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code refactoring

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This commit is contained in:
Marcel Christian Frommelt 2021-06-18 21:58:02 +09:00
parent 9db4afa06e
commit b6527552cd
1 changed files with 476 additions and 139 deletions
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615
main.py
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@ -1,13 +1,25 @@
import sys import sys
import pickle
from pyfiglet import Figlet
import warnings import warnings
import numpy as np import numpy as np
import xarray as xr import xarray as xr
import pandas as pd import pandas as pd
import pickle as pkl
import cartopy.crs as ccrs import cartopy.crs as ccrs
import astropy.units as unit import astropy.units as unit
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from dask import delayed from dask import delayed
from datetime import datetime from datetime import datetime
starttime = datetime.now()
print('----------------------------------------')
ascii_banner = Figlet(font="slant")
print(ascii_banner.renderText("BASTET"))
print("Ver. 1.0, 2021 by Marcel Frommelt")
print('----------------------------------------')
print("")
print("")
from netCDF4 import Dataset from netCDF4 import Dataset
from astropy.time import Time from astropy.time import Time
from scipy import interpolate from scipy import interpolate
@ -18,16 +30,19 @@ from input.natural_constants import *
from models.gravity import grav from models.gravity import grav
from models.valving import valving from models.valving import valving
from models.ballasting import ballasting from models.ballasting import ballasting
from dask.diagnostics import ProgressBar
from models.simple_atmosphere import T_air_simple, p_air_simple, rho_air_simple
from models.sun import sun_angles_analytical, tau from models.sun import sun_angles_analytical, tau
from models.drag import drag, cd_PalumboLow, cd_Palumbo, cd_PalumboHigh, cd_sphere from models.drag import drag, cd_PalumboLow, cd_Palumbo, cd_PalumboHigh, cd_PalumboMC, cd_sphere
from models.transformation import visible_cells, transform, radii from models.transformation import visible_cells, transform, radii, transform2
from multiprocessing import Process
starttime = datetime.now() starttime = datetime.now()
if not sys.warnoptions: if not sys.warnoptions:
warnings.simplefilter("ignore") warnings.simplefilter("ignore")
data = pd.read_excel(r'C:\Users\marcel\PycharmProjects\MasterThesis\Data_PoGo2016.xls', sheet_name='Tabelle3') # Tabelle3 data = pd.read_excel(r'C:\Users\marcel\PycharmProjects\MasterThesis\Data_PoGo2016.xls', sheet_name='SuperTIGER2') # Tabelle3
comp_time = pd.DataFrame(data, columns=['Time']).to_numpy().squeeze() comp_time = pd.DataFrame(data, columns=['Time']).to_numpy().squeeze()
comp_height = pd.DataFrame(data, columns=['Height']).to_numpy().squeeze() comp_height = pd.DataFrame(data, columns=['Height']).to_numpy().squeeze()
@ -35,43 +50,31 @@ comp_lat = pd.DataFrame(data, columns=['Latitude']).to_numpy().squeeze()
comp_lon = pd.DataFrame(data, columns=['Longitude']).to_numpy().squeeze() comp_lon = pd.DataFrame(data, columns=['Longitude']).to_numpy().squeeze()
print("") print("")
print("Initialising simulation...") print("INITIALISING SIMULATION...")
print("Launch location : longitude %.4f, latitude: %.4f" % (start_lon, start_lat)) print("")
print("Launch location:")
print("longitude: %.4f deg" % (start_lon))
print("latitude: %.4f deg" % (start_lat))
print("Launch time: " + str(start_utc) + " (UTC)") print("Launch time: " + str(start_utc) + " (UTC)")
print("") print("")
print("Reading ERA5-datasets, please wait.") print("Reading ERA5-datasets, please wait.")
ascend_data = xr.open_dataset("ascend_2019_kiruna.nc") first_file = Dataset(ERA5_float[0])
#float_data = xr.open_dataset("float_2019.nc") last_file = Dataset(ERA5_float[-1])
df = xr.open_mfdataset(['float1_2019.nc', 'float2_2019.nc', 'float3_2019.nc', 'float4_2019.nc'], combine='by_coords', concat_dim="time", parallel=True) tstart = int(first_file.variables['time'][0])
tend = int(last_file.variables['time'][-1])
startNC = Dataset('float1_2019.nc') first_file.close()
endNC = Dataset('float4_2019.nc') last_file.close()
start = int(startNC.variables['time'][0])
end = int(endNC.variables['time'][-1])
float_data = df.assign_coords(time=np.linspace(start, end, (end - start) + 1)) df1 = xr.open_mfdataset(ERA5_float, combine='by_coords', engine='netcdf4', concat_dim="time", parallel=True)
float_data = df1.assign_coords(time=np.linspace(tstart, tend, (tend - tstart) + 1))
single_data = xr.open_dataset("single_2019.nc")
ERAtime = float_data.variables['time'][:] # time
# ERA5 MULTI-LEVEL ASCENT (WIND + ATMOSPHERIC DATA DURING ASCENT)
ERAlat0 = ascend_data.variables['latitude'][:].values # latitude [deg]
ERAlon0 = ascend_data.variables['longitude'][:].values # longitude [deg]
ERAz_ascend = ascend_data.variables['z'][:].values / g # geopotential [m^-2/s^-2] to geopotential height [m]
ERApress_ascend = ascend_data.variables['level'][:].values # pressure level [-]
ERAtemp_ascend = ascend_data.variables['t'][:].values # air temperature in K
vw_x_ascend = ascend_data.variables['u'][:].values # v_x in [m/s]
vw_y_ascend = ascend_data.variables['v'][:].values # v_y in [m/s]
vw_z_ascend = ascend_data.variables['w'][:].values # v_z in [m/s]
# ERA5 MULTI-LEVEL FLOAT (WIND + ATMOSPHERIC DATA DURING FLOAT) # ERA5 MULTI-LEVEL FLOAT (WIND + ATMOSPHERIC DATA DURING FLOAT)
ERAtime = float_data.variables['time'][:] # time
ERAlat1 = float_data.variables['latitude'][:].values # latitude [deg] ERAlat1 = float_data.variables['latitude'][:].values # latitude [deg]
ERAlon1 = float_data.variables['longitude'][:].values # longitude [deg] ERAlon1 = float_data.variables['longitude'][:].values # longitude [deg]
ERAz_float = float_data.variables['z'][:].values / g # geopotential [m^-2/s^-2] to geopotential height [m] ERAz_float = float_data.variables['z'][:].values / g # geopotential [m^-2/s^-2] to geopotential height [m]
@ -81,6 +84,18 @@ vw_x_float = float_data.variables['u'][:].values # v_x in [m/s]
vw_y_float = float_data.variables['v'][:].values # v_y in [m/s] vw_y_float = float_data.variables['v'][:].values # v_y in [m/s]
vw_z_float = float_data.variables['w'][:].values # v_z in [m/s] vw_z_float = float_data.variables['w'][:].values # v_z in [m/s]
first_file = Dataset(ERA5_single[0])
last_file = Dataset(ERA5_single[-1])
tstart = int(first_file.variables['time'][0])
tend = int(last_file.variables['time'][-1])
first_file.close()
last_file.close()
df2 = xr.open_mfdataset(ERA5_single, combine='by_coords', engine='netcdf4', concat_dim="time", parallel=True)
single_data = df2.assign_coords(time=np.linspace(tstart, tend, (tend - tstart) + 1))
# ERA5 SINGLE-LEVEL (RADIATIVE ENVIRONMENT) # ERA5 SINGLE-LEVEL (RADIATIVE ENVIRONMENT)
@ -102,6 +117,32 @@ ERAtisr = single_data.variables['tisr'][:].values # hourly accumulated TOA
ERAstrdc = single_data.variables['strdc'][:].values # hourly accumulated surface thermal radiation downward clear-sky [J/m^2] ERAstrdc = single_data.variables['strdc'][:].values # hourly accumulated surface thermal radiation downward clear-sky [J/m^2]
ERAsp = single_data.variables['sp'][:].values # surface pressure in [Pa] ERAsp = single_data.variables['sp'][:].values # surface pressure in [Pa]
first_file = Dataset(ERA5_ascent[0])
last_file = Dataset(ERA5_ascent[-1])
tstart = int(first_file.variables['time'][0])
tend = int(last_file.variables['time'][-1])
first_file.close()
last_file.close()
df3 = xr.open_mfdataset(ERA5_ascent, combine='by_coords', engine='netcdf4', concat_dim="time", parallel=True)
ascent_data = df3.assign_coords(time=np.linspace(tstart, tend, (tend - tstart) + 1))
# ERA5 MULTI-LEVEL ASCENT (WIND + ATMOSPHERIC DATA DURING ASCENT)
ERAlat0 = ascent_data.variables['latitude'][:].values # latitude [deg]
ERAlon0 = ascent_data.variables['longitude'][:].values # longitude [deg]
ERAz_ascent = ascent_data.variables['z'][:].values / g # geopotential [m^-2/s^-2] to geopotential height [m]
ERApress_ascent = ascent_data.variables['level'][:].values # pressure level [-]
ERAtemp_ascent = ascent_data.variables['t'][:].values # air temperature in K
vw_x_ascent = ascent_data.variables['u'][:].values # v_x in [m/s]
vw_y_ascent = ascent_data.variables['v'][:].values # v_y in [m/s]
vw_z_ascent = ascent_data.variables['w'][:].values # v_z in [m/s]
ascent_data.close()
print("Finished reading ERA5-datasets.") print("Finished reading ERA5-datasets.")
lon_era2d0, lat_era2d0 = np.meshgrid(ERAlon0, ERAlat0) lon_era2d0, lat_era2d0 = np.meshgrid(ERAlon0, ERAlat0)
@ -116,10 +157,14 @@ print("")
tree0 = cKDTree(np.column_stack((xs0, ys0, zs0))) tree0 = cKDTree(np.column_stack((xs0, ys0, zs0)))
tree1 = cKDTree(np.column_stack((xs1, ys1, zs1))) tree1 = cKDTree(np.column_stack((xs1, ys1, zs1)))
tree2 = cKDTree(np.column_stack((xs2, ys2, zs2))) tree2 = cKDTree(np.column_stack((xs2, ys2, zs2)))
print("Built kd-trees") print("Built kd-trees.")
print("")
wflag1, wflag2, wflag3, wflag4, wflag5, wflag6, wflag7, wflag8, wflag9, wflag10, wflag11, wflag12, wflag13, wflag14, wflag15, wflag16, wflag17, wflag18, wflag19 = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
def ERA5Data(lon, lat, h, t, deltaT_ERA): flag_arr = np.zeros(20)
def ERA5Data(lon, lat, h, t, deltaT_ERA, flag_arr):
t_epoch = deltaT_ERA + t / 3600 t_epoch = deltaT_ERA + t / 3600
t_pre = int(t_epoch) t_pre = int(t_epoch)
@ -165,7 +210,7 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
interp4d_vw_z_post = np.ma.dot(w1, vw_z_float[t_post_ind, :, lat_ind1, lon_ind1]) / np.sum(w1) interp4d_vw_z_post = np.ma.dot(w1, vw_z_float[t_post_ind, :, lat_ind1, lon_ind1]) / np.sum(w1)
interp4d_vw_z = (interp4d_vw_z_post - interp4d_vw_z_pre) * (t_epoch - t_pre) + interp4d_vw_z_pre interp4d_vw_z = (interp4d_vw_z_post - interp4d_vw_z_pre) * (t_epoch - t_pre) + interp4d_vw_z_pre
pressure_hPa = np.array([1, 2, 3, 5, 7, 10, 20]) pressure_hPa = np.array([1, 2, 3, 5, 7, 10, 20, 30]) # !!!
pressure = 100 * pressure_hPa pressure = 100 * pressure_hPa
@ -176,27 +221,32 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
vw_z_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_z) vw_z_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_z)
except IndexError: except IndexError:
print("Error: Please check time range of ERA5 data!") if flag_arr[18] == 0:
print("Error: Please check time range of ERA5 data!")
flag_arr[18] = 1
else:
flag_arr[18] = 1
elif np.abs(lat - start_lat) <= 10.0 and np.abs(lon - start_lon) <= 10.0: elif np.abs(lat - start_lat) <= 10.0 and np.abs(lon - start_lon) <= 10.0:
try: try:
interp4d_temp_pre = np.ma.dot(w0, ERAtemp_ascend[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) interp4d_temp_pre = np.ma.dot(w0, ERAtemp_ascent[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
interp4d_temp_post = np.ma.dot(w0, ERAtemp_ascend[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) interp4d_temp_post = np.ma.dot(w0, ERAtemp_ascent[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
interp4d_temp = (interp4d_temp_post - interp4d_temp_pre) * (t_epoch - t_pre) + interp4d_temp_pre interp4d_temp = (interp4d_temp_post - interp4d_temp_pre) * (t_epoch - t_pre) + interp4d_temp_pre
interp4d_height_pre = np.ma.dot(w0, ERAz_ascend[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) interp4d_height_pre = np.ma.dot(w0, ERAz_ascent[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
interp4d_height_post = np.ma.dot(w0, ERAz_ascend[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) interp4d_height_post = np.ma.dot(w0, ERAz_ascent[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
interp4d_height = (interp4d_height_post - interp4d_height_pre) * (t_epoch - t_pre) + interp4d_height_pre interp4d_height = (interp4d_height_post - interp4d_height_pre) * (t_epoch - t_pre) + interp4d_height_pre
interp4d_vw_x_pre = np.ma.dot(w0, vw_x_ascend[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) interp4d_vw_x_pre = np.ma.dot(w0, vw_x_ascent[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
interp4d_vw_x_post = np.ma.dot(w0, vw_x_ascend[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) interp4d_vw_x_post = np.ma.dot(w0, vw_x_ascent[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
interp4d_vw_x = (interp4d_vw_x_post - interp4d_vw_x_pre) * (t_epoch - t_pre) + interp4d_vw_x_pre interp4d_vw_x = (interp4d_vw_x_post - interp4d_vw_x_pre) * (t_epoch - t_pre) + interp4d_vw_x_pre
interp4d_vw_y_pre = np.ma.dot(w0, vw_y_ascend[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) interp4d_vw_y_pre = np.ma.dot(w0, vw_y_ascent[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
interp4d_vw_y_post = np.ma.dot(w0, vw_y_ascend[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) interp4d_vw_y_post = np.ma.dot(w0, vw_y_ascent[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
interp4d_vw_y = (interp4d_vw_y_post - interp4d_vw_y_pre) * (t_epoch - t_pre) + interp4d_vw_y_pre interp4d_vw_y = (interp4d_vw_y_post - interp4d_vw_y_pre) * (t_epoch - t_pre) + interp4d_vw_y_pre
interp4d_vw_z_pre = np.ma.dot(w0, vw_z_ascend[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) interp4d_vw_z_pre = np.ma.dot(w0, vw_z_ascent[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
interp4d_vw_z_post = np.ma.dot(w0, vw_z_ascend[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0) interp4d_vw_z_post = np.ma.dot(w0, vw_z_ascent[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
interp4d_vw_z = (interp4d_vw_z_post - interp4d_vw_z_pre) * (t_epoch - t_pre) + interp4d_vw_z_pre interp4d_vw_z = (interp4d_vw_z_post - interp4d_vw_z_pre) * (t_epoch - t_pre) + interp4d_vw_z_pre
pressure_hPa = np.array( pressure_hPa = np.array(
@ -212,7 +262,12 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
vw_z_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_z) vw_z_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_z)
except IndexError: except IndexError:
print("Error: Check time range of ERA5 data!") if flag_arr[19] == 0:
print("Error: Check time range of ERA5 data!")
flag_arr[19] = 1
else:
flag_arr[19] = 1
else: else:
pass pass
@ -221,8 +276,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
tcc = (tcc_post - tcc_pre) * (t_epoch - t_pre) + tcc_pre tcc = (tcc_post - tcc_pre) * (t_epoch - t_pre) + tcc_pre
if isinstance(tcc, float) != True: if isinstance(tcc, float) != True:
print("WARNING: Corrupt ERA5 Data for parameter \"tcc\"!") if flag_arr[1] == 0:
print("Assuming simplified value for parameter \"tcc\".") print("WARNING: Corrupt or missing ERA5 Data for parameter \"tcc\"!")
print("Assuming simplified value for parameter \"tcc\".")
flag_arr[1] = 1
else:
flag_arr[1] = 1
tcc = cc tcc = cc
cbh_pre = np.ma.dot(w2, ERAcbh[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) cbh_pre = np.ma.dot(w2, ERAcbh[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
@ -230,8 +290,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
cbh = (cbh_post - cbh_pre) * (t_epoch - t_pre) + cbh_pre cbh = (cbh_post - cbh_pre) * (t_epoch - t_pre) + cbh_pre
if isinstance(tcc, float) != True: if isinstance(tcc, float) != True:
print("WARNING: Corrupt ERA5 Data for parameter \"cbh\"!") if flag_arr[2] == 0:
print("Assuming simplified value for parameter \"cbh\".") print("WARNING: Corrupt or missing ERA5 Data for parameter \"cbh\"!")
print("Assuming simplified value for parameter \"cbh\".")
flag_arr[2] = 1
else:
flag_arr[2] = 1
cbh = 2000 cbh = 2000
lcc_pre = np.ma.dot(w2, ERAlcc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) lcc_pre = np.ma.dot(w2, ERAlcc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
@ -239,8 +304,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
lcc = (lcc_post - lcc_pre) * (t_epoch - t_pre) + lcc_pre lcc = (lcc_post - lcc_pre) * (t_epoch - t_pre) + lcc_pre
if isinstance(lcc, float) != True: if isinstance(lcc, float) != True:
print("WARNING: Corrupt ERA5 Data for parameter \"lcc\"!") if flag_arr[3] == 0:
print("Assuming simplified value for parameter \"lcc\".") print("WARNING: Corrupt or missing ERA5 Data for parameter \"lcc\"!")
print("Assuming simplified value for parameter \"lcc\".")
flag_arr[3] = 1
else:
flag_arr[3] = 1
lcc = cc/3 lcc = cc/3
mcc_pre = np.ma.dot(w2, ERAmcc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) mcc_pre = np.ma.dot(w2, ERAmcc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
@ -248,8 +318,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
mcc = (mcc_post - mcc_pre) * (t_epoch - t_pre) + mcc_pre mcc = (mcc_post - mcc_pre) * (t_epoch - t_pre) + mcc_pre
if isinstance(mcc, float) != True: if isinstance(mcc, float) != True:
print("WARNING: Corrupt ERA5 Data for parameter \"mcc\"!") if flag_arr[4] == 0:
print("Assuming simplified value for parameter \"mcc\".") print("WARNING: Corrupt or missing ERA5 Data for parameter \"mcc\"!")
print("Assuming simplified value for parameter \"mcc\".")
flag_arr[4] = 1
else:
flag_arr[4] = 1
mcc = cc/3 mcc = cc/3
hcc_pre = np.ma.dot(w2, ERAhcc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) hcc_pre = np.ma.dot(w2, ERAhcc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
@ -257,8 +332,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
hcc = (hcc_post - hcc_pre) * (t_epoch - t_pre) + hcc_pre hcc = (hcc_post - hcc_pre) * (t_epoch - t_pre) + hcc_pre
if isinstance(hcc, float) != True: if isinstance(hcc, float) != True:
print("WARNING: Corrupt ERA5 Data for parameter \"hcc\"!") if flag_arr[5] == 0:
print("Assuming simplified value for parameter \"hcc\".") print("WARNING: Corrupt or missing ERA5 Data for parameter \"hcc\"!")
print("Assuming simplified value for parameter \"hcc\".")
flag_arr[5] = 1
else:
flag_arr[5] = 1
hcc = cc/3 hcc = cc/3
ssr_pre = np.ma.dot(w2, ERAssr[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) ssr_pre = np.ma.dot(w2, ERAssr[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
@ -270,8 +350,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
strn = ((strn_post - strn_pre) * (t_epoch - t_pre) + strn_pre) / 3600 strn = ((strn_post - strn_pre) * (t_epoch - t_pre) + strn_pre) / 3600
if isinstance(strn, float) != True: if isinstance(strn, float) != True:
print("WARNING: Corrupt ERA5 Data for parameter \"strn\"!") if flag_arr[6] == 0:
print("Assuming simplified value for parameter \"strn\".") print("WARNING: Corrupt or missing ERA5 Data for parameter \"strn\"!")
print("Assuming simplified value for parameter \"strn\".")
flag_arr[6] = 1
else:
flag_arr[6] = 1
strn = 0 strn = 0
skt_pre = np.ma.dot(w2, ERAskt[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) skt_pre = np.ma.dot(w2, ERAskt[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
@ -279,8 +364,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
skt = ((skt_post - skt_pre) * (t_epoch - t_pre) + skt_pre) skt = ((skt_post - skt_pre) * (t_epoch - t_pre) + skt_pre)
if isinstance(skt, float) != True: if isinstance(skt, float) != True:
print("WARNING: Corrupt ERA5 Data for parameter \"skt\"!") if flag_arr[7] == 0:
print("Assuming simplified value for parameter \"skt\".") print("WARNING: Corrupt or missing ERA5 Data for parameter \"skt\"!")
print("Assuming simplified value for parameter \"skt\".")
flag_arr[7] = 1
else:
flag_arr[7] = 1
skt = T_ground skt = T_ground
strd_pre = np.ma.dot(w2, ERAstrd[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) strd_pre = np.ma.dot(w2, ERAstrd[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
@ -288,8 +378,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
strd = ((strd_post - strd_pre) * (t_epoch - t_pre) + strd_pre) / 3600 strd = ((strd_post - strd_pre) * (t_epoch - t_pre) + strd_pre) / 3600
if isinstance(strd, float) != True: if isinstance(strd, float) != True:
print("WARNING: Corrupt ERA5 Data for parameter \"strd\"!") if flag_arr[8] == 0:
print("Assuming simplified value for parameter \"strd\".") print("WARNING: Corrupt or missing ERA5 Data for parameter \"strd\"!")
print("Assuming simplified value for parameter \"strd\".")
flag_arr[8] = 1
else:
flag_arr[8] = 1
strd = epsilon_ground * sigma * T_ground ** 4 strd = epsilon_ground * sigma * T_ground ** 4
strdc_pre = np.ma.dot(w2, ERAstrdc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) strdc_pre = np.ma.dot(w2, ERAstrdc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
@ -297,8 +392,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
strdc = ((strdc_post - strdc_pre) * (t_epoch - t_pre) + strdc_pre) / 3600 strdc = ((strdc_post - strdc_pre) * (t_epoch - t_pre) + strdc_pre) / 3600
if isinstance(strdc, float) != True: if isinstance(strdc, float) != True:
print("WARNING: Corrupt ERA5 Data for parameter \"strdc\"!") if flag_arr[9] == 0:
print("Assuming simplified value for parameter \"strdc\".") print("WARNING: Corrupt or missing ERA5 Data for parameter \"strdc\"!")
print("Assuming simplified value for parameter \"strdc\".")
flag_arr[9] = 1
else:
flag_arr[9] = 1
strdc = epsilon_ground * sigma * T_ground ** 4 strdc = epsilon_ground * sigma * T_ground ** 4
ssrd_pre = np.ma.dot(w2, ERAssrd[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) ssrd_pre = np.ma.dot(w2, ERAssrd[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
@ -306,14 +406,24 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
ssrd = ((ssrd_post - ssrd_pre) * (t_epoch - t_pre) + ssrd_pre) / 3600 ssrd = ((ssrd_post - ssrd_pre) * (t_epoch - t_pre) + ssrd_pre) / 3600
if isinstance(ssrd, float) != True: if isinstance(ssrd, float) != True:
print("WARNING: Corrupt ERA5 Data for parameter \"ssrd\"!") if flag_arr[10] == 0:
print("Assuming simplified value for parameter \"ssrd\".") print("WARNING: Corrupt or missing ERA5 Data for parameter \"ssrd\"!")
print("Assuming simplified value for parameter \"ssrd\".")
flag_arr[10] = 1
else:
flag_arr[10] = 1
ssrd = 1 ssrd = 1
ssr = 1 - Albedo ssr = 1 - Albedo
if isinstance(ssr, float) != True: if isinstance(ssr, float) != True:
print("WARNING: Corrupt ERA5 Data for parameter \"ssr\"!") if flag_arr[11] == 0:
print("Assuming simplified value for parameter \"ssr\".") print("WARNING: Corrupt or missing ERA5 Data for parameter \"ssr\"!")
print("Assuming simplified value for parameter \"ssr\".")
flag_arr[11] = 1
else:
flag_arr[11] = 1
ssrd = 1 ssrd = 1
ssr = 1 - Albedo ssr = 1 - Albedo
@ -326,8 +436,14 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
tisr = ((tisr_post - tisr_pre) * (t_epoch - t_pre) + tisr_pre) / 3600 tisr = ((tisr_post - tisr_pre) * (t_epoch - t_pre) + tisr_pre) / 3600
if isinstance(tisr, float) != True: if isinstance(tisr, float) != True:
print("WARNING: Corrupt ERA5 Data for parameter \"tisr\"!") if flag_arr[12] == 0:
print("Assuming simplified value for parameter \"tisr\".") print("WARNING: Corrupt or missing ERA5 Data for parameter \"tisr\"!")
print("Assuming simplified value for parameter \"tisr\".")
flag_arr[12] = 1
else:
flag_arr[12] = 1
utc = deltaT_ERA * unit.second * 3600 + Time('1900-01-01 00:00:00.0')
AZ, ELV = sun_angles_analytical(lat, lon, h, utc) AZ, ELV = sun_angles_analytical(lat, lon, h, utc)
MA = (357.52911 + 0.98560028 * (utc.jd - 2451545)) % 360 # in degree, reference: see folder "literature" MA = (357.52911 + 0.98560028 * (utc.jd - 2451545)) % 360 # in degree, reference: see folder "literature"
TA = MA + 2 * e * np.sin(np.deg2rad(MA)) + 5 / 4 * e ** 2 * np.sin(np.deg2rad(2 * MA)) TA = MA + 2 * e * np.sin(np.deg2rad(MA)) + 5 / 4 * e ** 2 * np.sin(np.deg2rad(2 * MA))
@ -335,8 +451,13 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
tisr = I_Sun * np.sin(np.deg2rad(ELV)) tisr = I_Sun * np.sin(np.deg2rad(ELV))
if isinstance(tsr, float) != True: if isinstance(tsr, float) != True:
print("WARNING: Corrupt ERA5 Data for parameter \"tsr\"!") if flag_arr[13] == 0:
print("Assuming simplified value for parameter \"tsr\".") print("WARNING: Corrupt or missing ERA5 Data for parameter \"tsr\"!")
print("Assuming simplified value for parameter \"tsr\".")
flag_arr[13] = 1
else:
flag_arr[13] = 1
tsr = (1 - Albedo) * tisr tsr = (1 - Albedo) * tisr
ttr_pre = np.ma.dot(w2, ERAttr[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2) ttr_pre = np.ma.dot(w2, ERAttr[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
@ -348,31 +469,55 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
p0 = (p0_post - p0_pre) * (t_epoch - t_pre) + p0_pre p0 = (p0_post - p0_pre) * (t_epoch - t_pre) + p0_pre
if isinstance(p0, float) != True: if isinstance(p0, float) != True:
print("WARNING: Corrupt ERA5 Data for parameter \"sp\"!") if flag_arr[14] == 0:
print("Assuming simplified value for parameter \"sp\".") print("WARNING: Corrupt or missing ERA5 Data for parameter \"sp\"!")
print("Assuming simplified value for parameter \"sp\".")
flag_arr[14] = 1
else:
flag_arr[14] = 1
p0 = 101325.0 p0 = 101325.0
if isinstance(ttr, float) != True: if isinstance(ttr, float) != True:
print("WARNING: Corrupt ERA5 Data for parameter \"ttr\"!") if flag_arr[15] == 0:
print("Assuming simplified value for parameter \"ttr\".") print("WARNING: Corrupt or missing ERA5 Data for parameter \"ttr\"!")
print("Assuming simplified value for parameter \"ttr\".")
flag_arr[15] = 1
else:
flag_arr[15] = 1
utc = deltaT_ERA * unit.second * 3600 + Time('1900-01-01 00:00:00.0')
AZ, ELV = sun_angles_analytical(lat, lon, h, utc) AZ, ELV = sun_angles_analytical(lat, lon, h, utc)
tau_atm, tau_atmIR = tau(ELV, h, p_air, p0) tau_atm, tau_atmIR = tau(ELV, h, p_air, p0)
HalfConeAngle = np.arcsin(R_E / (R_E + h)) HalfConeAngle = np.arcsin(R_E / (R_E + h))
ViewFactor = (1 - np.cos(HalfConeAngle)) / 2 ViewFactor = (1 - np.cos(HalfConeAngle)) / 2
ttr = epsilon_ground * sigma * T_ground ** 4 * tau_atmIR * ViewFactor * 2 ttr = epsilon_ground * sigma * T_ground ** 4 * tau_atmIR * ViewFactor * 2
if h > interp4d_height[0]: if h > np.amax(interp4d_height):
p_air = press_interp1d(interp4d_height[0]) if flag_arr[16] == 0:
T_air = temp_interp1d(interp4d_height[0]) print("WARNING: Balloon altitude above interpolation area!")
u = vw_x_interp1d(interp4d_height[0]) flag_arr[16] = 1
v = vw_y_interp1d(interp4d_height[0]) else:
w = -1 / grav(lat, h) * vw_z_interp1d(interp4d_height[0]) * R_air * T_air / p_air flag_arr[16] = 1
elif h < interp4d_height[-1]:
p_air = press_interp1d(interp4d_height[-1]) p_air = press_interp1d(np.amax(interp4d_height))
T_air = temp_interp1d(interp4d_height[-1]) T_air = temp_interp1d(np.amax(interp4d_height))
u = vw_x_interp1d(interp4d_height[-1]) u = vw_x_interp1d(np.amax(interp4d_height))
v = vw_y_interp1d(interp4d_height[-1]) v = vw_y_interp1d(np.amax(interp4d_height))
w = -1 / grav(lat, h) * vw_z_interp1d(interp4d_height[-1]) * R_air * T_air / p_air w = -1 / grav(lat, h) * vw_z_interp1d(np.amax(interp4d_height)) * R_air * T_air / p_air
elif h < np.amin(interp4d_height):
if flag_arr[17] == 0:
print("WARNING: Balloon altitude below interpolation area!")
flag_arr[17] = 1
else:
flag_arr[17] = 1
p_air = press_interp1d(np.amin(interp4d_height))
T_air = temp_interp1d(np.amin(interp4d_height))
u = vw_x_interp1d(np.amin(interp4d_height))
v = vw_y_interp1d(np.amin(interp4d_height))
w = -1 / grav(lat, h) * vw_z_interp1d(np.amin(interp4d_height)) * R_air * T_air / p_air
else: else:
p_air = press_interp1d(h) p_air = press_interp1d(h)
T_air = temp_interp1d(h) T_air = temp_interp1d(h)
@ -384,10 +529,15 @@ def ERA5Data(lon, lat, h, t, deltaT_ERA):
return p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt return p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt
t_start = Time(start_utc)
deltaT_ERA = (Time(start_utc).jd - Time('1900-01-01 00:00:00.0').jd) * 24.000000 m_gas_init = ((m_pl + m_film + m_bal_init) * (FreeLift / 100 + 1)) / (R_gas / R_air - 1)
p_air0, p00, T_air0, rho_air0, u0, v0, w0, cbh0, tcc0, lcc0, mcc0, hcc0, ssr0, strn0, strd0, strdc0, ssrd0, tsr0, ttr0, tisr0, skt0 = ERA5Data(
start_lon, start_lat, start_height, 0, deltaT_ERA) deltaT_ERA = (t_start.jd - Time('1900-01-01 00:00:00.0').jd) * 24.000000
p_air0, p00, T_air0, rho_air0, u0, v0, w0, cbh0, tcc0, lcc0, mcc0, hcc0, ssr0, strn0, strd0, strdc0, ssrd0, tsr0, ttr0, tisr0, skt0 = ERA5Data(start_lon, start_lat, start_height, 0, deltaT_ERA, flag_arr)
A_top0 = np.pi/4 * 1.383 ** 2 * (m_gas_init * R_gas * T_air0 / p_air0) ** (2/3)
y0 = [ y0 = [
start_lon, # start longitude [deg] start_lon, # start longitude [deg]
@ -404,8 +554,32 @@ y0 = [
] ]
def model(t, y, m_pl, m_film, c_virt): t_list, h_list, v_list = [], [], []
utc = Time(start_utc) + t * unit.second lat_list, lon_list = [], []
p_list, rho_list = [], []
Temp_list, Tgas_list, T_film_list = [], [], []
rhog_list = []
V_b_list = []
Q_Albedo_list = []
Q_IREarth_list = []
Q_Sun_list = []
Q_IRFilm_list = []
Q_IRout_list = []
Q_ConvExt_list = []
Q_ConvInt_list = []
utc_list = []
ssr_list = []
ssrd_list = []
ttr_list = []
strd_list = []
strn_list = []
tisr_list = []
tsr_list = []
def model(t, y, m_pl, m_film, c_virt, A_top0, t_start):
utc = t_start + t * unit.second
lon = y[0] # 1 lon = y[0] # 1
lat = y[1] # 2 lat = y[1] # 2
h = y[2] # 3 h = y[2] # 3
@ -430,21 +604,61 @@ def model(t, y, m_pl, m_film, c_virt):
else: else:
lat = lat lat = lat
if h > 53700:
h = 53700
elif h < 0:
h = 0
else:
h = h
h_list.append(h)
utc_list.append(utc)
lat_list.append(lat)
lon_list.append(lon)
Tgas_list.append(T_gas)
T_film_list.append(T_film)
r_lon, r_lat = radii(lat, h) # calculate radii for velocity conversion between cartesian and Earth reference frame r_lon, r_lat = radii(lat, h) # calculate radii for velocity conversion between cartesian and Earth reference frame
deltaT_ERA = (Time(start_utc).jd - Time('1900-01-01 00:00:00.0').jd) * 24.000000 # conversion to ERA5 time format deltaT_ERA = (t_start.jd - Time('1900-01-01 00:00:00.0').jd) * 24.000000 # conversion to ERA5 time format
AZ, ELV = sun_angles_analytical(lat, lon, h, utc)
MA = (357.52911 + 0.98560028 * (utc.jd - 2451545)) % 360 # in degree, reference: see folder "literature"
TA = MA + 2 * e * np.sin(np.deg2rad(MA)) + 5 / 4 * e ** 2 * np.sin(np.deg2rad(2 * MA))
I_Sun = 1367.5 * ((1 + e * np.cos(np.deg2rad(TA))) / (1 - e ** 2)) ** 2
HalfConeAngle = np.arcsin(R_E / (R_E + h))
ViewFactor = (1 - np.cos(HalfConeAngle)) / 2
try: try:
p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt = ERA5Data(lon, lat, h, t, deltaT_ERA) p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt = ERA5Data(lon, lat, h, t, deltaT_ERA, flag_arr)
tau_atm, tau_atmIR = tau(ELV, h, p_air, p0)
tau_atm0, tau_atmIR0 = tau(ELV, 0, p0, p0)
I_SunZ = I_Sun * tau_atm
I_Sun0 = I_Sun * tau_atm0
except: except:
# in case of solver (temporarily) exceeding interpolation area (with subsequent correction) # in case of solver (temporarily) exceeding interpolation area (with subsequent correction by the solver itself)
# or permanent drift out of interpolation area # or permanent drift out of interpolation area
if h >= 30000 or (np.abs(lat - start_lat) <= 10.0 and np.abs(lon - start_lon) <= 10.0): if h >= 30000 or (np.abs(lat - start_lat) <= 10.0 and np.abs(lon - start_lon) <= 10.0):
print("solver exceeds definition area") p0 = 101325
p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt = 0, 101325, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 p_air = p_air_simple(h)
tau_atm, tau_atmIR = tau(ELV, h, p_air, p0)
tau_atm0, tau_atmIR0 = tau(ELV, 0, p0, p0)
I_SunZ = I_Sun * tau_atm
I_Sun0 = I_Sun * tau_atm0
p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt = p_air_simple(h), 101325, T_air_simple(h), rho_air_simple(h), 0, 0, 0, 2000, cc, cc/3, cc/3, cc/3, (1 - Albedo), 0, (epsilon_ground * sigma * T_ground ** 4), (epsilon_ground * sigma * T_ground ** 4), 1, (1 - Albedo) * (I_Sun * np.sin(np.deg2rad(ELV))), (epsilon_ground * sigma * T_ground ** 4 * tau_atmIR * ViewFactor * 2), (I_Sun * np.sin(np.deg2rad(ELV))), T_ground
else: else:
print("instable trajectory!") p0 = 101325
p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt = 0, 101325, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 p_air = p_air_simple(h)
tau_atm, tau_atmIR = tau(ELV, h, p_air, p0)
tau_atm0, tau_atmIR0 = tau(ELV, 0, p0, p0)
I_SunZ = I_Sun * tau_atm
I_Sun0 = I_Sun * tau_atm0
p_air, p0, T_air, rho_air, u, v, w, cbh, tcc, lcc, mcc, hcc, ssr, strn, strd, strdc, ssrd, tsr, ttr, tisr, skt = p_air_simple(h), 101325, T_air_simple(h), rho_air_simple(h), 0, 0, 0, 2000, cc, cc/3, cc/3, cc/3, (1 - Albedo), 0, (epsilon_ground * sigma * T_ground ** 4), (epsilon_ground * sigma * T_ground ** 4), 1, (1 - Albedo) * (I_Sun * np.sin(np.deg2rad(ELV))), (epsilon_ground * sigma * T_ground ** 4 * tau_atmIR * ViewFactor * 2), (I_Sun * np.sin(np.deg2rad(ELV))), T_ground
p_gas = p_air p_gas = p_air
@ -464,23 +678,22 @@ def model(t, y, m_pl, m_film, c_virt):
dP_valve = grav(lat, h) * (rho_air - rho_gas) * h_valve dP_valve = grav(lat, h) * (rho_air - rho_gas) * h_valve
dP_duct = grav(lat, h) * (rho_air - rho_gas) * h_duct dP_duct = grav(lat, h) * (rho_air - rho_gas) * h_duct
if m_gas > m_gas_init: # limit gas mass to plausible value if m_gas < 0: # limit gas mass to plausible value
m_gas = m_gas_init #
elif m_gas < 0:
m_gas = 0 m_gas = 0
else:
pass
V_b = m_gas / rho_gas # calculate balloon volume from current gas mass and gas density V_b = m_gas / rho_gas # calculate balloon volume from current gas mass and gas density
rhog_list.append(rho_gas)
if V_b > V_design: if V_b > V_design:
c_duct = c_ducts c_duct = c_ducts
elif V_b < 0: elif V_b < 0:
c_duct = 0 c_duct = 0
V_b = 0 V_b = 1.0
else: else:
c_duct = 0 c_duct = 0
V_b_list.append(V_b)
if ballasting(utc) == True: if ballasting(utc) == True:
if m_bal >= 0: if m_bal >= 0:
mdot = m_baldot mdot = m_baldot
@ -518,27 +731,13 @@ def model(t, y, m_pl, m_film, c_virt):
A_surf1 = 4.94 * V_design ** (2 / 3) * (1 - np.cos(np.pi * L_goreB / L_goreDesign)) A_surf1 = 4.94 * V_design ** (2 / 3) * (1 - np.cos(np.pi * L_goreB / L_goreDesign))
A_eff = 0.65 * A_surf + 0.35 * A_surf1 A_eff = 0.65 * A_surf + 0.35 * A_surf1
A_top = np.pi / 4 * d_b ** 2 A_top = np.pi / 4 * d_b ** 2
A_top0 = A_top0
AZ, ELV = sun_angles_analytical(lat, lon, h, utc)
A_proj = A_top * (0.9125 + 0.0875 * np.cos(np.pi - 2 * np.deg2rad(ELV))) # projected area for sun radiation A_proj = A_top * (0.9125 + 0.0875 * np.cos(np.pi - 2 * np.deg2rad(ELV))) # projected area for sun radiation
A_drag = A_top * (0.9125 + 0.0875 * np.cos(np.pi - 2 * alpha)) # projected area for drag A_drag = A_top * (0.9125 + 0.0875 * np.cos(np.pi - 2 * alpha)) # projected area for drag
# CALCULATIONS FOR THERMAL MODEL # CALCULATIONS FOR THERMAL MODEL
tau_atm, tau_atmIR = tau(ELV, h, p_air, p0)
tau_atm0, tau_atmIR0 = tau(ELV, 0, p0, p0)
MA = (357.52911 + 0.98560028 * (utc.jd - 2451545)) % 360 # in degree, reference: see folder "literature"
TA = MA + 2 * e * np.sin(np.deg2rad(MA)) + 5 / 4 * e ** 2 * np.sin(np.deg2rad(2 * MA))
I_Sun = 1367.5 * ((1 + e * np.cos(np.deg2rad(TA))) / (1 - e ** 2)) ** 2
I_SunZ = I_Sun * tau_atm
I_Sun0 = I_Sun * tau_atm0
HalfConeAngle = np.arcsin(R_E / (R_E + h))
ViewFactor = (1 - np.cos(HalfConeAngle)) / 2
if simple == True: if simple == True:
q_IREarth = epsilon_ground * sigma * T_ground ** 4 * tau_atmIR q_IREarth = epsilon_ground * sigma * T_ground ** 4 * tau_atmIR
@ -680,12 +879,33 @@ def model(t, y, m_pl, m_film, c_virt):
Q_ConvExt = HC_external * A_eff * (T_air - T_film) Q_ConvExt = HC_external * A_eff * (T_air - T_film)
Q_ConvInt = HC_internal * A_eff * (T_film - T_gas) Q_ConvInt = HC_internal * A_eff * (T_film - T_gas)
if simple == True: Q_Albedo_list.append(Q_Albedo)
c_d = 0.47 # sphere Q_IREarth_list.append(Q_IREarth)
else: Q_Sun_list.append(Q_Sun)
c_d = cd_PalumboHigh(Fr, Re, A_top) # Ref. xx Q_IRFilm_list.append(Q_IRFilm)
Q_IRout_list.append(Q_IRout)
Q_ConvExt_list.append(Q_ConvExt)
Q_ConvInt_list.append(Q_ConvInt)
#c_d = 0.47 # cd_PalumboHigh(Fr, Re, A_top) #cd_sphere(Re) #0.47 # cd_Palumbo(Fr, Re, A_top) # 0.8 #cd_sphere(Re) # cd_palumbo(Fr, Re, A_top) # cd_sphere(Re) / 0.8 / 0.47 ssr_list.append(ssr)
ssrd_list.append(ssrd)
ttr_list.append(ttr)
strd_list.append(strd)
strn_list.append(strn)
tisr_list.append(tisr)
tsr_list.append(tsr)
if simple == True:
c_d = c_d
else:
if drag_model == 'PalumboHigh':
c_d = cd_PalumboHigh(Fr, Re, A_top, A_top0)
elif drag_model == 'Palumbo':
c_d = cd_Palumbo(Fr, Re, A_top, A_top0)
elif drag_model == 'PalumboLow':
c_d = cd_PalumboLow(Fr, Re, A_top, A_top0)
else:
c_d = cd_sphere(Re)
D = drag(c_d, rho_air, A_drag, v_rel) # calculate drag force D = drag(c_d, rho_air, A_drag, v_rel) # calculate drag force
@ -707,7 +927,12 @@ def model(t, y, m_pl, m_film, c_virt):
eqn7 = Q_ConvInt / (gamma * c_v * m_gas) - (gamma - 1) / gamma * (rho_air * grav(lat, h)) / (rho_gas * R_gas) * v_z eqn7 = Q_ConvInt / (gamma * c_v * m_gas) - (gamma - 1) / gamma * (rho_air * grav(lat, h)) / (rho_gas * R_gas) * v_z
eqn8 = (Q_Sun + Q_Albedo + Q_IREarth + Q_IRFilm + Q_ConvExt - Q_ConvInt - Q_IRout) / (c_f * m_film) eqn8 = (Q_Sun + Q_Albedo + Q_IREarth + Q_IRFilm + Q_ConvExt - Q_ConvInt - Q_IRout) / (c_f * m_film)
eqn9 = -(A_ducts * c_duct * np.sqrt(np.abs(2 * dP_duct * rho_gas))) - (A_valve * c2 * np.sqrt(np.abs(2 * dP_valve * rho_gas))) eqn9 = -(A_ducts * c_duct * np.sqrt(np.abs(2 * dP_duct * rho_gas))) - (A_valve * c2 * np.sqrt(np.abs(2 * dP_valve * rho_gas)))
if eqn9 > 0:
eqn9 = 0
eqn10 = c2dot eqn10 = c2dot
if m_bal > 0: if m_bal > 0:
eqn11 = -mdot eqn11 = -mdot
else: else:
@ -733,28 +958,143 @@ def below_float(t, y, m_pl, m_film, c_virt):
hit_ground = lambda t, x: at_ground(t, x, m_pl, m_film, c_virt) hit_ground = lambda t, x: at_ground(t, x, m_pl, m_film, c_virt)
hit_ground.terminal = True hit_ground.terminal = True
hit_ground.direction = -1 hit_ground.direction = -1
excess_ascend = lambda t, x: above_float(t, x, m_pl, m_film, c_virt) excess_ascent = lambda t, x: above_float(t, x, m_pl, m_film, c_virt)
excess_ascend.terminal = True excess_ascent.terminal = True
excess_ascend.direction = -1 excess_ascent.direction = -1
instable = lambda t, x: below_float(t, x, m_pl, m_film, c_virt) instable = lambda t, x: below_float(t, x, m_pl, m_film, c_virt)
instable.terminal = True instable.terminal = True
instable.direction = -1 instable.direction = -1
t0 = 2.0
tf = 30000
print("Beginning simulation") t0 = 0
tf = t_sim
sol = solve_ivp(fun=lambda t, x: model(t, x, m_pl, m_film, c_virt), t_span=[t0, tf], y0=y0, method='BDF', events=[hit_ground, excess_ascend, instable]) print("")
print("BEGINNING SIMULATION")
# tnew = np.linspace(0, sol.t[-1], len(Vol_list)) sol = solve_ivp(fun=lambda t, x: model(t, x, m_pl, m_film, c_virt, A_top0, t_start), t_span=[t0, tf], y0=y0, method='RK45', events=[hit_ground, excess_ascent, instable]) #, t_eval=comp_time
tnew = np.linspace(0, sol.t[-1], len(V_b_list))
print(sol.message) print(sol.message)
"""
lonsol = sol.y[0, :]
latsol = sol.y[1, :]
hsol = sol.y[2, :]
x_sol, y_sol, z_sol = transform2(lonsol, latsol, hsol)
x_test, y_test, z_test = transform2(comp_lon, comp_lat, comp_height)
delta = ((x_sol - x_test)**2 + (y_sol - y_test)**2 + (z_sol - z_test)**2)**(0.5)
val = 0
i = 0
for x in delta:
print(latsol[i])
print(comp_lat[i])
print(latsol[i] - comp_lat[i])
print(x)
val += x ** 2
i += 1
RMS = np.sqrt(val/i)
print('RMS')
print(RMS)
"""
print(datetime.now() - starttime) print(datetime.now() - starttime)
arr0 = np.linspace(0, sol.t[-1], len(V_b_list))
arr1 = np.asarray(utc_list)
arr2 = np.asarray(h_list)
arr3 = np.asarray(lat_list)
arr4 = np.asarray(lon_list)
arr5 = np.asarray(Tgas_list)
arr6 = np.asarray(T_film_list)
arr7 = np.asarray(rhog_list)
arr8 = np.asarray(V_b_list)
arr9 = np.asarray(Q_Albedo_list)
arr10 = np.asarray(Q_IREarth_list)
arr11 = np.asarray(Q_Sun_list)
arr12 = np.asarray(Q_IRFilm_list)
arr13 = np.asarray(Q_IRout_list)
arr14 = np.asarray(Q_ConvExt_list)
arr15 = np.asarray(Q_ConvInt_list)
arr16 = np.asarray(ssr_list)
arr17 = np.asarray(ssrd_list)
arr18 = np.asarray(ttr_list)
arr19 = np.asarray(strd_list)
arr20 = np.asarray(strn_list)
arr21 = np.asarray(tisr_list)
arr22 = np.asarray(tsr_list)
ind_list = []
for i in range(len(arr0)):
if arr0[i - 1] == arr0[i]:
ind_list.append(i)
arr0 = np.delete(arr0, ind_list)
arr1 = np.delete(arr1, ind_list)
arr2 = np.delete(arr2, ind_list)
arr3 = np.delete(arr3, ind_list)
arr4 = np.delete(arr4, ind_list)
arr5 = np.delete(arr5, ind_list)
arr6 = np.delete(arr6, ind_list)
arr7 = np.delete(arr7, ind_list)
arr8 = np.delete(arr8, ind_list)
arr9 = np.delete(arr9, ind_list)
arr10 = np.delete(arr10, ind_list)
arr11 = np.delete(arr11, ind_list)
arr12 = np.delete(arr12, ind_list)
arr13 = np.delete(arr13, ind_list)
arr14 = np.delete(arr14, ind_list)
arr15 = np.delete(arr15, ind_list)
arr16 = np.delete(arr16, ind_list)
arr17 = np.delete(arr17, ind_list)
arr18 = np.delete(arr18, ind_list)
arr19 = np.delete(arr19, ind_list)
arr20 = np.delete(arr20, ind_list)
arr21 = np.delete(arr21, ind_list)
arr22 = np.delete(arr22, ind_list)
df1 = pd.DataFrame(data={
'time [s]': arr0,
'UTC': arr1,
'Altitude [m]': arr2,
'Latitude [deg]': arr3,
'Longitude [deg]': arr4,
'T_gas [K]': arr5,
'T_film [K]': arr6,
'rho_gas [kg/m^3]': arr7,
'V_balloon [m^3]': arr8,
'Q_Albedo [W/m^2]': arr9,
'Q_IR_Earth [W/m^2]': arr10,
'Q_Sun [W/m^2]': arr11,
'Q_IRFilm [W/m^2]': arr12,
'Q_IRout [W/m^2]': arr13,
'Q_ConvExt [W/m^2]': arr14,
'Q_ConvInt [W/m^2]': arr15,
'SSR [W/m^2]': arr16,
'SSRD [W/m^2]': arr17,
'TTR [W/m^2]': arr18,
'STRD [W/m^2]': arr19,
'STRN [W/m^2]': arr20,
'TISR [W/m^2]': arr21,
'TSR [W/m^2]': arr22
})
df1.to_excel("output.xlsx")
plt.plot(sol.t, sol.y[2, :], 'k--', label='Simulation') plt.plot(sol.t, sol.y[2, :], 'k--', label='Simulation')
plt.plot(comp_time, comp_height, 'r-', label='PoGo Flight Test') plt.plot(comp_time, comp_height, 'r-', label='PoGo+ Flight Test')
plt.legend() plt.legend()
plt.title('high factor') plt.title('high factor')
plt.xlabel('time in s') plt.xlabel('time in s')
@ -762,17 +1102,14 @@ plt.ylabel('Balloon Altitude in m')
plt.show() plt.show()
plt.clf() plt.clf()
# ax = plt.axes(projection=ccrs.Mollweide()) ax = plt.axes(projection=ccrs.AzimuthalEquidistant(central_latitude=-90))
ax = plt.axes(projection=ccrs.AzimuthalEquidistant(central_latitude=90))
ax.coastlines() ax.coastlines()
ax.gridlines() ax.gridlines(draw_labels=True, linewidth=0.25, color='black')
ax.stock_img() ax.stock_img()
ax.set_extent([-120, 30, 60, 80], crs=ccrs.PlateCarree()) ax.set_extent([-120, 30, 60, 80], crs=ccrs.PlateCarree())
plt.plot(start_lon, start_lat, 'rx', transform=ccrs.Geodetic()) plt.plot(start_lon, start_lat, 'rx', transform=ccrs.Geodetic())
plt.plot(sol.y[0, :], sol.y[1, :], 'k--', transform=ccrs.Geodetic()) plt.plot(sol.y[0, :], sol.y[1, :], 'k--', transform=ccrs.Geodetic())
plt.plot(comp_lon, comp_lat, 'r-', transform=ccrs.Geodetic()) plt.plot(comp_lon, comp_lat, 'r-', transform=ccrs.Geodetic())
# plt.xticks()
# figname = "LatLon_%.2f_%.2f.png" % (Albedo, epsilon_ground)
# plt.savefig(os.path.join(rootdir, figname)) # plt.savefig(os.path.join(rootdir, figname))
plt.show() plt.show()