880 lines
37 KiB
Python
880 lines
37 KiB
Python
import sys
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import warnings
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import numpy as np
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import xarray as xr
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import pandas as pd
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import cartopy.crs as ccrs
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import astropy.units as unit
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import matplotlib.pyplot as plt
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from dask import delayed
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from datetime import datetime
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starttime = datetime.now()
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from netCDF4 import Dataset
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from astropy.time import Time
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from scipy import interpolate
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from scipy.spatial import cKDTree
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from scipy.integrate import solve_ivp
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from input.user_input import *
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from input.natural_constants import *
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from models.gravity import grav
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from models.valving import valving
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from models.ballasting import ballasting
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from models.sun import sun_angles_analytical, tau
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from models.drag import drag, cd_PalumboLow, cd_Palumbo, cd_PalumboHigh, cd_sphere
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from models.transformation import visible_cells, transform, radii
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from multiprocessing import Process
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if not sys.warnoptions:
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warnings.simplefilter("ignore")
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data = pd.read_excel(r'C:\Users\marcel\PycharmProjects\MasterThesis\Mappe1.xls', sheet_name='Tabelle3') # Tabelle3
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comp_time = pd.DataFrame(data, columns=['Time']).to_numpy().squeeze()
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comp_height = pd.DataFrame(data, columns=['Height']).to_numpy().squeeze()
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comp_lat = pd.DataFrame(data, columns=['Latitude']).to_numpy().squeeze()
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comp_lon = pd.DataFrame(data, columns=['Longitude']).to_numpy().squeeze()
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print("")
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print("Initialising simulation...")
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print("Launch location : longitude %.4f, latitude: %.4f" % (start_lon, start_lat))
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print("Launch time: " + str(start_utc) + " (UTC)")
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print("")
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print("Reading ERA5-datasets, please wait.")
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# ascend_data = xr.open_dataset("ascend_2019_kiruna.nc")
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# float_data = xr.open_dataset("float_2019.nc")
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first_file = Dataset('float1_2019.nc')
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last_file = Dataset('float4_2019.nc')
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tstart = int(first_file.variables['time'][0])
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tend = int(last_file.variables['time'][-1])
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first_file.close()
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last_file.close()
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df1 = xr.open_mfdataset(['float1_2019.nc', 'float2_2019.nc', 'float3_2019.nc', 'float4_2019.nc'], combine='by_coords', engine='netcdf4', concat_dim="time", parallel=True)
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float_data = df1.assign_coords(time=np.linspace(tstart, tend, (tend - tstart) + 1))
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df1.close()
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# ERA5 MULTI-LEVEL FLOAT (WIND + ATMOSPHERIC DATA DURING FLOAT)
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ERAtime = float_data.variables['time'][:] # time
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ERAlat1 = float_data.variables['latitude'][:].values # latitude [deg]
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ERAlon1 = float_data.variables['longitude'][:].values # longitude [deg]
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ERAz_float = float_data.variables['z'][:].values / g # geopotential [m^-2/s^-2] to geopotential height [m]
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ERApress_float = float_data.variables['level'][:].values # pressure level [-]
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ERAtemp_float = float_data.variables['t'][:].values # air temperature in [K]
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vw_x_float = float_data.variables['u'][:].values # v_x in [m/s]
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vw_y_float = float_data.variables['v'][:].values # v_y in [m/s]
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vw_z_float = float_data.variables['w'][:].values # v_z in [m/s]
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float_data.close()
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df2 = xr.open_mfdataset(['single1_2019.nc', 'single2_2019.nc', 'single3_2019.nc', 'single4_2019.nc'], combine='by_coords', engine='netcdf4', concat_dim="time", parallel=True)
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single_data = df2.assign_coords(time=np.linspace(tstart, tend, (tend - tstart) + 1))
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df2.close()
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# ERA5 SINGLE-LEVEL (RADIATIVE ENVIRONMENT)
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ERAlat2 = single_data.variables['latitude'][:].values # latitude [deg]
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ERAlon2 = single_data.variables['longitude'][:].values # longitude [deg]
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ERAtcc = single_data.variables['tcc'][:].values # total cloud cover [-]
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ERAskt = single_data.variables['skt'][:].values # skin (ground) temperature in [K]
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ERAcbh = single_data.variables['cbh'][:].values # cloud base height in [m]
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ERAlcc = single_data.variables['lcc'][:].values # low cloud cover [-]
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ERAmcc = single_data.variables['mcc'][:].values # medium cloud cover [-]
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ERAhcc = single_data.variables['hcc'][:].values # high cloud cover [-]
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ERAssr = single_data.variables['ssr'][:].values # hourly accumulated surface net solar radiation [J/m^2]
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ERAstrn = single_data.variables['str'][:].values # hourly accumulated surface net thermal radiation [J/m^2]
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ERAstrd = single_data.variables['strd'][:].values # hourly accumulated surface thermal radiation downwards [J/m^2]
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ERAssrd = single_data.variables['ssrd'][:].values # hourly accumulated surface solar radiation downwards [J/m^2]
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ERAtsr = single_data.variables['tsr'][:].values # hourly accumulated top net solar radiation [J/m^2]
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ERAttr = single_data.variables['ttr'][:].values # hourly accumulated top net thermal radiation [J/m^2]
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ERAtisr = single_data.variables['tisr'][:].values # hourly accumulated TOA incident solar radiation [J/m^2]
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ERAstrdc = single_data.variables['strdc'][:].values # hourly accumulated surface thermal radiation downward clear-sky [J/m^2]
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ERAsp = single_data.variables['sp'][:].values # surface pressure in [Pa]
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single_data.close()
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first_file = Dataset('ascend1_2019.nc')
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last_file = Dataset('ascend2_2019.nc')
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tstart = int(first_file.variables['time'][0])
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tend = int(last_file.variables['time'][-1])
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first_file.close()
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last_file.close()
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df3 = xr.open_mfdataset(['ascend1_2019.nc', 'ascend2_2019.nc'], combine='by_coords', engine='netcdf4', concat_dim="time", parallel=True)
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ascend_data = df3.assign_coords(time=np.linspace(tstart, tend, (tend - tstart) + 1))
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df3.close()
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# ERA5 MULTI-LEVEL ASCENT (WIND + ATMOSPHERIC DATA DURING ASCENT)
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ERAlat0 = ascend_data.variables['latitude'][:].values # latitude [deg]
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ERAlon0 = ascend_data.variables['longitude'][:].values # longitude [deg]
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ERAz_ascend = ascend_data.variables['z'][:].values / g # geopotential [m^-2/s^-2] to geopotential height [m]
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ERApress_ascend = ascend_data.variables['level'][:].values # pressure level [-]
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ERAtemp_ascend = ascend_data.variables['t'][:].values # air temperature in K
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vw_x_ascend = ascend_data.variables['u'][:].values # v_x in [m/s]
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vw_y_ascend = ascend_data.variables['v'][:].values # v_y in [m/s]
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vw_z_ascend = ascend_data.variables['w'][:].values # v_z in [m/s]
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ascend_data.close()
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print("Finished reading ERA5-datasets.")
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lon_era2d0, lat_era2d0 = np.meshgrid(ERAlon0, ERAlat0)
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lon_era2d1, lat_era2d1 = np.meshgrid(ERAlon1, ERAlat1)
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lon_era2d2, lat_era2d2 = np.meshgrid(ERAlon2, ERAlat2)
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xs0, ys0, zs0 = transform(lon_era2d0.flatten(), lat_era2d0.flatten())
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xs1, ys1, zs1 = transform(lon_era2d1.flatten(), lat_era2d1.flatten())
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xs2, ys2, zs2 = transform(lon_era2d2.flatten(), lat_era2d2.flatten())
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print("")
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tree0 = cKDTree(np.column_stack((xs0, ys0, zs0)))
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tree1 = cKDTree(np.column_stack((xs1, ys1, zs1)))
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tree2 = cKDTree(np.column_stack((xs2, ys2, zs2)))
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print("Built kd-trees")
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def ERA5Data(lon, lat, h, t, deltaT_ERA):
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t_epoch = deltaT_ERA + t / 3600
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t_pre = int(t_epoch)
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t_pre_ind = t_pre - int(ERAtime[0])
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t_post_ind = t_pre_ind + 1
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xt, yt, zt = transform(lon, lat) # current coordinates
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d0, inds0 = tree0.query(np.column_stack((xt, yt, zt)), k=4)
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d1, inds1 = tree1.query(np.column_stack((xt, yt, zt)), k=4) # longitude, latitude
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d2, inds2 = tree2.query(np.column_stack((xt, yt, zt)), k=visible_cells(h)) # longitude, latitude visible_cells(h)
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w0 = 1.0 / d0[0]
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w1 = 1.0 / d1[0]
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w2 = 1.0 / d2[0] ** 2
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lat_ind0 = np.unravel_index(inds0[0], lon_era2d0.shape)[0]
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lon_ind0 = np.unravel_index(inds0[0], lon_era2d0.shape)[1]
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lat_ind1 = np.unravel_index(inds1[0], lon_era2d1.shape)[0]
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lon_ind1 = np.unravel_index(inds1[0], lon_era2d1.shape)[1]
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lat_ind2 = np.unravel_index(inds2[0], lon_era2d2.shape)[0]
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lon_ind2 = np.unravel_index(inds2[0], lon_era2d2.shape)[1]
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if h >= 30000:
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try:
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interp4d_temp_pre = np.ma.dot(w1, ERAtemp_float[t_pre_ind, :, lat_ind1, lon_ind1]) / np.sum(w1)
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interp4d_temp_post = np.ma.dot(w1, ERAtemp_float[t_post_ind, :, lat_ind1, lon_ind1]) / np.sum(w1)
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interp4d_temp = (interp4d_temp_post - interp4d_temp_pre) * (t_epoch - t_pre) + interp4d_temp_pre
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interp4d_height_pre = np.ma.dot(w1, ERAz_float[t_pre_ind, :, lat_ind1, lon_ind1]) / np.sum(w1)
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interp4d_height_post = np.ma.dot(w1, ERAz_float[t_post_ind, :, lat_ind1, lon_ind1]) / np.sum(w1)
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interp4d_height = (interp4d_height_post - interp4d_height_pre) * (t_epoch - t_pre) + interp4d_height_pre
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interp4d_vw_x_pre = np.ma.dot(w1, vw_x_float[t_pre_ind, :, lat_ind1, lon_ind1]) / np.sum(w1)
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interp4d_vw_x_post = np.ma.dot(w1, vw_x_float[t_post_ind, :, lat_ind1, lon_ind1]) / np.sum(w1)
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interp4d_vw_x = (interp4d_vw_x_post - interp4d_vw_x_pre) * (t_epoch - t_pre) + interp4d_vw_x_pre
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interp4d_vw_y_pre = np.ma.dot(w1, vw_y_float[t_pre_ind, :, lat_ind1, lon_ind1]) / np.sum(w1)
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interp4d_vw_y_post = np.ma.dot(w1, vw_y_float[t_post_ind, :, lat_ind1, lon_ind1]) / np.sum(w1)
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interp4d_vw_y = (interp4d_vw_y_post - interp4d_vw_y_pre) * (t_epoch - t_pre) + interp4d_vw_y_pre
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interp4d_vw_z_pre = np.ma.dot(w1, vw_z_float[t_pre_ind, :, lat_ind1, lon_ind1]) / np.sum(w1)
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interp4d_vw_z_post = np.ma.dot(w1, vw_z_float[t_post_ind, :, lat_ind1, lon_ind1]) / np.sum(w1)
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interp4d_vw_z = (interp4d_vw_z_post - interp4d_vw_z_pre) * (t_epoch - t_pre) + interp4d_vw_z_pre
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pressure_hPa = np.array([1, 2, 3, 5, 7, 10, 20])
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pressure = 100 * pressure_hPa
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temp_interp1d = interpolate.interp1d(interp4d_height, interp4d_temp)
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press_interp1d = interpolate.interp1d(interp4d_height, pressure)
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vw_x_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_x)
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vw_y_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_y)
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vw_z_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_z)
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except IndexError:
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print("Error: Please check time range of ERA5 data!")
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elif np.abs(lat - start_lat) <= 10.0 and np.abs(lon - start_lon) <= 10.0:
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try:
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interp4d_temp_pre = np.ma.dot(w0, ERAtemp_ascend[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_temp_post = np.ma.dot(w0, ERAtemp_ascend[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_temp = (interp4d_temp_post - interp4d_temp_pre) * (t_epoch - t_pre) + interp4d_temp_pre
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interp4d_height_pre = np.ma.dot(w0, ERAz_ascend[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_height_post = np.ma.dot(w0, ERAz_ascend[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_height = (interp4d_height_post - interp4d_height_pre) * (t_epoch - t_pre) + interp4d_height_pre
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interp4d_vw_x_pre = np.ma.dot(w0, vw_x_ascend[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_vw_x_post = np.ma.dot(w0, vw_x_ascend[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_vw_x = (interp4d_vw_x_post - interp4d_vw_x_pre) * (t_epoch - t_pre) + interp4d_vw_x_pre
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interp4d_vw_y_pre = np.ma.dot(w0, vw_y_ascend[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_vw_y_post = np.ma.dot(w0, vw_y_ascend[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_vw_y = (interp4d_vw_y_post - interp4d_vw_y_pre) * (t_epoch - t_pre) + interp4d_vw_y_pre
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interp4d_vw_z_pre = np.ma.dot(w0, vw_z_ascend[t_pre_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_vw_z_post = np.ma.dot(w0, vw_z_ascend[t_post_ind, :, lat_ind0, lon_ind0]) / np.sum(w0)
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interp4d_vw_z = (interp4d_vw_z_post - interp4d_vw_z_pre) * (t_epoch - t_pre) + interp4d_vw_z_pre
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pressure_hPa = np.array(
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[1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400,
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450, 500, 550, 600, 650, 700, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, 1000])
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pressure = 100 * pressure_hPa
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temp_interp1d = interpolate.interp1d(interp4d_height, interp4d_temp)
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press_interp1d = interpolate.interp1d(interp4d_height, pressure)
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vw_x_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_x)
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vw_y_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_y)
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vw_z_interp1d = interpolate.interp1d(interp4d_height, interp4d_vw_z)
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except IndexError:
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print("Error: Check time range of ERA5 data!")
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else:
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pass
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tcc_pre = np.ma.dot(w2, ERAtcc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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tcc_post = np.ma.dot(w2, ERAtcc[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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tcc = (tcc_post - tcc_pre) * (t_epoch - t_pre) + tcc_pre
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if isinstance(tcc, float) != True:
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print("WARNING: Corrupt ERA5 Data for parameter \"tcc\"!")
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print("Assuming simplified value for parameter \"tcc\".")
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tcc = cc
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cbh_pre = np.ma.dot(w2, ERAcbh[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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cbh_post = np.ma.dot(w2, ERAcbh[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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cbh = (cbh_post - cbh_pre) * (t_epoch - t_pre) + cbh_pre
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if isinstance(tcc, float) != True:
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print("WARNING: Corrupt ERA5 Data for parameter \"cbh\"!")
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print("Assuming simplified value for parameter \"cbh\".")
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cbh = 2000
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lcc_pre = np.ma.dot(w2, ERAlcc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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lcc_post = np.ma.dot(w2, ERAlcc[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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lcc = (lcc_post - lcc_pre) * (t_epoch - t_pre) + lcc_pre
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if isinstance(lcc, float) != True:
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print("WARNING: Corrupt ERA5 Data for parameter \"lcc\"!")
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print("Assuming simplified value for parameter \"lcc\".")
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lcc = cc/3
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mcc_pre = np.ma.dot(w2, ERAmcc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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mcc_post = np.ma.dot(w2, ERAmcc[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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mcc = (mcc_post - mcc_pre) * (t_epoch - t_pre) + mcc_pre
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if isinstance(mcc, float) != True:
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print("WARNING: Corrupt ERA5 Data for parameter \"mcc\"!")
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print("Assuming simplified value for parameter \"mcc\".")
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mcc = cc/3
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hcc_pre = np.ma.dot(w2, ERAhcc[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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hcc_post = np.ma.dot(w2, ERAhcc[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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hcc = (hcc_post - hcc_pre) * (t_epoch - t_pre) + hcc_pre
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if isinstance(hcc, float) != True:
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print("WARNING: Corrupt ERA5 Data for parameter \"hcc\"!")
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print("Assuming simplified value for parameter \"hcc\".")
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hcc = cc/3
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ssr_pre = np.ma.dot(w2, ERAssr[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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ssr_post = np.ma.dot(w2, ERAssr[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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ssr = ((ssr_post - ssr_pre) * (t_epoch - t_pre) + ssr_pre) / 3600
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strn_pre = np.ma.dot(w2, ERAstrn[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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strn_post = np.ma.dot(w2, ERAstrn[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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strn = ((strn_post - strn_pre) * (t_epoch - t_pre) + strn_pre) / 3600
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if isinstance(strn, float) != True:
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print("WARNING: Corrupt ERA5 Data for parameter \"strn\"!")
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print("Assuming simplified value for parameter \"strn\".")
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strn = 0
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skt_pre = np.ma.dot(w2, ERAskt[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
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skt_post = np.ma.dot(w2, ERAskt[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
|
skt = ((skt_post - skt_pre) * (t_epoch - t_pre) + skt_pre)
|
|
|
|
if isinstance(skt, float) != True:
|
|
print("WARNING: Corrupt ERA5 Data for parameter \"skt\"!")
|
|
print("Assuming simplified value for parameter \"skt\".")
|
|
skt = T_ground
|
|
|
|
strd_pre = np.ma.dot(w2, ERAstrd[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
|
strd_post = np.ma.dot(w2, ERAstrd[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
|
strd = ((strd_post - strd_pre) * (t_epoch - t_pre) + strd_pre) / 3600
|
|
|
|
if isinstance(strd, float) != True:
|
|
print("WARNING: Corrupt ERA5 Data for parameter \"strd\"!")
|
|
print("Assuming simplified value for parameter \"strd\".")
|
|
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_post = np.ma.dot(w2, ERAstrdc[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
|
strdc = ((strdc_post - strdc_pre) * (t_epoch - t_pre) + strdc_pre) / 3600
|
|
|
|
if isinstance(strdc, float) != True:
|
|
print("WARNING: Corrupt ERA5 Data for parameter \"strdc\"!")
|
|
print("Assuming simplified value for parameter \"strdc\".")
|
|
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_post = np.ma.dot(w2, ERAssrd[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
|
ssrd = ((ssrd_post - ssrd_pre) * (t_epoch - t_pre) + ssrd_pre) / 3600
|
|
|
|
if isinstance(ssrd, float) != True:
|
|
print("WARNING: Corrupt ERA5 Data for parameter \"ssrd\"!")
|
|
print("Assuming simplified value for parameter \"ssrd\".")
|
|
ssrd = 1
|
|
ssr = 1 - Albedo
|
|
|
|
if isinstance(ssr, float) != True:
|
|
print("WARNING: Corrupt ERA5 Data for parameter \"ssr\"!")
|
|
print("Assuming simplified value for parameter \"ssr\".")
|
|
ssrd = 1
|
|
ssr = 1 - Albedo
|
|
|
|
tsr_pre = np.ma.dot(w2, ERAtsr[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
|
tsr_post = np.ma.dot(w2, ERAtsr[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
|
tsr = ((tsr_post - tsr_pre) * (t_epoch - t_pre) + tsr_pre) / 3600
|
|
|
|
tisr_pre = np.ma.dot(w2, ERAtisr[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
|
tisr_post = np.ma.dot(w2, ERAtisr[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
|
tisr = ((tisr_post - tisr_pre) * (t_epoch - t_pre) + tisr_pre) / 3600
|
|
|
|
if isinstance(tisr, float) != True:
|
|
print("WARNING: Corrupt ERA5 Data for parameter \"tisr\"!")
|
|
print("Assuming simplified value for parameter \"tisr\".")
|
|
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
|
|
tisr = I_Sun * np.sin(np.deg2rad(ELV))
|
|
|
|
if isinstance(tsr, float) != True:
|
|
print("WARNING: Corrupt ERA5 Data for parameter \"tsr\"!")
|
|
print("Assuming simplified value for parameter \"tsr\".")
|
|
tsr = (1 - Albedo) * tisr
|
|
|
|
ttr_pre = np.ma.dot(w2, ERAttr[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
|
ttr_post = np.ma.dot(w2, ERAttr[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
|
ttr = ((ttr_post - ttr_pre) * (t_epoch - t_pre) + ttr_pre) / 3600
|
|
|
|
p0_pre = np.ma.dot(w2, ERAsp[t_pre_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
|
p0_post = np.ma.dot(w2, ERAsp[t_post_ind, lat_ind2, lon_ind2]) / np.sum(w2)
|
|
p0 = (p0_post - p0_pre) * (t_epoch - t_pre) + p0_pre
|
|
|
|
if isinstance(p0, float) != True:
|
|
print("WARNING: Corrupt ERA5 Data for parameter \"sp\"!")
|
|
print("Assuming simplified value for parameter \"sp\".")
|
|
p0 = 101325.0
|
|
|
|
if isinstance(ttr, float) != True:
|
|
print("WARNING: Corrupt ERA5 Data for parameter \"ttr\"!")
|
|
print("Assuming simplified value for parameter \"ttr\".")
|
|
AZ, ELV = sun_angles_analytical(lat, lon, h, utc)
|
|
tau_atm, tau_atmIR = tau(ELV, h, p_air, p0)
|
|
HalfConeAngle = np.arcsin(R_E / (R_E + h))
|
|
ViewFactor = (1 - np.cos(HalfConeAngle)) / 2
|
|
ttr = epsilon_ground * sigma * T_ground ** 4 * tau_atmIR * ViewFactor * 2
|
|
|
|
if h > interp4d_height[0]:
|
|
print("Balloon altitude above interpolation area!")
|
|
print(h)
|
|
p_air = press_interp1d(interp4d_height[0])
|
|
T_air = temp_interp1d(interp4d_height[0])
|
|
u = vw_x_interp1d(interp4d_height[0])
|
|
v = vw_y_interp1d(interp4d_height[0])
|
|
w = -1 / grav(lat, h) * vw_z_interp1d(interp4d_height[0]) * R_air * T_air / p_air
|
|
elif h < interp4d_height[-1]:
|
|
print("Balloon altitude below interpolation area!")
|
|
p_air = press_interp1d(interp4d_height[-1])
|
|
T_air = temp_interp1d(interp4d_height[-1])
|
|
u = vw_x_interp1d(interp4d_height[-1])
|
|
v = vw_y_interp1d(interp4d_height[-1])
|
|
w = -1 / grav(lat, h) * vw_z_interp1d(interp4d_height[-1]) * R_air * T_air / p_air
|
|
else:
|
|
p_air = press_interp1d(h)
|
|
T_air = temp_interp1d(h)
|
|
u = vw_x_interp1d(h)
|
|
v = vw_y_interp1d(h)
|
|
w = -1 / grav(lat, h) * vw_z_interp1d(h) * R_air * T_air / p_air
|
|
|
|
rho_air = p_air / (R_air * T_air)
|
|
|
|
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
|
|
|
|
|
|
begin_time = datetime.now()
|
|
|
|
# start = Time(start_utc)
|
|
|
|
def model(t, y, m_pl, m_film, c_virt, t_start):
|
|
utc = t_start + t * unit.second
|
|
lon = y[0] # 1
|
|
lat = y[1] # 2
|
|
h = y[2] # 3
|
|
v_x = y[3] # 4
|
|
v_y = y[4] # 5
|
|
v_z = y[5] # 6
|
|
T_gas = y[6] # 7
|
|
T_film = y[7] # 8
|
|
m_gas = y[8] # 9
|
|
c2 = y[9] # 10
|
|
m_bal = y[10] # 11
|
|
|
|
if (lon % 360) > 180: # convert longitude to value in standard interval [-180, 180]
|
|
lon = (lon % 360) - 360
|
|
else:
|
|
lon = (lon % 360)
|
|
|
|
if lat > 90: # set plausible limits for latitudes
|
|
lat = 90
|
|
elif lat < -90:
|
|
lat = -90
|
|
else:
|
|
lat = lat
|
|
|
|
if h > 53700:
|
|
h = 53700
|
|
elif h < 0:
|
|
h = 0
|
|
else:
|
|
h = h
|
|
|
|
r_lon, r_lat = radii(lat, h) # calculate radii for velocity conversion between cartesian and Earth reference frame
|
|
|
|
deltaT_ERA = (t_start.jd - Time('1900-01-01 00:00:00.0').jd) * 24.000000 # conversion to ERA5 time format
|
|
|
|
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)
|
|
except:
|
|
# in case of solver (temporarily) exceeding interpolation area (with subsequent correction)
|
|
# 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):
|
|
print("solver exceeds definition area")
|
|
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
|
|
else:
|
|
print("instable trajectory!")
|
|
print(h)
|
|
print(c_virt)
|
|
print(m_gas)
|
|
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_gas = p_air
|
|
|
|
h_valve = 1.034 * V_design ** (1 / 3)
|
|
h_duct = 0.47 * h_valve
|
|
|
|
v_relx = u - v_x # relative wind velocity x-dir (balloon frame)
|
|
v_rely = v - v_y # relative wind velocity y-dir (balloon frame)
|
|
v_relz = w - v_z # relative wind velocity z-dir (balloon frame)
|
|
|
|
v_rel = np.sqrt(v_relx ** 2 + v_rely ** 2 + v_relz ** 2) # total relative wind velocity (balloon frame)
|
|
|
|
alpha = np.arcsin(v_relz / v_rel) # "angle of attack": angle between longitudinal axis and rel. wind (in [rad])
|
|
|
|
rho_gas = p_gas / (R_gas * T_gas) # calculate gas density through *ideal* gas equation
|
|
|
|
dP_valve = grav(lat, h) * (rho_air - rho_gas) * h_valve
|
|
dP_duct = grav(lat, h) * (rho_air - rho_gas) * h_duct
|
|
|
|
if m_gas < 0: # limit gas mass to plausible value
|
|
m_gas = 0 #
|
|
|
|
V_b = m_gas / rho_gas # calculate balloon volume from current gas mass and gas density
|
|
|
|
if V_b > V_design:
|
|
c_duct = c_ducts
|
|
elif V_b < 0:
|
|
c_duct = 0
|
|
V_b = 0
|
|
else:
|
|
c_duct = 0
|
|
|
|
if ballasting(utc) == True:
|
|
if m_bal >= 0:
|
|
mdot = m_baldot
|
|
else:
|
|
mdot = 0
|
|
else:
|
|
mdot = 0
|
|
|
|
if valving(utc) == True: # opening valve process
|
|
if c2 == 0:
|
|
c2 = 1.0
|
|
c2dot = 0
|
|
elif c_valve < c2 <= 1.0:
|
|
c2dot = (c_valve - 1.0) / t_open
|
|
else:
|
|
c2dot = 0
|
|
c2 = c_valve
|
|
|
|
if valving(utc) == False: # closing valve process
|
|
if c2 == 0:
|
|
c2dot = 0
|
|
elif c_valve <= c2 < 1.0:
|
|
c2dot = (1.0 - c_valve) / t_close
|
|
else:
|
|
c2dot = 0
|
|
c2 = 0
|
|
|
|
m_gross = m_pl + m_film + m_bal
|
|
m_tot = m_pl + m_film + m_gas
|
|
m_virt = m_tot + c_virt * rho_air * V_b
|
|
|
|
d_b = 1.383 * V_b ** (1 / 3) # calculate diameter of balloon from its volume
|
|
L_goreB = 1.914 * V_b ** (1 / 3)
|
|
A_surf = 4.94 * V_b ** (2 / 3)
|
|
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_top = np.pi / 4 * d_b ** 2
|
|
|
|
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_drag = A_top * (0.9125 + 0.0875 * np.cos(np.pi - 2 * alpha)) # projected area for drag
|
|
|
|
# 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:
|
|
|
|
q_IREarth = epsilon_ground * sigma * T_ground ** 4 * tau_atmIR
|
|
|
|
if ELV <= 0:
|
|
q_Albedo = 0
|
|
else:
|
|
q_Albedo = Albedo * I_Sun * np.sin(np.deg2rad(ELV))
|
|
|
|
Q_Albedo = alpha_VIS * A_surf * q_Albedo * ViewFactor * (1 + tau_VIS / (1 - r_VIS))
|
|
Q_IREarth = alpha_IR * A_surf * q_IREarth * ViewFactor * (1 + tau_IR / (1 - r_IR))
|
|
|
|
q_sun = I_SunZ
|
|
|
|
else:
|
|
|
|
if tcc <= 0.01:
|
|
|
|
q_IREarth1 = alpha_IR * A_surf * (strd - strn) * tau_atmIR * ViewFactor * (1 + tau_VIS / (1 - r_VIS))
|
|
q_IREarth2 = alpha_IR * A_surf * np.abs(ttr) * 0.5 * (1 + tau_IR / (1 - r_IR))
|
|
# q_IREarth2 = alpha_IR * A_surf * (0.04321906 * np.abs(ttr) + 84.67820281) * (1 + tau_IR / (1 - r_IR))
|
|
|
|
if h > 40000:
|
|
Q_IREarth = q_IREarth2
|
|
else:
|
|
Q_IREarth = (q_IREarth2 - q_IREarth1) / 40000 * h + q_IREarth1
|
|
|
|
if ELV <= 0:
|
|
q_Albedo1 = 0
|
|
q_Albedo2 = 0
|
|
else:
|
|
if ssrd == 0:
|
|
q_Albedo1 = 0
|
|
else:
|
|
q_Albedo1 = alpha_VIS * A_surf * (1 - ssr / ssrd) * I_Sun0 * tau_atmIR * np.sin(
|
|
np.deg2rad(ELV)) * ViewFactor * (1 + tau_VIS / (1 - r_VIS)) # !
|
|
if tisr == 0:
|
|
q_Albedo2 = 0
|
|
else:
|
|
q_Albedo2 = alpha_VIS * A_surf * (1 - tsr / tisr) * I_Sun * np.sin(np.deg2rad(ELV)) * 0.5 * (
|
|
1 + tau_VIS / (1 - r_VIS)) # !
|
|
if h > 40000:
|
|
Q_Albedo = q_Albedo2
|
|
else:
|
|
Q_Albedo = (q_Albedo2 - q_Albedo1) / 40000 * h + q_Albedo1
|
|
|
|
q_sun = I_SunZ
|
|
|
|
else:
|
|
q_IRground_bc = (strd - strn) + (strd - strdc)
|
|
q_IREarth_bc = alpha_IR * A_surf * q_IRground_bc * tau_atmIR * ViewFactor * (1 + tau_VIS / (1 - r_VIS))
|
|
q_sun_bc = I_SunZ * (1 - tcc)
|
|
q_Albedo_bc = alpha_VIS * A_surf * (1 - ssr / ssrd) * I_Sun0 * tau_atmIR * np.sin(
|
|
np.deg2rad(ELV)) * ViewFactor * (1 + tau_VIS / (1 - r_VIS))
|
|
|
|
# q_IREarth_ac = alpha_IR * A_surf * (0.04321906 * np.abs(ttr) + 84.67820281) * (1 + tau_IR / (1 - r_IR))
|
|
q_IREarth_ac = alpha_IR * A_surf * np.abs(ttr) * 0.5 * (1 + tau_VIS / (1 - r_VIS))
|
|
q_sun_ac = I_SunZ
|
|
q_Albedo_ac = alpha_VIS * A_surf * (1 - tsr / tisr) * I_Sun * np.sin(np.deg2rad(ELV)) * ViewFactor * (
|
|
1 + tau_VIS / (1 - r_VIS))
|
|
|
|
if h <= cbh: # "below clouds"
|
|
Q_IREarth = q_IREarth_bc
|
|
q_sun = q_sun_bc
|
|
if ELV <= 0:
|
|
Q_Albedo = 0
|
|
else:
|
|
Q_Albedo = q_Albedo_bc
|
|
elif h >= 12000: # "above clouds"
|
|
Q_IREarth = q_IREarth_ac
|
|
q_sun = q_sun_ac
|
|
if ELV <= 0:
|
|
Q_Albedo = 0
|
|
else:
|
|
Q_Albedo = q_Albedo_ac
|
|
elif h >= 6000:
|
|
if hcc >= 0.01:
|
|
Q_IREarth = ((h - 6000) / 6000 * hcc + mcc + lcc) / (hcc + mcc + lcc) * (
|
|
q_IREarth_ac - q_IREarth_bc) + q_IREarth_bc
|
|
q_sun = ((h - 6000) / 6000 * hcc + mcc + lcc) / (hcc + mcc + lcc) * (q_sun_ac - q_sun_bc) + q_sun_bc
|
|
if ELV <= 0:
|
|
Q_Albedo = 0
|
|
else:
|
|
Q_Albedo = ((h - 6000) / 6000 * hcc + mcc + lcc) / (hcc + mcc + lcc) * (
|
|
q_Albedo_ac - q_Albedo_bc) + q_Albedo_bc
|
|
else:
|
|
Q_IREarth = q_IREarth_ac
|
|
q_sun = q_sun_ac
|
|
if ELV <= 0:
|
|
Q_Albedo = 0
|
|
else:
|
|
Q_Albedo = q_Albedo_ac
|
|
elif h >= 2000:
|
|
if mcc > 0.01 or hcc > 0.01:
|
|
Q_IREarth = ((h - 2000) / 4000 * mcc + lcc) / (hcc + mcc + lcc) * (
|
|
q_IREarth_ac - q_IREarth_bc) + q_IREarth_bc
|
|
q_sun = ((h - 2000) / 4000 * mcc + lcc) / (hcc + mcc + lcc) * (q_sun_ac - q_sun_bc) + q_sun_bc
|
|
if ELV <= 0:
|
|
Q_Albedo = 0
|
|
else:
|
|
Q_Albedo = ((h - 2000) / 4000 * mcc + lcc) / (hcc + mcc + lcc) * (
|
|
q_Albedo_ac - q_Albedo_bc) + q_Albedo_bc
|
|
else:
|
|
Q_IREarth = q_IREarth_ac
|
|
q_sun = q_sun_ac
|
|
if ELV <= 0:
|
|
Q_Albedo = 0
|
|
else:
|
|
Q_Albedo = q_Albedo_ac
|
|
else:
|
|
Q_IREarth = (h / 2000 * lcc) / (hcc + mcc + lcc) * (q_IREarth_ac - q_IREarth_bc) + q_IREarth_bc
|
|
q_sun = (h / 2000 * lcc) / (hcc + mcc + lcc) * (q_sun_ac - q_sun_bc) + q_sun_bc
|
|
|
|
if ELV <= 0:
|
|
Q_Albedo = 0
|
|
else:
|
|
Q_Albedo = (h / 2000 * lcc) / (hcc + mcc + lcc) * (q_Albedo_ac - q_Albedo_bc) + q_Albedo_bc
|
|
|
|
my_air = (1.458 * 10 ** -6 * T_air ** 1.5) / (T_air + 110.4)
|
|
k_air = 0.0241 * (T_air / 273.15) ** 0.9
|
|
k_gas = 0.144 * (T_gas / 273.15) ** 0.7
|
|
Pr_air = 0.804 - 3.25 * 10 ** (-4) * T_air
|
|
Pr_gas = 0.729 - 1.6 * 10 ** (-4) * T_gas
|
|
Gr_air = (rho_air ** 2 * grav(lat, h) * np.abs(T_film - T_air) * d_b ** 3) / (T_air * my_air ** 2)
|
|
Nu_air = 2 + 0.45 * (Gr_air * Pr_air) ** 0.25
|
|
HC_free = Nu_air * k_air / d_b
|
|
Re = np.abs(v_rel) * d_b * rho_air / my_air
|
|
Fr = np.abs(v_rel) / np.sqrt(grav(lat, h) * d_b)
|
|
|
|
HC_forced = k_air / d_b * (2 + 0.41 * Re ** 0.55)
|
|
HC_internal = 0.13 * k_gas * (
|
|
(rho_gas ** 2 * grav(lat, h) * np.abs(T_film - T_gas) * Pr_gas) / (T_gas * my_air ** 2)) ** (
|
|
1 / 3)
|
|
HC_external = np.maximum(HC_free, HC_forced)
|
|
|
|
Q_Sun = alpha_VIS * A_proj * q_sun * (1 + tau_VIS / (1 - r_VIS))
|
|
Q_IRFilm = sigma * epsilon * alpha_IR * A_surf * T_film ** 4 * 1 / (1 - r_IR)
|
|
Q_IRout = sigma * epsilon * A_surf * T_film ** 4 * (1 + tau_IR / (1 - r_IR))
|
|
Q_ConvExt = HC_external * A_eff * (T_air - T_film)
|
|
Q_ConvInt = HC_internal * A_eff * (T_film - T_gas)
|
|
|
|
if simple == True:
|
|
c_d = 0.47 # sphere
|
|
else:
|
|
c_d = cd_PalumboHigh(Fr, Re, A_top) # Ref. xx
|
|
|
|
#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
|
|
|
|
D = drag(c_d, rho_air, A_drag, v_rel) # calculate drag force
|
|
|
|
if v_rel == 0:
|
|
Drag_x, Drag_y, Drag_z = 0, 0, 0
|
|
else:
|
|
Drag_x, Drag_y, Drag_z = D * v_relx / v_rel, D * v_rely / v_rel, D * v_relz / v_rel
|
|
|
|
F = grav(lat, h) * V_b * (rho_air - rho_gas) - grav(lat, h) * m_gross + Drag_z # gross inflation - weight + drag
|
|
|
|
a_x, a_y, a_z = Drag_x / m_virt, Drag_y / m_virt, F / m_virt
|
|
|
|
eqn1 = np.rad2deg(y[3] / r_lon)
|
|
eqn2 = np.rad2deg(y[4] / r_lat)
|
|
eqn3 = v_z
|
|
eqn4 = a_x
|
|
eqn5 = a_y
|
|
eqn6 = a_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)
|
|
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
|
|
|
|
if m_bal > 0:
|
|
eqn11 = -mdot
|
|
else:
|
|
eqn11 = 0
|
|
|
|
return [eqn1, eqn2, eqn3, eqn4, eqn5, eqn6, eqn7, eqn8, eqn9, eqn10, eqn11]
|
|
|
|
|
|
# DEFINITION OF EVENTS FOR SOLVER
|
|
|
|
def at_ground(t, y, m_pl, m_film, c_virt):
|
|
return y[2]
|
|
|
|
|
|
def above_float(t, y, m_pl, m_film, c_virt):
|
|
return 45000 - y[2]
|
|
|
|
|
|
def below_float(t, y, m_pl, m_film, c_virt):
|
|
return y[2] - 30050
|
|
|
|
|
|
hit_ground = lambda t, x: at_ground(t, x, m_pl, m_film, c_virt)
|
|
hit_ground.terminal = True
|
|
hit_ground.direction = -1
|
|
excess_ascend = lambda t, x: above_float(t, x, m_pl, m_film, c_virt)
|
|
excess_ascend.terminal = True
|
|
excess_ascend.direction = -1
|
|
instable = lambda t, x: below_float(t, x, m_pl, m_film, c_virt)
|
|
instable.terminal = True
|
|
instable.direction = -1
|
|
|
|
print("Beginning Monte-Carlo simulation")
|
|
|
|
#starttimes = [
|
|
# #'2019-05-23 05:00:00.000',
|
|
# #'2019-05-23 15:00:00.000',
|
|
# #'2019-06-07 05:00:00.000',
|
|
# #'2019-06-07 05:00:00.000',
|
|
# '2019-06-22 05:00:00.000',
|
|
# #'2019-06-22 05:00:00.000'
|
|
# ]
|
|
|
|
|
|
def monte_carlo(t_start, m_pl, m_film, m_bal_init, t_end, num_simulations, fig):
|
|
|
|
t0 = 0.0
|
|
tf = t_end
|
|
|
|
for i in range(num_simulations):
|
|
|
|
c_virt = c_virt_MC[i]
|
|
FreeLift = FL_MC[i]
|
|
|
|
print("----------------")
|
|
print("Starting Monte-Carlo-Run "+str(i))
|
|
print(c_virt)
|
|
print(FreeLift)
|
|
|
|
m_gas_init = ((m_pl + m_film + m_bal_init) * (FreeLift / 100 + 1)) / (R_gas / R_air - 1)
|
|
|
|
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)
|
|
|
|
y0 = [
|
|
start_lon, # start longitude [deg]
|
|
start_lat, # start latitude [deg]
|
|
start_height, # start altitude [m]
|
|
0, # initial v_x [m/s]
|
|
0, # initial v_y [m/s]
|
|
0, # initial v_z [m/s]
|
|
T_air0, # initial gas temperature [K] = initial air temperature [K]
|
|
T_air0, # initial film temperature [K] = initial air temperature [K]
|
|
m_gas_init, # initial lifting gas mass [kg]
|
|
0, # initial factor c2 [-]
|
|
m_bal_init # initial ballast mass [kg]
|
|
]
|
|
|
|
|
|
sol = solve_ivp(fun=lambda t, x: model(t, x, m_pl, m_film, c_virt, t_start), t_span=[t0, tf], y0=y0,
|
|
method='RK45', events=[hit_ground, excess_ascend, instable])
|
|
|
|
print(sol.t[-1]/(3600 * 24))
|
|
print(sol.message)
|
|
|
|
plt.figure(fig)
|
|
ax = plt.axes(projection=ccrs.AzimuthalEquidistant(central_latitude=90))
|
|
ax.coastlines()
|
|
ax.gridlines(draw_labels=True)
|
|
ax.stock_img()
|
|
ax.set_extent([-120, 30, 60, 80], crs=ccrs.PlateCarree())
|
|
plt.title("Start: " + str(t_start))
|
|
plt.plot(start_lon, start_lat, 'rx', transform=ccrs.Geodetic())
|
|
plt.plot(sol.y[0, :], sol.y[1, :], 'k--', transform=ccrs.Geodetic())
|
|
|
|
|
|
start1 = Time('2019-05-23 05:00:00.000')
|
|
start2 = Time('2019-05-23 15:00:00.000')
|
|
start3 = Time('2019-06-07 05:00:00.000')
|
|
start4 = Time('2019-06-07 15:00:00.000')
|
|
start5 = Time('2019-06-22 05:00:00.000')
|
|
start6 = Time('2019-06-22 15:00:00.000')
|
|
|
|
|
|
FL_values = [10.0, 15.0, 20.0]
|
|
FL_prob = [1/3, 1/3, 1/3]
|
|
|
|
num_simulations = 10
|
|
|
|
FL_MC = np.random.choice(FL_values, 10, p=FL_prob)
|
|
c_virt_MC = np.random.normal(0.375, 0.05, 10).round(2)
|
|
|
|
#m_pl = 917 #2174 # payload mass in [kg] (incl. flight-chain)
|
|
#m_bal_init = 540 #500 # initial ballast mass in [kg]
|
|
#m_film = 0.74074232733 * 1838 # mass balloon film in [kg]
|
|
#FreeLift = 10 # (initial) free lift in [%]
|
|
|
|
monte_carlo(start5, 917, 1361.48, 540, 2592000, 10, 1) # 3456000
|
|
# monte_carlo(start2, 917, 1361.48, 540, 432000, 10, 2)
|
|
# monte_carlo(start3, 917, 1361.48, 540, 432000, 10, 3)
|
|
|
|
"""
|
|
if __name__ == '__main__': # t_start m_film t_end fig
|
|
p1 = Process(target=monte_carlo(start1, 917, 1361.48, 540, 10000, 10, 1)) # 3456000
|
|
p1.start() # m_pl m_bal, num
|
|
p2 = Process(target=monte_carlo(start2, 917, 1361.48, 540, 10000, 10, 2)) # 3456000
|
|
p2.start() # t_start m_film t_end fig
|
|
p3 = Process(target=monte_carlo(start3, 917, 1361.48, 540, 10000, 10, 3)) # 3456000
|
|
p3.start() # t_start m_film t_end fig
|
|
|
|
p1.join()
|
|
p2.join()
|
|
p3.join()
|
|
"""
|
|
|
|
print(datetime.now() - starttime)
|
|
|
|
plt.figure(1)
|
|
plt.show()
|
|
|
|
plt.figure(2)
|
|
plt.show()
|
|
|
|
plt.figure(3)
|
|
plt.show()
|