAQAAWrq1i6V//Uja5zBp8jXZrgZ1IFwDAAC0ZLEy6ZFvSfmdpRP/IkWZ1duS8e4AAAC0ZDOmJW65d/r9Uqfe2a4G9WDkGgAAoKVa907iseZjzpSGH5XtapABwjUAAEBL5C49+xMpt4P0pZ9nuxpkiHANAADQEr3zrLTseemwy6VOvbJdDTJEuAYAAGhp3KV//z+p+xBpwnnZrgYNQLgGAABoaZY+LX2yQPrij6VobrarQQMQrgEAAFqaV34ndRskjTo525WggQjXAAAALcmaedKq/0r7X8A9rVshwjUAAEBL8t/bpJxCafTp2a4Eu4FwDQAA0FLs3CoteEgaeZJUuEe2q8FuIFwDAAC0FMtekMq3Svueku1KsJsI1wAAAC3F4selwu7SwIOzXQl2E+EaAACgJYiVJR4cU3w0FzK2YoRrAACAlmDFy1LZJqlkarYrQSMQrgEAAFqCd6dLOQXS4C9muxI0AuEaAAAg2yrKpcVPSIMPlXILs10NGoFwDQAAkG0LH5U2fSR94RvZrgSNRLgGAADIJnfptRuknsOloZOzXQ0aiXANAACQTe+/JH08Xzrou1KEaNba8Q4CAABk02t/kjr2lkbx4Ji2gHANAACQLZ8skpY9L+1/npRbkO1qEALCNQAAQLb850Ypt4M0ngsZ2wrCNQAAQDZsWiPNf1Aa+zWpQ/dsV4OQEK4BAACy4c1bJK+QDrgw25UgRIRrAACA5la2WZp1e+JR590HZ7sahIhwDQAA0Nzm3iPt2Cgd9L1sV4KQNUu4NrPbzWytmb2d0vZbM1tiZvPN7J9mtkfKtp+Y2TIzW2pmRzZHjQAAAM2iIib95yZp74Ok/uOzXQ1C1lwj13dKmlKjbbqkke6+r6R3JP1EksysVNJpkkYE+9xkZtFmqhMAAKBpzbtX2vhh4qExaHOaJVy7+0xJG2q0PefusWD1dUn9g+XjJd3v7mXu/r6kZZImNEedAAAATeqTRdKzV0l7HygVHZXtatAEWsqc6/+R9HSw3E/SypRtq4I2AACA1uvzD6V7TpTyOkgn3sqjztuorL+rZnalpJikvyeb0nTzWvY9z8xmmdmsdevWNVWJAAAAjbNtg/S3E6Wd26SvPSztsXe2K0ITyWq4NrNzJB0r6Ux3TwboVZIGpHTrL2l1uv3d/VZ3H+/u43v16tW0xQIAAOyOnVulv5+cGLk+/T5pzxHZrghNKGvh2symSLpM0lR335ay6XFJp5lZvpkNljRM0pvZqBEAAKBRKsqlf5wrrZ4jffV2adDB2a4ITSynOU5iZvdJOkxSTzNbJelnStwdJF/SdDOTpNfd/QJ3X2hmD0papMR0ke+4e0Vz1AkAABAad+nx70vvPicd+wep5NhsV4Rm0Czh2t1PT9N8Wx39fynpl01XEQAAQBN7/urEbfcOu0Ia//VsV4NmkvULGgEAANqc/9wkvfoHafw3pEMvzXY1aEaEawAAgDAteEh69idSyVTp6N9Klu5GaGirCNcAAABhWfaC9M8LpIETpRP/IkV4yHR7Q7gGAAAIwwf/ke4/U+pVLJ1+r5RbkO2KkAWEawAAgMZaPVe69xSpa3/prH9KBV2zXRGyhHANAADQGGsXJ56+WLiHdPZjUicebNeeEa4BAAB214b3pLtPkKJ5iWDdtV+2K0KWNct9rgEAANqcjR9Jdx0vVeyUvv6U1H2fbFeEFoBwDQAA0FBb1kl3Hy/t+Fw653Gpd0m2K0ILQbgGAABoiC3rpLunShtXJS5e7Ds22xWhBSFcAwAAZGrLOumu46TPVkhnPCANPDDbFaGF4YJGAACATNQM1vscmu2K0AIxcg0AAFCfLWuDYP2BdOaD0uAvZrsitFCEawAAgLpUC9b/kAYfku2K0IIxLQQAAKA2m1ZLdx4rff4hwRoZYeQaAAAgnQ3vJW63t21DIlgPmpjtitAKEK4BAABq+vht6Z4TpYpy6ZwnpH77ZbsitBJMCwEAAEi18k3pzqMli0hff5pgjQYhXAMAACQt/3diKkhhd+l/npV6F2e7IrQyhGsAAABJeus+6e+nSN0GJ4J1t4HZrgitEOEaAAC0b+7SjGnSoxcknrj49aekzntmuyq0UlzQCAAA2q/YTumJ70vz7pNGnyEd90cpJy/bVaEVI1wDAID2aftn0gNnSSteliZdKX3xx5JZtqtCK0e4BgAA7c+6d6T7z5A+WyF95VZp9KnZrghtBOEaAAC0L0v+JT1yvpSTL539mDTo4GxXhDaEcA0AANqHeFx6aZr00q+lvmOlU++RuvbPdlVoYwjXAACg7du2QXr029I7z0hjzpSO+Z2UW5DtqtAGEa4BAEDb9sFr0sPflLaslY76rTThW1y4iCZDuAYAAG1TvEJ6+Xppxq+kPQZK35yemA4CNCHCNQAAaHs2rpL+eUHiNnujTk5MAynoku2q0A4QrgEAQNvhLs39m/TslYmR6+NvksacwTQQNBvCNQAAaBs2fpR42uKy56VBh0hT/yR1H5ztqtDOEK4BAEDrFo9Lc+6Upv9MiscSFy1+4ZtSJJLtytAOEa4BAEDr9dEc6V8/klbPCUarb5C675PtqtCOEa4BAEDrs22D9O//J826XerYSzrxL4kLF5lbjSwjXAMAgNYjVia9+Rfp5eukHRul/c+XJl0hFXTNdmWAJMI1AABoDeJxacGD0r9/KW38UBpyuDT5GmmvUdmuDKiGcA0AAFqueFxa/Lg087fSJ29LfUYn5lUPmZTtyoC0CNcAAKDlqYhJbz+ceMLi+qVS9yHSSbdJI07kLiBo0QjXAACg5dixSXrrXumN/5M+WyH1HhGE6q9IkWi2qwPq1Szh2sxul3SspLXuPjJo6y7pAUmDJK2QdIq7fxZs+4mkb0iqkPR9d3+2OeoEAABZsu4d6c1bpXn3STu3SP2/IB35K6loCiPVaFWaa+T6Tkk3Sro7pe1ySS+4+zQzuzxYv8zMSiWdJmmEpL6SnjezInevaKZaAQBAc9i5VVr0uPTW36UVL0vRvMS0j/3Pk/qNy3Z1wG5plnDt7jPNbFCN5uMlHRYs3yVphqTLgvb73b1M0vtmtkzSBEn/aY5aAQBAE6qISR++Js17QFr0aGKUutsg6fCrpP3OlTr1ynKBQONkc871nu6+RpLcfY2Z9Q7a+0l6PaXfqqANAAC0Rjs2Se/NkJb8S3r3WWn7Z1Je58Q86jFnSHsfyMNf0Ga0xAsa0/3t8rQdzc6TdJ4k7b333k1ZEwAAyNT2z6WVbySmeqx4RVozT/K4VNgtMYd6+NHS0COkvI7ZrhQIXTbD9Sdm1icYte4jaW3QvkrSgJR+/SWtTncAd79V0q2SNH78+LQBHAAANKGtn0qfLJBWvyWteSvx+tn7iW3RvMSFiV/8sTT4i9KAA6RoSxzXA8KTzT/hj0s6R9K04PWxlPZ7zex3SlzQOEzSm1mpEAAASDu3SRtXSp99IK1/p+pr3VJp+4aqfnvsLfUZI439WiJUD5gg5RZmrWwgG5rrVnz3KXHxYk8zWyXpZ0qE6gfN7BuSPpR0siS5+0Ize1DSIkkxSd/hTiEAADSR8u3Slk+kzZ9IWz5OvG5aJX3+YdXX1nXV9+nQQ+o5XCo5Tuo1XOpdkgjVHbpn5VsAWhJzbxuzKcaPH++zZs3KdhkAADS/iphUUSbFgq+dWxMXDSa/dnxetbztU2nzx1WBumzjrseL5kldByRGoiu/Bkp7DJB6DJM69mj2bxFoScxstruPT7eNiU8AADSEeyLAVpRJsZ1VobZiZ43X1O119Quhv8czq72gq1TYXeq0Z2K0eZ9JUqfeUue9pE57VS136MmDW4DdRLgGALRc7lI8FkIgTdevAf1jO6q2xcvD+/4iOVI0X8rJq/Ganxg9zsmXcgoSoTi5Xl//aF7iLhyF3RPTNAq7Jb4KuvL4cKAZEK4BoD1zlyrKE8Gx2ld5VdhM/YrV7FdbW1n649a5f8o+qf3S341196QLotVe86WCLpkF2EyDbmW/NNsIu0CbQ7gGgKZUGV5Tg2NZSlvNIJvSd5e2WsJp2iBc3/4pbWGL5CSCYzQ3ESqTyzn51dtSR2Qrv3Krgme1vil9Ghxqg+3RXB5UAqDJEa4BtE7xiupBcpfl8lraaywnpxzU1aciXZ/ka2oQrtFWOfIasuRUgmhuSojM3bUtr4MU3aNGKA22J/uma9vlmLWE47RBOJfRWADtGuEaQMIu0wOC5XgmIbW2PjXa4rW0V1uu2aeWmjK9gKuhkqOukdyU0JmraiOryde8jlK0W/VR1dSvdO2VbalBNl2/WoJwJJcLzQCgBSNcA02p2uhqBiOkGY2ipgugtQXZBoTiMC/SqmmXYFojpEZSlnM71NK3rqCb5jj1nTNdO8EVANBIhGu0DjVHVStDaI1wWDN4Vo6UxtKMwpanGU1NN1qb2q+2fWNpatvZdKOrFq0nMKYEx9zClHmt6QJoA8JnzSBab4gNLthinisAoJ0gXLc3qbe1anQwrTnS2tAAW18dKW3xWNP+XCJpQmXlRVl5UjRlqkBOgZTfpapv5b6pffIaEEAzDakpF4kxpxUAgBaJcN0Y7onHxtYMprUGztrCZVME0zoCclNKO1+1tjmsqVMActKHyF1GSWuZSpBRv1rqiOQwsgoAAEJBuG6sa/s0zXErf+1fc3Q0N31bbpfwg2ltI7c1A3LqsQipAACgHSNcN4aZNPmaRAgNI5hyURUAAECrRrhurIMvynYFAAAAaCEYHgUAAABCQrgGAAAAQkK4BgAAAEJCuAYAAABCQrgGAAAAQkK4BgAAAEJCuAYAAABCQrgGAAAAQkK4BgAAAELS4HBtZh3NLNoUxQAAAACtWb3h2swiZnaGmf3LzNZKWiJpjZktNLPfmtmwpi8TAAAAaPkyGbl+UdIQST+RtJe7D3D33pIOkfS6pGlm9rUmrBEAAABoFXIy6PMldy+v2ejuGyQ9LOlhM8sNvTIAAACglak3XCeDtZn1kHSKpB2SFkpa4O7bU/sAAAAA7VlDLmj8p6Rekq6V9FtJG81sSZNUBQAAALRCmUwLSers7teY2YnufqiZnSRpaFMVBgAAALQ2DRm53hG8lplZobs/LOnoJqgJAAAAaJUaMnJ9nZl1l/SApNvN7DVJ/ZqmLAAAAKD1yXjk2t0fdvcN7v47SU9JGiDp+CarDAAAAGhl6h25NrNzJF2vRBB/UtJ33P1vTV0YAAAA0NpkMnL9v5ImSyqW9IESdwsBAAAAUEMmc643ufvcYPl/zeyNpiwIAAAAaK0yCdd9zOw8SYslLZHE0xgBAACANDIJ1z+TtK+kMyWNktTJzJ6SNE/SfHe/rwnrAwAAAFqNTML1Mkn/dPd1kmRm/ZUI26MkHSOJcA0AAAAos3D9vKS1ZhaX9Lak+ZIWSHpO0h+arjQAAACgdckkXH9f0v9IelDSfyQVSRon6VxJJZL2aqriAAAAgNak3lvxufuNkg6W5JJ+L6lc0kXuPsndGx2szeyHZrbQzN42s/vMrMDMupvZdDN7N3jt1tjzAAAAAE0toyc0uvt2d/+1pMMkDZX0ppnt39iTm1k/JUbGx7v7SElRSadJulzSC+4+TNILwToAAADQomXyhMZDlJj+URy89pa0WVKPEGsoNLNySR0krZb0EyWCvCTdJWmGpMtCOh8AAADQJDKZc/2SErfdu0/SDe6+IqyTu/tHZnadpA8lbZf0nLs/Z2Z7uvuaoM8aM+udbv/g/tvnSdLee+8dVlkAAADAbslkWsi3Jb2qxG333jCzRWb2gJldZWYnNObkwVzq4yUNltRXUkcz+1qm+7v7re4+3t3H9+rVqzGlAAAAAI1W78i1u9+Sul7jPtcnSXq0Eef/kqT3U+6h/YikgyR9YmZ9glHrPpLWNuIcAAAAQLPIZM61ubsn1919laRVkp6qrU8DfCjpADProMS0kCMkzZK0VdI5kqYFr4/txrEBAACAZpXJnOsXzexhSY+5+4fJRjPLkzRRifD7oqQ7G3pyd3/DzB6SNEdSTNJcSbdK6iTpQTP7hhIB/OSGHhsAAAC1q4hXqMIrFIvHVB4vVyweS3x5rGo53XrKV7lX7VcRr6jWv9oxk328ou5z1XW+Gset8Ar9+Yg/a3Sv0dn+UVaTSbieosRDZO4zs8GSPpdUqMR87eck/d7d39rdAtz9Z5J+VqO5TIlRbAAAgKxxd1V4IoRWxBPBMLmcDIqpITXmserrNYJltdC5m8GyZoitFnbj5ZXtlf2CvjXDrmt3Jh3snpxIjnIjuYpaVDmRnKovq1rOjeQqJ5JT2acwp7D6tpS+0UhUOZajbvkt71Eomcy53iHpJkk3mVmupJ6Strv7501cGwAAaGHcPW2ATAbO1GBX4buup4bRiniFyr3G+m6E10zPXevxazlf8pjNzWTVAmjaYBkE02QgzYnkqCCnoFpgjUaiVdtT9q8t3NZsqxl2a4bgTM8VtajMrNl/jtmSych1JXcvl7SmiWoBAKDFSY5cxj1eGeRqW04NadWWa77W1be2/imBMu7xBi/HPKZ4PP32hgTWCq/I2nsRtWjiKyVcRiPRauGvcrtVX8+L5iknp2rEs9prSoCsfE3XL5P+6favLcjWMmobjUSz9jNG4zUoXAMA2pbU4Jga4JLrcY/v0pa6XtkWtKeuJ/dNbastAIYdIpPnrSsMN+TYLUnEIpUBLt1yMpzVuRyJKs/y6g6U6QJkjddqYTZd2K0r/Ca3NyAst6fRT7RehGsArV5dQbBmkGpUn5SRv2ohMl4jUNY4Rs1gWq1PmkBbW5+MakkJtDGPVQ/Aab6nuMez/fbVqr4QmclyZYhMCZi7e7xk0MtkOXnuiEUqA2O67Q0Jw8k2AibQshGugSxw92qhJ+1rmoC1y2sQrlzeoP61njs4RlzxzGpIN1LZgNHPekNmjZqrhcWUANnSmawyfCVDUmr4Sy4nw1fqejJQRSJVbbmRXOVbfrW2HEvZL5KyX13HjtTeJ7W+umqpDIG1nbdGjYRIAG1dxuHazE6W9Iy7bzazqyTtJ+n/ufucJqsOWZUuAKYLWOmCW0MCY81fS2cSMOvqE1ZoDfOYNfs05xXaYakZwjIJcbUGtEg0MfexZkALgma1sBipEexSzl0tUNYTFpPr6dp2qTdN6E0XIFPPu0uNhEQAaJcaMnL9v+7+DzObKOlISddJ+j9J+zdJZa3EE8ufqHVksL4gVlsIq2u0sTmP2ZJ/XVybZJBJGwTTjKSlBq5a+0SiyrXcUI+ZdnsGx9zlNRlGFamzf8bHrKNGAABQv4aE6+QVHcdI+j93f8zMrg6/pNblyleubPAoZF3BKm0wTDOCtktwqiUA1nquDI9Z27bdOWZdITTTsFffOQEAALKpIeH6IzO7RdKXJP3azPKVeJBMu/avr/yreiDOILTyK2IAAIC2qSHh+hQlntZ4nbt/bmZ7Sfpx05TVegzoMiDbJQAAAKCFaMjI8zGSprv7u8EFjTdJWt80ZQEAAACtT0PC9f8GdwpJXtB4lxIXNAIAAABQw8L1Lhc0SsoLvyQAAACgdWpIuE5e0HiqpKe4oBEAAACoriHh+BRJz0o60t0/l9RdXNAIAAAAVGpIuN4uqaOk04P1XEmfh10QAAAA0Fo1JFzfJOkAVYXrzZL+HHpFAAAAQCvVkPtc7+/u+5nZXEly98/MjAsaAQAAgEBDRq7LzSwqJZ71bWa9JMWbpCoAAACgFWpIuL5B0j8l9TazX0p6RdK1TVIVAAAA0AplNC3EzEzSTEmzJR0hySSd4O6Lm7A2AAAAoFXJKFy7u5vZo+4+TtKSJq4JAAAAaJUaMi3kdTP7QpNVAgAAALRyDblbyCRJ55vZB5K2KjE1xN193yapDAAAAGhlGhKuj2qyKgAAAIA2oCHTQi509w9SvyRd2FSFAQAAAK1NQ8L15DRtjGYDAAAAgXqnhZjZt5UYod7HzOYnmyV1kvRqE9YGAAAAtCqZzLm+V9LTkn4l6fKU9s3uvqFJqgIAAABaoUymhRRJ2uHupwfzrA9V4mmNV5tZ9yatDgAAAGhFMgnXt0jaKUlm9kVJ0yTdLWmjpFubrjQAAACgdclkWkg0ZfrHqZJudfeHJT1sZm81WWUAAABAK5PJyHXUzJIh/AhJ/07Z1pD7ZAMAAABtWibh+D5JL5nZeknbJb0sSWY2VImpIQAAAACUQbh291+a2QuS+kh6zt092BSR9L2mLA4AAABoTTKa1uHur6dpeyf8cgAAAIDWqyFPaAQAAABQB8I1AAAAEBLCNQAAABCSrIdrM9vDzB4ysyVmttjMDjSz7mY23czeDV67ZbtOAAAAoD5ZD9eS/ijpGXcvljRa0mJJl0t6wd2HSXohWAcAAABatKyGazPrIumLkm6TJHff6e6fSzpe0l1Bt7sknZCN+gAAAICGyPbI9T6S1km6w8zmmtlfzayjpD3dfY0kBa+9s1kkAAAAkIlsh+scSftJ+j93HytpqxowBcTMzjOzWWY2a926dU1VIwAAAJCRbIfrVZJWufsbwfpDSoTtT8ysjyQFr2vT7ezut7r7eHcf36tXr2YpGAAAAKhNVsO1u38saaWZDQ+ajpC0SNLjks4J2s6R9FgWygMAAAAaJKPHnzex70n6u5nlSXpP0teVCP0Pmtk3JH0o6eQs1gcAAABkJOvh2t3fkjQ+zaYjmrkUAAAAoFGyPecaAAAAaDMI1wAAAEBICNcAAABASAjXAAAAQEgI1wAAAEBICNcAAABASAjXAAAAQEgI1wAAAEBICNcAAABASAjXAAAAQEgI1wAAAEBICNcAAABASAjXAAAAQEgI1wAAAEBICNcAAABASAjXAAAAQEgI1wAAAEBICNcAAABASAjXAAAAQEgI1wAAAEBICNcAAABASAjXAAAAQEgI1wAAAEBICNcAAABASAjXAAAAQEgI1wAAAEBICNcAAABASAjXAAAAQEgI1wAAAEBICNcAAABASAjXAAAAQEgI1wAAAEBICNcAAABASAjXAAAAQEgI1wAAAEBICNcAAABASAjXAAAAQEgI1wAAAEBICNcAAABASAjXAAAAQEhaRLg2s6iZzTWzJ4P17mY23czeDV67ZbtGAAAAoD4tIlxLukjS4pT1yyW94O7DJL0QrAMAAAAtWtbDtZn1l3SMpL+mNB8v6a5g+S5JJzRzWQAAAECDZT1cS/qDpEslxVPa9nT3NZIUvPbOQl0AAABAg2Q1XJvZsZLWuvvs3dz/PDObZWaz1q1bF3J1AAAAQMNke+T6YElTzWyFpPslHW5m90j6xMz6SFLwujbdzu5+q7uPd/fxvXr1aq6aAQAAgLSyGq7d/Sfu3t/dB0k6TdK/3f1rkh6XdE7Q7RxJj2WpRAAAACBj2R65rs00SZPN7F1Jk4N1AAAAoEXLyXYBSe4+Q9KMYPlTSUdksx4AAACgoVrqyDUAAADQ6hCuAQAAgJAQrgEAAICQEK4BAACAkBCuAQAAgJAQrgEAAICQEK4BAACAkBCuAQAAgJAQrgEAAICQEK4BAACAkBCuAQAAgJAQrgEAAICQEK4BAACAkBCuAQAAgJAQrgEAAICQEK4BAACAkBCuAQAAgJAQrgEAAICQEK4BAACAkBCuAQAAgJAQrgEAAICQEK4BAACAkBCuAQAAgJAQrgEAAICQEK4BAACAkBCuAQAAgJAQrgEAAICQEK4BAACAkBCuAQAAgJAQrgEAAICQEK4BAACAkBCuAQAAgJAQrgEAAICQEK4BAACAkBCuAQAAgJAQrgEAAICQEK4BAACAkBCuAQAAgJAQrgEAAICQEK4BAACAkGQ1XJvZADN70cwWm9lCM7soaO9uZtPN7N3gtVs26wQAAAAyke2R65ikH7l7iaQDJH3HzEolXS7pBXcfJumFYB0AAABo0bIart19jbvPCZY3S1osqZ+k4yXdFXS7S9IJWSkQAAAAaIBsj1xXMrNBksZKekPSnu6+RkoEcEm9a9nnPDObZWaz1q1b12y1AgAAAOm0iHBtZp0kPSzpB+6+KdP93P1Wdx/v7uN79erVdAUCAAAAGch6uDazXCWC9d/d/ZGg+RMz6xNs7yNpbbbqAwAAADKV7buFmKTbJC1299+lbHpc0jnB8jmSHmvu2gAAAICGysny+Q+WdJakBWb2VtB2haRpkh40s29I+lDSydkpDwAAAMhcVsO1u78iyWrZfERz1gIAAAA0VtbnXAMAAABtBeEaAAAACAnhGgAAAAgJ4RoAAAAICeEaAAAACAnhGgAAAAgJ4RoAAAAICeEaAAAACAnhGgAAAAgJ4RoAAAAICeEaAAAACAnhGgAAAAgJ4RoAAAAICeEaAAAACAnhGgAAAAgJ4RoAAAAICeEaAAAACAnhGgAAAAgJ4RoAAAAICeEaAAAACAnhGgAAAAgJ4RoAAAAICeEaAAAACAnhGgAAAAgJ4RoAAAAICeEaAAAACAnhGgAAAAgJ4RoAAAAICeEaAAAACAnhGgAAAAgJ4RoAAAAICeEaAAAACAnhGgAAAAgJ4RoAAAAICeEaAAAACAnhGgAAAAgJ4RoAAAAICeEaAAAACAnhGgAAAAhJiw7XZjbFzJaa2TIzuzzb9QAAAAB1abHh2syikv4s6ShJpZJON7PS7FYFAAAA1K7FhmtJEyQtc/f33H2npPslHZ/lmgAAAIBateRw3U/SypT1VUEbAAAA0CLlZLuAOliaNq/Wwew8SecFq1vMbGmTV9V8ekpan+0i0Ox439sv3vv2ife9/eK9b90G1rahJYfrVZIGpKz3l7Q6tYO73yrp1uYsqrmY2Sx3H5/tOtC8eN/bL9779on3vf3ivW+7WvK0kP9KGmZmg80sT9Jpkh7Pck0AAABArVrsyLW7x8zsu5KelRSVdLu7L8xyWQAAAECtWmy4liR3f0rSU9muI0va5HQX1Iv3vf3ivW+feN/bL977Nsrcvf5eAAAAAOrVkudcAwAAAK0K4boFMLPuZjbdzN4NXrvV0TdqZnPN7MnmrBFNI5P33swKzOxNM5tnZgvN7OfZqBXhyvC9H2BmL5rZ4uC9vygbtSI8mf57b2a3m9laM3u7uWtEuMxsipktNbNlZnZ5mu1mZjcE2+eb2X7ZqBPhIVy3DJdLesHdh0l6IVivzUWSFjdLVWgOmbz3ZZIOd/fRksZImmJmBzRfiWgimbz3MUk/cvcSSQdI+o6ZlTZjjQhfpv/e3ylpSnMVhaZhZlFJf5Z0lKRSSaen+Tt8lKRhwdd5kv6vWYtE6AjXLcPxku4Klu+SdEK6TmbWX9Ixkv7aPGWhGdT73nvClmA1N/jiYonWL5P3fo27zwmWNyvxwZon1bZuGf177+4zJW1opprQdCZIWubu77n7Tkn3K/FnINXxku4O/q1/XdIeZtanuQtFeAjXLcOe7r5GSvxnKql3Lf3+IOlSSfFmqgtNL6P3PpgO9JaktZKmu/sbzVcimkimf+8lSWY2SNJYSbz3rVuD3ne0ev0krUxZX6VdPyBn0getSIu+FV9bYmbPS9orzaYrM9z/WElr3X22mR0WYmloYo197yXJ3SskjTGzPST908xGujtzMVu4MN774DidJD0s6QfuvimM2tB0wnrf0SZYmraav3nMpA9aEcJ1M3H3L9W2zcw+MbM+7r4m+FXQ2jTdDpY01cyOllQgqYuZ3ePuX2uikhGSEN771GN9bmYzlJiLSbhu4cJ4780sV4lg/Xd3f6SJSkWIwvw7j1ZvlaQBKev9Ja3ejT5oRZgW0jI8LumcYPkcSY/V7ODuP3H3/u4+SIlHwf+bYN0m1Pvem1mvYMRaZlYo6UuSljRXgWgymbz3Juk2SYvd/XfNWBuaTr3vO9qU/0oaZmaDzSxPif+/H6/R53FJZwd3DTlA0sbk1CG0ToTrlmGapMlm9q6kycG6zKyvmbXXJ1S2F5m8930kvWhm85X4h3q6u3MrxtYvk/f+YElnSTrczN4Kvo7OTrkISUb/3pvZfZL+I2m4ma0ys29kpVo0irvHJH1X0rNKXJD8oLsvNLMLzOyCoNtTkt6TtEzSXyRdmJViERqe0AgAAACEhJFrAAAAICSEawAAACAkbfpuIbNnz+6dk5PzV0kjxQcJtExxSW/HYrFvjhs3jrsGAADQyrXpcJ2Tk/PXvfbaq6RXr16fRSIRJpejxYnH47Zu3brSjz/++K+Spma7HgAA0DhtfTR3ZK9evTYRrNFSRSIR79Wr10YlfrsCAABaubYeriMEa7R0wZ/Rtv53EQCAdoH/0JtYhw4dxmbrnCtWrMidMmXKPs19/rqMHTu2uDH7X3755ekeKVynQw89dOj69euju3O+mTNndjj33HMHSNKTTz7Zefr06R2T237zm9/0uvHGG3vsznEBAEDbRLhu5crLy2vdNmjQoPJnnnnmvTDOE4vFQtl/7ty5GT9ZMN05b7jhhj4NPfdLL720rGfPnhUN3U+SvvjFL2678847V0rSv//9784vv/xyp+S2Sy+9dN13v/vdT3fnuGg4M/uKmbmZ1fsBzcxey6DPlgz6XG1ml2R6zMacqyUwsz3MrNYHWJjZ981ssZn9vblqMLNBZvZ2c5wrzfZCM3vJzDL+cG5meWY208za9DVNAGpHuG4mTz75ZOcJEyYMnzJlyj6DBw8eMXXq1MHxeFyS1K9fv1E//OEP+5aWlpYUFRWVzp07t0CSNm3aFDn55JMHjRw5sqSkpKT0nnvu2UOSbrjhhh5HHXXUPocffvjQQw45pKi2cy5dujRv2LBhI5L7fPnLXx5yyCGHDBs4cODICy64oH+y3yOPPNJlzJgxxaWlpSVHHXXUPhs3bowk67rkkkv6jBs3bvjtt9/erbbzXHzxxX1POOGEwQcccEDRwIEDR15//fU9k9/z/vvvX3TccccNHj58+AipalQ9Ho/r/PPP7z9s2LARRUVFpX/5y1+61bZP0oUXXtivrKwsUlxcXDp16tTBknT11VfvOWzYsBHDhg0bcc011/ROV1+/fv1GrVmzJkeSfvzjH/cZPHjwiIMOOmjYcccdN/inP/3pnpI0YcKE4d/+9rf7jRo1qmTQoEEjn3nmmU7JeiZNmjR06dKleXfffXevm2++ec/i4uLSZ555ptPFF1/cN7n/a6+9Vjh69OjioqKi0smTJw9Zt25dtK7jYrecLukVJR4fXCd3PyjskzfFMVugPVT30+EulHS0u5+ZbAge2Rzm/yX11ZDWbtZR37n+R9Ij7p7xh3N33ynpBUmnNrAWAG1Eu/lk/eOH5g145+PNHcI8ZtFenbf99qujV2baf/HixYVvvfXWe4MGDSofN25c8fTp0zsdeeSRWySpZ8+esUWLFi2eNm1ar2nTpu35wAMPfHDFFVf0mTRp0qZ//OMfK9avXx8dP358ydSpUzdJ0pw5czrNnz9/4Z577pnxP/qLFi3qMG/evEWFhYXxoUOHjrzkkks+6dixo1977bV9Zs6c+U6XLl3iV1555V6/+MUv9rzuuuvWSFJBQUF89uzZSzP53mbPnr148+bN0bFjx5aedNJJGyVp/vz5HefOnbuwuLh4Z2r/u+++e48FCxYULl68eOGaNWtyJkyYUPLlL395S1373HTTTR/deeedvZcsWbJIkl5++eUO9957b4/Zs2cvdneNGzeu5Igjjth88MEHb09X48yZMzs88cQT3RYsWLCovLzcxowZUzp27Nhtye2xWMwWLFiw+IEHHuh6zTXX9J0yZco7yW3Dhw/fefbZZ6/r1KlTxTXXXPOJJD333HNdktvPPffcwb///e8/POaYY7b84Ac/6HvZZZf1vf3221fWd1xkxsw6KfEo8EmSHpd0tZl9QdJtkiZIikp6U9Kp7v62mW1x907Bvo9KGiCpQNIf3f3Wes51paSzJa2UtE7S7KB9i7t3MrOOkh6U1D847y8kvSHpmeB1rKR3JJ3t7tvSHD9tPWZ2tqRLJLmk+e5+lpl9TdL3JeUFx74w2PcZJT5oHCBpnqQ7JP1cUm9JZ7r7m8Exa9v/6WD/gyR9JOl4d9+uxKO4h5jZW5Kmu/uPU+q+WdI+kh43s9slnSfpRUkHSjrBzE5SIoxK0l/d/Q9mNijTWlNUq0HSnyVFzewvNesNjv90fXXU8XOv9fsNnCnpjJSfwT8kfSJpTHCsM4OfwwGSXnb35CPKH5X0K0lNNsIPoOVi5LoZjRo1auuQIUPKo9GoRowYsW358uV5yW1nnHHGZ5I0YcKEbStXrsyXpBkzZnT5/e9/36e4uLh04sSJw8vKymzZsmV5knTIIYdsakiwlqSJEydu6tGjR0WHDh186NChO5YvX54/Y8aMjsuXLy+YMGFCcXFxcen999/f48MPP6ys6+yzz/4sk2MfddRRn3fq1Mn79OkTO/DAAze9/PLLHSVp33333VozJEvSyy+/3PmUU07ZkJOTowEDBsT233//La+88kqHuvapacaMGZ2OPvroz7t06RLv2rVr/JhjjvnsxRdf7FxX/2Sd3bp1i0+ePPnz1O0nn3zyZ5J00EEHbV21alVe2oOk8emnn0Y3b94cPeaYY7ZI0re+9a1PX3/99coR6t09Lqo5QdIz7v6OpA1mtp+7/1eJoP3/JP1G0j3unm76wP+4+zhJ4yV938xqnSdvZuOUGBkfK+lESV9I022KpNXuPtrdRyoRHiVpuKRb3X1fSZtU+4joLvWY2QhJV0o63N1HS7rIzEqUGP082N3HSKpQIsxJ0lBJf5S0r6RiJQLgRCXC+RXB91LX/sMk/dndR0j6XNJJQfvlkpa7+5iaQdPdL5C0WokPOP8Mvt+73X2spJ6Svi5pfyWC5rfMLHm9Sb211pCuhtrqVQPqSPfnoNbv18zyJO3j7itSmkdJes/dJ0q6S4kPd5cpcbefE80sP+j3ttL/2QHQDrSbkeuGjDA3lfz8/Mo7l0SjUcViMUuuFxQUuCTl5OR4st3d9dBDDy0bPXp0WepxXnnllY4dOnSIN/T8eXl5qef38vJyc3dNnDhx0xNPPPF+un06d+6c0XnMLO16bXW6134Tl0y/t7qOsTv9U94DVVRUWJ2dG6CpjtvOnC7pD8Hy/cH6HEnXSPqvpB1KjNCm830z+0qwPECJoFbbXPlDJP0zOeJsZo+n6bNA0nVm9mtJT7r7y2bWTdJKd3816HNPUM91GdbzBUkPuft6SXL3DWZ2hqRxkv4b/H0qlLRW0kxJ77v7gqDGhZJecHc3swWSBgXHPqKe/d8K+s1O2achPnD314PliUr83LYGNT2ixM/y8QxrrU9d9WZSx1yl/7l/XMc5eyoR5BUcq0CJaSR/CJq2S7rN3dcE27dJ2ilJ7l5hZjvNrLO7b87wewTQRjBy3YJNmjRp0/XXX79ncm72q6++Whj2OQ477LCts2bN6vT222/nS9LmzZsj8+fPz0/X99prr+117bXX9kq37emnn95j27Zt9vHHH0dff/31zhMnTtxa13kPPfTQzQ899FD3WCym1atX57z55pudDjnkkDr3kRIfPsrKykySDj/88C1PPfXUHps3b45s2rQp8tRTT3WbNGlSrf+RHXbYYVueffbZrtu2bbONGzdGnn/++T3qO1+qzp07V2zevHmXC5t69OhR0aVLl4rkfOrbbrutx4EHHtgqLmBrDYIRxsMl/dXMVkj6saRTLZEYu0vqJKmzEr/ur7nvYZK+JOnAYER4brp+NdT5KSwYPR+nRMj+lZn9tJb9djlOHfVYmv4m6a5gVHWMuw9396uDbakfuOMp63FVDZpkun+Fdm+gJfXva10fGjOptT511VtvHbv552B7jT4jJM1x9+SH/9FKTLWRmfVX4rcZqe9hvhIf+gC0M4TrFmzatGmrY7GYFRcXlw4bNmzEVVdd1S/sc/Tt2zd2yy23rDjttNP2KSoqKh03blzxggUL0v6ns2TJksIePXqknYoyduzYrUccccSw/fffv+SSSy5ZM2jQoNpvYyLprLPO+nzEiBHbS0pKRhx22GFFP//5z1ftvffe9d6S5Mwzz1xXUlJSOnXq1METJ07cdsYZZ3y63377lYwbN67krLPOWlfbfGtJOvTQQ7dNmTJlY2lp6Yijjz56yL777ru1a9euGU+tOemkkz7/17/+tUfygsbUbXfcccf7l112Wf+ioqLS+fPnF06bNm11psdFvb6qxK/9B7r7IHcfIOl9JUYpb5X0v0rMbf11mn27SvrM3bcFdxk5oJ5zzZT0leAuEZ0lHVezg5n1lbTN3e9RYmR6v2DT3mZ2YLCcvPgy03pekHRKcsqKmXUP2r5qZr2TbWY2sJ76U+3O/puV+KDSUDOVmO/cIZiT/hVJL+/GcRpTQ1111PZzr/Vc7v6ZEnO9k/8ejlJiznjSvpLmB8ujU5aTHwjXuXud/w4CaJusob9ab03mzZu3YvTo0euzXUdbMWnSpKFPP/308uQ0h6SLL764b+qFfi3Zxo0bI127do1v3rw5cuCBBw6/+eabP5g4ceIuF501t3nz5vUcPXr0oGzX0RKZ2QxJ09z9mZS270v6oaS57n5icKu01yT9xN3/nXLxYb4SF5f1k7RUUi9JV7v7jNSLHmucL3lB4weSVkla5O7XpRzzSEm/VWLktVzStyWtl/SUEuHuIEnvSjorZXpJJvWco8SofEXwfZ1rZqdK+okSAyHlkr6jxFSGJ4P53jKzO4P1h4IL/FK3ZbL/JZI6JUe1zexeJYLj02nmIa9QYs5yp9RjBNsuVvoLGjOqtcZ5KmtQ4oLGtPWmO0YtddT1c6/r+71N0n3u/ryZXS/pTXd/IAjci919cNDvJ0p84PpjsP5VJUbJf1TzewPQ9hGu0WitKVwfd9xxg999993CsrIyO+200z791a9+Vdecy2ZDuG7d6gqKaL2CiyEvdvezGrjfI0p80Kv3TksA2p52c0Ejms7vfve7VjMForYLNwGgJnefa2YvmlnUM7zXdXCXkUcJ1kD7RbgGgEYKbtfGqHUb5O63N7D/Tkl3N1E5AFoBLmgEAAAAQkK4BgAAAEJCuAYAAABCQrhuYh06dBhbf6+mOeeKFStyp0yZsk9znz/VcccdN7ioqKj05z//ee9rrrmm9+bNmxv1Z+43v/lNrxtvvLHWx1fX59BDDx26fv366Pr166PTpk2rfCBOS/hZAQCA1o9b8TWxDh06jN22bdvcpjp+eXm5cnNzm/ycsVhMOTkNu/71ww8/zDnggANKVq9evUCS+vXrN2rWrFmL+/TpU+/DYhpz3kwsXbo079hjjx327rvvLgz94LuBW/EBANA2MHLdTJ588snOEyZMGD5lypR9Bg8ePGLq1KmDk48179ev36gf/vCHfUtLS0uKiopK586dWyBJmzZtipx88smDRo4cWVJSUlJ6zz337CFJN9xwQ4+jjjpqn8MPP3zoIYccUlTbOZcuXZo3bNiwEcl9vvzlLw855JBDhg0cOHDkBRdc0D/Z75FHHukyZsyY4tLS0pKjjjpqn40bN0aSdV1yySV9xo0bN/z222/vVtt5XnzxxQ5jx44tLikpKR07dmzxvHnz8iXpS1/6UtGGDRtyi4uLS3/0ox/1Wbt2be6hhx5atP/++xft7nkvvvjivj/96U/3lKSXXnqpQ1FRUemYMWOKzz///P6ZfK/9+vUbtWbNmpwf/ehH/VeuXJlfXFxcev755/dP/Vlt27bNvvrVrw4qKioqLSkpKX3iiSc613dcAAAAqT3diu/R7wzQ2kUdQj1m79JtOuHPKzPtvnjx4sK33nrrvUGDBpWPGzeuePr06Z2OPPLILZLUs2fP2KJFixZPmzat17Rp0/Z84IEHPrjiiiv6TJo0adM//vGPFevXr4+OHz++ZOrUqZskac6cOZ3mz5+/cM8998z48d2LFi3qMG/evEWFhYXxoUOHjrzkkks+6dixo1977bV9Zs6c+U6XLl3iV1555V6/+MUv9rzuuuvWSFJBQUF89uzZdd6vdfTo0TvefPPNJbm5uXr00Uc7X3rppf2fffbZ5U888cSyY489dtiSJUsWSdL999/f86WXXnqnT58+sTVr1uQ09rzf/OY3B990000rJk+evPXCCy+s9mj4dN/r0KFDKx9FfP3116869thjC5O1LV26NC+57de//nVvSXrnnXcWzZ07t+Doo48etnz58rczOS4AAGjf2k+4bgFGjRq1dciQIeWSNGLEiG3Lly+vDHRnnHHGZ5I0YcKEbY8//ng3SZoxY0aXZ599do8bbrhhL0kqKyuzZcuW5UnSIYccsqkhwVqSJk6cuKlHjx4VkjR06NAdy5cvz9+wYUN0+fLlBRMmTCiWpPLychs3btyW5D5nn332Z/Udd8OGDdFTTz118IoVKwrMzMvLy62+fWbMmNGxMeddv359dOvWrZHJkydvlaRzzjlnw/Tp0/eo63vNNAS/9tprnb73ve+tlaSxY8fu6Nu3784FCxYUNPa4AACg7Ws/4boBI8xNJT8/v3KCezQaVSwWqwyhBQUFLkk5OTmebHd3PfTQQ8tGjx5dlnqcV155pWOHDh3iDT1/Xl5e6vm9vLzc3F0TJ07cVNuTCzt37lzveS677LJ+hx566Obp06cvX7p0ad7hhx8+vL59Gnve+q4VSPe91ldTJsduzHEBAEDbx5zrFmzSpEmbrr/++j2Tc7NfffXVwrDPcdhhh22dNWtWp7fffjtfkjZv3hyZP39+frq+1157ba9rr722V832TZs2Rfv3779Tkm655ZaetZ2rY8eOFcl51Q05bzq9evWq6NixY/yFF17oKEl/+9vfume6ryR17dq1YuvWrWn//E+cOHHLPffc012S5s+fn79mzZq8fffdd0dDjg8AANonwnULNm3atNWxWMyKi4tLhw0bNuKqq67qV/9eDdO3b9/YLbfcsuK0007bp6ioqHTcuHHFySkQNS1ZsqQwOSUi1WWXXfbx1Vdf3X+//fYrrqiofabKOeecs/6oo44atv/++xc15Ly1ueWWW1Z8+9vfHjhmzJhid1fnzp0zniaz1157VYwbN27LsGHDRpx//vnVLky89NJL11ZUVFhRUVHpqaeeOuSWW25ZUVhY2HZvqwMAAELDrfiQsUmTJg19+umnlyensGTbxo0bI127do1L0hVXXLHXmjVrcu+4446sT//ZHdyKDwCAtqH9zLlGo7344ovLsl1DqgcffLDr9ddf36eiosL69etXdu+9967Idk0AAKB9I1yj1frWt7712be+9a1672YCAADQXJhzDQAAAISEcA0AAACEhHANAAAAhIRwDQAAAISEcN3Eli9fnnvEEUcMGThw4MgBAwaM/PrXvz5gx44d9T7V7/LLL9+rsec+6aSTBt1xxx3dJOnUU08dOHv27AbdRxoAAAANQ7huQvF4XCeccMLQqVOnfv7BBx+8/f7777+9devWyEUXXVTvw2BuuOGGPg09XywWq3XbAw888MG4ceMa/ZTB8vLyxh4CAACgzSJcN6Ennniic35+fvyiiy76VJJycnJ08803r3zggQd6bt68OXLDDTf0OPvss/dO9p80adLQJ598svOFF17Yr6ysLFJcXFw6derUwZJ00003dR81alRJcXFx6RlnnDEwGaQ7dOgw9gc/+EHffffdt/iFF17oVFstEyZMGD5z5swOyX2+973v9Rs+fHjp6NGji1euXJkjSatXr8458sgjh4wcObJk5MiRJc8991xHSbr44ov7nn766QMPPvjgYSeeeOLgJvuBAQAAtHLt5j7X//vq/w5Y9tmyDmEec2i3odt+cfAvan0i4IIFCwpHjx69LbWte/fu8T59+uxctGhRfm373XTTTR/deeedvZcsWbJIkubMmVPw0EMPdZ81a9aS/Px8/9rXvrb3zTff3OO73/3up9u3b4+MHDly+x/+8IfVmda9ffv2yIEHHrjlT3/600cXXHBB/z/96U+9fvOb36w5//zzB1x88cWfHHnkkVvefffdvCOPPHLYe++9t1CS5s+f3+GNN95Y0qlTpxbxdEYAAICWqN2E62xwd5nZLmE0aM/4OM8880znt99+u8Po0aNLJGnHjh2R3r17xyQpGo3q3HPPbdCDVHJzc/20007bKEnjxo3b+vzzz3eRpFdffbXLu+++W5jst2XLluhnn30WkaQpU6Z8TrAGAACoW7sJ13WNMDeVUaNGbX/ssce6pbZt2LAh8vHHH+eVlJSUzZo1qzAej1duKysrSztNx93t5JNP/vTPf/7zRzW35eXlxXNyGvY25uTkeCQSSS4rFotZcB7NmjVrcboQ3bFjx3jNNgAAAFTHnOsmNHXq1M07duyI3HjjjT2kxAWHF1544YCTTz55fefOneNDhgzZuXDhwg4VFRVatmxZ7vz58zsm983JyfGysjKTpClTpmx68sknu3300Uc5kvTJJ59E33nnnbyw6504ceKmX//6172T66+99lphXf0BAABQHeG6CUUiET366KPLHnnkkW4DBw4cOXjw4JH5+fnxG2644SNJmjx58pYBAwaUDR8+fMRFF100oLS0tHJ+9plnnrmupKSkdOrUqYPHjRu346qrrvroiCOOKCoqKio9/PDDi1auXJkbdr233nrryjlz5nQsKioqHTJkyIgbb7yxV9jnAAAAaMvMve1Oo503b96K0aNHr892HUB95s2b13P06NGDsl0HAABoHEauAQAAgJAQrgEAAICQEK4BAACAkLT1cB2Px+OZ31AayILgzyi3OgQAoA1o6+H67XXr1nUlYKOlisfjtm7duq6S3s52LQAAoPHa9ENkYrHYNz/++OO/fvzxxyPV9j9IoHWKS3o7Fot9M9uFAACAxmvTt+IDAAAAmhOjuQAAAEBICNcAAABASAjXAAAAQEgI1wAAAEBICNcAAABASP4/H76ff4r1FNwAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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" ] diff --git a/bamboo/cooling.py b/bamboo/cooling.py index fa60e0f..1cc7083 100644 --- a/bamboo/cooling.py +++ b/bamboo/cooling.py @@ -48,7 +48,7 @@ def black_body(T): """ return SIGMA*T**4 -def h_gas_1(D, M, T, rho, gamma, R, mu, k, Pr): +def h_gas_rpe(D, M, T, rho, gamma, R, mu, k, Pr): """Get the convective heat transfer coefficient on the gas side. Uses Eqn (8-22) on page 312 or RPE 7th edition (Reference [2]). I believe this is just a form of the Dittius-Boelter equation. Note: @@ -73,7 +73,7 @@ def h_gas_1(D, M, T, rho, gamma, R, mu, k, Pr): return 0.026 * (rho*v)**0.8 / (D**0.2) * (Pr**0.4) * k/(mu**0.8) -def h_gas_2(D, cp_inf, mu_inf, Pr_inf, rho_inf, v_inf, rho_am, mu_am, mu0): +def h_gas_bartz(D, cp_inf, mu_inf, Pr_inf, rho_inf, v_inf, rho_am, mu_am, mu0): """Bartz equation, using Equation (8-23) from page 312 of RPE 7th edition (Reference [2]). 'am' refers to the gas being at the 'arithmetic mean' of the wall and freestream temperatures. @@ -94,7 +94,7 @@ def h_gas_2(D, cp_inf, mu_inf, Pr_inf, rho_inf, v_inf, rho_am, mu_am, mu0): return (0.026/D**0.2) * (cp_inf*mu_inf**0.2)/(Pr_inf**0.6) * (rho_inf * v_inf)**0.8 * (rho_am/rho_inf) * (mu_am/mu0)**0.2 -def h_gas_3(c_star, At, A, pc, Tc, M, Tw, mu, cp, gamma, Pr): +def h_gas_bartz_sigma(c_star, At, A, pc, Tc, M, Tw, mu, cp, gamma, Pr): """Bartz heat transfer equation using the sigma correlation, from Reference [6]. Args: @@ -119,7 +119,7 @@ def h_gas_3(c_star, At, A, pc, Tc, M, Tw, mu, cp, gamma, Pr): return (0.026)/(Dt**0.2) * (mu**0.2*cp/Pr**0.6) * (pc/c_star)**0.8 * (At/A)**0.9 * sigma -def h_coolant_1(A, D, mdot, mu, k, c_bar, rho): +def h_coolant_rpe(A, D, mdot, mu, k, c_bar, rho): """Get the convective heat transfer coefficient for the coolant side. Uses the equation from page 317 of RPE 7th edition (Reference [2]). @@ -138,7 +138,7 @@ def h_coolant_1(A, D, mdot, mu, k, c_bar, rho): v = mdot / (rho*A) return 0.023*c_bar * (mdot/A) * (D*v*rho/mu)**(-0.2) * (mu*c_bar/k)**(-2/3) -def h_coolant_2(rho, V, D, mu_bulk, mu_wall, Pr, k): +def h_coolant_sieder_tate(rho, V, D, mu_bulk, mu_wall, Pr, k): """Sieder-Tate equation for convective heat transfer coefficient. Args: @@ -158,7 +158,7 @@ def h_coolant_2(rho, V, D, mu_bulk, mu_wall, Pr, k): return Nu*k/D -def h_coolant_3(rho, V, D, mu, Pr, k): +def h_coolant_dittus_boelter(rho, V, D, mu, Pr, k): """Dittus-Boelter equation for convective heat transfer coefficient. Args: @@ -277,7 +277,6 @@ def relStrength(self, T, ignoreLowTemp = False, ignoreHighTemp = False): return np.sum([self.polyCoeffs[index] * T**index for index in range(self.polyOrder)]) - class TransportProperties: """Container for transport properties of a fluid. @@ -490,9 +489,32 @@ def rho(self, T, p): elif self.model == "CoolProp": return PropsSI("DMASS", "T", T, "P", p, self.coolprop_name) +class ThermalCircuit: + def __init__(self, T1, T2, R): + """Class for solving thermal circuits. + + Args: + T1 (float): Temperature at start + T2 (float): Temperature at end + R (list): List of resistances between T1 and T2 + + Attributes: + Qdot (float): Heat transfer rate (positive in the direction of T1 --> T2) + T (list): List of temperatures in between each resistance, including T1 and T2 at either end. i.e. [T1, ..., T2]. + """ + self.R = R + self.T1 = T1 + self.T2 = T2 + + self.Qdot = (T1 - T2)/sum(R) + self.T = np.zeros(len(R) + 1) + self.T[0] = T1 + + for i in range(1, len(R)): + self.T[i] = self.T[i-1] - self.Qdot*R[i-1] class CoolingJacket: - """Container for cooling jacket information - e.g. for regenerative cooling. + """Container for cooling jacket information - e.g. for regenerative cooling. All channels are assumed to have rectangular cross sections. Args: inner_wall (Material): Wall material on the inner side of the cooling jacket. @@ -502,19 +524,16 @@ class CoolingJacket: mdot_coolant (float): Coolant mass flow rate (kg/s) xs (list): x positions that the cooling jacket starts and ends at, [x_min, x_max]. Defaults to [-1000, 1000]. configuration (str, optional): Options include 'spiral' and 'vertical'. Defaults to "vertical". + has_ablative (bool, optional): Whether or not the engine has an ablative. Keyword Args: - channel_shape (str, optional): Used if configuration = 'spiral'. Options include 'rectangle', 'semi-circle', and 'custom'. - blockage_ratio (str, optional): Can be used if configuration = "spiral". This is the proportion (by area) of the channel cross section occupied by ribs. - channel_height (float, optional): If using configuration = 'vertical' or channel_shape = 'rectangle', this is the height of the channels (m). - channel_width (float, optional): If using channel_shape = 'rectangle', this is the width of the channels (m). If using channel_shape = 'semi-circle', this is the diameter of the semi circle (m). - custom_effective_diameter (float, optional): If using channel_shape = 'custom', this is the effective diameter you want to use. - custom_flow_area (float, optional): If using channel_shape = 'custom', this is the flow you want to use. + blockage_ratio (float): Only relevant if configuration = 'vertical'. This is the proportion (by area) of the channel cross section occupied by ribs. + number_of_ribs (int): Only relevant if configuration = 'vertical' and 'blockage_ratio' !=0. This is the number of ribs present in the cooling channel. + channel_height (float): This is the height of the channels, in the radial direction (m). + channel_width (float): Only relevant if configuration = 'spiral'. This is the width of the cooling channels (m). outer_wall (Material): Wall material for the outer liner. """ - def __init__(self, geometry, inner_wall, inlet_T, inlet_p0, coolant_transport, mdot_coolant, xs = [-1000, 1000], configuration = "spiral", has_ablative = False, **kwargs): - - self.ymax = geometry.chamber_radius + def __init__(self, inner_wall, inlet_T, inlet_p0, coolant_transport, mdot_coolant, xs = [-1000, 1000], configuration = "spiral", **kwargs): self.inner_wall = inner_wall self.coolant_transport = coolant_transport self.mdot_coolant = mdot_coolant @@ -522,47 +541,44 @@ def __init__(self, geometry, inner_wall, inlet_T, inlet_p0, coolant_transport, m self.inlet_T = inlet_T self.inlet_p0 = inlet_p0 self.configuration = configuration - self.has_ablative = has_ablative if "outer_wall" in kwargs: self.outer_wall = kwargs["outer_wall"] if self.configuration == 'spiral': - self.channel_shape = kwargs['channel_shape'] - - if self.channel_shape == "rectangle": - #Page 317 of RPE 7th Edition - self.channel_width = kwargs["channel_width"] - self.channel_height = kwargs["channel_height"] - self.perimeter = 2*self.channel_width + 2*self.channel_height - self.flow_area = self.channel_width*self.channel_height - self.hydraulic_radius = self.flow_area/self.perimeter - self.effective_diameter = 4*self.hydraulic_radius - - if self.channel_shape == "semi-circle": - self.channel_width = kwargs["channel_width"] - self.perimeter = self.channel_width + np.pi*self.channel_width/2 - self.flow_area = np.pi*self.channel_width**2/8 - self.hydraulic_radius = self.flow_area/self.perimeter - self.effective_diameter = 4*self.hydraulic_radius - - if self.channel_shape == "custom": - self.flow_area = kwargs["custom_flow_area"] - self.effective_diameter = kwargs["custom_effective_diameter"] + + #Page 317 of RPE 7th Edition + self.channel_width = kwargs["channel_width"] + self.channel_height = kwargs["channel_height"] + self.perimeter = 2*self.channel_width + 2*self.channel_height + self.flow_area = self.channel_width*self.channel_height + self.hydraulic_radius = self.flow_area/self.perimeter + self.effective_diameter = 4*self.hydraulic_radius + elif self.configuration == 'vertical': self.channel_height = kwargs["channel_height"] if "blockage_ratio" in kwargs: self.blockage_ratio = kwargs["blockage_ratio"] + + if "number_of_ribs" in kwargs: + if type(kwargs["number_of_ribs"]) is not int: + raise ValueError("Keyword argument 'number_of_ribs' must be an integer") + else: + self.number_of_ribs = kwargs["number_of_ribs"] + else: + raise ValueError("Must also specify 'number_of_ribs' if you want to specify 'blockage_ratio'") + else: - self.blockage_ratio = 0 + self.blockage_ratio = 0.0 + self.number_of_ribs = 0 - def A(self, x = None, y = None): + def A(self, x, y): """Get coolant channel cross flow cross sectional area. Args: x (float, optional): x position - does not currently affect anything. - y (float, optional): The radius of the engine (m) (NOT the radius of the cooling channel). Only required for 'vertical' channels. + y (float, optional): y distance from engine centreline to the inner wall of the cooling channel (m). Returns: float: Cooling channel flow area (m^2) @@ -572,45 +588,47 @@ def A(self, x = None, y = None): return self.flow_area elif self.configuration == 'vertical': - if self.has_ablative is True: - y = self.ymax - # Ignore the nozzle contours - jacket has constant radius if an ablative insert is present return np.pi*((y + self.channel_height)**2 - y**2) * (1 - self.blockage_ratio) else: raise ValueError(f"The cooling jacket configuration {self.configuration} is not recognised. Try 'spiral' or 'vertical'. ") - def D(self, x = None, y = None): - """Get the 'effective diameter' of the cooling channel. This is equal 4*hydraulic_radius, with hydraulic_radius = channel_area / channel_perimeter. + def D(self, x, y): + """Get the 'effective diameter' of the cooling channel. This is equal 4*channel_area / wetted_channel_perimeter. Args: - x (float, optional): Axial position along the engine. This parameter may have no effect on the output. Defaults to None. - y (float, optional): The radius of the engine (m) (NOT the radius of the cooling channel). Only required for 'vertical' channels. + x (float, optional): Axial position along the engine. + y (float, optional): y distance from engine centreline to the inner wall of the cooling channel (m). + + Note: + Not entirely sure if I calculated the perimeter correctly when including blockage ratio. Returns: float: Effective diameter (m) """ - if self.has_ablative is True: - y = self.ymax - # Ignore the nozzle contours - jacket has constant radius if an ablative insert is present - if self.configuration == 'spiral': return self.effective_diameter elif self.configuration == 'vertical': - perimeter = 2*np.pi*y + 2*np.pi*(y + self.channel_height) - return 4*self.A(x, y)/perimeter + if self.blockage_ratio == 0.0: + perimeter = 2*np.pi*y + 2*np.pi*(y + self.channel_height) + return 4*self.A(x, y)/perimeter + + else: + #Not entirely sure if I calculated the perimeter correctly with blockage ratio + perimeter = (2*np.pi*y + 2*np.pi*(y + self.channel_height))*(1 - self.blockage_ratio) + 2*self.number_of_ribs*self.channel_height + return 4*self.A(x, y)/perimeter else: raise ValueError(f"The cooling jacket configuration {self.configuration} is not recognised. Try 'spiral' or 'vertical'. ") - def coolant_velocity(self, rho_coolant, x = None, y = None): + def coolant_velocity(self, rho_coolant, x, y): """Get coolant velocity using mdot = rho*V*A. Args: rho_coolant (float): Coolant density (kg/m^3) x (float, optional): x position - does not currently affect anything. - y (float, optional): Is The radius of the engine (m) (NOT the radius of the cooling channel). Only required for 'vertical' channels. + y (float, optional): y distance from engine centreline to the inner wall of the cooling channel (m). Returns: float: Coolant velocity (m/s) diff --git a/bamboo/main.py b/bamboo/main.py index 09fa449..4a476f5 100644 --- a/bamboo/main.py +++ b/bamboo/main.py @@ -43,6 +43,7 @@ import bamboo.cooling as cool import json import matplotlib.patches +import thermo.mixture R_BAR = 8.3144621e3 #Universal gas constant (J/K/kmol) g0 = 9.80665 #Standard gravitational acceleration (m/s^2) @@ -287,40 +288,6 @@ def get_exit_area(perfect_gas, chamber_conditions, p_amb): Me = M_from_p(p_amb, chamber_conditions.p0, perfect_gas.gamma) return (chamber_conditions.mdot * (perfect_gas.cp*chamber_conditions.T0)**0.5 )/(m_bar(Me, perfect_gas.gamma) * chamber_conditions.p0) -def show_conical_shape(A1, At, A2, div_half_angle = 15, conv_half_angle=45): - """Legacy function. Plots the shape of a conical nozzle with the specified half angle. - - Args: - A1 (Chamber area): Chamber area (m^2) - At (Throat area): Throat area (m^2) - A2 (float) : Exit plane area (m^2) - div_half_angle (float, optional): Cone half angle for the diverging section (deg). Defaults to 15. - conv_half_angle (float, optional): Cone half angle for the converging section (deg). Defaults to 45. - """ - - #Convert angles to radians - div_half_angle = div_half_angle*np.pi/180 - conv_half_angle = conv_half_angle*np.pi/180 - - #Convert areas to radii - r1 = (A1/np.pi)**0.5 - rt = (At/np.pi)**0.5 - r2 = (A2/np.pi)**0.5 - - x = [0, (r1-rt)/np.tan(conv_half_angle), (r1-rt)/np.tan(conv_half_angle) + (r2-rt)/np.tan(div_half_angle)] - y_pos = [r1, rt, r2] - y_neg = [-r1, -rt, -r2] - - plt.plot(x, y_pos, color='b') - plt.plot(x, y_neg, color='b') - plt.gca().set_aspect('equal', adjustable='box') - plt.grid() - plt.xlabel("x (m)") - plt.ylabel("y (m)") - plt.title("r1={:.5g} m, rt={:.5g} m, r2={:.5g} m".format(r1,rt,r2)) - plt.show() - - class PerfectGas: """Object to store exhaust gas properties. Assumes a perfect gas (ideal gas with constant cp, cv and gamma). Only two properties need to be specified. @@ -615,7 +582,7 @@ def __init__(self, perfect_gas, chamber_conditions, nozzle): #Engine geometry functions def y(self, x, up_to = 'contour'): - """Get y position up to a specified part of the engine (e.g. inner contour, ablative inner or outer wall, etc.) + """Get y position up to a specified part of the engine (e.g. inner contour, inner or outer side of the ablative, inner or outer side of the inner liner). Args: x (float): x position (m). x = 0 is the throat, x > 0 is the nozzle diverging section. @@ -1009,10 +976,10 @@ def plot_geometry(self, number_of_points = 1000, minimal = False, legend = True) #If using a spiral cooling jacket if self.cooling_jacket.configuration == 'spiral': - D = self.cooling_jacket.D(x[0], wall_outer[0]) - A = self.cooling_jacket.A(x[0], wall_outer[0]) + W = self.cooling_jacket.channel_width + H = self.cooling_jacket.channel_height - regen_xs = np.linspace(xmin, xmax, int((xmax - xmin)/D)) + regen_xs = np.linspace(xmin, xmax, int((xmax - xmin)/W)) if len(regen_xs) > 5000: print(f"WARNING: Large number of channels to plot for the cooling jacket ({len(regen_xs)}) - this may take a while.") @@ -1022,9 +989,9 @@ def plot_geometry(self, number_of_points = 1000, minimal = False, legend = True) for i in range(len(regen_xs) - 1): y_jacket_inner = np.interp(regen_xs[i], x, wall_outer) - #We'll show the coolant channels as rectangles, with a diameter equal to the equivelant diameter, and area equal to the flow area. - axs.add_patch(matplotlib.patches.Rectangle([regen_xs[i], y_jacket_inner], D, A/D, color = 'green', fill = False)) - axs.add_patch(matplotlib.patches.Rectangle([regen_xs[i], -y_jacket_inner-A/D], D, A/D, color = 'green', fill = False)) + #Plot the cooling channels as rectangles with the right width and height. + axs.add_patch(matplotlib.patches.Rectangle([regen_xs[i], y_jacket_inner], W, H, color = 'green', fill = False)) + axs.add_patch(matplotlib.patches.Rectangle([regen_xs[i], -y_jacket_inner-H], W, H, color = 'green', fill = False)) #If using a vertical channels cooling jacket if self.cooling_jacket.configuration == 'vertical': @@ -1113,40 +1080,37 @@ def add_geometry(self, chamber_length, chamber_area, inner_wall_thickness, style outer_wall_thickness (float or array): Thickness of the outer liner wall (m). Can be constant (float), or variable (array). """ - self.geometry = EngineGeometry(self.nozzle, chamber_length, chamber_area, inner_wall_thickness, - style, **kwargs) + self.geometry = EngineGeometry(self.nozzle, chamber_length, chamber_area, inner_wall_thickness, style, **kwargs) self.has_geometry = True def add_cooling_jacket(self, inner_wall, inlet_T, inlet_p0, coolant_transport, mdot_coolant, xs = [-1000, 1000], configuration = "spiral", **kwargs): """Container for cooling jacket information - e.g. for regenerative cooling. Args: - inner_wall (Material): Inner wall material. + inner_wall (Material): Wall material on the inner side of the cooling jacket. inlet_T (float): Inlet coolant temperature (K) inlet_p0 (float): Inlet coolant stagnation pressure (Pa) coolant_transport (TransportProperties): Container for the coolant transport properties. mdot_coolant (float): Coolant mass flow rate (kg/s) - xs (list): x position that the cooling jacket starts and ends at in the form [x_start, x_end]. Defaults to [-1000, 1000]. + xs (list): x positions that the cooling jacket starts and ends at, [x_min, x_max]. Defaults to [-1000, 1000]. configuration (str, optional): Options include 'spiral' and 'vertical'. Defaults to "vertical". + has_ablative (bool, optional): Whether or not the engine has an ablative. Keyword Args: - channel_shape (str, optional): Used if configuration = 'spiral'. Options include 'rectangle', 'semi-circle', and 'custom'. - channel_height (float, optional): If using configuration = 'vertical' or channel_shape = 'rectangle', this is the height of the channels (m). - channel_width (float, optional): If using channel_shape = 'rectangle', this is the width of the channels (m). If using channel_shape = 'semi-circle', this is the diameter of the semi circle (m). - custom_effective_diameter (float, optional): If using channel_shape = 'custom', this is the effective diameter you want to use. - custom_flow_area (float, optional): If using channel_shape = 'custom', this is the flow you want to use. - outer_wall (Material): Wall material for the outer liner. + blockage_ratio (float): Only relevant if configuration = 'vertical'. This is the proportion (by area) of the channel cross section occupied by ribs. + number_of_ribs (int): Only relevant if configuration = 'vertical' and 'blockage_ratio' != 0. This is the number of ribs present in the cooling channel. + channel_height (float): This is the height of the channels, in the radial direction (m). + channel_width (float): Only relevant if configuration = 'spiral'. This is the width of the cooling channels (m). + outer_wall (Material): Wall material for the outer liner. """ - self.cooling_jacket = cool.CoolingJacket(self.geometry, - inner_wall, + self.cooling_jacket = cool.CoolingJacket(inner_wall, inlet_T, inlet_p0, coolant_transport, mdot_coolant, xs, configuration, - self.has_ablative, **kwargs) self.has_cooling_jacket = True @@ -1189,6 +1153,7 @@ def add_ablative(self, ablative_material, wall_material = None, xs = [-1000, 100 ablative_thickness = ablative_thickness) self.has_ablative = True + #Cooling system functions def map_thickness_profile(self, thickness, number_of_points): """Stretches an array of any size so that it has the required 'number_of_points', whilst maintaining the same values at indexes [0] and [-1]. @@ -1215,7 +1180,7 @@ def map_thickness_profile(self, thickness, number_of_points): return mapped_thickness - def channel_geometry(self, number_of_sections = 1000): + def coolant_path_length(self, number_of_sections = 1000): """Finds the path length of the coolant in the jacket from engine geometry and channel configuration. Number_of_sections must be equal to number_of_points when used in a heating analysis. @@ -1238,15 +1203,13 @@ def channel_geometry(self, number_of_sections = 1000): section_turns = axis_length/(pitch*number_of_sections) # Number of turns per discrete section for i in range(number_of_sections-1): - if self.has_ablative is True: - y = self.geometry.chamber_radius - else: - y = self.y(discretised_x[i], up_to = "wall out") - # Ignore the nozzle contours - jacket has constant radius if an ablative insert is present - - radius_avg = (y + self.y(discretised_x[i+1], up_to = 'wall out'))/2 - discretised_length.append(section_turns * np.sqrt(pitch**2 + (radius_avg*2*np.pi)**2)) + y_i = self.y(discretised_x[i], up_to = "wall out") + y_iplus1 = self.y(discretised_x[i+1], up_to = "wall out") + # Find the average radius for this section and use it to determine the spiral section length + radius_avg = (y_i + y_iplus1)/2 + discretised_length.append(section_turns * np.sqrt(pitch**2 + (radius_avg*2*np.pi)**2)) + return discretised_length @@ -1262,12 +1225,12 @@ def channel_geometry(self, number_of_sections = 1000): else: raise AttributeError("Invalid cooling channel configuration") - def coolant_friction_factor(self, T, p, x, y = None): + def coolant_friction_factor(self, T, p, x, y): """Determine the friction factor of the coolant at the current position. Formula from reference [5] page 29. Args: x (float): Axial position - y (float, optional): The radius of the engine (m) (NOT the radius of the cooling channel). Only required for 'vertical' channels. + y (float, optional): y distance from engine centreline to the inner wall of the cooling channel (m). T (float): Coolant temperature at x p (float): Coolant pressure at x Returns: @@ -1282,12 +1245,12 @@ def coolant_friction_factor(self, T, p, x, y = None): return ((0.79*np.log(reynolds)) - 1.64)**(-2) - def Q_coolant(self, T, p, x = None, y = None): + def coolant_dynamic_pressure(self, T, p, x = None, y = None): """Determine dynamic pressure of coolant. Args: x (float, optional): Axial position. Only required for 'vertical' channels. - y (float, optional): The radius of the engine (m) (NOT the radius of the cooling channel). Only required for 'vertical' channels. + y (float, optional): y distance from engine centreline to the inner wall of the cooling channel (m). T (float): Coolant temperature at x p (float): Coolant pressure at x @@ -1305,7 +1268,7 @@ def coolant_p0_drop(self, friction_factor, dl, T, p, x = None, y = None): Args: friction_factor (float): Dimensionless friction factor x (float, optional): Axial position. Only required for 'vertical' channels. - y (float, optional): The radius of the engine (m) (NOT the radius of the cooling channel). Only required for 'vertical' channels. + y (float, optional): y distance from engine centreline to the inner wall of the cooling channel (m). dl (float): Length to evaluate pressure drop over - an increment along the channel, not the engine axis T (float): Coolant temperature p (float): Coolant pressure @@ -1314,42 +1277,10 @@ def coolant_p0_drop(self, friction_factor, dl, T, p, x = None, y = None): """ D = self.cooling_jacket.D(x, y) - Q = self.Q_coolant(T=T, p=p, x=x, y=y) + Q = self.coolant_dynamic_pressure(T=T, p=p, x=x, y=y) return friction_factor*dl*Q/D - def regen_thermal_circuit(self, r, h_gas, h_coolant, wall_material, inner_wall_thickness, T_gas, T_coolant): - """ - q is per unit length along the nozzle wall (axially) - positive when heat is flowing to the coolant. - Uses the idea of thermal circuits and resistances - we have three resistors in series. - - Args: - r (float): Radius to the inner wall of the engine (m) - h_gas (float): Gas side convective heat transfer coefficient - h_coolant (float): Coolant side convective heat transfer coefficient - wall_material (Material): Material object for the inner wall, needed for thermal conductivity - inner_wall_thickness (float): Thickness of the inner wall at x position (m) - T_gas (float): Free stream gas temperature (K) - T_coolant (float): Coolant temperature (K) - - Returns: - float, float, float, float: q_dot, R_gas, R_wall, R_coolant - """ - - r_in = r - r_out = r_in + inner_wall_thickness - - A_in = 2*np.pi*r_in #Inner area per unit length (i.e. just the inner circumference) - A_out = 2*np.pi*r_out #Outer area per unit length (i.e. just the outer circumference) - - R_gas = 1/(h_gas*A_in) - R_wall = np.log(r_out/r_in)/(2*np.pi*wall_material.k) - R_coolant = 1/(h_coolant*A_out) - - q_dot = (T_gas - T_coolant)/(R_gas + R_wall + R_coolant) #Heat flux per unit length - - return q_dot, R_gas, R_wall, R_coolant - def ablative_thermal_circuit(self, r, h_gas, ablative_material, ablative_thickness, T_gas, T_wall): """ q is per unit length along the nozzle wall (axially) - positive when heat is flowing to the coolant. @@ -1378,56 +1309,20 @@ def ablative_thermal_circuit(self, r, h_gas, ablative_material, ablative_thickne return q_dot, R_gas, R_ablative - def regen_ablative_thermal_circuit(self, r, h_gas, h_coolant, wall_material, inner_wall_thickness, T_gas, T_coolant, ablative_material, ablative_thickness): - """Combined regenerative and ablative cooling thermal circuit. - q is per unit length along the nozzle wall (axially) - positive when heat is flowing to the coolant. - q_Adot is the heat flux per unit area along the nozzle wall. - Uses the idea of thermal circuits and resistances - we have three resistors in series. - - Args: - r (float): Radius to the contour of the engine (m) - h_gas (float): Gas side convective heat transfer coefficient - h_coolant (float): Coolant side convective heat transfer coefficient - wall_material (Material): Material object for the inner wall, needed for thermal conductivity - inner_wall_thickness (float): Thickness of the inner wall at x position (m) - T_gas (float): Free stream gas temperature (K) - T_coolant (float): Coolant temperature (K) - ablative_material (Material): Material object for the ablative material, needed for thermal conductivity - ablative_thickness (float): Thickness of the ablative material (m) - - Returns: - float, float, float, float, float: q_dot, R_gas, R_ablative, R_wall, R_coolant - """ - - r_ablative_in = r - r_ablative_out = r_ablative_in + ablative_thickness - - r_wall_in = r_ablative_out - r_wall_out = r_wall_in + inner_wall_thickness - - A_wall_in = 2*np.pi*r_wall_in #Inner area per unit length (i.e. just the inner circumference) - A_wall_out = 2*np.pi*r_wall_out #Outer area per unit length (i.e. just the outer circumference) - - R_gas = 1/(h_gas*A_wall_in) - R_wall = np.log(r_wall_out/r_wall_in)/(2*np.pi*wall_material.k) - R_coolant = 1/(h_coolant*A_wall_out) - R_ablative = np.log(r_ablative_out/r_ablative_in)/(2*np.pi*ablative_material.k) - - q_dot = (T_gas - T_coolant)/(R_gas + R_ablative + R_wall + R_coolant) #Heat flux per unit length - - return q_dot, R_gas, R_ablative, R_wall, R_coolant, - - def steady_heating_analysis(self, number_of_points=1000, h_gas_model = "3", h_coolant_model = "2", to_json = "heating_output.json"): + def steady_heating_analysis(self, number_of_points = 1000, h_gas_model = "bartz-sigma", h_coolant_model = "sieder-tate", to_json = "heating_output.json"): """Steady state heating analysis. Can be used for regenarative cooling, or combined regenerative and ablative cooling. Args: number_of_points (int, optional): Number of discrete points to divide the engine into. Defaults to 1000. - h_gas_model (str, optional): Equation to use for the gas side convective heat transfer coefficients. Options are '1', '2' and '3'. Defaults to "3". - h_coolant_model (str, optional): Equation to use for the coolant side convective heat transfer coefficients. Options are '1', '2' and '3'. Defaults to "2". + h_gas_model (str, optional): Equation to use for the gas side convective heat transfer coefficients. Options are 'rpe', 'bartz' and 'bartz-sigma'. Defaults to "bartz-sigma". + h_coolant_model (str, optional): Equation to use for the coolant side convective heat transfer coefficients. Options are 'rpe', 'sieder-tate' and 'dittus-boelter'. Defaults to "sieder-tate". to_json (str or bool, optional): Directory to export a .JSON file to, containing simulation results. If False, no .JSON file is saved. Defaults to 'heating_output.json'. Note: - See the bamboo.cooling module for explanations of each h_gas and h_coolant option. Defaults are Bartz (using sigma correlation) for gas side, and Sieder-Tate for coolant side. These are believed to be the most accurate. + See the bamboo.cooling module for details of each h_gas and h_coolant option. Defaults are Bartz (using sigma correlation) for gas side, and Sieder-Tate for coolant side. These are believed to be the most accurate. + + Note: + Questionable decisions (especially when using mixtures) were made when implementing the Sieder-Tate method with wall temperature > coolant boiling temperature. Ideally these should be looked into in more detail. Returns: dict: Results of the simulation. Contains the following dictionary keys: @@ -1437,7 +1332,6 @@ def steady_heating_analysis(self, number_of_points=1000, h_gas_model = "3", h_co - "T_coolant" : Coolant temperature (K) - "T_gas" : Exhaust gas freestream temperature (K) - "q_dot" : Heat transfer rate per unit length (axially along the engine) (W/m) - - "q_Adot": Heat transfer rate per unit area (W/m^2) - "h_gas" : Convective heat transfer rate for the exhaust gas side - "h_coolant" : Convective heat transfer rate for the coolant side - "boil_off_position" : x position of any coolant boil off. Equal to None if the coolant does not boil. @@ -1459,7 +1353,7 @@ def steady_heating_analysis(self, number_of_points=1000, h_gas_model = "3", h_co print("WARNING: h_gas_model = '2' seems to provide questionable results (if it works at all) - use it with caution. ") '''Initialise variables and arrays''' - #To keep track of any coolant boiling + #To keep track of any coolant boiling, and if the coolant pressure drops below chamber pressure. boil_off_position = None too_low_pressure = False @@ -1468,7 +1362,7 @@ def steady_heating_analysis(self, number_of_points=1000, h_gas_model = "3", h_co dx = discretised_x[0] - discretised_x[1] #Calculation of coolant channel length per "section" - channel_length = self.channel_geometry(number_of_sections=number_of_points) #number_of_sections must be equal to number_of_points + channel_length = self.coolant_path_length(number_of_sections=number_of_points) #number_of_sections must be equal to number_of_points #Data arrays to return T_wall_inner = np.full(len(discretised_x), float('NaN')) #Gas side wall temperature @@ -1514,8 +1408,8 @@ def steady_heating_analysis(self, number_of_points=1000, h_gas_model = "3", h_co if self.has_cooling_jacket and self.cooling_jacket.xs[0] <= x <= self.cooling_jacket.xs[1]: #Gas side heat transfer coefficient - if h_gas_model == "1": - h_gas[i] = cool.h_gas_1(2*self.y(x), + if h_gas_model == "rpe": + h_gas[i] = cool.h_gas_rpe(2*self.y(x), self.M(x), T_gas[i], self.rho(x), @@ -1525,8 +1419,8 @@ def steady_heating_analysis(self, number_of_points=1000, h_gas_model = "3", h_co k_gas[i], Pr_gas[i]) - elif h_gas_model == "2": - #We need the previous wall temperature to use h_gas_2. If we're on the first step, assume wall temperature = freestream temperature. + elif h_gas_model == "bartz": + #We need the previous wall temperature to use h_gas_bartz. If we're on the first step, assume wall temperature = freestream temperature. if i == 0: gamma = self.perfect_gas.gamma R = self.perfect_gas.R @@ -1552,7 +1446,7 @@ def steady_heating_analysis(self, number_of_points=1000, h_gas_model = "3", h_co T0 = self.chamber_conditions.T0 mu0 = self.exhaust_transport.mu(T = T0, p = p0) - h_gas[i] = cool.h_gas_2(D, cp_inf, mu_inf, Pr_inf, rho_inf, v_inf, rho_am, mu_am, mu0) + h_gas[i] = cool.h_gas_bartz(D, cp_inf, mu_inf, Pr_inf, rho_inf, v_inf, rho_am, mu_am, mu0) else: gamma = self.perfect_gas.gamma @@ -1579,12 +1473,12 @@ def steady_heating_analysis(self, number_of_points=1000, h_gas_model = "3", h_co T0 = self.chamber_conditions.T0 mu0 = self.exhaust_transport.mu(T = T0, p = p0) - h_gas[i] = cool.h_gas_2(D, cp_inf, mu_inf, Pr_inf, rho_inf, v_inf, rho_am, mu_am, mu0) + h_gas[i] = cool.h_gas_bartz(D, cp_inf, mu_inf, Pr_inf, rho_inf, v_inf, rho_am, mu_am, mu0) - elif h_gas_model == "3": - #We need the previous wall temperature to use h_gas_3. If we're on the first step, assume wall temperature = freestream temperature. + elif h_gas_model == "bartz-sigma": + #We need the previous wall temperature to use h_gas_bartz_sigma. If we're on the first step, assume wall temperature = freestream temperature. if i == 0: - h_gas[i] = cool.h_gas_3(self.c_star, + h_gas[i] = cool.h_gas_bartz_sigma(self.c_star, self.nozzle.At, self.A(x), self.chamber_conditions.p0, @@ -1596,9 +1490,9 @@ def steady_heating_analysis(self, number_of_points=1000, h_gas_model = "3", h_co self.perfect_gas.gamma, Pr_gas[i]) - #Use h_gas_3() for all subsequent steps + #For all other steps else: - h_gas[i] = cool.h_gas_3(self.c_star, + h_gas[i] = cool.h_gas_bartz_sigma(self.c_star, self.nozzle.At, self.A(x), self.chamber_conditions.p0, @@ -1611,22 +1505,36 @@ def steady_heating_analysis(self, number_of_points=1000, h_gas_model = "3", h_co Pr_gas[i]) else: - raise AttributeError(f"Could not find the h_gas_model '{h_gas_model}'. Try '1', '2' or '3'.") + raise AttributeError(f"Could not find the h_gas_model '{h_gas_model}'. Try 'rpe', 'bartz' or 'bartz-sigma'.") #Calculate the current coolant temperature if i == 0: T_coolant[i] = self.cooling_jacket.inlet_T p0_coolant[i] = self.cooling_jacket.inlet_p0 - p_coolant[i] = p0_coolant[i] - self.Q_coolant(T=T_coolant[i], p=p0_coolant[i], x=x, y=self.y(x)) + p_coolant[i] = p0_coolant[i] - self.coolant_dynamic_pressure(T=T_coolant[i], p=p0_coolant[i], x=x, y=self.y(x)) else: #Increase in coolant temperature, q*dx = mdot*Cp*dT T_coolant[i] = T_coolant[i-1] + (q_dot[i-1]*dx)/(self.cooling_jacket.mdot_coolant*cp_coolant[i-1]) #Pressure drop in coolant channel - friction_factor = self.coolant_friction_factor(T=T_coolant[i], p=p_coolant[i-1], x=x, y=self.y(x)) - p0_coolant[i] = p0_coolant[i-1] - self.coolant_p0_drop(friction_factor, dl=channel_length[i-1], T=T_coolant[i], p=p_coolant[i-1], x=x, y=self.y(x)) - p_coolant[i] = p0_coolant[i] - self.Q_coolant(T=T_coolant[i], p=p_coolant[i-1], x=x, y=self.y(x)) # Update static pressure of coolant + friction_factor = self.coolant_friction_factor(T = T_coolant[i], + p = p_coolant[i-1], + x = x, + y = self.y(x, up_to = "wall out")) + + p0_coolant[i] = p0_coolant[i-1] - self.coolant_p0_drop(friction_factor, + dl = channel_length[i-1], + T = T_coolant[i], + p = p_coolant[i-1], + x = x, + y = self.y(x, up_to = "wall out")) + + #Update static pressure of coolant + p_coolant[i] = p0_coolant[i] - self.coolant_dynamic_pressure(T = T_coolant[i], + p = p_coolant[i-1], + x = x, + y = self.y(x, up_to = "wall out")) if too_low_pressure == False and p0_coolant[i] < self.chamber_conditions.p0: too_low_pressure = True @@ -1644,8 +1552,8 @@ def steady_heating_analysis(self, number_of_points=1000, h_gas_model = "3", h_co v_coolant[i] = self.cooling_jacket.coolant_velocity(rho_coolant[i], x=x, y = self.y(x, up_to = "wall in")) #Coolant side heat transfer coefficient - if h_coolant_model == "1": - h_coolant[i] = cool.h_coolant_1(self.cooling_jacket.A(x=x, y=self.y(x=x, up_to = "wall in")), + if h_coolant_model == "rpe": + h_coolant[i] = cool.h_coolant_rpe(self.cooling_jacket.A(x=x, y=self.y(x=x, up_to = "wall in")), self.cooling_jacket.D(x=x, y=self.y(x=x, up_to = "wall in")), self.cooling_jacket.mdot_coolant, mu_coolant[i], @@ -1653,30 +1561,38 @@ def steady_heating_analysis(self, number_of_points=1000, h_gas_model = "3", h_co cp_coolant[i], rho_coolant[i]) - elif h_coolant_model == "2": + elif h_coolant_model == "sieder-tate": #This model requires the cooling channel wall temperature, which hasn't been calculated in the first step. #Assume the wall temperature = coolant temperature for the first step. if i == 0: - h_coolant[i] = cool.h_coolant_2(rho_coolant[i], - v_coolant[i], - self.cooling_jacket.D(x=x, y=self.y(x=x, up_to = "wall in")), - mu_coolant[i], - self.cooling_jacket.coolant_transport.mu(T = T_coolant[i], p = p_coolant[i]), - Pr_coolant[i], - k_coolant[i]) + h_coolant[i] = cool.h_coolant_sieder_tate(rho_coolant[i], + v_coolant[i], + self.cooling_jacket.D(x=x, y=self.y(x=x, up_to = "wall in")), + mu_coolant[i], + self.cooling_jacket.coolant_transport.mu(T = T_coolant[i], p = p_coolant[i]), + Pr_coolant[i], + k_coolant[i]) else: + #Get inside wall temperature + liquid_wall_temp = T_wall_outer[i-1] + #If the wall temperature is above the boiling temperature of the fluid, cap the wall temperature used in Sieder-Tate to the boiling temperature of the liquid. #Currently doesn't do anything for CoolProp models. if self.cooling_jacket.coolant_transport.model == "thermo": self.cooling_jacket.coolant_transport.thermo_object.calculate(P = p_coolant[i]) - if self.cooling_jacket.coolant_transport.thermo_object.Tb < T_wall_outer[i-1]: + #If using a mixture, take the lowest boiling point and cap the temperature at that. + if type(self.cooling_jacket.coolant_transport.thermo_object) is thermo.mixture.Mixture: + if min(self.cooling_jacket.coolant_transport.thermo_object.Tbs) < T_wall_outer[i-1]: + liquid_wall_temp = min(self.cooling_jacket.coolant_transport.thermo_object.Tbs) - 0.001 #Make it a bit smaller just to avoid thermo using the gas phase. + + #If using a pure substance + elif self.cooling_jacket.coolant_transport.thermo_object.Tb < T_wall_outer[i-1]: liquid_wall_temp = self.cooling_jacket.coolant_transport.thermo_object.Tb - 0.001 #Make it a bit smaller just to avoid thermo using the gas phase. - else: - liquid_wall_temp = T_wall_outer[i-1] - h_coolant[i] = cool.h_coolant_2(rho_coolant[i], + + h_coolant[i] = cool.h_coolant_sieder_tate(rho_coolant[i], v_coolant[i], self.cooling_jacket.D(x=x, y=self.y(x=x, up_to = "wall in")), mu_coolant[i], @@ -1684,52 +1600,71 @@ def steady_heating_analysis(self, number_of_points=1000, h_gas_model = "3", h_co Pr_coolant[i], k_coolant[i]) - elif h_coolant_model == "3": - h_coolant[i] = cool.h_coolant_3(rho_coolant[i], - v_coolant[i], - self.cooling_jacket.D(x=x, y=self.y(x=x, up_to = "wall in")), - mu_coolant[i], - Pr_coolant[i], - k_coolant[i]) + elif h_coolant_model == "dittus-boelter": + h_coolant[i] = cool.h_coolant_dittus_boelter(rho_coolant[i], + v_coolant[i], + self.cooling_jacket.D(x=x, y=self.y(x=x, up_to = "wall in")), + mu_coolant[i], + Pr_coolant[i], + k_coolant[i]) else: - raise AttributeError(f"Could not find the h_coolant_model '{h_coolant_model}'") + raise AttributeError(f"Could not find the h_coolant_model '{h_coolant_model}'. Try 'rpe', 'sieder-tate' or 'dittus-boelter'.") #Check for coolant boil off if boil_off_position == None and self.cooling_jacket.coolant_transport.check_liquid(T = T_coolant[i], p = p_coolant[i]) == False: print(f"WARNING: Coolant boiled off at x = {x} m") boil_off_position = x - #Get thermal circuit properties + #Thermal circuit analysis + #Combined ablative and regen: if self.has_ablative and self.ablative.xs[0] <= x <= self.ablative.xs[1]: - #Thermal circuit - q_dot[i], R_gas[i], R_ablative[i], R_wall[i], R_coolant[i] = self.regen_ablative_thermal_circuit(self.y(x), - h_gas[i], - h_coolant[i], - self.ablative.wall_material, - self.thickness(x, layer = 'wall'), - T_gas[i], - T_coolant[i], - self.ablative.ablative_material, - self.thickness(x, layer = 'ablative')) - - #Calculate wall temperatures using the thermal circuit idea - T_ablative_inner[i] = T_gas[i] - q_dot[i]*R_gas[i] - T_wall_inner[i] = T_ablative_inner[i] - q_dot[i]*R_ablative[i] - T_wall_outer[i] = T_wall_inner[i] - q_dot[i]*R_wall[i] - + #Geometry + r_ablative_in = self.y(x, up_to = 'ablative in') + r_ablative_out = self.y(x, up_to = 'ablative out') + + r_wall_in = self.y(x, up_to = 'wall in') + r_wall_out = self.y(x, up_to = 'wall out') + + #Areas per unit length (i.e. just circumference) + A_gas = 2 * np.pi * self.y(x, up_to = 'contour') + A_coolant = 2 * np.pi * r_wall_out + + #Thermal resistances + R_gas[i] = 1/(h_gas[i]*A_gas) + R_wall[i] = np.log(r_wall_out/r_wall_in)/(2*np.pi*self.ablative.wall_material.k) + R_coolant[i] = 1/(h_coolant[i]*A_coolant) + R_ablative[i] = np.log(r_ablative_out/r_ablative_in)/(2*np.pi*self.ablative.ablative_material.k) + + #Thermal circuit object + thermal_circuit = cool.ThermalCircuit(T_gas[i], T_coolant[i], [R_gas[i], R_ablative[i], R_wall[i], R_coolant[i]]) + + q_dot[i] = thermal_circuit.Qdot + T_ablative_inner[i] = thermal_circuit.T[1] + T_wall_inner[i] = thermal_circuit.T[2] + T_wall_outer[i] = thermal_circuit.T[3] + + #Regen but no ablative: else: - q_dot[i], R_gas[i], R_wall[i], R_coolant[i] = self.regen_thermal_circuit(self.y(x), - h_gas[i], - h_coolant[i], - self.cooling_jacket.inner_wall, - self.thickness(x, layer = 'wall'), - T_gas[i], - T_coolant[i]) - - #Calculate temperatures - T_wall_inner[i] = T_gas[i] - q_dot[i]*R_gas[i] - T_wall_outer[i] = T_wall_inner[i] - q_dot[i]*R_wall[i] + #Geometry + r_wall_in = self.y(x, up_to = 'wall in') + r_wall_out = self.y(x, up_to = 'wall out') + + #Areas per unit length (i.e. just circumference) + A_gas = 2 * np.pi * self.y(x, up_to = 'contour') + A_coolant = 2 * np.pi * r_wall_out + + R_gas[i] = 1/(h_gas*A_gas) + R_wall[i] = np.log(r_wall_out/r_wall_in)/(2*np.pi*self.cooling_jacket.inner_wall.k) + R_coolant[i] = 1/(h_coolant*A_coolant) + + #Thermal circuit object + thermal_circuit = cool.ThermalCircuit(T_gas[i], T_coolant[i], [R_gas[i], R_wall[i], R_coolant[i]]) + + q_dot[i] = thermal_circuit.Qdot + T_wall_inner[i] = thermal_circuit.T[1] + T_wall_outer[i] = thermal_circuit.T[2] + else: T_wall_inner[i] = T_gas[i] T_wall_outer[i] = T_gas[i] @@ -1777,7 +1712,7 @@ def transient_heating_analysis(self, number_of_points=1000, dt = 0.1, t_max = 10 """This is used exclusive for pure ablative cooling, without any regenerative cooling jacket. Note: - This function is outdated and does not incorporate many new features that have been added to Bamboo. + This function is outdated and likely no longer functional, as it does not incorporate many new features that have been added to Bamboo. Args: number_of_points (int, optional): [description]. Defaults to 1000. @@ -1936,6 +1871,7 @@ def transient_heating_analysis(self, number_of_points=1000, dt = 0.1, t_max = 10 return output_dict + #Stress analysis functions def run_stress_analysis(self, heating_result, condition="steady", **kwargs): """Perform stress analysis on the liner, using a cooling result. Results should be taken only as a first approximation of some key stresses. diff --git a/docs/_autosummary/bamboo.cooling.html b/docs/_autosummary/bamboo.cooling.html index 186ff69..931a879 100644 --- a/docs/_autosummary/bamboo.cooling.html +++ b/docs/_autosummary/bamboo.cooling.html @@ -189,24 +189,24 @@

black_body(T)

Get the black body radiation emitted over a hemisphere, at a given temperature.

-

h_coolant_1(A, D, mdot, mu, k, c_bar, rho)

+

h_coolant_dittus_boelter(rho, V, D, mu, Pr, k)

+

Dittus-Boelter equation for convective heat transfer coefficient.

+ +

h_coolant_rpe(A, D, mdot, mu, k, c_bar, rho)

Get the convective heat transfer coefficient for the coolant side.

-

h_coolant_2(rho, V, D, mu_bulk, mu_wall, Pr, k)

+

h_coolant_sieder_tate(rho, V, D, mu_bulk, …)

Sieder-Tate equation for convective heat transfer coefficient.

-

h_coolant_3(rho, V, D, mu, Pr, k)

-

Dittus-Boelter equation for convective heat transfer coefficient.

- -

h_gas_1(D, M, T, rho, gamma, R, mu, k, Pr)

-

Get the convective heat transfer coefficient on the gas side.

- -

h_gas_2(D, cp_inf, mu_inf, Pr_inf, rho_inf, …)

+

h_gas_bartz(D, cp_inf, mu_inf, Pr_inf, …)

Bartz equation, using Equation (8-23) from page 312 of RPE 7th edition (Reference [2]).

-

h_gas_3(c_star, At, A, pc, Tc, M, Tw, mu, …)

+

h_gas_bartz_sigma(c_star, At, A, pc, Tc, M, …)

Bartz heat transfer equation using the sigma correlation, from Reference [6].

+

h_gas_rpe(D, M, T, rho, gamma, R, mu, k, Pr)

+

Get the convective heat transfer coefficient on the gas side.

+

Classes

@@ -219,13 +219,16 @@

Ablative(ablative_material, wall_material[, …])

Container for refractory or ablative properties.

-

CoolingJacket(geometry, inner_wall, inlet_T, …)

+

CoolingJacket(inner_wall, inlet_T, inlet_p0, …)

Container for cooling jacket information - e.g.

Material(E, sigma_y, poisson, alpha, k, **kwargs)

Class used to specify a material and its properties.

-

TransportProperties([model, force_phase])

+

ThermalCircuit(T1, T2, R)

+

Class for solving thermal circuits.

+ +

TransportProperties([model, force_phase])

Container for transport properties of a fluid.

diff --git a/docs/_autosummary/bamboo.main.html b/docs/_autosummary/bamboo.main.html index 73289b1..6e7c3d5 100644 --- a/docs/_autosummary/bamboo.main.html +++ b/docs/_autosummary/bamboo.main.html @@ -240,9 +240,6 @@

rao_theta_n(area_ratio[, length_fraction])

Returns the contour angle at the inflection point of the bell nozzle, by interpolating data.

-

show_conical_shape(A1, At, A2[, …])

-

Legacy function.

-

Classes

diff --git a/docs/_sources/_autosummary/bamboo.cooling.rst.txt b/docs/_sources/_autosummary/bamboo.cooling.rst.txt index eb039ab..4a182b1 100644 --- a/docs/_sources/_autosummary/bamboo.cooling.rst.txt +++ b/docs/_sources/_autosummary/bamboo.cooling.rst.txt @@ -14,12 +14,12 @@ .. autosummary:: black_body - h_coolant_1 - h_coolant_2 - h_coolant_3 - h_gas_1 - h_gas_2 - h_gas_3 + h_coolant_dittus_boelter + h_coolant_rpe + h_coolant_sieder_tate + h_gas_bartz + h_gas_bartz_sigma + h_gas_rpe @@ -32,6 +32,7 @@ Ablative CoolingJacket Material + ThermalCircuit TransportProperties diff --git a/docs/_sources/_autosummary/bamboo.main.rst.txt b/docs/_sources/_autosummary/bamboo.main.rst.txt index 578b99b..711da4d 100644 --- a/docs/_sources/_autosummary/bamboo.main.rst.txt +++ b/docs/_sources/_autosummary/bamboo.main.rst.txt @@ -24,7 +24,6 @@ p0 rao_theta_e rao_theta_n - show_conical_shape diff --git a/docs/build/doctrees/_autosummary/bamboo.cooling.doctree b/docs/build/doctrees/_autosummary/bamboo.cooling.doctree index 5b8b937..b97d37b 100644 Binary files a/docs/build/doctrees/_autosummary/bamboo.cooling.doctree and b/docs/build/doctrees/_autosummary/bamboo.cooling.doctree differ diff --git a/docs/build/doctrees/_autosummary/bamboo.doctree b/docs/build/doctrees/_autosummary/bamboo.doctree index 966f670..4223da6 100644 Binary files a/docs/build/doctrees/_autosummary/bamboo.doctree and b/docs/build/doctrees/_autosummary/bamboo.doctree differ diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle index ae44b7e..f36491c 100644 Binary files a/docs/build/doctrees/environment.pickle and b/docs/build/doctrees/environment.pickle differ diff --git a/docs/build/doctrees/index.doctree b/docs/build/doctrees/index.doctree index 0c2dce4..036133e 100644 Binary files a/docs/build/doctrees/index.doctree and b/docs/build/doctrees/index.doctree differ diff --git a/docs/genindex.html b/docs/genindex.html index 8ee8c79..d68ed87 100644 --- a/docs/genindex.html +++ b/docs/genindex.html @@ -267,17 +267,19 @@

C

  • ChamberConditions (class in bamboo.main)
    • -
  • channel_geometry() (bamboo.main.Engine method) -
    • - - +
      -
    • show_conical_shape() (in module bamboo.main) -
    • sigma_y (bamboo.cooling.Material attribute)
    • steady_heating_analysis() (bamboo.main.Engine method) @@ -544,6 +540,8 @@

      S

      T

        +
      • thickness() (bamboo.main.Engine method) +
      • thrust() (bamboo.main.Engine method)
      • transient_heating_analysis() (bamboo.main.Engine method) diff --git a/docs/index.html b/docs/index.html index f071c4a..aa894ae 100644 --- a/docs/index.html +++ b/docs/index.html @@ -433,23 +433,6 @@

        Welcome to Bamboo’s documentation! -
        -bamboo.main.show_conical_shape(A1, At, A2, div_half_angle=15, conv_half_angle=45)
        -

        Legacy function. Plots the shape of a conical nozzle with the specified half angle.

        -
        -
        Parameters
        -
          -

        • A1 (Chamber area) – Chamber area (m^2)

          • -

        • At (Throat area) – Throat area (m^2)

          • -

        • A2 (float) – Exit plane area (m^2)

          • -

        • div_half_angle (float, optional) – Cone half angle for the diverging section (deg). Defaults to 15.

          • -

        • conv_half_angle (float, optional) – Cone half angle for the converging section (deg). Defaults to 45.

          • -
        -
        -
        -
        -
        class bamboo.main.PerfectGas(**kwargs)
        @@ -807,7 +790,7 @@

        Welcome to Bamboo’s documentation!
        y(x, up_to='contour')
        -

        Get y position up to a specified part of the engine (e.g. inner contour, ablative inner or outer wall, etc.)

        +

        Get y position up to a specified part of the engine (e.g. inner contour, inner or outer side of the ablative, inner or outer side of the inner liner).

        Parameters
          @@ -1097,22 +1080,22 @@

          Welcome to Bamboo’s documentation!
          Parameters
            -

          • inner_wall (Material) – Inner wall material.

            • +

          • inner_wall (Material) – Wall material on the inner side of the cooling jacket.

          • inlet_T (float) – Inlet coolant temperature (K)

          • inlet_p0 (float) – Inlet coolant stagnation pressure (Pa)

          • coolant_transport (TransportProperties) – Container for the coolant transport properties.

          • mdot_coolant (float) – Coolant mass flow rate (kg/s)

            • -

          • xs (list) – x position that the cooling jacket starts and ends at in the form [x_start, x_end]. Defaults to [-1000, 1000].

            • +

          • xs (list) – x positions that the cooling jacket starts and ends at, [x_min, x_max]. Defaults to [-1000, 1000].

          • configuration (str, optional) – Options include ‘spiral’ and ‘vertical’. Defaults to “vertical”.

            • +

          • has_ablative (bool, optional) – Whether or not the engine has an ablative.

          Keyword Arguments
            -

          • channel_shape (str, optional) – Used if configuration = ‘spiral’. Options include ‘rectangle’, ‘semi-circle’, and ‘custom’.

            • -

          • channel_height (float, optional) – If using configuration = ‘vertical’ or channel_shape = ‘rectangle’, this is the height of the channels (m).

            • -

          • channel_width (float, optional) – If using channel_shape = ‘rectangle’, this is the width of the channels (m). If using channel_shape = ‘semi-circle’, this is the diameter of the semi circle (m).

            • -

          • custom_effective_diameter (float, optional) – If using channel_shape = ‘custom’, this is the effective diameter you want to use.

            • -

          • custom_flow_area (float, optional) – If using channel_shape = ‘custom’, this is the flow you want to use.

            • +

          • blockage_ratio (float) – Only relevant if configuration = ‘vertical’. This is the proportion (by area) of the channel cross section occupied by ribs.

            • +

          • number_of_ribs (int) – Only relevant if configuration = ‘vertical’ and ‘blockage_ratio’ !=0. This is the number of ribs present in the cooling channel.

            • +

          • channel_height (float) – This is the height of the channels, in the radial direction (m).

            • +

          • channel_width (float) – Only relevant if configuration = ‘spiral’. This is the width of the cooling channels (m).

          • outer_wall (Material) – Wall material for the outer liner.

          @@ -1163,8 +1146,8 @@

          Welcome to Bamboo’s documentation! -
          -channel_geometry(number_of_sections=1000)
          +
          +coolant_path_length(number_of_sections=1000)
          Finds the path length of the coolant in the jacket from engine geometry and channel configuration.

          Number_of_sections must be equal to number_of_points when used in a heating analysis.

          @@ -1188,7 +1171,7 @@

          Welcome to Bamboo’s documentation!
          -coolant_friction_factor(T, p, x, y=None)
          +coolant_friction_factor(T, p, x, y)
          Determine the friction factor of the coolant at the current position.

          Formula from reference [5] page 29.

          @@ -1197,7 +1180,7 @@

          Welcome to Bamboo’s documentation!Parameters

          • x (float) – Axial position

            • -

          • y (float, optional) – The radius of the engine (m) (NOT the radius of the cooling channel). Only required for ‘vertical’ channels.

            • +

          • y (float, optional) – y distance from engine centreline to the inner wall of the cooling channel (m).

          • T (float) – Coolant temperature at x

          • p (float) – Coolant pressure at x

          @@ -1212,14 +1195,14 @@

          Welcome to Bamboo’s documentation! -
          -Q_coolant(T, p, x=None, y=None)
          +
          +coolant_dynamic_pressure(T, p, x=None, y=None)

          Determine dynamic pressure of coolant.

          Parameters

          • x (float, optional) – Axial position. Only required for ‘vertical’ channels.

            • -

          • y (float, optional) – The radius of the engine (m) (NOT the radius of the cooling channel). Only required for ‘vertical’ channels.

            • +

          • y (float, optional) – y distance from engine centreline to the inner wall of the cooling channel (m).

          • T (float) – Coolant temperature at x

          • p (float) – Coolant pressure at x

          @@ -1241,7 +1224,7 @@

          Welcome to Bamboo’s documentation! -
          -regen_thermal_circuit(r, h_gas, h_coolant, wall_material, inner_wall_thickness, T_gas, T_coolant)
          -

          q is per unit length along the nozzle wall (axially) - positive when heat is flowing to the coolant. -Uses the idea of thermal circuits and resistances - we have three resistors in series.

          -
          -
          Parameters
          -
            -

          • r (float) – Radius to the inner wall of the engine (m)

            • -

          • h_gas (float) – Gas side convective heat transfer coefficient

            • -

          • h_coolant (float) – Coolant side convective heat transfer coefficient

            • -

          • wall_material (Material) – Material object for the inner wall, needed for thermal conductivity

            • -

          • inner_wall_thickness (float) – Thickness of the inner wall at x position (m)

            • -

          • T_gas (float) – Free stream gas temperature (K)

            • -

          • T_coolant (float) – Coolant temperature (K)

            • -
          -
          -
          Returns
          -

          q_dot, R_gas, R_wall, R_coolant

          -
          -
          Return type
          -

          float, float, float, float

          -
          -
          -

          -
          ablative_thermal_circuit(r, h_gas, ablative_material, ablative_thickness, T_gas, T_wall)
          @@ -1309,53 +1266,27 @@

          Welcome to Bamboo’s documentation! -
          -regen_ablative_thermal_circuit(r, h_gas, h_coolant, wall_material, inner_wall_thickness, T_gas, T_coolant, ablative_material, ablative_thickness)
          -

          Combined regenerative and ablative cooling thermal circuit. -q is per unit length along the nozzle wall (axially) - positive when heat is flowing to the coolant. -q_Adot is the heat flux per unit area along the nozzle wall. -Uses the idea of thermal circuits and resistances - we have three resistors in series.

          -
          -
          Parameters
          -
            -

          • r (float) – Radius to the contour of the engine (m)

            • -

          • h_gas (float) – Gas side convective heat transfer coefficient

            • -

          • h_coolant (float) – Coolant side convective heat transfer coefficient

            • -

          • wall_material (Material) – Material object for the inner wall, needed for thermal conductivity

            • -

          • inner_wall_thickness (float) – Thickness of the inner wall at x position (m)

            • -

          • T_gas (float) – Free stream gas temperature (K)

            • -

          • T_coolant (float) – Coolant temperature (K)

            • -

          • ablative_material (Material) – Material object for the ablative material, needed for thermal conductivity

            • -

          • ablative_thickness (float) – Thickness of the ablative material (m)

            • -
          -
          -
          Returns
          -

          q_dot, R_gas, R_ablative, R_wall, R_coolant

          -
          -
          Return type
          -

          float, float, float, float, float

          -
          -
          -

          -
          -steady_heating_analysis(number_of_points=1000, h_gas_model='3', h_coolant_model='2', to_json='heating_output.json')
          +steady_heating_analysis(number_of_points=1000, h_gas_model='bartz-sigma', h_coolant_model='sieder-tate', to_json='heating_output.json')

          Steady state heating analysis. Can be used for regenarative cooling, or combined regenerative and ablative cooling.

          Parameters

          • number_of_points (int, optional) – Number of discrete points to divide the engine into. Defaults to 1000.

            • -

          • h_gas_model (str, optional) – Equation to use for the gas side convective heat transfer coefficients. Options are ‘1’, ‘2’ and ‘3’. Defaults to “3”.

            • -

          • h_coolant_model (str, optional) – Equation to use for the coolant side convective heat transfer coefficients. Options are ‘1’, ‘2’ and ‘3’. Defaults to “2”.

            • +

          • h_gas_model (str, optional) – Equation to use for the gas side convective heat transfer coefficients. Options are ‘rpe’, ‘bartz’ and ‘bartz-sigma’. Defaults to “bartz-sigma”.

            • +

          • h_coolant_model (str, optional) – Equation to use for the coolant side convective heat transfer coefficients. Options are ‘rpe’, ‘sieder-tate’ and ‘dittus-boelter’. Defaults to “sieder-tate”.

          • to_json (str or bool, optional) – Directory to export a .JSON file to, containing simulation results. If False, no .JSON file is saved. Defaults to ‘heating_output.json’.

          Note

          -

          See the bamboo.cooling module for explanations of each h_gas and h_coolant option. Defaults are Bartz (using sigma correlation) for gas side, and Sieder-Tate for coolant side. These are believed to be the most accurate.

          +

          See the bamboo.cooling module for details of each h_gas and h_coolant option. Defaults are Bartz (using sigma correlation) for gas side, and Sieder-Tate for coolant side. These are believed to be the most accurate.

          +
          +
          +

          Note

          +

          Questionable decisions (especially when using mixtures) were made when implementing the Sieder-Tate method with wall temperature > coolant boiling temperature. Ideally these should be looked into in more detail.

          Returns
          @@ -1367,7 +1298,6 @@

          Welcome to Bamboo’s documentation!

          Note

          -

          This function is outdated and does not incorporate many new features that have been added to Bamboo.

          +

          This function is outdated and likely no longer functional, as it does not incorporate many new features that have been added to Bamboo.

          Parameters
          @@ -1491,8 +1421,8 @@

          Welcome to Bamboo’s documentation! -
          -bamboo.cooling.h_gas_1(D, M, T, rho, gamma, R, mu, k, Pr)
          +
          +bamboo.cooling.h_gas_rpe(D, M, T, rho, gamma, R, mu, k, Pr)

          Get the convective heat transfer coefficient on the gas side. Uses Eqn (8-22) on page 312 or RPE 7th edition (Reference [2]). I believe this is just a form of the Dittius-Boelter equation.

          Note

          @@ -1522,8 +1452,8 @@

          Welcome to Bamboo’s documentation! -
          -bamboo.cooling.h_gas_2(D, cp_inf, mu_inf, Pr_inf, rho_inf, v_inf, rho_am, mu_am, mu0)
          +
          +bamboo.cooling.h_gas_bartz(D, cp_inf, mu_inf, Pr_inf, rho_inf, v_inf, rho_am, mu_am, mu0)

          Bartz equation, using Equation (8-23) from page 312 of RPE 7th edition (Reference [2]). ‘am’ refers to the gas being at the ‘arithmetic mean’ of the wall and freestream temperatures.

          @@ -1550,8 +1480,8 @@

          Welcome to Bamboo’s documentation! -
          -bamboo.cooling.h_gas_3(c_star, At, A, pc, Tc, M, Tw, mu, cp, gamma, Pr)
          +
          +bamboo.cooling.h_gas_bartz_sigma(c_star, At, A, pc, Tc, M, Tw, mu, cp, gamma, Pr)

          Bartz heat transfer equation using the sigma correlation, from Reference [6].

          Parameters
          @@ -1579,8 +1509,8 @@

          Welcome to Bamboo’s documentation! -
          -bamboo.cooling.h_coolant_1(A, D, mdot, mu, k, c_bar, rho)
          +
          +bamboo.cooling.h_coolant_rpe(A, D, mdot, mu, k, c_bar, rho)

          Get the convective heat transfer coefficient for the coolant side. Uses the equation from page 317 of RPE 7th edition (Reference [2]).

          @@ -1605,8 +1535,8 @@

          Welcome to Bamboo’s documentation! -
          -bamboo.cooling.h_coolant_2(rho, V, D, mu_bulk, mu_wall, Pr, k)
          +
          +bamboo.cooling.h_coolant_sieder_tate(rho, V, D, mu_bulk, mu_wall, Pr, k)

          Sieder-Tate equation for convective heat transfer coefficient.

          Parameters
          @@ -1630,8 +1560,8 @@

          Welcome to Bamboo’s documentation! -
          -bamboo.cooling.h_coolant_3(rho, V, D, mu, Pr, k)
          +
          +bamboo.cooling.h_coolant_dittus_boelter(rho, V, D, mu, Pr, k)

          Dittus-Boelter equation for convective heat transfer coefficient.

          Parameters
          @@ -1958,11 +1888,49 @@

          Welcome to Bamboo’s documentation! +
          +class bamboo.cooling.ThermalCircuit(T1, T2, R)
          +

          Bases: object

          +

          Class for solving thermal circuits.

          +
          +
          Parameters
          +
            +

          • T1 (float) – Temperature at start

            • +

          • T2 (float) – Temperature at end

            • +

          • R (list) – List of resistances between T1 and T2

            • +
          +
          +
          +
          +
          +Qdot
          +

          Heat transfer rate (positive in the direction of T1 –> T2)

          +
          +
          Type
          +

          float

          +
          +
          +
          + +
          +
          +T
          +

          List of temperatures in between each resistance, including T1 and T2 at either end. i.e. [T1, …, T2].

          +
          +
          Type
          +

          list

          +
          +
          +
          + +

          +
          -class bamboo.cooling.CoolingJacket(geometry, inner_wall, inlet_T, inlet_p0, coolant_transport, mdot_coolant, xs=[- 1000, 1000], configuration='spiral', has_ablative=False, **kwargs)
          +class bamboo.cooling.CoolingJacket(inner_wall, inlet_T, inlet_p0, coolant_transport, mdot_coolant, xs=[- 1000, 1000], configuration='spiral', **kwargs)

          Bases: object

          -

          Container for cooling jacket information - e.g. for regenerative cooling.

          +

          Container for cooling jacket information - e.g. for regenerative cooling. All channels are assumed to have rectangular cross sections.

          Parameters
          -A(x=None, y=None)
          +A(x, y)

          Get coolant channel cross flow cross sectional area.

          Parameters

          • x (float, optional) – x position - does not currently affect anything.

            • -

          • y (float, optional) – The radius of the engine (m) (NOT the radius of the cooling channel). Only required for ‘vertical’ channels.

            • +

          • y (float, optional) – y distance from engine centreline to the inner wall of the cooling channel (m).

          Returns
          @@ -2009,34 +1976,40 @@

          Welcome to Bamboo’s documentation!
          -D(x=None, y=None)
          -

          Get the ‘effective diameter’ of the cooling channel. This is equal 4*hydraulic_radius, with hydraulic_radius = channel_area / channel_perimeter.

          +D(x, y) +

          Get the ‘effective diameter’ of the cooling channel. This is equal 4*channel_area / wetted_channel_perimeter.

          Parameters
            -

          • x (float, optional) – Axial position along the engine. This parameter may have no effect on the output. Defaults to None.

            • -

          • y (float, optional) – The radius of the engine (m) (NOT the radius of the cooling channel). Only required for ‘vertical’ channels.

            • +

          • x (float, optional) – Axial position along the engine.

            • +

          • y (float, optional) – y distance from engine centreline to the inner wall of the cooling channel (m).

          -
          Returns
          -

          Effective diameter (m)

          +
          +
          +

          Note

          +

          Not entirely sure if I calculated the perimeter correctly when including blockage ratio.

          +
          +
          +
          Returns
          +

          Effective diameter (m)

          -
          Return type
          -

          float

          +
          Return type
          +

          float

          -coolant_velocity(rho_coolant, x=None, y=None)
          +coolant_velocity(rho_coolant, x, y)

          Get coolant velocity using mdot = rho*V*A.

          Parameters

          • rho_coolant (float) – Coolant density (kg/m^3)

          • x (float, optional) – x position - does not currently affect anything.

            • -

          • y (float, optional) – Is The radius of the engine (m) (NOT the radius of the cooling channel). Only required for ‘vertical’ channels.

            • +

          • y (float, optional) – y distance from engine centreline to the inner wall of the cooling channel (m).

          Returns
          diff --git a/docs/objects.inv b/docs/objects.inv index e71c7a4..04c55d2 100644 Binary files a/docs/objects.inv and b/docs/objects.inv differ diff --git a/docs/searchindex.js b/docs/searchindex.js index 79b51f9..09149a9 100644 --- a/docs/searchindex.js +++ b/docs/searchindex.js @@ -1 +1 @@ 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to Bamboo\u2019s documentation!"],titleterms:{bamboo:[0,1,2,3,4],cool:[1,4],document:4,indic:4,main:[2,4],modul:4,plot:[3,4],tabl:4,welcom:4}}) \ No newline at end of file diff --git a/docs/source/_autosummary/bamboo.cooling.rst b/docs/source/_autosummary/bamboo.cooling.rst index eb039ab..4a182b1 100644 --- a/docs/source/_autosummary/bamboo.cooling.rst +++ b/docs/source/_autosummary/bamboo.cooling.rst @@ -14,12 +14,12 @@ .. autosummary:: black_body - h_coolant_1 - h_coolant_2 - h_coolant_3 - h_gas_1 - h_gas_2 - h_gas_3 + h_coolant_dittus_boelter + h_coolant_rpe + h_coolant_sieder_tate + h_gas_bartz + h_gas_bartz_sigma + h_gas_rpe @@ -32,6 +32,7 @@ Ablative CoolingJacket Material + ThermalCircuit TransportProperties diff --git a/docs/source/_autosummary/bamboo.main.rst b/docs/source/_autosummary/bamboo.main.rst index 578b99b..711da4d 100644 --- a/docs/source/_autosummary/bamboo.main.rst +++ b/docs/source/_autosummary/bamboo.main.rst @@ -24,7 +24,6 @@ p0 rao_theta_e rao_theta_n - show_conical_shape