-
Notifications
You must be signed in to change notification settings - Fork 53
/
tab_thermo_data_generator.py
173 lines (133 loc) · 6.81 KB
/
tab_thermo_data_generator.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
"""
Sample script showing how to generate tabular thermodynamic
data for use with tabular thermodynamics
This scrip generates a pickle file called 'air_jetA.pkl' which is
equivalent to the default tabular thermo data in pyCycle.
You can generate a custom pickle file, then provide the path to that file
as the thermo_spec for tabular therm
"""
import numpy as np
import openmdao.api as om
import pickle
from pycycle.thermo.thermo import Thermo, ThermoAdd
from pycycle.constants import CEA_AIR_COMPOSITION, CEA_AIR_FUEL_COMPOSITION, ALLOWED_THERMOS
from pycycle.thermo.cea.species_data import janaf, wet_air
class TabThermoGenAir(om.Group):
def initialize(self):
self.options.declare('thermo_data', default=False,
desc='thermodynamic data specific to this element', recordable=False)
self.options.declare('thermo_method', default='CEA', values=ALLOWED_THERMOS,
desc='Method for computing thermodynamic properties')
def setup(self):
thermo_data = self.options['thermo_data']
thermo_method = self.options['thermo_method']
flow = Thermo(mode='total_TP', fl_name='flow',
method=thermo_method,
thermo_kwargs={'composition':CEA_AIR_COMPOSITION,
'spec':thermo_data})
self.add_subsystem('flow', flow, promotes_inputs=['T','P'],
promotes_outputs=['flow:*'])
self.set_input_defaults('T', val=273.15, units='degK')
self.set_input_defaults('P', val=101325, units='Pa')
class TabThermoGenAirFuel(om.Group):
def initialize(self):
self.options.declare('fuel_type', default="Jet-A(g)",
desc='Type of fuel.')
self.options.declare('thermo_data', default=False,
desc='thermodynamic data specific to this element', recordable=False)
self.options.declare('thermo_method', default='CEA', values=ALLOWED_THERMOS,
desc='Method for computing thermodynamic properties')
def setup(self):
fuel_type = self.options['fuel_type']
thermo_data = self.options['thermo_data']
thermo_method = self.options['thermo_method']
# Compute mixed air-fuel composition
self.thermo_add_comp = ThermoAdd(method=thermo_method, mix_mode='reactant',
thermo_kwargs={'spec':thermo_data,
'inflow_composition':CEA_AIR_COMPOSITION,
'mix_composition':fuel_type})
self.add_subsystem('mix_fuel', self.thermo_add_comp,
promotes=[('Fl_I:stat:W','W'), ('mix:ratio', 'FAR'), ('Fl_I:tot:composition','composition'),
('Fl_I:tot:h','h'), ('mix:W','Wfuel'), 'Wout'])
# Compute properties of air-fuel mixture
vit_flow = Thermo(mode='total_TP', fl_name='flow',
method=thermo_method,
thermo_kwargs={'composition':CEA_AIR_FUEL_COMPOSITION,
'spec':thermo_data})
self.add_subsystem('vitiated_flow', vit_flow, promotes_inputs=['T','P'],
promotes_outputs=['flow:*'])
self.connect("mix_fuel.composition_out", "vitiated_flow.composition")
self.set_input_defaults('W', val=1.0, units='kg/s')
self.set_input_defaults('FAR', val=0.02)
self.set_input_defaults('T', val=273.15, units='degK')
self.set_input_defaults('P', val=101325, units='Pa')
if __name__ == "__main__":
# FAR - lower: 0.0, upper 0.05
# P - lower: 0.886280 Pa, upper: 10132500 Pa
# T - lower: 196.650 degK, upper: 2500 degK
# FAR_range = [0, 0.03]
# P_range = [1e6]
# T_range = [1500]
FAR_range = np.linspace(0.0, 0.05, num=20)
# WAR_range = np.linspace(0.0, 1.0, num=5)
# P_range = np.linspace(1, 1e7, num=30)
P_range = np.logspace(0, 7, num=110)
T_range = np.linspace(100, 3500, num=100)
numFAR = len(FAR_range)
numP = len(P_range)
numT = len(T_range)
h = np.empty([numFAR,numP,numT])
S = np.empty([numFAR,numP,numT])
gamma = np.empty([numFAR,numP,numT])
Cp = np.empty([numFAR,numP,numT])
Cv = np.empty([numFAR,numP,numT])
rho = np.empty([numFAR,numP,numT])
R = np.empty([numFAR,numP,numT])
#############################################
# Pure Air - Run this for FAR=0 cases
#############################################
p = om.Problem()
p.model = TabThermoGenAir(thermo_data=janaf, thermo_method='CEA')
p.setup(check=False)
p.set_solver_print(level=-1)
for j, P in enumerate(P_range):
p['P'] = P
for k, T in enumerate(T_range):
p['T'] = T
p.run_model()
print('FAR: %.3f, P: %.1f, T: %.1f' %(0.0,p['P'][0],p['T'][0]))
h[0,j,k] = p.get_val('flow:h', units='J/kg')[0]
S[0,j,k] = p.get_val('flow:S', units='J/kg/degK')[0]
gamma[0,j,k] = p.get_val('flow:gamma')[0]
Cp[0,j,k] = p.get_val('flow:Cp', units='J/kg/degK')[0]
Cv[0,j,k] = p.get_val('flow:Cv', units='J/kg/degK')[0]
rho[0,j,k] = p.get_val('flow:rho', units='kg/m**3')[0]
R[0,j,k] = p.get_val('flow:R', units='J/kg/degK')[0]
#############################################
# Vitiated Air - Run this for FAR>0 cases
#############################################
p2 = om.Problem()
p2.model = TabThermoGenAirFuel(fuel_type='Jet-A(g)', thermo_data=wet_air, thermo_method='CEA')
p2.setup(check=False)
p2.set_solver_print(level=-1)
for i, FAR in enumerate(FAR_range):
if i==0:
pass
else:
p2['FAR'] = FAR
for j, P in enumerate(P_range):
p2['P'] = P
for k, T in enumerate(T_range):
p2['T'] = T
p2.run_model()
print('FAR: %.3f, P: %.1f, T: %.1f' %(p2['FAR'][0],p2['P'][0],p2['T'][0]))
h[i,j,k] = p2.get_val('flow:h', units='J/kg')[0]
S[i,j,k] = p2.get_val('flow:S', units='J/kg/degK')[0]
gamma[i,j,k] = p2.get_val('flow:gamma')[0]
Cp[i,j,k] = p2.get_val('flow:Cp', units='J/kg/degK')[0]
Cv[i,j,k] = p2.get_val('flow:Cv', units='J/kg/degK')[0]
rho[i,j,k] = p2.get_val('flow:rho', units='kg/m**3')[0]
R[i,j,k] = p2.get_val('flow:R', units='J/kg/degK')[0]
thermo_data_dict = {'T':T_range, 'P':P_range, 'FAR':FAR_range, 'h':h, 'S':S, 'gamma':gamma, 'Cp':Cp,
'Cv':Cv, 'rho':rho, 'R':R}
pickle.dump( thermo_data_dict, open('air_jetA.pkl', 'wb'))