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Parameter relationship calculations for T_070_360_SAL1744
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Porosity_EffectiveStress_Relation.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Porosity Relation\n",
"\n",
"We want to get an empirical realtionship between effective stress and sample porosity for the Whillan's till. Only increases in load will be considered, the unload/reload portions of the test will be removed since the sample is overconsolidated during those portions of the experiment. We seek to fit two forms for comparison:\n",
"\n",
"$$\\phi = \\phi_0e^{-\\zeta \\sigma_z'}$$\n",
"\n",
"$$\\phi = \\phi_0 + \\xi\\text{ ln}(\\sigma_z')$$\n",
"\n",
"* $\\phi$ = porosity\n",
"* $\\phi_0$ = porosity at zero stress\n",
"* $\\zeta$ = empirical constant (similar to Athy's relation)\n",
"* $\\xi$ = empirical constant\n",
"* $\\sigma_z'$ = effective stress [Pa]"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"scrolled": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from scipy.optimize import curve_fit\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"# Read in the data we are interested in\n",
"poro_df = pd.read_table('data_products/porosity.txt', usecols=[0,10,11,12])\n",
"poro_df.rename(columns={'# Stage':'Stage'}, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Stage</th>\n",
" <th>Porosity</th>\n",
" <th>VoidRatio</th>\n",
" <th>EffectiveStress</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>109</td>\n",
" <td>0.235880</td>\n",
" <td>0.308695</td>\n",
" <td>111.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>104</td>\n",
" <td>0.203516</td>\n",
" <td>0.255517</td>\n",
" <td>10008.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>100</td>\n",
" <td>0.222337</td>\n",
" <td>0.285904</td>\n",
" <td>5010.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>99</td>\n",
" <td>0.222045</td>\n",
" <td>0.285422</td>\n",
" <td>4912.71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>94</td>\n",
" <td>0.259752</td>\n",
" <td>0.350899</td>\n",
" <td>1010.24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>89</td>\n",
" <td>0.267209</td>\n",
" <td>0.364646</td>\n",
" <td>510.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>84</td>\n",
" <td>0.277802</td>\n",
" <td>0.384663</td>\n",
" <td>60.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>78</td>\n",
" <td>0.266366</td>\n",
" <td>0.363078</td>\n",
" <td>261.16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>72</td>\n",
" <td>0.261137</td>\n",
" <td>0.353431</td>\n",
" <td>512.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>66</td>\n",
" <td>0.257100</td>\n",
" <td>0.346076</td>\n",
" <td>760.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>60</td>\n",
" <td>0.253354</td>\n",
" <td>0.339323</td>\n",
" <td>1011.83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>54</td>\n",
" <td>0.255499</td>\n",
" <td>0.343182</td>\n",
" <td>911.51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>48</td>\n",
" <td>0.259289</td>\n",
" <td>0.350055</td>\n",
" <td>810.36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>42</td>\n",
" <td>0.264284</td>\n",
" <td>0.359219</td>\n",
" <td>712.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>36</td>\n",
" <td>0.266626</td>\n",
" <td>0.363561</td>\n",
" <td>612.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>30</td>\n",
" <td>0.269983</td>\n",
" <td>0.369831</td>\n",
" <td>510.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>24</td>\n",
" <td>0.273691</td>\n",
" <td>0.376825</td>\n",
" <td>409.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>16</td>\n",
" <td>0.277488</td>\n",
" <td>0.384060</td>\n",
" <td>310.16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>10</td>\n",
" <td>0.278995</td>\n",
" <td>0.386954</td>\n",
" <td>212.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0</td>\n",
" <td>0.280871</td>\n",
" <td>0.390571</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Stage Porosity VoidRatio EffectiveStress\n",
"0 109 0.235880 0.308695 111.08\n",
"1 104 0.203516 0.255517 10008.08\n",
"2 100 0.222337 0.285904 5010.08\n",
"3 99 0.222045 0.285422 4912.71\n",
"4 94 0.259752 0.350899 1010.24\n",
"5 89 0.267209 0.364646 510.23\n",
"6 84 0.277802 0.384663 60.25\n",
"7 78 0.266366 0.363078 261.16\n",
"8 72 0.261137 0.353431 512.10\n",
"9 66 0.257100 0.346076 760.19\n",
"10 60 0.253354 0.339323 1011.83\n",
"11 54 0.255499 0.343182 911.51\n",
"12 48 0.259289 0.350055 810.36\n",
"13 42 0.264284 0.359219 712.08\n",
"14 36 0.266626 0.363561 612.21\n",
"15 30 0.269983 0.369831 510.12\n",
"16 24 0.273691 0.376825 409.40\n",
"17 16 0.277488 0.384060 310.16\n",
"18 10 0.278995 0.386954 212.22\n",
"19 0 0.280871 0.390571 0.00"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display the data, so we can select the stages we want \n",
"# Stages with higher than before seen effective stresses\n",
"poro_df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"# Turn effective stresses from kPa to Pa\n",
"poro_df['EffectiveStress'] *= 1000"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"# Clip out just the stages we have selected\n",
"stages_to_use = np.array([10, 16, 24, 30, 36, 42, 48, 54, 60, 99, 100, 104])\n",
"poro_df = poro_df.loc[poro_df['Stage'].isin(stages_to_use)]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Stage</th>\n",
" <th>Porosity</th>\n",
" <th>VoidRatio</th>\n",
" <th>EffectiveStress</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>104</td>\n",
" <td>0.203516</td>\n",
" <td>0.255517</td>\n",
" <td>10008080</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>100</td>\n",
" <td>0.222337</td>\n",
" <td>0.285904</td>\n",
" <td>5010080</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>99</td>\n",
" <td>0.222045</td>\n",
" <td>0.285422</td>\n",
" <td>4912710</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>60</td>\n",
" <td>0.253354</td>\n",
" <td>0.339323</td>\n",
" <td>1011830</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>54</td>\n",
" <td>0.255499</td>\n",
" <td>0.343182</td>\n",
" <td>911510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>48</td>\n",
" <td>0.259289</td>\n",
" <td>0.350055</td>\n",
" <td>810360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>42</td>\n",
" <td>0.264284</td>\n",
" <td>0.359219</td>\n",
" <td>712080</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>36</td>\n",
" <td>0.266626</td>\n",
" <td>0.363561</td>\n",
" <td>612210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>30</td>\n",
" <td>0.269983</td>\n",
" <td>0.369831</td>\n",
" <td>510120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>24</td>\n",
" <td>0.273691</td>\n",
" <td>0.376825</td>\n",
" <td>409400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>16</td>\n",
" <td>0.277488</td>\n",
" <td>0.384060</td>\n",
" <td>310160</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>10</td>\n",
" <td>0.278995</td>\n",
" <td>0.386954</td>\n",
" <td>212220</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Stage Porosity VoidRatio EffectiveStress\n",
"1 104 0.203516 0.255517 10008080\n",
"2 100 0.222337 0.285904 5010080\n",
"3 99 0.222045 0.285422 4912710\n",
"10 60 0.253354 0.339323 1011830\n",
"11 54 0.255499 0.343182 911510\n",
"12 48 0.259289 0.350055 810360\n",
"13 42 0.264284 0.359219 712080\n",
"14 36 0.266626 0.363561 612210\n",
"15 30 0.269983 0.369831 510120\n",
"16 24 0.273691 0.376825 409400\n",
"17 16 0.277488 0.384060 310160\n",
"18 10 0.278995 0.386954 212220"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check to make sure it all worked\n",
"poro_df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# x values to compute fit curves at\n",
"x = np.linspace(0.11, 10e6, 100)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Exponential Relation\n",
"phi_0: 0.270781170164\n",
"zeta: -3.25101774361e-08\n"
]
}
],
"source": [
"# Fit the data with an exponential\n",
"lny = np.log(poro_df['Porosity'])\n",
"coeffs = np.polyfit(poro_df['EffectiveStress'], lny, 1)\n",
"\n",
"k = coeffs[0]\n",
"c = np.exp(coeffs[1])\n",
"\n",
"y_fit_exp = c * np.exp(k * x)\n",
"print \"Exponential Relation\"\n",
"print \"phi_0: \", c\n",
"print \"zeta: \", k"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log Relation\n",
"phi_0: 0.535913327595\n",
"xi: -0.0203988544134\n"
]
}
],
"source": [
"# Fit the data with a log relation\n",
"lnx = np.log(poro_df['EffectiveStress'])\n",
"coeffs = np.polyfit(lnx, poro_df['Porosity'], 1)\n",
"\n",
"b = coeffs[0]\n",
"a = coeffs[1]\n",
"\n",
"y_fit_log = a + b * np.log(x)\n",
"print \"Log Relation\"\n",
"print \"phi_0: \", a\n",
"print \"xi: \", b"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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wZpOnAJ38niJqcOLM+RvGz0BLYCQwHXgO2A90tNZ+5neph3CeUDoAp4g/F+ht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\nIiIiIiIpRAm6iIiIiEgK+f+vjJn05ZfUZwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ab59710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Setup figure and axes\n",
"# Generally plots is ~1.33x width to height (10,7.5 or 12,9)\n",
"fig = plt.figure(figsize=(12, 9))\n",
"ax1 = plt.subplot(111)\n",
"\n",
"# Set labels and tick sizes\n",
"ax1.set_xlabel(r'Effective Stress [MPa]', fontsize=18)\n",
"ax1.set_ylabel(r'Porosity', fontsize=18)\n",
"ax1.tick_params(axis='both', which='major', labelsize=16)\n",
"\n",
"# Plotting\n",
"ax1.plot(poro_df['EffectiveStress']/1e6, poro_df['Porosity'], 'ko')\n",
"ax1.plot(x/1e6, y_fit_exp, color='g', linestyle='--', linewidth=2)\n",
"ax1.plot(x/1e6, y_fit_log, color='b', linestyle='--', linewidth=2)\n",
"\n",
"# Set limits\n",
"ax1.set_xlim(0, 11)\n",
"ax1.set_ylim(0.15, 0.35)\n",
"\n",
"# Add fit text\n",
"ax1.text(1, 0.33, r'Exponential Fit', color='g', fontsize=20)\n",
"ax1.text(1, 0.315, r'Log Fit', color='b', fontsize=20)\n",
"\n",
"ax1.grid()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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