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124 lines
4.0 KiB
Python
124 lines
4.0 KiB
Python
# NPFLAME1 - A nonpremixed counterflow flame.
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#
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# This script computes an atmospheric-pressure ethane/air
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# counterflow flame using GRI-Mech 3.0.
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# Run time on a Mac G4: ~ 5 minutes
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#
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from Cantera import *
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from Cantera.OneD import *
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from Cantera.OneD.CounterFlame import CounterFlame
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from Cantera.num import array
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##################################################################
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# parameter values
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#
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# These are grouped here to simplify changing flame conditions
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p = OneAtm # pressure
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tin_f = 300.0 # fuel inlet temperature
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tin_o = 300.0 # oxidizer inlet temperature
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mdot_o = 0.72 # kg/m^2/s
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mdot_f = 0.24 # kg/m^2/s
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comp_o = 'O2:0.21, N2:0.78, AR:0.01'; # air composition
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comp_f = 'C2H6:1'; # fuel composition
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# distance between inlets is 2 cm; start with an evenly-spaced 6-point
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# grid
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initial_grid = 0.02*array([0.0, 0.2, 0.4, 0.6, 0.8, 1.0],'d')
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tol_ss = [1.0e-5, 1.0e-9] # [rtol, atol] for steady-state
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# problem
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tol_ts = [1.0e-3, 1.0e-9] # [rtol, atol] for time stepping
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loglevel = 1 # amount of diagnostic output (0
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# to 5)
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refine_grid = 1 # 1 to enable refinement, 0 to
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# disable
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################ create the gas object ########################
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#
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# This object will be used to evaluate all thermodynamic, kinetic,
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# and transport properties
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#
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# Here we use GRI-Mech 3.0 with mixture-averaged transport
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# properties. To use your own mechanism, use function
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# IdealGasMix('mech.cti') to read a mechanism in Cantera format. If
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# you need to convert from Chemkin format, use the ck2cti utility
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# program first.
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gas = GRI30('Mix')
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# create an object representing the counterflow flame configuration,
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# which consists of a fuel inlet on the left, the flow in the middle,
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# and the oxidizer inlet on the right. Class CounterFlame creates this
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# configuration.
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f = CounterFlame(gas = gas, grid = initial_grid)
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# Set the state of the two inlets
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f.fuel_inlet.set(massflux = mdot_f,
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mole_fractions = comp_f,
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temperature = tin_f)
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f.oxidizer_inlet.set(massflux = mdot_o,
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mole_fractions = comp_o,
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temperature = tin_o)
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# set the error tolerances
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f.set(tol = tol_ss, tol_time = tol_ts)
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# construct the initial solution estimate. To do so, it is necessary
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# to specify the fuel species. If a fuel mixture is being used,
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# specify a representative species here for the purpose of
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# constructing an initial guess.
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f.init(fuel = 'C2H6')
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# show the starting estimate
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f.showSolution()
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# First disable the energy equation and solve the problem without
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# refining the grid
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f.set(energy = 'off')
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f.solve(loglevel, 0)
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# Now specify grid refinement criteria, turn on the energy equation,
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# and solve the problem again. The ratio parameter controls the
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# maximum size ratio between adjacent cells; slope and curve should be
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# between 0 and 1 and control adding points in regions of high
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# gradients and high curvature, respectively. If prune > 0, points
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# will be removed if the relative slope and curvature for all
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# components fall below the prune level. Set prune < min(slope,
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# curve), or to zero to disable removing grid points.
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f.setRefineCriteria(ratio = 200.0, slope = 0.1, curve = 0.2, prune = 0.02)
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f.set(energy = 'on')
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f.solve(1)
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# Save the solution
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f.save('npflame1.xml')
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# write the velocity, temperature, and mole fractions to a CSV file
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z = f.flame.grid()
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T = f.T()
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u = f.u()
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V = f.V()
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fcsv = open('npflame1.csv','w')
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writeCSV(fcsv, ['z (m)', 'u (m/s)', 'V (1/s)', 'T (K)']
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+ list(gas.speciesNames()))
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for n in range(f.flame.nPoints()):
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f.setGasState(n)
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writeCSV(fcsv, [z[n], u[n], V[n], T[n]]+list(gas.moleFractions()))
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fcsv.close()
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print 'solution saved to npflame1.csv'
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f.showSolution()
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f.showStats()
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