forked from lfierce2/LagrangianDroplets
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathprocess.py
496 lines (396 loc) · 19.1 KB
/
process.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 9 09:37:27 2022
@author: Laura Fierce
"""
import matplotlib.pyplot as plt
import pickle
import numpy as np
import run
import parcels
from netCDF4 import Dataset
from scipy.optimize import fsolve
import scipy.constants as c
import random
M_dry_air = 28.9647/1000. # kg/mol
M_h2o = 18./1000.
M_co2 = 44./1000.
M_nacl = 58.44/1000.
def plot_some_trajectories(parcel_nums,output_dir,avg_SS=0.,Slims=[-10,10],Dlims=[0.,25.],add_spatial_trajectories=False,plot_dir = 'figures/'):
fig, axs1 = plt.subplots(len(parcel_nums))
axs2 = np.array([])
# axs3 = np.array([])
fig.set_size_inches(3.5,len(parcel_nums)*1.8)
ylab_idx = np.floor(len(axs1)/2)
particle_trace_dir= output_dir + 'particle_traces/avgSS_' + str(int(avg_SS*100.)).zfill(6) + '/'
parcel_trace_dir= output_dir + 'parcel_traces/'
for pp,(parcel_num,ax1) in enumerate(zip(parcel_nums,axs1)):
particle_trace = pickle.load(open(particle_trace_dir + 'particle_' + str(parcel_num).zfill(9) + '.pkl','rb'))
ts,lnDp,Tp,T,wv,S,S_crit,D_crit,Ddry,kappa = run.unravel_particle_trace(particle_trace)
Dp=np.exp(lnDp)
# fig, ax1 = plt.subplots(figsize=(4.5,2.5))
ax2 = ax1.twinx()
np.hstack([axs2,ax2])
hln1, = ax1.plot(ts,S*100,linewidth=2.)
hln2, = ax2.plot(ts,Dp*1e6,linewidth=2.,color='C3')
if pp == ylab_idx:
ax1.set_ylabel('supersaturation [%]')
ax2.set_ylabel('droplet diameter [$\mu$m]',rotation=270,labelpad=18)
if pp < (len(parcel_nums) - 1):
ax1.set_xticklabels('')
ax2.set_xticklabels('')
else:
ax1.set_xlabel('time [s]')
ax1.set_xlim([min(ts),max(ts)])
ax2.set_xlim([min(ts),max(ts)])
ax1.yaxis.label.set_color(hln1.get_color())
ax2.yaxis.label.set_color(hln2.get_color())
ax1.spines["right"].set_edgecolor(hln1.get_color())
ax2.spines["right"].set_edgecolor(hln2.get_color())
ax1.tick_params(axis='y', colors=hln1.get_color())
ax2.tick_params(axis='y', colors=hln2.get_color())
ax2.hlines(D_crit*1e6,0.,max(ts),linestyle=':',color=hln2.get_color())
ax1.hlines(S_crit*100.,0.,max(ts),linestyle=':',color=hln1.get_color())
print(S_crit*100.)
ax1.set_ylim(Slims)
ax2.set_ylim(Dlims)
if add_spatial_trajectories:
big_ax_position = ax1.get_position()
dy = big_ax_position.y1 - big_ax_position.y0
dx = big_ax_position.x1 - big_ax_position.x0
little_ax_position = [big_ax_position.x0-dx*0.6, big_ax_position.y0 + dy*0.4, dx/3.,dy/1.8]
ax3 = fig.add_axes(little_ax_position,projection='3d')
ax3.set_xticks([],labels='')
ax3.set_yticks([],labels='')
ax3.set_zticks([],labels='')
ax3.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
ax3.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
ax3.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
ax3 = plot_spatial_trajectory(parcel_num,parcel_trace_dir,ax3)
plt.tight_layout()
fig.savefig(plot_dir + 'trajectories.pdf',dpi=1000)
# plt.show()
if add_spatial_trajectories:
return fig,ax1,ax2,ax3
else:
return fig,ax1,ax2
def plot_spatial_trajectory(parcel_num,parcel_trace_dir,ax):
trajectory = run.read_parcel_trace(parcel_num,parcel_trace_dir = parcel_trace_dir)
xdata = trajectory['x']
ydata = trajectory['y']
zdata = trajectory['z']
ax.plot3D(xdata, ydata, zdata, 'gray')
ax.set_xlim([0.,2.])
ax.set_ylim([0.,2.])
ax.set_zlim([0.,1.])
def plot_activated_fraction_mean_SS(avg_SS, output_dir,plot_dir = 'figures/'):
all_particle_trace_dir = output_dir+'/particle_traces'
N_tot = []
N_ccn = []
N_ccn_kohler = []
N_ccn_tau0 = []
mean_SS = []
for aa, SS in enumerate(avg_SS):
SS_thresh = SS
particle_trace_dir = all_particle_trace_dir + '/avgSS_' + str(int(SS*1000)).zfill(6) +'/'
particle_traces = run.read_particle_traces(particle_trace_dir)
time, Dp, Tp, T, mixing_ratio, SS_env, SS_crit, Dp_crit, Ddry, kappa = run.unravel_particle_traces(particle_traces)
Dp=np.exp(Dp)
Ntot_t = np.zeros([len(time)])
Nccn_t = np.zeros([len(time)])
Nccn_t_tau0 = np.zeros([len(time)])
Nccn_kohler_t = np.zeros([len(time)])
mean_SS.append(np.mean(SS))
N0 = 1.
Ns = np.zeros(Dp.shape)
wps = np.zeros(Dp.shape)
for ii in range(Dp.shape[0]):
N_vs_t,wp_vs_t = N_vs_t__settling(N0,time,Dp[ii,:],Tp[ii,:],SS_env[ii,:]+1.,rho_p=1000.,ht=1.)
Ns[ii,:] = N_vs_t
wps[ii,:] = wp_vs_t
Dp_bins = np.logspace(-7,-4,100);
plt.hist(Dp[:,-1],bins=Dp_bins,weights=Ns[:,-1]); plt.xscale('log'); plt.title(SS); plt.show();
print(aa,SS)
for tt in range(len(time)):
Ntot_t[tt] = sum(Ns[:,tt])
Nccn_t[tt] = sum(Ns[:,tt]*(Dp[:,tt]>=Dp_crit))
Nccn_kohler_t[tt] = sum(Ns[:,tt]*(SS_crit <= SS_thresh))
Nccn_t_tau0[tt] = sum(Ns[:,tt]*(SS_crit <= (SS_env[:,tt]+SS)))
N_tot.append(Ntot_t)
N_ccn.append(Nccn_t)
N_ccn_kohler.append(Nccn_kohler_t)
N_ccn_tau0.append(Nccn_t_tau0)
N_tot = np.array(N_tot)
N_ccn = np.array(N_ccn)
N_ccn_kohler = np.array(N_ccn_kohler)
mean_SS = np.array(mean_SS)*100
fig, ax = plt.subplots(1,1)
hln_uniform, = ax.plot(np.array([min(mean_SS),np.mean(SS_crit*100.),np.mean(SS_crit*100.),max(mean_SS)]),np.array([0.,0.,1.,1.]),color='C1',linewidth=3.)
hln_variable, = ax.plot(mean_SS,(N_ccn/N_tot)[:,len(time)-1],linewidth=2.,color='C2')
hln_tau0, = ax.plot(mean_SS,(N_ccn_tau0/N_tot)[:,len(time)-1],linestyle='--',linewidth=2.,color='C2')
fig.set_size_inches(3.5,2.5)
ax.set_xlabel('mean supersaturation (%)')
ax.set_ylabel('activated fraction')
ax.set_xlim([-4.,3.])
ax.set_ylim([0.,1.001])
ax.legend([hln_variable,hln_tau0,hln_uniform],['turbulent','$\\tau_{\mathrm{evap}}=0$','uniform'],fontsize=10,loc='upper left',handletextpad=0.5)
filename = plot_dir + '/activated_fraction_mean_SS.png'
plt.tight_layout()
fig.savefig(filename,dpi=1000)
return fig,ax,[hln_variable,hln_tau0,hln_uniform]
def get_s_pdf(avg_SS=0.,LES_dir='/Users/fier887/OneDrive - PNNL/Documents/shared_files/ICLASS/LES/OUT_3D/'):
SS_all = np.array([])
for ii in range(0,3001,25):
## Load the variable from the LES output
filename = LES_dir+'PiChamber_huji_19K_surfmod_trj_32_000009'+str(ii).zfill(4)+'.nc'
LES_array = Dataset(filename)
T_t = LES_array['TABS'][:] # Temperature, Kelvin, dimensions=(t,z,y,x)
Qv_t = LES_array['QV'][:]/1000 # Water Vapor, kg water/kg air, dimensions=(t,z,y,x)
SS_t = parcels.SuperSaturation(Qv_t, T_t)*100.
SS_all = np.hstack([SS_all,SS_t.ravel()])
SS_true_mean = np.mean(SS_all)
N,SS_edges,patches = plt.hist(SS_all-SS_true_mean+avg_SS,100)
# SS_mids = output[1]
dSS = SS_edges[1] - SS_edges[0]
SS_mids = SS_edges[:-1]+dSS/2.
pdf = N/(sum(N)*dSS)
return SS_mids,pdf
def get_nonoverlapping_xy(N_samples,r_closest=0.1):
xs = np.array([np.random.uniform()])
ys = np.array([np.random.uniform()])
while len(xs) < N_samples:
x = np.random.uniform()
y = np.random.uniform()
r = np.hstack([np.sqrt((xs-x)**2 + (ys-y)**2)])
if all(r>r_closest):
xs = np.append(xs,x)
ys = np.append(ys,y)
return xs, ys
def plot_activated_fraction_mean_SS__with_particles(avg_SS,output_dir,plot_dir = 'figures/',ss_plot = [-0.03,0],tt_plot=-1,N_particles=30,r_closest=0.1,droplets_above=True):
all_particle_trace_dir_uniform = output_dir+'/particle_traces_uniformS'
all_particle_trace_dir = output_dir + '../output_LagrangeDroplets_old/particle_traces'
fig = plt.figure(constrained_layout=True)
fig.set_size_inches(3.5,5.)
plt.show()
nrows = 3
if droplets_above:
gs = fig.add_gridspec(nrows=nrows, ncols=len(ss_plot),height_ratios=[1,1,3],wspace=0.1)
else:
gs = fig.add_gridspec(nrows=nrows, ncols=len(ss_plot),height_ratios=[3,1,1])
Dp_uniform = np.zeros(len(ss_plot))
frac_aerosol_uniform = np.zeros(len(ss_plot))
ax_particles = []
for ii in range(2):
ax_ii = []
for jj in range(len(ss_plot)):
if droplets_above:
ax_ii.append(fig.add_subplot(gs[ii,jj]))
else:
ax_ii.append(fig.add_subplot(gs[nrows-2+ii,jj]))
ax_particles.append(ax_ii)
ax_particles = np.array(ax_particles)
if droplets_above:
ax = fig.add_subplot(gs[2:,:])
else:
ax = fig.add_subplot(gs[:-2,:])
N_tot = []
N_ccn = []
N_ccn_kohler = []
N_ccn_tau0 = []
mean_SS = []
jj = 0
col_inactive = np.array([106, 106, 106])/255.#np.array([212, 213, 214])/255.#np.array([181, 5, 2])/255.#
edge_col_inactive = np.array([0.,0.,0.])#np.array([59, 60, 61])/255.
col_active = np.array([92, 154, 255])/255.
edge_col_active =np.array([50, 94, 166])/255.#np.array([0.,0.,0.])#
multiplier = 1.8e6
leg_strs = ['0.2~$\mu m$','1~$\mu m$','5~$\mu m$','CCN-active','CCN-inactive']
marker_sizes = np.array([0.2e-7,1.e-6,5e-6,1e6,1e6])*multiplier
marker_cols = np.zeros([5,3])
marker_cols[:2,:] = 1.
marker_cols[3,:] = col_active
marker_cols[4,:] = col_inactive
marker_edgecols = np.zeros([5,3])
marker_cols[3,:] = edge_col_active
marker_cols[4,:] = edge_col_inactive
linecol_uniform = 'C1'
linecol_turbulent = 'C2'
cols = np.zeros([N_particles,3])
cols_uniform = np.zeros([N_particles,3])
edge_cols = np.zeros([N_particles,3])
edge_cols_uniform = np.zeros([N_particles,3])
xs,ys = get_nonoverlapping_xy(N_particles,r_closest=r_closest);
for aa, SS in enumerate(avg_SS):
SS = np.round(SS,8)
particle_trace_dir = all_particle_trace_dir + '/avgSS_' + str(int(avg_SS[aa]*1000)).zfill(6) +'/'
particle_traces = run.read_particle_traces(particle_trace_dir)
random_index = random.sample(range(len(particle_traces)),N_particles)
these_particles = random_index[:N_particles]
if SS in ss_plot:
particle_trace_dir_uniform = all_particle_trace_dir_uniform + '/avgSS_' + str(int(avg_SS[aa]*1000)).zfill(6) +'/'
particle_traces_uniform = run.read_particle_traces(particle_trace_dir_uniform)
time, Dp_uniform_all, Tp, T, mixing_ratio, SS_env, SS_crit, Dp_crit_uniform, Ddry_uniform, kappa = run.unravel_particle_traces(particle_traces_uniform)
if len(Dp_uniform_all) == 1:
Dp_uniform_all=np.exp(Dp_uniform_all[0])
else:
Dp_uniform_all=np.exp(Dp_uniform_all)
Dp_uniform[jj] = Dp_uniform_all[tt_plot]
frac_aerosol_uniform[jj] = Ddry_uniform**3/Dp_uniform_all[tt_plot]**3
print('aa',aa,'SS',SS,'Dp_uniform[jj]',Dp_uniform[jj])
time, Dp, Tp, T, mixing_ratio, SS_env, SS_crit, Dp_crit, Ddry, kappa = run.unravel_particle_traces(particle_traces)
Dp=np.exp(Dp)
Ntot_t = np.zeros([len(time)])
Nccn_t = np.zeros([len(time)])
Nccn_t_tau0 = np.zeros([len(time)])
Nccn_kohler_t = np.zeros([len(time)])
mean_SS.append(np.mean(SS))
for tt in range(len(time)):
Ntot_t[tt] = len(Dp[:,tt])
Nccn_t[tt] = sum(Dp[:,tt]>=Dp_crit)
Nccn_kohler_t[tt] = sum(SS_crit <= SS)
Nccn_t_tau0[tt] = sum(SS_crit <= (SS_env[:,tt]+SS))
N_tot.append(Ntot_t)
N_ccn.append(Nccn_t)
N_ccn_kohler.append(Nccn_kohler_t)
N_ccn_tau0.append(Nccn_t_tau0)
print('SS',SS,'SS in ss_plot',SS in ss_plot)
if SS in ss_plot:
for ii in range(N_particles):
if Dp_uniform[jj]>=Dp_crit_uniform[0]:
cols_uniform[ii,:] = col_active
edge_cols_uniform[ii,:] = edge_col_active
else:
cols_uniform[ii,:] = col_inactive
edge_cols_uniform[ii,:] = edge_col_inactive
for ii in range(N_particles):
if Dp[these_particles[ii],tt_plot]>=Dp_crit[these_particles[ii]]:
cols[ii,:] = col_active
edge_cols[ii,:] = edge_col_active
else:
cols[ii,:] = col_inactive
edge_cols[ii,:] = edge_col_inactive
hscat_uniform = ax_particles[0,jj].scatter(xs,ys,np.ones(N_particles)*Dp_uniform[jj]*multiplier,cols_uniform,edgecolors=edge_cols_uniform,linewidth=0.25)
ax_particles[1,jj].scatter(xs,ys,Dp[these_particles,tt_plot]*multiplier,cols,edgecolors=edge_cols,linewidth=0.2)
print('aa',aa,'SS',SS,'mean Dp',np.mean(Dp[:,tt_plot]),'gm Dp',np.exp(np.mean(np.log(Dp[:,tt_plot]))))
ax_particles[0,jj].set_xticks([])
ax_particles[0,jj].set_yticks([])
ax_particles[1,jj].set_xticks([])
ax_particles[1,jj].set_yticks([])
ax_particles[0,jj].spines['left'].set_color(linecol_uniform)
ax_particles[0,jj].spines['right'].set_color(linecol_uniform)
ax_particles[0,jj].spines['top'].set_color(linecol_uniform)
ax_particles[0,jj].spines['bottom'].set_color(linecol_uniform)
ax_particles[1,jj].spines['left'].set_color(linecol_turbulent)
ax_particles[1,jj].spines['right'].set_color(linecol_turbulent)
ax_particles[1,jj].spines['top'].set_color(linecol_turbulent)
ax_particles[1,jj].spines['bottom'].set_color(linecol_turbulent)
ax_particles[0,jj].set_title('$s=$' + str(int(SS*100))+'%',fontsize=10)
ax_particles[0,jj].patch.set_facecolor('w')
ax_particles[1,jj].patch.set_facecolor('w')
# ax_particles[0,jj].set_title('r$\bar{s}=$' + str(int(SS*100))+'%',fontsize=10)
jj += 1
# ax_particles[0,jj].hist(Dp[:,-1],bins=Dp_bins); plt.xscale('log'); plt.title(SS);# plt.show();
N_tot = np.array(N_tot)
N_ccn = np.array(N_ccn)
N_ccn_kohler = np.array(N_ccn_kohler)
mean_SS = np.array(mean_SS)*100
# SS_mids_avg0,s_pdf = get_s_pdf(avg_SS=0.,LES_dir='/Users/fier887/OneDrive - PNNL/Documents/shared_files/ICLASS/LES/OUT_3D/')
# grid = plt.GridSpec(9,3,hspace=0.4)
# fig = plt.figure()
# ax = fig.add_subplot(grid[3:,:])
# ax1 = fig.add_subplot(grid[0,:])
# ax2 = fig.add_subplot(grid[1,:])
# ax3 = fig.add_subplot(grid[2,:])
# fig.set_size_inches(3.5,3.5)
# ax1.plot(SS_mids_avg0-2.,s_pdf)
# ax2.plot(SS_mids_avg0,s_pdf)
# ax3.plot(SS_mids_avg0+2.,s_pdf)
#plt.legend(loc='upper left')
# fig, ax = plt.subplots(1,1)
hln_uniform, = ax.plot(np.array([min(mean_SS),np.mean(SS_crit*100.),np.mean(SS_crit*100.),max(mean_SS)]),np.array([0.,0.,1.,1.]),color=linecol_uniform,linewidth=3.)
hln_variable, = ax.plot(mean_SS,(N_ccn/N_tot)[:,len(time)-1],linewidth=2.,color=linecol_turbulent)
hln_tau0, = ax.plot(mean_SS,(N_ccn_tau0/N_tot)[:,len(time)-1],linestyle='--',linewidth=2.,color=linecol_turbulent)
# ax.plot(mean_SS,(N_ccn_kohler/N_tot)[:,len(time)-1],linestyle='--',linewidth=2.,color='C1')
# ax.plot(mean_SS,(N_ccn_kohler/N_tot)[:,len(time)-1],linestyle='--',linewidth=2.,color='C1')
ax.set_xlabel('mean supersaturation, $s$')#', ' + r'$\bar{s}$')
ax.set_ylabel('activated fraction')
ax.set_xlim([-4.,3.])
ax.set_ylim([0.,1.001])
ss_ticks = np.arange(-4.,3.)
ax.set_xticks([int(ss_tick) for ss_tick in ss_ticks])
ax.set_xticklabels([str(int(ss_tick))+'%' for ss_tick in ss_ticks])
ax.legend(
[hln_uniform,hln_variable,hln_tau0],
['uniform $s$','with turbulent\nfluctuations in $s$','with fluctuations\nbut $\\tau_{\mathrm{evap}}=0$'],
frameon=False,fontsize=9,loc='upper left',handletextpad=0.5)
filename = plot_dir + '/activated_fraction_mean_SS.pdf'
plt.tight_layout()
fig.patch.set_alpha(0.)
fig.savefig(filename,dpi=1000)
# fig.savefig(filename,dpi=1000,transparent=True)
return fig,ax,[hln_variable,hln_tau0,hln_uniform]
def N_vs_t__settling(N0,ts,Dp_vs_t,Tv_vs_t,Sv_vs_t,rho_p=1000.,ht=1.):
dt = ts[1] - ts[0]
N_vs_t = np.zeros(Dp_vs_t.shape)
N_vs_t[0] = N0
wp_vs_t = np.zeros(Dp_vs_t.shape)
for tt in range(0,len(Dp_vs_t)):
wp_terminal = get_terminal_velocity(Dp_vs_t[tt],rho_p,Tv_vs_t[tt],Sv_vs_t[tt],x_co2=410e-6,p=101325.)
wp_vs_t[tt] = wp_terminal
if tt > 0:
N_vs_t[tt] = N_vs_t[tt-1]*np.exp(-wp_terminal*dt/ht)
return N_vs_t, wp_vs_t
def get_terminal_velocity(Dp,rho_p,Tv_inf,S_inf,x_co2=410e-6,p=101325.):
# eqn. 9.49 from Seinfeld and Pandis
rho_g = get_air_density(Tv_inf,S_inf,x_co2=x_co2,p=p)
# break out function -- make it jit
wp_terminal = fsolve(lambda wp: wp - np.sqrt(c.g*(4*Dp*rho_p)/(3*rho_g*get_drag_coefficient(wp-0.,Dp,Tv_inf,S_inf))),1.)
return wp_terminal
def get_drag_coefficient(velocity_diff,Dp,Tv,S):
Re = get_particle_Re(velocity_diff,Dp,Tv,S)
if Re == 0:
Cd = 0.
elif Re<=1.:
Cd = 24/Re
else:
Cd = (1 + 0.15*Re**0.687)*24/Re
return Cd
def get_particle_Re(velocity_diff,Dp,Tv,S,x_co2=410e-6,p=101325.):
rho_air = get_air_density(Tv,S,x_co2=x_co2,p=p)
mu = get_dynamic_viscosity_of_air(Tv)
nu = mu/rho_air
Re = abs(velocity_diff)*Dp/nu
return Re
def get_dynamic_viscosity_of_air(Tv):
# http://www-mdp.eng.cam.ac.uk/web/library/enginfo/aerothermal_dvd_only/aero/fprops/propsoffluids/node5.html
mu0 = 17.15e-6
T0 = 273.15
mu = mu0*(Tv/T0)**0.7
return mu
def get_air_density(Tv,S,x_co2=410e-6,p=101325.):
x_h2o = get_h2o_mixing_ratio_from_Sv(S,Tv,p=p)
rho_air = (M_dry_air*(1-x_h2o-x_co2) + x_h2o*M_h2o + x_co2*M_co2)*p/(c.R*Tv)#*(1+x_h2o+x_co2))
return rho_air
def get_h2o_mixing_ratio_from_Sv(S,Tv,p=101325.):
p_v = get_vapor_pressure(S,Tv)
mixing_ratio = p_v/p#(p - p_v)
return mixing_ratio
def get_vapor_pressure(S,Tv):
p_sat = get_saturation_vapor_pressure(Tv)
p_v = S*p_sat
return p_v # Pa
def get_saturation_vapor_pressure(Tv_K):
Tv = Tv_K-273.15
p_sat = 611.21*np.exp((18.678 - Tv/234.5)*(Tv/(257.14+Tv)))
return p_sat # Pa
def get_Dwet(Ddry, kappa, RH, temp):
import numpy as np
from scipy.optimize import brentq
if RH>0 and kappa>0:
sigma_w = 0.072; rho_w = 1000; M_w = 18/1e3; R=8.314;
A = 4*sigma_w*M_w/(R*temp*rho_w)
zero_this = lambda gf: RH/np.exp(A/(Ddry*gf))-(gf**3-1)/(gf**3-(1-kappa))
return Ddry*brentq(zero_this,1,10000)
else:
return Ddry