def solve(self, phi, T, width, P):
gas = ct.Solution('h2o2.xml')
gas.TPX = T, ct.one_atm * P, {'H2': phi, 'O2': 0.5, 'AR': 1.5}
sim = ct.BurnerFlame(gas=gas, width=width)
sim.burner.mdot = gas.density * 0.15
sim.solve(loglevel=0, auto=True)
self.assertGreater(sim.T[1], T)
def test_blowoff(self):
gas = ct.Solution('h2o2.cti')
gas.set_equivalence_ratio(0.4, 'H2', 'O2:1.0, AR:5')
gas.TP = 300, ct.one_atm
sim = ct.BurnerFlame(gas=gas, width=0.1)
sim.burner.mdot = 1.2
sim.set_refine_criteria(ratio=3, slope=0.3, curve=0.5, prune=0)
sim.solve(loglevel=0, auto=True)
# nonreacting solution
self.assertNear(sim.T[-1], sim.T[0], 1e-6)
self.assertNear(sim.velocity[-1], sim.velocity[0], 1e-6)
self.assertArrayNear(sim.Y[:, 0], sim.Y[:, -1], 1e-6, atol=1e-6)
def test_fixed_temp(self):
gas = ct.Solution('h2o2.xml')
gas.TPX = 400, 2 * ct.one_atm, {'H2': 0.7, 'O2': 0.5, 'AR': 1.5}
sim = ct.BurnerFlame(gas=gas, width=0.05)
sim.burner.mdot = gas.density * 0.15
sim.flame.set_fixed_temp_profile([0, 0.1, 0.9, 1],
[400, 1100, 1100, 500])
sim.energy_enabled = False
sim.solve(loglevel=0, refine_grid=True)
self.assertNear(sim.T[0], 400)
self.assertNear(sim.T[-1], 500)
self.assertNear(max(sim.T), 1100)
def test_restart(self):
gas = ct.Solution('h2o2.cti')
gas.set_equivalence_ratio(0.4, 'H2', 'O2:1.0, AR:5')
gas.TP = 300, ct.one_atm
sim = ct.BurnerFlame(gas=gas, width=0.1)
sim.burner.mdot = 1.2
sim.set_refine_criteria(ratio=3, slope=0.3, curve=0.5, prune=0)
sim.solve(loglevel=0, auto=True)
arr = sim.to_solution_array()
sim.burner.mdot = 1.1
sim.set_initial_guess(data=arr)
sim.solve(loglevel=0, auto=True)
# Check continuity
rhou = sim.burner.mdot
for rhou_j in sim.density * sim.velocity:
self.assertNear(rhou_j, rhou, 1e-4)
def setup(phi):
# parameter values
p = ct.one_atm # pressure
tburner = 300.0 # burner temperature
mdot = 1.2 # kg/m^2/s
width = 0.05 # m
gas = ct.Solution('h2o2.cti')
# set state to that of the unburned gas at the burner
gas.set_equivalence_ratio(phi, 'H2', 'O2:1.0, AR:5')
gas.TP = tburner, p
# Create the stagnation flow object with a non-reactive surface.
sim = ct.BurnerFlame(gas=gas, width=width)
# set the mass flow rate at the inlet
sim.burner.mdot = mdot
sim.set_refine_criteria(ratio=3, slope=0.16, curve=0.3, prune=0.1)
return sim
Requires: cantera >= 2.5.0
"""
import cantera as ct
p = 0.05 * ct.one_atm
tburner = 373.0
mdot = 0.06
reactants = 'H2:1.5, O2:1, AR:7' # premixed gas composition
width = 0.5 # m
loglevel = 1 # amount of diagnostic output (0 to 5)
gas = ct.Solution('h2o2.yaml')
gas.TPX = tburner, p, reactants
f = ct.BurnerFlame(gas, width=width)
f.burner.mdot = mdot
f.set_refine_criteria(ratio=3.0, slope=0.05, curve=0.1)
f.show_solution()
f.transport_model = 'Mix'
f.solve(loglevel, auto=True)
f.save('h2_burner_flame.xml', 'mix',
'solution with mixture-averaged transport')
f.transport_model = 'Multi'
f.solve(loglevel) # don't use 'auto' on subsequent solves
f.show_solution()
f.save('h2_burner_flame.xml', 'multi',
'solution with multicomponent transport')
sensSpecies = 'CO'
initial_grid = np.linspace(0.0, 0.03, 10) # m
#initial_grid = np.linspace(0.0, 0.01805882, 10) # m
tol_ss = [1.0e-5, 1.0e-13] # [rtol atol] for steady-state problem
tol_ts = [1.0e-4, 1.0e-10] # [rtol atol] for time stepping
loglevel = 1 # amount of diagnostic output (0 to 5)
filename = 'C:\\Users\\HP USER\\Google Drive\\Burke Group\\Codes\\TMRP codes\\dooley\\chem'
gas = ct.Solution(filename+'.cti')
gas.TPX = tburner, p, reactants
v=0.909
gas2=ct.Solution(filename+'.cti')
gas2.TPX=300,p,reactants
mdot=v*gas2.density
#mdot=0.021
f = ct.BurnerFlame(gas, initial_grid)
f.burner.mdot = mdot
f.set_initial_guess()
# read temperature vs. position data from a file.
# The file is assumed to have one z, T pair per line, separated by a comma.
#zloc, tvalues = np.genfromtxt('C:\\Users\\HP USER\\Google Drive\\Burke group\\codes\\paperFigs\\mf1p0_T_profile.csv', delimiter=',', comments='#').T
#zloc, tvalues = np.genfromtxt('C:\\Users\\HP USER\\Desktop\\kevin\\t_profile_38torr.txt', delimiter=',', comments='#').T
data=pd.read_csv('C:\\Users\\HP USER\\Google Drive\\Burke group\\codes\\paperFigs\\mf1p0_T_profile.csv',header=3)
zloc=data['z']
tvalues=data['T']
zloc /= max(zloc)
#print(tvalues)
p = 0.12 * ct.one_atm
tburner = 300.0
reactants = 'AR:0.55, O2:0.2143, C2H2:0.2357, A4:0' # premixed gas composition
grid = np.linspace(0, 0.09, 20) # m
grid_points = len(grid)
loglevel = 1 # amount of diagnostic output (0 to 5)
# Create gas object
gas = ct.Solution('abf_mech90torr.cti')
gas.TPX = tburner, p, reactants
mdot = 0.204 * gas.density # kg/(m^2*s)
# Create flame object
f = ct.BurnerFlame(gas, grid=grid)
f.burner.mdot = mdot
f.energy_enabled = False
zloc, tvalues = get_temp_prof(t_distance_vals, temp_vals, points=grid_points)
f.flame.set_fixed_temp_profile(zloc, tvalues)
conc_grid, cvalues = get_conc_prof(c_distance_vals,
concentration_vals,
points=grid_points)
f.X[gas.species_index('A4'), :] = cvalues
f.set_refine_criteria(ratio=3.0, slope=0.05, curve=0.1)
f.show_solution()
f.transport_model = 'Mix'
f.solve(loglevel, refine_grid=False)
f.save('c2h2_o2_ar_burner_flame.xml', 'mix',
'solution with mixture-averaged transport')
loglevel = 1 # amount of diagnostic output (0 to 5)
refine_grid = True # 'True' to enable refinement
################ create the gas object ########################
#
# This object will be used to evaluate all thermodynamic, kinetic, and
# transport properties. It is created with two transport managers, to enable
# switching from mixture-averaged to multicomponent transport on the last
# solution.
gas = ct.Solution('gri30.xml', 'gri30_mix')
# set its state to that of the unburned gas at the burner
gas.TPX = tburner, p, comp
# create the BurnerFlame object.
f = ct.BurnerFlame(gas=gas, grid=initial_grid)
# set the properties at the burner
f.burner.mdot = mdot
f.burner.X = comp
f.burner.T = tburner
# read in the fixed temperature profile
[zloc, tvalues] = getTempData('tdata.dat')
# set the temperature profile to the values read in
f.flame.set_fixed_temp_profile(zloc, tvalues)
f.flame.set_steady_tolerances(default=tol_ss)
f.flame.set_transient_tolerances(default=tol_ts)
def burner_flame(gas,grid,mdot,data=pd.DataFrame(columns=['z','T']),kinetic_sens=0,physical_sens=0,observables=[],physical_params=['T','P'],energycon=False,soret=True):
#when energycon is off treat flame as burner stabilized, with known T-profile
simtype = 'burner flame'
baseConditions=gas.TPX
tol_ss = [1.0e-5, 1.0e-13] # [rtol atol] for steady-state problem
tol_ts = [1.0e-4, 1.0e-10] # [rtol atol] for time stepping
loglevel = 1 # amount of diagnostic output (0 to 5)
f = ct.BurnerFlame(gas, width=grid)
f.burner.mdot = mdot
f.set_initial_guess()
# read temperature vs. position data from a file.
# The file is assumed to have one z, T pair per line, separated by a comma.
if data.empty==False and energycon==False:
zloc=data['z']
tvalues=data['T']
#zloc, tvalues = np.genfromtxt('C:\\Users\\HP USER\\Desktop\\kevin\\harrington_78torr_Tprofile_chemkin.txt', delimiter=',', comments='#').T
#zloc, tvalues = np.genfromtxt('C:\\Users\\HP USER\\Desktop\\kevin\\t_profile_38torr.txt', delimiter=',', comments='#').T
zloc /= max(zloc)
#print(tvalues)
f.flame.set_fixed_temp_profile(zloc, tvalues) #sets a fixed temperature profile for the flame simulation. May come from a measurement. Requires no energy conservation
elif data.empty==False and energycon!='off':
raise Exception('User has supplied fixed temperature dataset but energy conservation is not off. Remove dataset or turn energy conservation off')
f.flame.set_steady_tolerances(default=tol_ss) #Set steady tolerances
f.flame.set_transient_tolerances(default=tol_ts) #Set transient tolerances
f.show_solution()
f.energy_enabled = energycon #This must be set to false for a burner stabilized flame with known T-profile
f.transport_model = 'Multi' #Sets to multicomponent transport for simulation. Needs to be set this way to use Soret effect
f.set_max_jac_age(10, 10) #Age limits on Jacobian-leave as is for best results
f.solve(loglevel, refine_grid=False) #Solve for initial estimate without grid refinement
f.soret_enabled = soret #Enable Soret effect. Remember transport must be set to multi. Mix causes failure
f.set_refine_criteria(ratio=2.0, slope=0.05, curve=0.5) #Establishes refinement criteria for grid
#print('mixture-averaged flamespeed = ', f.u[0])
f.transport_model = 'Multi' #This block solves problem again with grid refinement on
f.solve(loglevel, refine_grid=True)
f.show_solution()
print('multicomponent flamespeed = ', f.u[0])
#solution = f
##Begin section to calculate sensitivities
dk = 0.010
solution = f.X
if kinetic_sens==1 and bool(observables):
#Calculate kinetic sensitivities
sensIndex = [f.grid.tolist(),gas.reaction_equations(),observables]
S = np.zeros((len(f.grid),gas.n_reactions,len(observables)))
#print(solution.X[solution.flame.component_index(observables[0])-4,len(f.grid)-1])
#a=solution.X[solution.flame.component_index(observables[0])-4,len(f.grid)-1]
for m in range(gas.n_reactions):
gas.set_multiplier(1.0)
gas.set_multiplier(1+dk,m)
f.solve(loglevel=1,refine_grid=False)
for i in np.arange(len(observables)):
for k in np.arange(len(f.grid)):
S[k,m,i]=f.X[f.flame.component_index(observables[i])-4,k]-solution[f.flame.component_index(observables[i])-4,k]
#print(solution.X[solution.flame.component_index(observables[i])-4,k])
#print(f.X[f.flame.component_index(observables[i])-4,k])
S[k,m,i]=np.divide(S[k,m,i],solution[f.flame.component_index(observables[i])-4,k])
S[k,m,i]=np.divide(S[k,m,i],dk)
if physical_sens==1 and bool(observables):
#Calculate physical sensitivities
gas.set_multiplier(1.0)
psensIndex = [f.grid.tolist(),physical_params,observables]
pS = np.zeros((len(f.grid),len(physical_params),len(observables)))
for m in range(len(physical_params)):
gas.TPX=baseConditions
if physical_params[m]=='T':
gas.TPX=baseConditions[0]+dk,baseConditions[1],baseConditions[2]
elif physical_params[m]=='P':
gas.TPX=baseConditions[0],baseConditions[1]+dk,baseConditions[2]
f.solve(loglevel=1,refine_grid=False)
for i in np.arange(len(observables)):
for k in np.arange(len(f.grid)):
pS[k,m,i] =np.log10(solution[f.flame.component_index(observables[i])-4,k])-np.log10(f.X[f.flame.component_index(observables[i])-4,k])
pS[k,m,i] = np.divide(pS[k,m,i],np.log10(dk))
elif kinetic_sens==1 and bool(observables)==False:
raise Exception('Please supply a list of observables in order to run kinetic sensitivity analysis')
elif physical_sens==1 and bool(observables)==False:
raise Exception('Please supply a list of observables in order to run physical sensitivity analysis')
gas.set_multiplier(1.0)
gas.TP = baseConditions[0],baseConditions[1]
f.solve(loglevel=1,refine_grid=False)
solution=pd.DataFrame(columns=f.flame.component_names)
#for i in f.flame.component_names:
#solution[i]=f.solution(i)
for i in np.arange(len(f.flame.component_names)):
if i<=3:
solution[f.flame.component_names[i]]=f.solution(i)
else:
solution[f.flame.component_names[i]]=f.X[i-4,:]
if kinetic_sens==1 and bool(observables) and physical_sens!=1:
results = model_data(simtype,kinetic_sens=S,Solution=solution,Index=sensIndex)
return results
elif physical_sens==1 and bool(observables) and kinetic_sens!=1:
results = model_data(simtype,Solution=solution,pIndex=psensIndex,physical_sens=pS)
return results
elif kinetic_sens==1 and physical_sens==1 and bool(observables):
results = model_data(simtype,Solution=solution,pIndex=psensIndex,Index=sensIndex,physical_sens=pS,kinetic_sens=S)
return results
elif kinetic_sens!=1 and physical_sens!=1:
Index = [f.grid.tolist()]
results = model_data(simtype,Solution=solution,Index=Index)
return results
else:
print('Something went wrong with the parameters given. Kinetic and physical sens may be set to either 0 or 1, and require a list of observables')
