From 99bb41bc539cbbb898736e386848011f11079cf1 Mon Sep 17 00:00:00 2001 From: Yuan Li <43114076+liyuan1993@users.noreply.github.com> Date: Fri, 30 Nov 2018 12:25:34 -0500 Subject: [PATCH 1/6] Some sensitivities of different parameters Calculated heat transfer coefficient of Propane. You can use this to calculate the heat rate. --- Pipeline Icing.ipynb | 252 ++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 249 insertions(+), 3 deletions(-) diff --git a/Pipeline Icing.ipynb b/Pipeline Icing.ipynb index e149316..156c168 100644 --- a/Pipeline Icing.ipynb +++ b/Pipeline Icing.ipynb @@ -2,16 +2,16 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.4242161224603258" + "-3.6136086249067567" ] }, - "execution_count": 5, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -46,6 +46,252 @@ "Temp_Surface" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "95e9f678d605479b816bb502bcaa5e79", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-187.0), Output()), …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "56af15d7af53473e84e69e5b294aa21f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-150.0), Output()), …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bbd1c33b0d8e49afb58b12abf4d5136c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-187.0), Output()), …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8b6b06fc82e741dd9c64f38c4feeb1f1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-187.0), Output()), …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3fa14e06f6e349ee8bccc5006cd10597", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-187.0), Output()), …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from ipywidgets import widgets, interact, fixed\n", + "%matplotlib inline\n", + "import seaborn as sbn\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import scipy\n", + "import scipy.interpolate\n", + "\n", + "\n", + "@interact (temperature=widgets.FloatSlider(value=-120, min=-187, max=-45))\n", + "def Prandtl_liquidPP(temperature):\n", + " T = [-187.7, -173, -153, -133, -113.2, -93.2, -73.2, -53.2, -42.4]\n", + " Pr_PP= [99.44, 35.88, 15.11, 8.89, 6.29, 4.94, 4.14, 3.63, 3.43]\n", + " interpolation = scipy.interpolate.interp1d(T, Pr_PP, kind=\"quadratic\")\n", + " return float(interpolation(temperature))\n", + "\n", + "@interact (temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", + "def ThermalConductivity_liquidPP(temperature):\n", + " T = [-150, -125, -100, -75, -50,-44]\n", + " k_PP= 1e-3*np.array ([193, 179, 164, 149,134, 129])\n", + " interpolation = scipy.interpolate.interp1d(T, k_PP, kind=\"quadratic\")\n", + " return float(interpolation(temperature))\n", + "\n", + "@interact (temperature=widgets.FloatSlider(value=-120, min=-187, max=-45))\n", + "#Density calculation\n", + "def density_liquidPP(temperature):\n", + " T = [-187.7, -173, -153, -133, -113.2, -93.2, -73.2, -53.2, -42.4]\n", + " rho_PP = [733.1, 718.2, 697.8, 677.6, 657.3, 636.7, 615.5, 593.5 ,581.2]\n", + " interpolation = scipy.interpolate.interp1d(T, rho_PP, kind=\"quadratic\")\n", + " return float(interpolation(temperature))\n", + "\n", + "@interact (temperature=widgets.FloatSlider(value=-120, min=-187, max=-45))\n", + "#Dynamic Viscosity Calculation\n", + "def DynamicViscosity_liquidPP(temperature):\n", + " T = [-187.7, -173, -153, -133, -113.2, -93.2, -73.2, -53.2, -42.4]\n", + " miu_D_PP = 1e-6*np.array([10970, 3778, 1502, 822.9, 535.5, 382.5, 288.2, 224.3,197.9])\n", + " interpolation = scipy.interpolate.interp1d(T, miu_D_PP, kind=\"quadratic\")\n", + " return float(interpolation(temperature))\n", + "@interact (temperature=widgets.FloatSlider(value=-120, min=-187, max=-45))\n", + "#Kinematic Viscosity Calculation\n", + "def KinematicViscosity_liquidPP(temperature):\n", + " T = [-187.7, -173, -153, -133, -113.2, -93.2, -73.2, -53.2, -42.4]\n", + " miu_K_PP = 1e-6*np.array([14.72, 5.26, 2.152, 1.214, 0.8147, 0.6008,0.4683, 0.3779, 0.3405])\n", + " interpolation = scipy.interpolate.interp1d(T, miu_K_PP, kind=\"quadratic\")\n", + " return float(interpolation(temperature))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "76733b82e3be470ab6fe8a04fb875362", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=10.0, description='velocity', max=20.0), FloatSlider(value=1.0, descri…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"Reynolds Number calculation\"\n", + "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", + " temperature=widgets.FloatSlider(value=-120, min=-187, max=-45))\n", + "def Reynolds_liquidPP(velocity, lengthscale, temperature):\n", + " Reynolds_liquidPP = density_liquidPP(temperature) * velocity * lengthscale / DynamicViscosity_liquidPP(temperature)\n", + " return Reynolds_liquidPP" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e7ad5f3be69b454cab43cea2aa42d603", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=10.0, description='velocity', max=20.0), FloatSlider(value=1.0, descri…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", + " temperature=widgets.FloatSlider(value=-120, min=-187, max=-45))\n", + "def Nusselt_liquidPP(velocity, lengthscale, temperature):\n", + " Re_PP = Reynolds_liquidPP(velocity, lengthscale, temperature)\n", + " Pr_PP = Prandtl_liquidPP(temperature)\n", + " Nusselt_liquidPP = 0.023 * Re_PP**(4/5) * Pr_PP**0.4\n", + " return Nusselt_liquidPP" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b884e22381aa4fc29ea71c12e13c053f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=10.0, description='velocity', max=20.0), FloatSlider(value=1.0, descri…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Boundary layer thickness of Propane\n", + "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", + " temperature=widgets.FloatSlider(value=-120, min=-187, max=-45))\n", + "def Boundary_PP(velocity, lengthscale, temperature):\n", + " mu_k_PP = KinematicViscosity_liquidPP (temperature)\n", + " Pr_PP = Prandtl_liquidPP(temperature)\n", + " Boundary_PP = 0.37 * lengthscale / (velocity*lengthscale/mu_k_PP)**0.2\n", + " return Boundary_PP" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a1f707ae90af4ac7a6766a772f2bc333", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=10.0, description='velocity', max=20.0), FloatSlider(value=1.0, descri…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Heat transfer coeffecient of Propane from -150 C to -45 C\n", + "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", + " temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", + "def heat_transfer_coefficient_PP (velocity, lengthscale, temperature):\n", + " Nu_PP = Nusselt_liquidPP(velocity, lengthscale, temperature)\n", + " k_PP = ThermalConductivity_liquidPP(temperature)\n", + " L = Boundary_PP(velocity, lengthscale, temperature)\n", + " h_PP = Nu_PP * k_PP / lengthscale\n", + " return h_PP" + ] + }, { "cell_type": "code", "execution_count": null, From eda46c940fd35dcacd548436fabc8521483f8c7a Mon Sep 17 00:00:00 2001 From: Yuan Li <43114076+liyuan1993@users.noreply.github.com> Date: Sun, 2 Dec 2018 14:39:37 -0500 Subject: [PATCH 2/6] Tried Morris Method and Scatter Plots. Tried Morris Method and Scatter Plots for heat coefficient. But there're errors: only size-1 arrays can be converted to Python scalars. --- Pipeline Icing.ipynb | 242 +++++++++++++++++++++++++++++++++++++------ 1 file changed, 212 insertions(+), 30 deletions(-) diff --git a/Pipeline Icing.ipynb b/Pipeline Icing.ipynb index 156c168..65139d1 100644 --- a/Pipeline Icing.ipynb +++ b/Pipeline Icing.ipynb @@ -48,18 +48,18 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "95e9f678d605479b816bb502bcaa5e79", + "model_id": "1f7bc2411dca48228684172e0af85e23", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-187.0), Output()), …" + "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-150.0), Output()), …" ] }, "metadata": {}, @@ -68,7 +68,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "56af15d7af53473e84e69e5b294aa21f", + "model_id": "d7c4f0d954d54091aee5582f8cc07d9a", "version_major": 2, "version_minor": 0 }, @@ -82,12 +82,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bbd1c33b0d8e49afb58b12abf4d5136c", + "model_id": "d07196c02dee4ac6971f1ed67854cd47", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-187.0), Output()), …" + "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-150.0), Output()), …" ] }, "metadata": {}, @@ -96,12 +96,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8b6b06fc82e741dd9c64f38c4feeb1f1", + "model_id": "dfca2b159ef24ecc89a14fa142b4ef49", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-187.0), Output()), …" + "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-150.0), Output()), …" ] }, "metadata": {}, @@ -110,12 +110,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3fa14e06f6e349ee8bccc5006cd10597", + "model_id": "b7dcf5c116554a3d99c9970758431903", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-187.0), Output()), …" + "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-150.0), Output()), …" ] }, "metadata": {}, @@ -132,7 +132,7 @@ "import scipy.interpolate\n", "\n", "\n", - "@interact (temperature=widgets.FloatSlider(value=-120, min=-187, max=-45))\n", + "@interact (temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", "def Prandtl_liquidPP(temperature):\n", " T = [-187.7, -173, -153, -133, -113.2, -93.2, -73.2, -53.2, -42.4]\n", " Pr_PP= [99.44, 35.88, 15.11, 8.89, 6.29, 4.94, 4.14, 3.63, 3.43]\n", @@ -146,7 +146,7 @@ " interpolation = scipy.interpolate.interp1d(T, k_PP, kind=\"quadratic\")\n", " return float(interpolation(temperature))\n", "\n", - "@interact (temperature=widgets.FloatSlider(value=-120, min=-187, max=-45))\n", + "@interact (temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", "#Density calculation\n", "def density_liquidPP(temperature):\n", " T = [-187.7, -173, -153, -133, -113.2, -93.2, -73.2, -53.2, -42.4]\n", @@ -154,14 +154,15 @@ " interpolation = scipy.interpolate.interp1d(T, rho_PP, kind=\"quadratic\")\n", " return float(interpolation(temperature))\n", "\n", - "@interact (temperature=widgets.FloatSlider(value=-120, min=-187, max=-45))\n", + "@interact (temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", "#Dynamic Viscosity Calculation\n", "def DynamicViscosity_liquidPP(temperature):\n", " T = [-187.7, -173, -153, -133, -113.2, -93.2, -73.2, -53.2, -42.4]\n", " miu_D_PP = 1e-6*np.array([10970, 3778, 1502, 822.9, 535.5, 382.5, 288.2, 224.3,197.9])\n", " interpolation = scipy.interpolate.interp1d(T, miu_D_PP, kind=\"quadratic\")\n", " return float(interpolation(temperature))\n", - "@interact (temperature=widgets.FloatSlider(value=-120, min=-187, max=-45))\n", + "\n", + "@interact (temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", "#Kinematic Viscosity Calculation\n", "def KinematicViscosity_liquidPP(temperature):\n", " T = [-187.7, -173, -153, -133, -113.2, -93.2, -73.2, -53.2, -42.4]\n", @@ -172,13 +173,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "76733b82e3be470ab6fe8a04fb875362", + "model_id": "ae883b0356e247e19aeca079cf6b5cf2", "version_major": 2, "version_minor": 0 }, @@ -192,22 +193,25 @@ ], "source": [ "\"Reynolds Number calculation\"\n", - "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", - " temperature=widgets.FloatSlider(value=-120, min=-187, max=-45))\n", + "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), \n", + " lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", + " temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", "def Reynolds_liquidPP(velocity, lengthscale, temperature):\n", - " Reynolds_liquidPP = density_liquidPP(temperature) * velocity * lengthscale / DynamicViscosity_liquidPP(temperature)\n", + " rho_PP = density_liquidPP(temperature)\n", + " miu_D_PP = DynamicViscosity_liquidPP(temperature)\n", + " Reynolds_liquidPP = rho_PP * velocity * lengthscale / miu_D_PP\n", " return Reynolds_liquidPP" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e7ad5f3be69b454cab43cea2aa42d603", + "model_id": "40863332a9c84e56a8e23fb1aeae8ce1", "version_major": 2, "version_minor": 0 }, @@ -220,8 +224,9 @@ } ], "source": [ - "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", - " temperature=widgets.FloatSlider(value=-120, min=-187, max=-45))\n", + "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), \n", + " lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", + " temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", "def Nusselt_liquidPP(velocity, lengthscale, temperature):\n", " Re_PP = Reynolds_liquidPP(velocity, lengthscale, temperature)\n", " Pr_PP = Prandtl_liquidPP(temperature)\n", @@ -231,13 +236,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b884e22381aa4fc29ea71c12e13c053f", + "model_id": "e30f92c82a7648ca8a326cb74f00157a", "version_major": 2, "version_minor": 0 }, @@ -251,8 +256,9 @@ ], "source": [ "#Boundary layer thickness of Propane\n", - "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", - " temperature=widgets.FloatSlider(value=-120, min=-187, max=-45))\n", + "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), \n", + " lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", + " temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", "def Boundary_PP(velocity, lengthscale, temperature):\n", " mu_k_PP = KinematicViscosity_liquidPP (temperature)\n", " Pr_PP = Prandtl_liquidPP(temperature)\n", @@ -262,13 +268,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a1f707ae90af4ac7a6766a772f2bc333", + "model_id": "99195fd57f6d47a79aefcb661d415bd4", "version_major": 2, "version_minor": 0 }, @@ -282,7 +288,8 @@ ], "source": [ "#Heat transfer coeffecient of Propane from -150 C to -45 C\n", - "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", + "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), \n", + " lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", " temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", "def heat_transfer_coefficient_PP (velocity, lengthscale, temperature):\n", " Nu_PP = Nusselt_liquidPP(velocity, lengthscale, temperature)\n", @@ -292,6 +299,181 @@ " return h_PP" ] }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "#Morris Method\n", + "import SALib\n", + "from SALib.sample import morris as ms\n", + "from SALib.analyze import morris as ma\n", + "from SALib.plotting import morris as mp" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "morris_problem = {\n", + " # There are three variables\n", + " 'num_vars': 3,\n", + " 'names': ['velocity', 'lengthscale', 'temperature'],\n", + " # These are their ranges over which we'll move the variables\n", + " 'bounds': [[0, 20], #velocity (m/s)\n", + " [1, 2], # length (m)\n", + " [-150, -45], # temperature (C) \n", + " ],\n", + " 'groups': None\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(40000, 3)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_levels = 4\n", + "grid_jump = 2\n", + "trajectories = int(1e4)\n", + "sample = ms.sample(morris_problem, trajectories, num_levels, grid_jump)\n", + "sample.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 40000)\n", + "[[ 6.66666667 6.66666667 20. ... 13.33333333\n", + " 13.33333333 0. ]\n", + " [ 1.33333333 2. 2. ... 1.66666667\n", + " 1. 1. ]\n", + " [ -45. -45. -45. ... -150.\n", + " -150. -150. ]]\n" + ] + } + ], + "source": [ + "print(sample.T.shape)\n", + "print(sample.T)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "only size-1 arrays can be converted to Python scalars", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheat_transfer_coefficient_PP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mheat_transfer_coefficient_PP\u001b[0;34m(velocity, lengthscale, temperature)\u001b[0m\n\u001b[1;32m 4\u001b[0m temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n\u001b[1;32m 5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mheat_transfer_coefficient_PP\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mNu_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNusselt_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mk_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mThermalConductivity_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mL\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBoundary_PP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mNusselt_liquidPP\u001b[0;34m(velocity, lengthscale, temperature)\u001b[0m\n\u001b[1;32m 3\u001b[0m temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n\u001b[1;32m 4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mNusselt_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mRe_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mReynolds_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mPr_PP\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mrho_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdensity_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mmiu_D_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDynamicViscosity_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mReynolds_liquidPP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrho_PP\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mvelocity\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlengthscale\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mmiu_D_PP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mdensity_liquidPP\u001b[0;34m(temperature)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mrho_PP\u001b[0m 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\u001b[0;32mreturn\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0minteract\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwidgets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFloatSlider\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m120\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m45\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: only size-1 arrays can be converted to Python scalars" + ] + } + ], + "source": [ + "output = heat_transfer_coefficient_PP(*sample.T)\n", + "print(output.shape)\n", + "print(output)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "only size-1 arrays can be converted to Python scalars", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheat_transfer_coefficient_PP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m Si = ma.analyze(morris_problem, \n\u001b[1;32m 3\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint_to_console\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mheat_transfer_coefficient_PP\u001b[0;34m(velocity, lengthscale, temperature)\u001b[0m\n\u001b[1;32m 4\u001b[0m temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n\u001b[1;32m 5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mheat_transfer_coefficient_PP\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mNu_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNusselt_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mk_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mThermalConductivity_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mL\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBoundary_PP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mNusselt_liquidPP\u001b[0;34m(velocity, lengthscale, temperature)\u001b[0m\n\u001b[1;32m 3\u001b[0m temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n\u001b[1;32m 4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mNusselt_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mRe_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mReynolds_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mPr_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPrandtl_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mNusselt_liquidPP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.023\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mRe_PP\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mPr_PP\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m0.4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mReynolds_liquidPP\u001b[0;34m(velocity, lengthscale, temperature)\u001b[0m\n\u001b[1;32m 4\u001b[0m temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n\u001b[1;32m 5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mReynolds_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mrho_PP\u001b[0m \u001b[0;34m=\u001b[0m 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+ } + ], + "source": [ + "output = heat_transfer_coefficient_PP(*sample.T)\n", + "Si = ma.analyze(morris_problem, \n", + " sample, \n", + " output, \n", + " print_to_console=False, \n", + " grid_jump=grid_jump, \n", + " num_levels=num_levels)\n", + "print(\"{:20s} {:>7s} {:>7s} {:>7s}\".format(\"Name\", \"mu\", \"mu_star\", \"sigma\"))\n", + "for name, s1, st, mean in zip(morris_problem['names'], Si['mu'], Si['mu_star'], Si['sigma']):\n", + " print(\"{:20s} {:=7.2f} {:=7.2f} {:=7.2f}\".format(name, s1, st, mean))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Scatter plots\n", + "number_sims = 1000\n", + "mc_velocity = np.random.uniform(0, 20, number_sims)\n", + "mc_lengthscale = np.random.uniform(1, 2, number_sims)\n", + "mc_temperature = np.random.uniform(-150, -45, number_sims)\n", + "data = np.array((mc_velocity, mc_lengthscale, mc_temperature))\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for thing in data:\n", + " print(thing.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "heat_transfer_coefficient_PP(data[0], data[1], data[2])" + ] + }, { "cell_type": "code", "execution_count": null, From 9ff159210388e7786a6ea22d2c7377ba4f920271 Mon Sep 17 00:00:00 2001 From: Yuan Li <43114076+liyuan1993@users.noreply.github.com> Date: Thu, 6 Dec 2018 08:28:30 -0500 Subject: [PATCH 3/6] changed the equation and morris work --- Pipeline Icing.ipynb | 536 ++++++++++++++++++------------------------- 1 file changed, 226 insertions(+), 310 deletions(-) diff --git a/Pipeline Icing.ipynb b/Pipeline Icing.ipynb index 65139d1..736b07c 100644 --- a/Pipeline Icing.ipynb +++ b/Pipeline Icing.ipynb @@ -2,120 +2,30 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 15, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "-3.6136086249067567" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from math import log\n", - "Fluid_temp=-10 #F\n", - "Air_temp=25 #C\n", - "Fluid_h=10 #w/m2k\n", - "Water_h=2250 #w/m2k\n", - "Air_h=50 #w/m2k\n", - "Pipe_k=16 #w/m*k\n", - "Pipe_Length=1 #m\n", - "L=Pipe_Length\n", - "Pipe_Radius_Internal=4 #cm\n", - "Ri=Pipe_Radius_Internal\n", - "Pipe_Radius_External=4.2 #cm\n", - "Ro=Pipe_Radius_External\n", - "Tf=Fluid_temp\n", - "Ice_Radius=100 #cm\n", - "Rice=Ice_Radius\n", - "Resistance_fluid= 1/(Fluid_h)*(2)*(3.14159)*(Ri)*(L)\n", - "Resistance_Pipe=(log((Ro/Ri),2.71828))/(2*3.14*Pipe_k*L)\n", - "Resistance_Iceonwall= 1 / (Water_h)*(2)*(3.14159)*(Ro)*(L)\n", - "Resistance_IceExterior= 1 / (Water_h)*(2)*(3.14159)*(Rice)*(L)\n", - "Resistance_AirandIce = 1 / (Air_h)*(2)*(3.14159)*(Rice)*(L)\n", - "Resistance_Total=Resistance_fluid + Resistance_Pipe + Resistance_Iceonwall + Resistance_IceExterior + Resistance_AirandIce\n", - "UA=1/Resistance_Total\n", - "q=(UA)*(Fluid_temp-Air_temp)\n", - "UA_final=1/Resistance_AirandIce\n", - "Temp_Surface=(q/(UA_final))+(Air_temp)\n", - "Temp_Surface" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1f7bc2411dca48228684172e0af85e23", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-150.0), Output()), …" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d7c4f0d954d54091aee5582f8cc07d9a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-150.0), Output()), …" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d07196c02dee4ac6971f1ed67854cd47", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-150.0), Output()), …" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "dfca2b159ef24ecc89a14fa142b4ef49", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-150.0), Output()), …" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "Temperatures are in degrees C\n", + "h is in w/m2k\n", + "k is in w2/m2k\n", + "length is in meters\n", + "pipe radius is in cm\n", + "time is in minutes\n" + ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b7dcf5c116554a3d99c9970758431903", + "model_id": "020c94fb8f1c48a0aa9fc01faafecc8b", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-150.0), Output()), …" + "interactive(children=(FloatSlider(value=-10.0, description='Fluid_Temp', max=50.0, min=-273.0, step=1.0), Floa…" ] }, "metadata": {}, @@ -123,163 +33,181 @@ } ], "source": [ - "from ipywidgets import widgets, interact, fixed\n", + "from math import log\n", + "from math import sqrt\n", + "from ipywidgets import widgets, interact\n", + "from IPython.display import display\n", + "%matplotlib inline\n", + "import seaborn as sbn\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from IPython.core.pylabtools import figsize\n", + "from ipywidgets import widgets, interact\n", + "from IPython.display import display\n", "%matplotlib inline\n", "import seaborn as sbn\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import scipy\n", - "import scipy.interpolate\n", + "from IPython.core.pylabtools import figsize\n", + "figsize(12, 10)\n", + "sbn.set_context(\"talk\", font_scale=1)\n", + "figsize(12, 10)\n", + "sbn.set_context(\"talk\", font_scale=1)\n", + "def Rice(Fluid_temp,\n", + " Air_temp,\n", + " Fluid_h,\n", + " Pipe_k,\n", + " Pipe_Radius_Internal,\n", + " Pipe_Radius_External,\n", + " Pipe_Length=1,\n", + " Water_h=2250,\n", + " Air_h=50,\n", + " Tsurf=273.15\n", + " ):\n", "\n", + " Tair=(Air_temp+273.15)\n", + " WA_h=Air_h/Water_h\n", + " L=Pipe_Length\n", + " Ri=(Pipe_Radius_Internal*0.01) #m\n", + " Ro=(Pipe_Radius_External*0.01) #m\n", + " Tfluid=(Fluid_temp+273.15)\n", + " Resistance_fluid = 1/(Fluid_h)*(2)*(3.14159)*(Ri)*(L)\n", + " Resistance_Pipe=(Ro-Ri)/(2*3.14*Pipe_k)\n", + " Resistance_Iceonwall= 1 / (Water_h)*(2)*(3.14159)*(Ro)*(L)\n", + " Resistance_Sub=Resistance_fluid+Resistance_Pipe+Resistance_Iceonwall\n", "\n", - "@interact (temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", - "def Prandtl_liquidPP(temperature):\n", - " T = [-187.7, -173, -153, -133, -113.2, -93.2, -73.2, -53.2, -42.4]\n", - " Pr_PP= [99.44, 35.88, 15.11, 8.89, 6.29, 4.94, 4.14, 3.63, 3.43]\n", - " interpolation = scipy.interpolate.interp1d(T, Pr_PP, kind=\"quadratic\")\n", - " return float(interpolation(temperature))\n", + " Rice=-100*((WA_h+1)*(Tsurf-Tair)-(Tfluid-Tair))/(((Resistance_Sub)+(Tsurf-Tair))*(Air_h*2*3.14159*L))\n", + " return Rice\n", + "def Rate(Air_temp,\n", + " Fluid_temp,\n", + " time,\n", + " k=2.22,\n", + " roe=1000,\n", + " Hia=10,\n", + " fusion=334 #j\n", + " ):\n", "\n", - "@interact (temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", - "def ThermalConductivity_liquidPP(temperature):\n", - " T = [-150, -125, -100, -75, -50,-44]\n", - " k_PP= 1e-3*np.array ([193, 179, 164, 149,134, 129])\n", - " interpolation = scipy.interpolate.interp1d(T, k_PP, kind=\"quadratic\")\n", - " return float(interpolation(temperature))\n", + " Tair=(Air_temp+273.15)\n", + " Tfluid=(Fluid_temp+273.15)\n", + " t=(time*30)\n", "\n", - "@interact (temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", - "#Density calculation\n", - "def density_liquidPP(temperature):\n", - " T = [-187.7, -173, -153, -133, -113.2, -93.2, -73.2, -53.2, -42.4]\n", - " rho_PP = [733.1, 718.2, 697.8, 677.6, 657.3, 636.7, 615.5, 593.5 ,581.2]\n", - " interpolation = scipy.interpolate.interp1d(T, rho_PP, kind=\"quadratic\")\n", - " return float(interpolation(temperature))\n", + " Rate=sqrt(((2*k)/(roe*fusion))*(abs(Tfluid-Tair))*t+((k/Hia)*(k/Hia)))-(k/Hia)\n", + " return Rate\n", "\n", - "@interact (temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", - "#Dynamic Viscosity Calculation\n", - "def DynamicViscosity_liquidPP(temperature):\n", - " T = [-187.7, -173, -153, -133, -113.2, -93.2, -73.2, -53.2, -42.4]\n", - " miu_D_PP = 1e-6*np.array([10970, 3778, 1502, 822.9, 535.5, 382.5, 288.2, 224.3,197.9])\n", - " interpolation = scipy.interpolate.interp1d(T, miu_D_PP, kind=\"quadratic\")\n", - " return float(interpolation(temperature))\n", + "print (\"Temperatures are in degrees C\\nh is in w/m2k\\nk is in w2/m2k\\nlength is in meters\\npipe radius is in cm\\ntime is in minutes\")\n", + "@interact(Fluid_Temp=widgets.FloatSlider(value=-10, min=-273, max=50, step=1), \n", + " Air_Temp=widgets.FloatSlider(value=25, min=10, max=100, step=1),\n", + " h_of_Fluid=widgets.FloatSlider(value=10, min=0.1, max=100, step=.1), \n", + " k_of_Pipe=widgets.FloatSlider(value=16, min=0.1, max=100, step=0.1), \n", + " Pipe_R_int=widgets.FloatSlider(value=1, min=0.1, max=20, step=0.1),\n", + " Pipe_R_ext=widgets.FloatSlider(value=1, min=0.1, max=20, step=0.1),\n", + " time=widgets.FloatSlider(value=5, min=0, max=100, step=.5))\n", + "def final(Fluid_Temp,Air_Temp,h_of_Fluid,k_of_Pipe,Pipe_R_int,Pipe_R_ext,time):\n", + " ball = Rice(Fluid_temp = Fluid_Temp,\n", + " Air_temp = Air_Temp,\n", + " Fluid_h = h_of_Fluid,\n", + " Pipe_k = k_of_Pipe,\n", + " Pipe_Radius_Internal = Pipe_R_int,\n", + " Pipe_Radius_External = Pipe_R_ext,\n", + " Pipe_Length = 1,\n", + " Water_h=2250,\n", + " Air_h=50,\n", + " Tsurf=273.15\n", + " )\n", + " growth = Rate(Fluid_temp = Fluid_Temp,\n", + " Air_temp = Air_Temp,\n", + " time = time,\n", + " k=2.22,\n", + " roe=1000,\n", + " Hia=10,\n", + " fusion=334\n", + " )\n", "\n", - "@interact (temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", - "#Kinematic Viscosity Calculation\n", - "def KinematicViscosity_liquidPP(temperature):\n", - " T = [-187.7, -173, -153, -133, -113.2, -93.2, -73.2, -53.2, -42.4]\n", - " miu_K_PP = 1e-6*np.array([14.72, 5.26, 2.152, 1.214, 0.8147, 0.6008,0.4683, 0.3779, 0.3405])\n", - " interpolation = scipy.interpolate.interp1d(T, miu_K_PP, kind=\"quadratic\")\n", - " return float(interpolation(temperature))" + " \n", + " if Pipe_R_int > Pipe_R_ext:\n", + " return print(\"The internal radius cannot be larger that the external radius\")\n", + " if ball>0 and growth0 and growth>=ball:\n", + " return print(\"The current thickness of the ice is {} cm\".format(round(ball, 2)),\"\\nThe max thickness of the ice is {} cm\".format(round(ball, 2)))\n", + " else:\n", + " return print(\"The thickness of the ice is 0 cm\")" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ae883b0356e247e19aeca079cf6b5cf2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(FloatSlider(value=10.0, description='velocity', max=20.0), FloatSlider(value=1.0, descri…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "\"Reynolds Number calculation\"\n", - "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), \n", - " lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", - " temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", - "def Reynolds_liquidPP(velocity, lengthscale, temperature):\n", - " rho_PP = density_liquidPP(temperature)\n", - " miu_D_PP = DynamicViscosity_liquidPP(temperature)\n", - " Reynolds_liquidPP = rho_PP * velocity * lengthscale / miu_D_PP\n", - " return Reynolds_liquidPP" + "number_sims = 1000\n", + "\n", + "# Make some random data in the correct ranges\n", + "mc_Fluid_Temp = np.random.uniform(2.3, 22, number_sims)\n", + "mc_Air_Temp = np.random.uniform(50, 100, number_sims)\n", + "mc_h_of_Fluid = np.random.uniform(0, 80, number_sims)\n", + "mc_k_of_Pipe = np.random.uniform(0, 80, number_sims)\n", + "mc_Ri = np.random.uniform(0.5, 24, number_sims)\n", + "mc_Ro = np.random.uniform(0.5, 24, number_sims)\n", + "\n", + "\n", + "data = np.array((mc_Fluid_Temp, \n", + " mc_Air_Temp, \n", + " mc_h_of_Fluid, \n", + " mc_k_of_Pipe, \n", + " mc_Ri,\n", + " mc_Ro\n", + " ))\n", + "\n", + "y = Rice(mc_Fluid_Temp, \n", + " mc_Air_Temp, \n", + " mc_h_of_Fluid, \n", + " mc_k_of_Pipe, \n", + " mc_Ri,\n", + " mc_Ro\n", + " )\n" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "40863332a9c84e56a8e23fb1aeae8ce1", - "version_major": 2, - "version_minor": 0 - }, "text/plain": [ - "interactive(children=(FloatSlider(value=10.0, description='velocity', max=20.0), FloatSlider(value=1.0, descri…" + "(6, 1000)" ] }, + "execution_count": 11, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), \n", - " lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", - " temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", - "def Nusselt_liquidPP(velocity, lengthscale, temperature):\n", - " Re_PP = Reynolds_liquidPP(velocity, lengthscale, temperature)\n", - " Pr_PP = Prandtl_liquidPP(temperature)\n", - " Nusselt_liquidPP = 0.023 * Re_PP**(4/5) * Pr_PP**0.4\n", - " return Nusselt_liquidPP" + "data.shape" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e30f92c82a7648ca8a326cb74f00157a", - "version_major": 2, - "version_minor": 0 - }, "text/plain": [ - "interactive(children=(FloatSlider(value=10.0, description='velocity', max=20.0), FloatSlider(value=1.0, descri…" + "Text(0,0.5,'Max Power (kW)')" ] }, + "execution_count": 14, "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Boundary layer thickness of Propane\n", - "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), \n", - " lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", - " temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", - "def Boundary_PP(velocity, lengthscale, temperature):\n", - " mu_k_PP = KinematicViscosity_liquidPP (temperature)\n", - " Pr_PP = Prandtl_liquidPP(temperature)\n", - " Boundary_PP = 0.37 * lengthscale / (velocity*lengthscale/mu_k_PP)**0.2\n", - " return Boundary_PP" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ + "output_type": "execute_result" + }, { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "99195fd57f6d47a79aefcb661d415bd4", - "version_major": 2, - "version_minor": 0 - }, + "image/png": 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\n", "text/plain": [ - "interactive(children=(FloatSlider(value=10.0, description='velocity', max=20.0), FloatSlider(value=1.0, descri…" + "
" ] }, "metadata": {}, @@ -287,62 +215,53 @@ } ], "source": [ - "#Heat transfer coeffecient of Propane from -150 C to -45 C\n", - "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), \n", - " lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", - " temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", - "def heat_transfer_coefficient_PP (velocity, lengthscale, temperature):\n", - " Nu_PP = Nusselt_liquidPP(velocity, lengthscale, temperature)\n", - " k_PP = ThermalConductivity_liquidPP(temperature)\n", - " L = Boundary_PP(velocity, lengthscale, temperature)\n", - " h_PP = Nu_PP * k_PP / lengthscale\n", - " return h_PP" + "plt.subplot(241)\n", + "plt.scatter(mc_Fluid_Temp, y, alpha=0.2)\n", + "plt.title(\"Connector size (kW)\")\n", + "plt.ylabel(\"Max Power (kW)\")" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "#Morris Method\n", "import SALib\n", "from SALib.sample import morris as ms\n", "from SALib.analyze import morris as ma\n", - "from SALib.plotting import morris as mp" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ + "from SALib.plotting import morris as mp\n", "morris_problem = {\n", - " # There are three variables\n", - " 'num_vars': 3,\n", - " 'names': ['velocity', 'lengthscale', 'temperature'],\n", - " # These are their ranges over which we'll move the variables\n", - " 'bounds': [[0, 20], #velocity (m/s)\n", - " [1, 2], # length (m)\n", - " [-150, -45], # temperature (C) \n", - " ],\n", + " # There are six variables\n", + " 'num_vars': 7,\n", + " # These are their names\n", + " 'names': ['Fluid_Temp', 'Air_Tem', 'h_of_Fluid', 'k_of_Pipe', 'Pipe_Length', 'Pipe_R_in', 'Pipe_R_ext'],\n", + " # These are their plausible ranges over which we'll move the variables\n", + " 'bounds': [[-273, 50], \n", + " [10, 100], \n", + " [0.1, 100], \n", + " [0.1, 100], \n", + " [0.1, 100], \n", + " [0.1, 20], \n", + " [0.1, 20] \n", + " ],\n", + " # I don't want to group any of these variables together\n", " 'groups': None\n", " }" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(40000, 3)" + "(80000, 7)" ] }, - "execution_count": 23, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -357,20 +276,27 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(3, 40000)\n", - "[[ 6.66666667 6.66666667 20. ... 13.33333333\n", - " 13.33333333 0. ]\n", - " [ 1.33333333 2. 2. ... 1.66666667\n", - " 1. 1. ]\n", - " [ -45. -45. -45. ... -150.\n", - " -150. -150. ]]\n" + "(7, 80000)\n", + "[[ 5.00000000e+01 5.00000000e+01 5.00000000e+01 ... -1.65333333e+02\n", + " -1.65333333e+02 -1.65333333e+02]\n", + " [ 1.00000000e+02 1.00000000e+02 1.00000000e+02 ... 1.00000000e+01\n", + " 7.00000000e+01 7.00000000e+01]\n", + " [ 3.34000000e+01 3.34000000e+01 1.00000000e+02 ... 1.00000000e+02\n", + " 1.00000000e+02 1.00000000e+02]\n", + " ...\n", + " [ 6.67000000e+01 1.00000000e-01 1.00000000e-01 ... 1.00000000e+02\n", + " 1.00000000e+02 3.34000000e+01]\n", + " [ 1.00000000e-01 1.00000000e-01 1.00000000e-01 ... 1.33666667e+01\n", + " 1.33666667e+01 1.33666667e+01]\n", + " [ 2.00000000e+01 2.00000000e+01 2.00000000e+01 ... 1.33666667e+01\n", + " 1.33666667e+01 1.33666667e+01]]\n" ] } ], @@ -381,54 +307,46 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 19, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "only size-1 arrays can be converted to Python scalars", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheat_transfer_coefficient_PP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mheat_transfer_coefficient_PP\u001b[0;34m(velocity, lengthscale, temperature)\u001b[0m\n\u001b[1;32m 4\u001b[0m temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n\u001b[1;32m 5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mheat_transfer_coefficient_PP\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mNu_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNusselt_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mk_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mThermalConductivity_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mL\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBoundary_PP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mNusselt_liquidPP\u001b[0;34m(velocity, lengthscale, temperature)\u001b[0m\n\u001b[1;32m 3\u001b[0m temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n\u001b[1;32m 4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mNusselt_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mRe_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mReynolds_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mPr_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPrandtl_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mNusselt_liquidPP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.023\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mRe_PP\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mPr_PP\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m0.4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mReynolds_liquidPP\u001b[0;34m(velocity, lengthscale, temperature)\u001b[0m\n\u001b[1;32m 4\u001b[0m temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n\u001b[1;32m 5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mReynolds_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mrho_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdensity_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mmiu_D_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDynamicViscosity_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mReynolds_liquidPP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrho_PP\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mvelocity\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlengthscale\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mmiu_D_PP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mdensity_liquidPP\u001b[0;34m(temperature)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mrho_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m733.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m718.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m697.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m677.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m657.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m636.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m615.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m593.5\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0;36m581.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0minterpolation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterp1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrho_PP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"quadratic\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0minteract\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwidgets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFloatSlider\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m120\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m45\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: only size-1 arrays can be converted to Python scalars" + "name": "stdout", + "output_type": "stream", + "text": [ + "(80000,)\n", + "[-0.0085251 -0.00831175 -0.00831154 ... 0.42928123 0.05639386\n", + " 0.05594397]\n" ] } ], "source": [ - "output = heat_transfer_coefficient_PP(*sample.T)\n", + "output = Rice(*sample.T)\n", "print(output.shape)\n", "print(output)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 20, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "only size-1 arrays can be converted to Python scalars", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheat_transfer_coefficient_PP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m Si = ma.analyze(morris_problem, \n\u001b[1;32m 3\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint_to_console\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mheat_transfer_coefficient_PP\u001b[0;34m(velocity, lengthscale, temperature)\u001b[0m\n\u001b[1;32m 4\u001b[0m temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n\u001b[1;32m 5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mheat_transfer_coefficient_PP\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mNu_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNusselt_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mk_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mThermalConductivity_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mL\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBoundary_PP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m 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converted to Python scalars" + "name": "stdout", + "output_type": "stream", + "text": [ + "Name mu mu_star sigma\n", + "Fluid_Temp - 10.89 10.90 30.35\n", + "Air_Tem - 7.58 9.41 29.00\n", + "h_of_Fluid - 1.13 1.43 12.88\n", + "k_of_Pipe 0.21 0.28 3.11\n", + "Pipe_Length 0.97 1.30 11.08\n", + "Pipe_R_in 0.02 0.03 0.25\n", + "Pipe_R_ext - 11.53 14.08 34.06\n" ] } ], "source": [ - "output = heat_transfer_coefficient_PP(*sample.T)\n", "Si = ma.analyze(morris_problem, \n", " sample, \n", " output, \n", @@ -442,36 +360,34 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Scatter plots\n", - "number_sims = 1000\n", - "mc_velocity = np.random.uniform(0, 20, number_sims)\n", - "mc_lengthscale = np.random.uniform(1, 2, number_sims)\n", - "mc_temperature = np.random.uniform(-150, -45, number_sims)\n", - "data = np.array((mc_velocity, mc_lengthscale, mc_temperature))\n", - "data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for thing in data:\n", - " print(thing.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "heat_transfer_coefficient_PP(data[0], data[1], data[2])" + "fig, (ax1, ax2) = plt.subplots(1,2)\n", + "mp.horizontal_bar_plot(ax1, Si, param_dict={})\n", + "mp.covariance_plot(ax2, Si, {})" ] }, { From 0abe9c2bc01ec19fc1dbe177e50e0cb13d9940e7 Mon Sep 17 00:00:00 2001 From: Yuan Li <43114076+liyuan1993@users.noreply.github.com> Date: Thu, 6 Dec 2018 08:30:45 -0500 Subject: [PATCH 4/6] Changed the equation morris work --- Pipeline Icing.ipynb | 33 --------------------------------- 1 file changed, 33 deletions(-) diff --git a/Pipeline Icing.ipynb b/Pipeline Icing.ipynb index 736b07c..6de54a6 100644 --- a/Pipeline Icing.ipynb +++ b/Pipeline Icing.ipynb @@ -188,39 +188,6 @@ "data.shape" ] }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0,0.5,'Max Power (kW)')" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.subplot(241)\n", - "plt.scatter(mc_Fluid_Temp, y, alpha=0.2)\n", - "plt.title(\"Connector size (kW)\")\n", - "plt.ylabel(\"Max Power (kW)\")" - ] - }, { "cell_type": "code", "execution_count": 16, From 0aed5cd1a4215a25a23d6c8875ab0892e9831d07 Mon Sep 17 00:00:00 2001 From: Yuan Li <43114076+liyuan1993@users.noreply.github.com> Date: Thu, 6 Dec 2018 08:44:15 -0500 Subject: [PATCH 5/6] modify the equation --- Pipeline Icing.ipynb | 70 ++++++++++++++++++++++---------------------- 1 file changed, 35 insertions(+), 35 deletions(-) diff --git a/Pipeline Icing.ipynb b/Pipeline Icing.ipynb index 6de54a6..5801167 100644 --- a/Pipeline Icing.ipynb +++ b/Pipeline Icing.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 15, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -20,7 +20,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "020c94fb8f1c48a0aa9fc01faafecc8b", + "model_id": "9c65b8f183b3425f9345db53d2dd2a9f", "version_major": 2, "version_minor": 0 }, @@ -72,7 +72,7 @@ " Ro=(Pipe_Radius_External*0.01) #m\n", " Tfluid=(Fluid_temp+273.15)\n", " Resistance_fluid = 1/(Fluid_h)*(2)*(3.14159)*(Ri)*(L)\n", - " Resistance_Pipe=(Ro-Ri)/(2*3.14*Pipe_k)\n", + " Resistance_Pipe=(Ro/Ri)/(2*3.14*Pipe_k)\n", " Resistance_Iceonwall= 1 / (Water_h)*(2)*(3.14159)*(Ro)*(L)\n", " Resistance_Sub=Resistance_fluid+Resistance_Pipe+Resistance_Iceonwall\n", "\n", @@ -136,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -179,7 +179,7 @@ "(6, 1000)" ] }, - "execution_count": 11, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -190,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -228,7 +228,7 @@ "(80000, 7)" ] }, - "execution_count": 17, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -243,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -253,17 +253,17 @@ "(7, 80000)\n", "[[ 5.00000000e+01 5.00000000e+01 5.00000000e+01 ... -1.65333333e+02\n", " -1.65333333e+02 -1.65333333e+02]\n", - " [ 1.00000000e+02 1.00000000e+02 1.00000000e+02 ... 1.00000000e+01\n", - " 7.00000000e+01 7.00000000e+01]\n", - " [ 3.34000000e+01 3.34000000e+01 1.00000000e+02 ... 1.00000000e+02\n", - " 1.00000000e+02 1.00000000e+02]\n", + " [ 1.00000000e+02 1.00000000e+02 4.00000000e+01 ... 4.00000000e+01\n", + " 4.00000000e+01 1.00000000e+02]\n", + " [ 1.00000000e-01 1.00000000e-01 1.00000000e-01 ... 1.00000000e-01\n", + " 1.00000000e-01 1.00000000e-01]\n", " ...\n", - " [ 6.67000000e+01 1.00000000e-01 1.00000000e-01 ... 1.00000000e+02\n", - " 1.00000000e+02 3.34000000e+01]\n", - " [ 1.00000000e-01 1.00000000e-01 1.00000000e-01 ... 1.33666667e+01\n", - " 1.33666667e+01 1.33666667e+01]\n", - " [ 2.00000000e+01 2.00000000e+01 2.00000000e+01 ... 1.33666667e+01\n", - " 1.33666667e+01 1.33666667e+01]]\n" + " [ 6.67000000e+01 6.67000000e+01 6.67000000e+01 ... 6.67000000e+01\n", + " 6.67000000e+01 6.67000000e+01]\n", + " [ 2.00000000e+01 2.00000000e+01 2.00000000e+01 ... 6.73333333e+00\n", + " 6.73333333e+00 6.73333333e+00]\n", + " [ 1.00000000e-01 1.33666667e+01 1.33666667e+01 ... 1.00000000e-01\n", + " 1.00000000e-01 1.00000000e-01]]\n" ] } ], @@ -274,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -282,8 +282,8 @@ "output_type": "stream", "text": [ "(80000,)\n", - "[-0.0085251 -0.00831175 -0.00831154 ... 0.42928123 0.05639386\n", - " 0.05594397]\n" + "[-1.73500773e+00 2.70238019e-03 2.32964176e-03 ... 1.46178038e+01\n", + " 1.46176730e+01 5.41911051e+00]\n" ] } ], @@ -295,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -303,13 +303,13 @@ "output_type": "stream", "text": [ "Name mu mu_star sigma\n", - "Fluid_Temp - 10.89 10.90 30.35\n", - "Air_Tem - 7.58 9.41 29.00\n", - "h_of_Fluid - 1.13 1.43 12.88\n", - "k_of_Pipe 0.21 0.28 3.11\n", - "Pipe_Length 0.97 1.30 11.08\n", - "Pipe_R_in 0.02 0.03 0.25\n", - "Pipe_R_ext - 11.53 14.08 34.06\n" + "Fluid_Temp - 10.14 10.63 31.56\n", + "Air_Tem - 7.23 9.00 28.41\n", + "h_of_Fluid - 1.16 1.60 13.88\n", + "k_of_Pipe 0.61 0.99 8.74\n", + "Pipe_Length 1.45 2.02 13.94\n", + "Pipe_R_in - 0.09 0.61 6.80\n", + "Pipe_R_ext - 10.49 13.27 34.30\n" ] } ], @@ -327,22 +327,22 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] From aae4d833011c2293dc97f971c7862f5dbab38505 Mon Sep 17 00:00:00 2001 From: Yuan Li <43114076+liyuan1993@users.noreply.github.com> Date: Thu, 6 Dec 2018 12:54:03 -0500 Subject: [PATCH 6/6] The OAT for fluid_h --- Pipeline Icing_heat_transfer_of_fluid.ipynb | 495 ++++++++++++++++++++ 1 file changed, 495 insertions(+) create mode 100644 Pipeline Icing_heat_transfer_of_fluid.ipynb diff --git a/Pipeline Icing_heat_transfer_of_fluid.ipynb b/Pipeline Icing_heat_transfer_of_fluid.ipynb new file mode 100644 index 0000000..b7978b7 --- /dev/null +++ b/Pipeline Icing_heat_transfer_of_fluid.ipynb @@ -0,0 +1,495 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-3.6136086249067567" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from math import log\n", + "Fluid_temp=-10 #F\n", + "Air_temp=25 #C\n", + "Fluid_h=10 #w/m2k\n", + "Water_h=2250 #w/m2k\n", + "Air_h=50 #w/m2k\n", + "Pipe_k=16 #w/m*k\n", + "Pipe_Length=1 #m\n", + "L=Pipe_Length\n", + "Pipe_Radius_Internal=4 #cm\n", + "Ri=Pipe_Radius_Internal\n", + "Pipe_Radius_External=4.2 #cm\n", + "Ro=Pipe_Radius_External\n", + "Tf=Fluid_temp\n", + "Ice_Radius=100 #cm\n", + "Rice=Ice_Radius\n", + "Resistance_fluid= 1/(Fluid_h)*(2)*(3.14159)*(Ri)*(L)\n", + "Resistance_Pipe=(log((Ro/Ri),2.71828))/(2*3.14*Pipe_k*L)\n", + "Resistance_Iceonwall= 1 / (Water_h)*(2)*(3.14159)*(Ro)*(L)\n", + "Resistance_IceExterior= 1 / (Water_h)*(2)*(3.14159)*(Rice)*(L)\n", + "Resistance_AirandIce = 1 / (Air_h)*(2)*(3.14159)*(Rice)*(L)\n", + "Resistance_Total=Resistance_fluid + Resistance_Pipe + Resistance_Iceonwall + Resistance_IceExterior + Resistance_AirandIce\n", + "UA=1/Resistance_Total\n", + "q=(UA)*(Fluid_temp-Air_temp)\n", + "UA_final=1/Resistance_AirandIce\n", + "Temp_Surface=(q/(UA_final))+(Air_temp)\n", + "Temp_Surface" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c5ee0d7655914130ab94199bbe5ad340", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-150.0), Output()), …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b93c5c57d56f40fdaa0d2e1f4c0c968a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-150.0), Output()), …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ac2a6552661a4cf59d1c5d3f15b35a20", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-150.0), Output()), …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8c98fed3f34240caa71de92d73513e33", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-150.0), Output()), …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d6b69eb1849b41f5a6a72ad638544d78", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=-120.0, description='temperature', max=-45.0, min=-150.0), Output()), …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from ipywidgets import widgets, interact, fixed\n", + "%matplotlib inline\n", + "import seaborn as sbn\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import scipy\n", + "import scipy.interpolate\n", + "\n", + "\n", + "@interact (temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", + "def Prandtl_liquidPP(temperature):\n", + " T = [-187.7, -173, -153, -133, -113.2, -93.2, -73.2, -53.2, -42.4]\n", + " Pr_PP= [99.44, 35.88, 15.11, 8.89, 6.29, 4.94, 4.14, 3.63, 3.43]\n", + " interpolation = scipy.interpolate.interp1d(T, Pr_PP, kind=\"quadratic\")\n", + "\n", + " return float(interpolation(temperature))\n", + " \n", + "@interact (temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", + "def ThermalConductivity_liquidPP(temperature):\n", + " T = [-150, -125, -100, -75, -50,-44]\n", + " k_PP= 1e-3*np.array ([193, 179, 164, 149,134, 129])\n", + " interpolation = scipy.interpolate.interp1d(T, k_PP, kind=\"quadratic\")\n", + " print('Thermal Conductivity (W/mK)')\n", + " return float(interpolation(temperature))\n", + "\n", + "@interact (temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", + "#Density calculation\n", + "def density_liquidPP(temperature):\n", + " T = [-187.7, -173, -153, -133, -113.2, -93.2, -73.2, -53.2, -42.4]\n", + " rho_PP = [733.1, 718.2, 697.8, 677.6, 657.3, 636.7, 615.5, 593.5 ,581.2]\n", + " interpolation = scipy.interpolate.interp1d(T, rho_PP, kind=\"quadratic\")\n", + "\n", + " return float(interpolation(temperature))\n", + "\n", + "@interact (temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", + "#Dynamic Viscosity Calculation\n", + "def DynamicViscosity_liquidPP(temperature):\n", + " T = [-187.7, -173, -153, -133, -113.2, -93.2, -73.2, -53.2, -42.4]\n", + " miu_D_PP = 1e-6*np.array([10970, 3778, 1502, 822.9, 535.5, 382.5, 288.2, 224.3,197.9])\n", + " interpolation = scipy.interpolate.interp1d(T, miu_D_PP, kind=\"quadratic\")\n", + "\n", + " return float(interpolation(temperature))\n", + "\n", + "@interact (temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", + "#Kinematic Viscosity Calculation\n", + "def KinematicViscosity_liquidPP(temperature):\n", + " T = [-187.7, -173, -153, -133, -113.2, -93.2, -73.2, -53.2, -42.4]\n", + " miu_K_PP = 1e-6*np.array([14.72, 5.26, 2.152, 1.214, 0.8147, 0.6008,0.4683, 0.3779, 0.3405])\n", + " interpolation = scipy.interpolate.interp1d(T, miu_K_PP, kind=\"quadratic\")\n", + " print('Kinematic Viscosity m^2/s')\n", + " return float(interpolation(temperature))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7cd68e76ccb942559d3d05f0fafbde49", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=10.0, description='velocity', max=20.0), FloatSlider(value=1.0, descri…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"Reynolds Number calculation\"\n", + "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), \n", + " lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", + " temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", + "def Reynolds_liquidPP(velocity, lengthscale, temperature):\n", + " rho_PP = density_liquidPP(temperature)\n", + " miu_D_PP = DynamicViscosity_liquidPP(temperature)\n", + " Reynolds_liquidPP = rho_PP * velocity * lengthscale / miu_D_PP\n", + " return Reynolds_liquidPP" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d581d9475a774381afbdfdefaf44d12f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=10.0, description='velocity', max=20.0), FloatSlider(value=1.0, descri…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), \n", + " lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", + " temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", + "def Nusselt_liquidPP(velocity, lengthscale, temperature):\n", + " Re_PP = Reynolds_liquidPP(velocity, lengthscale, temperature)\n", + " Pr_PP = Prandtl_liquidPP(temperature)\n", + " Nusselt_liquidPP = 0.023 * Re_PP**(4/5) * Pr_PP**0.4\n", + " return Nusselt_liquidPP" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "78ec96a6042c4abd9d739d385f341eca", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=10.0, description='velocity', max=20.0), FloatSlider(value=1.0, descri…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Boundary layer thickness of Propane\n", + "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), \n", + " lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", + " temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", + "def Boundary_PP(velocity, lengthscale, temperature):\n", + " mu_k_PP = KinematicViscosity_liquidPP (temperature)\n", + " Pr_PP = Prandtl_liquidPP(temperature)\n", + " Boundary_PP = 0.37 * lengthscale / (velocity*lengthscale/mu_k_PP)**0.2\n", + " return Boundary_PP" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "95945f0c9487406685673ee145b1a7a8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(FloatSlider(value=10.0, description='velocity', max=20.0), FloatSlider(value=1.0, descri…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Heat transfer coeffecient of Propane from -150 C to -45 C\n", + "@interact (velocity=widgets.FloatSlider(value=10, min=0, max=20), \n", + " lengthscale=widgets.FloatSlider(value=1, min=0.5, max=2), \n", + " temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n", + "def heat_transfer_coefficient_PP (velocity, lengthscale, temperature):\n", + " Nu_PP = Nusselt_liquidPP(velocity, lengthscale, temperature)\n", + " k_PP = ThermalConductivity_liquidPP(temperature)\n", + " L = Boundary_PP(velocity, lengthscale, temperature)\n", + " h_PP = Nu_PP * k_PP / lengthscale\n", + " return h_PP" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "#Morris Method\n", + "import SALib\n", + "from SALib.sample import morris as ms\n", + "from SALib.analyze import morris as ma\n", + "from SALib.plotting import morris as mp" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "morris_problem = {\n", + " # There are three variables\n", + " 'num_vars': 3,\n", + " 'names': ['velocity', 'lengthscale', 'temperature'],\n", + " # These are their ranges over which we'll move the variables\n", + " 'bounds': [[0, 20], #velocity (m/s)\n", + " [1, 2], # length (m)\n", + " [-150, -45], # temperature (C) \n", + " ],\n", + " 'groups': None\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(40000, 3)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_levels = 4\n", + "grid_jump = 2\n", + "trajectories = int(1e4)\n", + "sample = ms.sample(morris_problem, trajectories, num_levels, grid_jump)\n", + "sample.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 40000)\n", + "[[ 0. 0. 0. ... 0.\n", + " 0. 0. ]\n", + " [ 1.33333333 2. 2. ... 1.66666667\n", + " 1.66666667 1. ]\n", + " [-115. -115. -45. ... -115.\n", + " -45. -45. ]]\n" + ] + } + ], + "source": [ + "print(sample.T.shape)\n", + "print(sample.T)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "only size-1 arrays can be converted to Python scalars", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheat_transfer_coefficient_PP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mheat_transfer_coefficient_PP\u001b[0;34m(velocity, lengthscale, temperature)\u001b[0m\n\u001b[1;32m 4\u001b[0m temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n\u001b[1;32m 5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mheat_transfer_coefficient_PP\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mNu_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNusselt_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mk_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mThermalConductivity_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mL\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBoundary_PP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mNusselt_liquidPP\u001b[0;34m(velocity, lengthscale, temperature)\u001b[0m\n\u001b[1;32m 3\u001b[0m temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n\u001b[1;32m 4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mNusselt_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mRe_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mReynolds_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mPr_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPrandtl_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mNusselt_liquidPP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.023\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mRe_PP\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mPr_PP\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m0.4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mReynolds_liquidPP\u001b[0;34m(velocity, lengthscale, temperature)\u001b[0m\n\u001b[1;32m 4\u001b[0m temperature=widgets.FloatSlider(value=-120, min=-150, max=-45))\n\u001b[1;32m 5\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mReynolds_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvelocity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengthscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mrho_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdensity_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mmiu_D_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDynamicViscosity_liquidPP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mReynolds_liquidPP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrho_PP\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mvelocity\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlengthscale\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mmiu_D_PP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mdensity_liquidPP\u001b[0;34m(temperature)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mrho_PP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m733.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m718.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m697.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m677.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m657.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m636.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m615.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m593.5\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0;36m581.2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0minterpolation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterp1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrho_PP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"quadratic\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0minteract\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwidgets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFloatSlider\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m120\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m45\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: only size-1 arrays can be converted to Python scalars" + ] + } + ], + "source": [ + "output = heat_transfer_coefficient_PP(*sample.T)\n", + "print(output.shape)\n", + "print(output)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = heat_transfer_coefficient_PP(*sample.T)\n", + "Si = ma.analyze(morris_problem, \n", + " sample, \n", + " output, \n", + " print_to_console=False, \n", + " grid_jump=grid_jump, \n", + " num_levels=num_levels)\n", + "print(\"{:20s} {:>7s} {:>7s} {:>7s}\".format(\"Name\", \"mu\", \"mu_star\", \"sigma\"))\n", + "for name, s1, st, mean in zip(morris_problem['names'], Si['mu'], Si['mu_star'], Si['sigma']):\n", + " print(\"{:20s} {:=7.2f} {:=7.2f} {:=7.2f}\".format(name, s1, st, mean))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Scatter plots\n", + "number_sims = 1000\n", + "mc_velocity = np.random.uniform(0, 20, number_sims)\n", + "mc_lengthscale = np.random.uniform(1, 2, number_sims)\n", + "mc_temperature = np.random.uniform(-150, -45, number_sims)\n", + "data = np.array((mc_velocity, mc_lengthscale, mc_temperature))\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for thing in data:\n", + " print(thing.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "heat_transfer_coefficient_PP(data[0], data[1], data[2])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}