{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "323e0127",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pvlib\n",
    "import pandas as pd\n",
    "from scipy.optimize import newton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91a2bc16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# input instructions:\n",
    "# df is a dataframe containing columns 'Wind Speed','Temperature', 'DNI', 'DHI', and 'GHI' with solar irradiance data, with a datetime index corresponding to the timestamps of the data\n",
    "# lat and long are the latitude and longitude of the collector location\n",
    "# hemisphere is either 'northern' or 'southern' to determine surface azimuth for solar calculations\n",
    "# T_amb is a pandas Series containing ambient temperature data corresponding to the timestamps in 'times'\n",
    "def evacuated_tube_collector(Ac,beta,Cb,Cp,D,Di,epsilon_g,epsilon_p,k,k_air,m_dot,nu,nu_air,rho,tau,alpha,T_water,t,W,lat,long,df,hemisphere):    \n",
    "    \n",
    "    sig=5.67e-8 # Stefan-Boltzmann constant\n",
    "\n",
    "    T_fi=T_water # inlet temperature\n",
    "    T_fo=T_fi+3 # estimate temperature grows by 3 degrees Kelvin\n",
    "\n",
    "    # extract values from df\n",
    "    T_amb=df['Temperature']+273.15 # convert ambient temperature from Celsius to Kelvin\n",
    "    T_g=df['Temperature']+273.15 # assume pipe temperature is approximately ambient\n",
    "    V_wind=df['Wind Speed'] # extract wind speed data\n",
    "\n",
    "    # fluid pipe fluid dynamics\n",
    "    Re_int=4*m_dot/(np.pi*Di*nu) # fluid pipe Reynolds number\n",
    "    Pr_int=rho*nu*Cp/k # fluid pipe Prandtl number\n",
    "    f=(0.79*np.log(Re_int)-1.64)**-2 # fluid pipe friction factor\n",
    "    Nu=(f/8)*(Re_int-1000)*Pr_int/(1+12.7*np.sqrt(f/8)*(Pr_int**(2/3)-1)) # fluid pipe Nusselt number, using Gnielinski correlation -- assume 3e3 < Re < 5e6 and 0.5 < Pr < 2000\n",
    "    h_fi=Nu*k/Di # fluid pipe convective heat transfer coefficient\n",
    "\n",
    "    # external heat transfer\n",
    "    Re_ext=D*V_wind/nu_air # Reynolds number for external convection\n",
    "    Pr_air=0.7 # Prandtl number for air at room temperature\n",
    "    Nu_ext=0.3+0.62*Re_ext**(1/2)*Pr_air**(1/3)/(1+(0.4/Pr_air)**(2/3))**(1/4)*(1+(Re_ext/282000)**(5/8))**(4/5) # Nusselt number for external convection, using the Churchill-Bernstein correlation for flow over a cylinder\n",
    "    h_ext=k_air*Nu_ext/D # external convective heat transfer coefficient\n",
    "\n",
    "    T_pm=(T_fo+T_fi)/2 # mean fluid temperature\n",
    "    hr_pc=sig*(1/(1/epsilon_g+1/epsilon_p-1))*(T_pm**2-T_g**2)*(T_pm+T_g) # radiative heat transfer coefficient between fluid and pipe\n",
    "    hr_ca=sig*epsilon_g*(T_g**2-T_amb**2)*(T_g+T_amb) # radiative heat transfer coefficient between pipe and air\n",
    "\n",
    "    # overall loss coefficient\n",
    "    Ul=(1/hr_pc+1/(h_ext+hr_ca))**-1\n",
    "\n",
    "    # fin and collector efficiency\n",
    "    m=np.sqrt(Ul/(k*t)) # fin parameter\n",
    "    F=np.tanh(m*(W-D)/2)/(m*(W-D)/2) # fin efficiency\n",
    "    F_prime=(1/Ul)/((1/(Ul*(D+(W-D)*F)))+1/Cb+1/(np.pi*Di*h_fi)) # collector efficiency factor\n",
    "    Fr=m_dot*Cp/(Ac*Ul)*(1-np.exp(-Ac*Ul*F_prime/(m_dot*Cp))) # heat removal factor\n",
    "\n",
    "    # solar data\n",
    "    times=df.index # extract timestamps from dataframe index\n",
    "    solpos=pvlib.solarposition.get_solarposition(times, lat, long) # get solar position data\n",
    "    if hemisphere=='southern': # determine surface azimuth based on hemisphere\n",
    "        surface_azimuth=0\n",
    "    elif hemisphere=='northern':\n",
    "        surface_azimuth=180\n",
    "    it_data=pvlib.irradiance.get_total_irradiance( # calculate plane of array irradiance data\n",
    "        surface_tilt=beta,\n",
    "        surface_azimuth=surface_azimuth,\n",
    "        dni=df['DNI'],\n",
    "        dhi=df['DHI'],\n",
    "        ghi=df['GHI'],\n",
    "        solar_zenith=solpos['zenith'],\n",
    "        solar_azimuth=solpos['azimuth']\n",
    "    )\n",
    "    It=it_data['poa_global'] # total plane of array irradiance\n",
    "    \n",
    "   # useful heat gain calculation\n",
    "    Qu=Ac*Fr*(It*tau*alpha-Ul*(T_fi-T_amb)) # useful heat gain\n",
    "    Qu=Qu.clip(lower=0) # ensure useful heat gain is not negative\n",
    "    return Qu, Ul\n",
    "\n",
    "# input instructions:\n",
    "# N is an integer corresponding to the number of tubes in the collector\n",
    "# N_parallel_rows is an integer corresponding to the number of rows of tubes connected in parallel\n",
    "# N/N_parallel_rows must be an integer value\n",
    "# Qu_initial is a list of useful heat gain values for initial tubes, calculated from the evacuated_tube_collector function\n",
    "# Note: this function looks at simple configurations. You must run multiple iterations of this function to analyze more complex configurations\n",
    "def config(Qu_initial,Ul_initial,T_in,N,N_parallel_rows,Ac,beta,Cb,Cp,D,Di,epsilon_g,epsilon_p,k,k_air,m_dot,nu,nu_air,rho,tau,alpha,t,W,lat,long,df,hemisphere):\n",
    "    m_dot_per_row=m_dot/N_parallel_rows\n",
    "    N_each_row=range(1,N//N_parallel_rows+1)\n",
    "    T_out=0\n",
    "    T_out_collector={}\n",
    "    Qu_collector={}\n",
    "    Ul_collector={}\n",
    "    T_in_i=T_in\n",
    "    Qu_i=Qu_initial\n",
    "    Ul_i=Ul_initial\n",
    "    for i in N_each_row:\n",
    "        if i==1:\n",
    "            Qu_i=Qu_initial\n",
    "            Ul_i=Ul_initial\n",
    "        else:\n",
    "            Qu_i,Ul_i=evacuated_tube_collector(Ac,beta,Cb,Cp,D,Di,epsilon_g,epsilon_p,k,k_air,m_dot_per_row,nu,nu_air,rho,tau,alpha,T_in_i,t,W,lat,long,df,hemisphere) # set T_water to T_in_i\n",
    "        T_out_i=T_in_i+Qu_i/(m_dot_per_row*Cp) # calculate outlet temperature for each tube in the row\n",
    "        T_out_collector[i]=T_out_i # store outlet temperature for each tube in the row\n",
    "        Ul_collector[i]=Ul_i # store overall loss coefficient for each tube in the row\n",
    "        T_in_i=T_out_i # outlet of one tube becomes inlet for the next tube in series\n",
    "        Qu_collector[i]=Qu_i # store useful heat gain for each tube in the row\n",
    "    T_out=T_out_i # final outlet temperature\n",
    "    Qu_total=sum(Qu_collector.values())*N_parallel_rows # total useful heat gain\n",
    "    return Qu_total,T_out,T_out_collector,Qu_collector,Ul_collector\n",
    "\n",
    "# input instructions:\n",
    "# almost identical to evacuated_tube_collector() except T_out replaces Ac\n",
    "def area(T_out,beta,Cb,Cp,D,Di,epsilon_g,epsilon_p,k,k_air,m_dot,nu,nu_air,rho,tau,alpha,T_water,t,W,lat,long,df,hemisphere):    \n",
    "    \n",
    "    sig=5.67e-8 # Stefan-Boltzmann constant\n",
    "\n",
    "    T_fi=T_water # inlet temperature\n",
    "    T_fo=T_fi+3 # estimate temperature grows by 3 degrees Kelvin\n",
    "\n",
    "    # extract values from df\n",
    "    T_amb=df['Temperature']+273.15 # convert ambient temperature from Celsius to Kelvin\n",
    "    T_g=df['Temperature']+273.15 # assume pipe temperature is approximately ambient\n",
    "    V_wind=df['Wind Speed'] # extract wind speed data\n",
    "\n",
    "    # fluid pipe fluid dynamics\n",
    "    Re_int=4*m_dot/(np.pi*Di*nu) # fluid pipe Reynolds number\n",
    "    Pr_int=rho*nu*Cp/k # fluid pipe Prandtl number\n",
    "    f=(0.79*np.log(Re_int)-1.64)**-2 # fluid pipe friction factor\n",
    "    Nu=(f/8)*(Re_int-1000)*Pr_int/(1+12.7*np.sqrt(f/8)*(Pr_int**(2/3)-1)) # fluid pipe Nusselt number, using Gnielinski correlation -- assume 3e3 < Re < 5e6 and 0.5 < Pr < 2000\n",
    "    h_fi=Nu*k/Di # fluid pipe convective heat transfer coefficient\n",
    "\n",
    "    # external heat transfer\n",
    "    Re_ext=D*V_wind/nu_air # Reynolds number for external convection\n",
    "    Pr_air=0.7 # Prandtl number for air at room temperature\n",
    "    Nu_ext=0.3+0.62*Re_ext**(1/2)*Pr_air**(1/3)/(1+(0.4/Pr_air)**(2/3))**(1/4)*(1+(Re_ext/282000)**(5/8))**(4/5) # Nusselt number for external convection, using the Churchill-Bernstein correlation for flow over a cylinder\n",
    "    h_ext=k_air*Nu_ext/D # external convective heat transfer coefficient\n",
    "\n",
    "    T_pm=(T_fo+T_fi)/2 # mean fluid temperature\n",
    "    hr_pc=sig*(1/(1/epsilon_g+1/epsilon_p-1))*(T_pm**2-T_g**2)*(T_pm+T_g) # radiative heat transfer coefficient between fluid and pipe\n",
    "    hr_ca=sig*epsilon_g*(T_g**2-T_amb**2)*(T_g+T_amb) # radiative heat transfer coefficient between pipe and air\n",
    "\n",
    "    # overall loss coefficient\n",
    "    Ul=(1/hr_pc+1/(h_ext+hr_ca))**-1\n",
    "\n",
    "    # fin and collector efficiency\n",
    "    m=np.sqrt(Ul/(k*t)) # fin parameter\n",
    "    F=np.tanh(m*(W-D)/2)/(m*(W-D)/2) # fin efficiency\n",
    "    F_prime=(1/Ul)/((1/(Ul*(D+(W-D)*F)))+1/Cb+1/(np.pi*Di*h_fi)) # collector efficiency factor\n",
    "\n",
    "    # solar data\n",
    "    times=df.index # extract timestamps from dataframe index\n",
    "    solpos=pvlib.solarposition.get_solarposition(times, lat, long) # get solar position data\n",
    "    if hemisphere=='southern': # determine surface azimuth based on hemisphere\n",
    "        surface_azimuth=0\n",
    "    elif hemisphere=='northern':\n",
    "        surface_azimuth=180\n",
    "    it_data=pvlib.irradiance.get_total_irradiance( # calculate plane of array irradiance data\n",
    "        surface_tilt=beta,\n",
    "        surface_azimuth=surface_azimuth,\n",
    "        dni=df['DNI'],\n",
    "        dhi=df['DHI'],\n",
    "        ghi=df['GHI'],\n",
    "        solar_zenith=solpos['zenith'],\n",
    "        solar_azimuth=solpos['azimuth']\n",
    "    )\n",
    "    It=it_data['poa_global'] # total plane of array irradiance\n",
    "    \n",
    "    # conservative design point: size for the 20th percentile of daylight irradiance\n",
    "    # and the Ul/F_prime that correspond to that SAME timestamp (not independently picked)\n",
    "    threshold = It > It[It>0].quantile(0.20)\n",
    "    worst_idx = It[threshold].idxmin()  # timestamp of worst daylight irradiance\n",
    "\n",
    "    It_val = It[worst_idx]\n",
    "    Ul_val = Ul[worst_idx]\n",
    "    F_prime_val = F_prime[worst_idx]\n",
    "    T_amb_val = T_amb[worst_idx]\n",
    "   \n",
    "    Qu_target = (T_out - T_water) * (m_dot * Cp)\n",
    "\n",
    "    def residual(Ac):\n",
    "        Fr = m_dot*Cp/(Ac*Ul_val)*(1-np.exp(-Ac*Ul_val*F_prime_val/(m_dot*Cp)))\n",
    "        Qu = Ac*Fr*(It_val*tau*alpha - Ul_val*(T_fi - T_amb_val))\n",
    "        return Qu - Qu_target\n",
    "\n",
    "    Ac_solution = newton(residual, x0=0.001)  # x0 is your initial guess for Ac\n",
    "    return Ac_solution\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d0b17c71",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.11/site-packages/pandas/core/arraylike.py:396: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test case\n",
    "df=pd.read_csv(\"/Users/shoon/Desktop/ESL/SAM Downloaded Weather Files/ashburn_va_39.0523_-77.4827_nsrdb-GOES-tmy-v4-0-0_60_tmy.csv\", skiprows=2) # ashburn tmy data\n",
    "df['Year']=2026 # set year to 2026 to deal with tmy year handling\n",
    "df['times']=pd.to_datetime(df[['Year','Month','Day','Hour','Minute']])\n",
    "df=df.set_index('times')\n",
    "df.index=df.index.tz_localize('Etc/GMT+5') # localize to correct timezone\n",
    "Qu_test,Ul_test=evacuated_tube_collector(Ac=0.08,beta=30,Cb=0.9,Cp=4186,D=0.05,Di=0.04,epsilon_g=0.9,epsilon_p=0.8,k=205,k_air=0.025,m_dot=0.01,nu=1e-6,nu_air=1.5e-5,rho=1000,tau=0.9,alpha=0.9,T_water=308.15,t=0.00012,W=0.06,lat=39.05,long=-77.5,df=df,hemisphere='northern')\n",
    "plt.figure()\n",
    "plt.plot(Qu_test['2026-12-21 08:30:00-05:00':'2026-12-22 08:30:00-05:00'])\n",
    "# continue with config\n",
    "config_test=config(Qu_initial=Qu_test,Ul_initial=Ul_test,T_in=308.15,N=90,N_parallel_rows=3,Ac=0.08,beta=30,Cb=0.9,Cp=4186,D=0.05,Di=0.04,epsilon_g=0.9,epsilon_p=0.8,k=205,k_air=0.025,m_dot=0.01,nu=1e-6,nu_air=1.5e-5,rho=1000,tau=0.9,alpha=0.9,t=0.00012,W=0.06,lat=39.05,long=-77.5,df=df,hemisphere='northern')\n",
    "# config_test[0] returns Qu_total as a pandas series\n",
    "# config_test[1] returns T_out as a pandas series\n",
    "# config_test[2] returns T_out_collector as a dictionary of series per tube\n",
    "# config_test[3] returns Qu_collector as a dictionary of series per tube\n",
    "# config_test[4] returns Ul_collector as a dictionary of series per tube\n",
    "x_vals=range(1,31)\n",
    "y_vals=[config_test[3][i]['2026-06-21 14:30:00-05:00'] for i in x_vals]\n",
    "y_vals_2=[config_test[2][i]['2026-06-21 14:30:00-05:00'] for i in x_vals]\n",
    "fig,ax1=plt.subplots()\n",
    "ax1.plot(x_vals,y_vals,color='blue',label='Qu')\n",
    "ax1.set_ylabel('Useful Heat Gain Qu (W)',color='blue')\n",
    "ax1.set_xlabel('Tube Number in Row')\n",
    "ax2=ax1.twinx()\n",
    "ax2.plot(x_vals,y_vals_2,color='red',label='T_out')\n",
    "ax2.set_ylabel('Outlet Temperature T_out (K)',color='red')\n",
    "fig.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cf065069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mass flow test\n",
    "m_dot_test=np.arange(0.01, 0.1, 0.005)\n",
    "Qu_total_mdottest={}\n",
    "T_out_mdottest={}\n",
    "for i in m_dot_test:\n",
    "    Qu_mdottest,Ul_mdottest=evacuated_tube_collector(Ac=0.08,beta=30,Cb=0.9,Cp=4186,D=0.05,Di=0.04,epsilon_g=0.9,epsilon_p=0.8,k=205,k_air=0.025,m_dot=i,nu=1e-6,nu_air=1.5e-5,rho=1000,tau=0.9,alpha=0.9,T_water=308.15,t=0.00012,W=0.06,lat=39.05,long=-77.5,df=df,hemisphere='northern')\n",
    "    Qu_total_mdottest[i],T_out_mdottest[i]=config(Qu_initial=Qu_test,Ul_initial=Ul_test,T_in=308.15,N=90,N_parallel_rows=3,Ac=0.08,beta=30,Cb=0.9,Cp=4186,D=0.05,Di=0.04,epsilon_g=0.9,epsilon_p=0.8,k=205,k_air=0.025,m_dot=i,nu=1e-6,nu_air=1.5e-5,rho=1000,tau=0.9,alpha=0.9,t=0.00012,W=0.06,lat=39.05,long=-77.5,df=df,hemisphere='northern')[0:2]\n",
    "y_vals_m=[Qu_total_mdottest[i]['2026-06-21 14:30:00-05:00'] for i in m_dot_test]\n",
    "y_vals_2_m=[T_out_mdottest[i]['2026-06-21 14:30:00-05:00'] for i in m_dot_test]\n",
    "fig_m,ax1_m=plt.subplots()\n",
    "ax1_m.plot(m_dot_test,y_vals_m,color='blue',label='Qu')\n",
    "ax1_m.set_ylabel('Total Useful Heat Gain Qu (W)',color='blue')\n",
    "ax1_m.set_xlabel('Mass Flow Rate (kg/s)')\n",
    "ax2_m=ax1_m.twinx()\n",
    "ax2_m.plot(m_dot_test,y_vals_2_m,color='red',label='T_out')\n",
    "ax2_m.set_ylabel('Outlet Temperature T_out (K)',color='red')\n",
    "fig_m.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba9a2bc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# T_in test\n",
    "T_in_test=np.arange(298.15, 318.15, 2)\n",
    "Qu_total_T_in_test={}\n",
    "T_out_T_in_test={}\n",
    "for i in T_in_test:\n",
    "    Qu_T_in_test,Ul_T_in_test=evacuated_tube_collector(Ac=0.08,beta=30,Cb=0.9,Cp=4186,D=0.05,Di=0.04,epsilon_g=0.9,epsilon_p=0.8,k=205,k_air=0.025,m_dot=0.01,nu=1e-6,nu_air=1.5e-5,rho=1000,tau=0.9,alpha=0.9,T_water=i,t=0.00012,W=0.06,lat=39.05,long=-77.5,df=df,hemisphere='northern')\n",
    "    Qu_total_T_in_test[i],T_out_T_in_test[i]=config(Qu_initial=Qu_T_in_test,Ul_initial=Ul_T_in_test,T_in=i,N=90,N_parallel_rows=3,Ac=0.08,beta=30,Cb=0.9,Cp=4186,D=0.05,Di=0.04,epsilon_g=0.9,epsilon_p=0.8,k=205,k_air=0.025,m_dot=0.01,nu=1e-6,nu_air=1.5e-5,rho=1000,tau=0.9,alpha=0.9,t=0.00012,W=0.06,lat=39.05,long=-77.5,df=df,hemisphere='northern')[0:2]\n",
    "y_vals_T_in=[Qu_total_T_in_test[i]['2026-06-21 14:30:00-05:00'] for i in T_in_test]\n",
    "y_vals_2_T_in=[T_out_T_in_test[i]['2026-06-21 14:30:00-05:00']-i for i in T_in_test]\n",
    "fig_T_in,ax1_T_in=plt.subplots()\n",
    "ax1_T_in.plot(T_in_test,y_vals_T_in,color='blue',label='Qu',lw=3)\n",
    "ax1_T_in.set_ylabel('Total Useful Heat Gain Qu (W)',color='blue')\n",
    "ax1_T_in.set_xlabel('Inlet Temperature T_in (K)')\n",
    "ax2_T_in=ax1_T_in.twinx()\n",
    "ax2_T_in.plot(T_in_test,y_vals_2_T_in,color='red',label='T_out')\n",
    "ax2_T_in.set_ylabel('Change in Temperature ∆T (K)',color='red')\n",
    "fig_T_in.legend()\n",
    "plt.show()\n",
    "# same slope!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0f15899d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.11/site-packages/pandas/core/arraylike.py:396: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/pandas/core/arraylike.py:396: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/pandas/core/arraylike.py:396: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/pandas/core/arraylike.py:396: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/pandas/core/arraylike.py:396: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/pandas/core/arraylike.py:396: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/pandas/core/arraylike.py:396: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/pandas/core/arraylike.py:396: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/pandas/core/arraylike.py:396: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/pandas/core/arraylike.py:396: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75.98076211353317\n",
      "75.98076211353317\n",
      "75.98076211353317\n",
      "75.98076211353317\n",
      "75.98076211353317\n",
      "75.98076211353317\n",
      "75.98076211353317\n",
      "75.98076211353317\n",
      "75.98076211353317\n",
      "75.98076211353317\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# area test\n",
    "T_out_test=np.arange(353.15,373.15,2)\n",
    "A_test ={}\n",
    "\n",
    "for i in T_out_test:\n",
    "    A_test[i]=area(T_out=i,beta=30,Cb=0.9,Cp=4186,D=0.05,Di=0.04,epsilon_g=0.9,epsilon_p=0.8,k=205,k_air=0.025,m_dot=0.01,nu=1e-6,nu_air=1.5e-5,rho=1000,tau=0.9,alpha=0.9,T_water=308.15,t=0.00012,W=0.06,lat=39.05,long=-77.5,df=df,hemisphere='northern')\n",
    "y_vals = [A_test[i] for i in T_out_test]\n",
    "\n",
    "plt.plot(T_out_test, y_vals)\n",
    "plt.xlabel('Target Outlet Temperature T_out (K)')\n",
    "plt.ylabel('Required Collector Area Ac (m²)')\n",
    "plt.title('Required Area vs Target Outlet Temperature')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
