Warning
This documentation is for an old version of Cantera. You can find docs for newer versions here.
Start by opening an interactive Python session, e.g. by running IPython. Import the Cantera Python module by running:
>>> import cantera as ct
When using Cantera, the first thing you usually need is an object representing some phase of matter. Here, we’ll create a gas mixture:
>>> gas1 = ct.Solution('gri30.xml')
To view the state of the mixture, call the gas1 object as if it were a function:
>>> gas1()
You should see something like this:
gri30:
temperature 300 K
pressure 101325 Pa
density 0.0818891 kg/m^3
mean mol. weight 2.01588 amu
1 kg 1 kmol
----------- ------------
enthalpy 26470.1 5.336e+04 J
internal energy -1.21087e+06 -2.441e+06 J
entropy 64913.9 1.309e+05 J/K
Gibbs function -1.94477e+07 -3.92e+07 J
heat capacity c_p 14311.8 2.885e+04 J/K
heat capacity c_v 10187.3 2.054e+04 J/K
X Y Chem. Pot. / RT
------------- ------------ ------------
H2 1 1 -15.7173
[ +52 minor] 0 0
What you have just done is to create an object, gas1 that implements GRI- Mech 3.0, the 53-species, 325-reaction natural gas combustion mechanism developed by Gregory P. Smith, David M. Golden, Michael Frenklach, Nigel W. Moriarty, Boris Eiteneer, Mikhail Goldenberg, C. Thomas Bowman, Ronald K. Hanson, Soonho Song, William C. Gardiner, Jr., Vitali V. Lissianski, and Zhiwei Qin. See http://www.me.berkeley.edu/gri_mech/ for more information.
The gas1 object has properties you would expect for a gas mixture - it has a temperature, a pressure, species mole and mass fractions, etc. As we’ll soon see, it has many more properties.
The summary of the state of gas1 printed above shows that new objects created from the gri30.xml input file start out with a temperature of 300 K, a pressure of 1 atm, and have a composition that consists of only one species, in this case hydrogen. There is nothing special about H2 - it just happens to be the first species listed in the input file defining GRI-Mech 3.0. In general, whichever species is listed first will initially have a mole fraction of 1.0, and all of the others will be zero.
The state of the object can easily be changed. For example:
>>> gas1.TP = 1200, 101325
sets the temperature to 1200 K and the pressure to 101325 Pa (Cantera always uses SI units). After this statement, calling gas1() results in:
gri30:
temperature 1200 K
pressure 101325 Pa
density 0.0204723 kg/m^3
mean mol. weight 2.01588 amu
1 kg 1 kmol
----------- ------------
enthalpy 1.32956e+07 2.68e+07 J
internal energy 8.34619e+06 1.682e+07 J
entropy 85227.6 1.718e+05 J/K
Gibbs function -8.89775e+07 -1.794e+08 J
heat capacity c_p 15377.9 3.1e+04 J/K
heat capacity c_v 11253.4 2.269e+04 J/K
X Y Chem. Pot. / RT
------------- ------------ ------------
H2 1 1 -17.9775
[ +52 minor] 0 0
Notice that the temperature has been changed as requested, but the pressure has changed too. The density and composition have not.
Thermodynamics generally requires that two properties in addition to composition information be specified to fix the intensive state of a substance (or mixture). The state of the mixture can be set using several combinations of two properties. The following are all equivalent:
>>> gas1.TP = 1200, 101325 # temperature, pressure
>>> gas1.TD = 1200, 0.0204723 # temperature, density
>>> gas1.HP = 1.32956e7, 101325 # specific enthalpy, pressure
>>> gas1.UV = 8.34619e6, 1/0.0204723 # specific internal energy, specific volume
>>> gas1.SP = 85227.6, 101325 # specific entropy, pressure
>>> gas1.SV = 85227.6, 1/0.0204723 # specific entropy, specific volume
In each case, the values of the extensive properties must be entered per unit mass.
Properties may be read independently or together:
>>> gas1.T
1200.0
>>> gas1.h
13295567.68
>>> gas1.UV
(8346188.494954427, 48.8465747765848)
The composition can be set in terms of either mole fractions (X) or mass fractions (Y):
>>> gas1.X = 'CH4:1, O2:2, N2:7.52'
When the composition alone is changed, the temperature and density are held constant. This means that the pressure and other intensive properties will change. The composition can also be set in conjunction with the intensive properties of the mixture:
>>> gas1.TPX = 1200, 101325, 'CH4:1, O2:2, N2:7.52'
>>> gas1()
results in:
gri30:
temperature 1200 K
pressure 101325 Pa
density 0.280629 kg/m^3
mean mol. weight 27.6332 amu
1 kg 1 kmol
----------- ------------
enthalpy 861943 2.382e+07 J
internal energy 500879 1.384e+07 J
entropy 8914.3 2.463e+05 J/K
Gibbs function -9.83522e+06 -2.718e+08 J
heat capacity c_p 1397.26 3.861e+04 J/K
heat capacity c_v 1096.38 3.03e+04 J/K
X Y Chem. Pot. / RT
------------- ------------ ------------
O2 0.190114 0.220149 -28.7472
CH4 0.095057 0.0551863 -35.961
N2 0.714829 0.724665 -25.6789
[ +50 minor] 0 0
The composition above was specified using a string. The format is a comma- separated list of <species name>:<relative mole numbers> pairs. The mole numbers will be normalized to produce the mole fractions, and therefore they are “relative” mole numbers. Mass fractions can be set in this way too by changing X to Y in the above statements.
The composition can also be set using an array, which must have the same size as the number of species. For example, to set all 53 mole fractions to the same value, do this:
>>> gas1.X = np.ones(53) # NumPy array of 53 ones
Or, to set all the mass fractions to equal values:
>>> gas1.Y = np.ones(53)
When setting the state, you can control what properties are held constant by passing the special value None to the property setter. For example, to change the specific volume to 2.1 m^3/kg while holding entropy constant:
>>> gas1.SV = None, 2.1
Or to set the mass fractions while holding temperature and pressure constant:
>>> gas1.TPX = None, None, 'CH4:1.0, O2:0.5'
In previous example, we created an object that models an ideal gas mixture with the species and reactions of GRI-Mech 3.0, using the gri30.xml input file included with Cantera. This is a “pre-processed” XML input file written in a format that is easy for Cantera to parse. Cantera also supports an input file format that is easier to write, called CTI. Several reaction mechanism files in this format are included with Cantera, including ones that model high- temperature air, a hydrogen/oxygen reaction mechanism, and a few surface reaction mechanisms. These files are usually located in the data subdirectory of the Cantera installation directory, e.g. C:\\Program Files\\Cantera\\data on Windows or /usr/local/cantera/data/ on Unix/Linux/Mac OS X machines, depending on how you installed Cantera and the options you specified.
If for some reason Cantera has difficulty finding where these files are on your system, set environment variable CANTERA_DATA to the directory or directories (separated using ; on Windows or : on other operating systems) where they are located. Alternatively, you can call function add_directory to add a directory to the Cantera search path:
>>> ct.add_directory('/usr/local/cantera/my_data_files')
Cantera input files are plain text files, and can be created with any text editor. See the document Defining Phases for more information.
A Cantera input file may contain more than one phase specification, or may contain specifications of interfaces (surfaces). Here we import definitions of two bulk phases and the interface between them from file diamond.cti:
>>> gas2 = ct.Solution('diamond.cti', 'gas')
>>> diamond = ct.Solution('diamond.cti', 'diamond')
>>> diamond_surf = ct.Interface('diamond.cti' , 'diamond_100',
[gas2, diamond])
Note that the bulk (i.e., 3D or homogeneous) phases that participate in the surface reactions must also be passed as arguments to Interface.
See Converting CK-format files in the Working with Input Files documentation.
In addition to the Sphinx-generated Python Module Documentation, documentation of the Python classes and their methods can be accessed from within the Python interpreter as well.
Suppose you have created a Cantera object and want to know what methods are available for it, and get help on using the methods:
>>> g = ct.Solution('gri30.xml')
To get help on the Python class that this object is an instance of:
>>> help(g)
For a simple list of the properties and methods of this object:
>>> dir(g)
To get help on a specific method, e.g. the species_index method:
>>> help(g.species_index)
For properties, getting the documentation is slightly trickier, as the usual method will give you the help for the result, e.g.:
>>> help(g.T)
will provide help on Python’s float class. To get the help for the temperature property, ask for the attribute of the class object itself:
>>> help(g.__class__.T)
If you are using the IPython shell, help can also be obtained using the ? syntax:
In[1]: g.species_index?
To set a gas mixture to a state of chemical equilibrium, use the equilibrate method:
>>> import cantera as ct
>>> g = ct.Solution('gri30.xml')
>>> g.TPX = 300.0, ct.one_atm, 'CH4:0.95,O2:2,N2:7.52'
>>> g.equilibrate('TP')
The above statement sets the state of object g to the state of chemical equilibrium holding temperature and pressure fixed. Alternatively, the specific enthalpy and pressure can be held fixed:
>>> g.TPX = 300.0, ct.one_atm, 'CH4:0.95,O2:2,N2:7.52'
>>> g.equilibrate('HP')
Other options are:
- ‘UV’ fixed specific internal energy and specific volume
- ‘SV’ fixed specific entropy and specific volume
- ‘SP’ fixed specific entropy and pressure
How can you tell if equilibrate has correctly found the chemical equilibrium state? One way is verify that the net rates of progress of all reversible reactions are zero. Here is the code to do this:
>>> g.TPX = 300.0, ct.one_atm, 'CH4:0.95,O2:2,N2:7.52'
>>> g.equilibrate('HP')
>>> rf = g.forward_rates_of_progress
>>> rr = g.reverse_rates_of_progress
>>> for i in range(g.n_reactions):
>>> if g.is_reversible(i) and rf[i] != 0.0:
>>> print(' %4i %10.4g ' % (i, (rf[i] - rr[i])/rf[i]))
If the magnitudes of the numbers in this list are all very small, then each reversible reaction is very nearly equilibrated, which only occurs if the gas is in chemical equilibrium.
You might be wondering how equilibrate works. (Then again, you might not). Method equilibrate invokes Cantera’s chemical equilibrium solver, which uses an element potential method. The element potential method is one of a class of equivalent nonstoichiometric methods that all have the characteristic that the problem reduces to solving a set of M nonlinear algebraic equations, where M is the number of elements (not species). The so-called stoichiometric methods, on the other hand, (including Gibbs minimization), require solving K nonlinear equations, where K is the number of species (usually K >> M). See Smith and Missen, “Chemical Reaction Equilibrium Analysis” for more information on the various algorithms and their characteristics.
Cantera uses a damped Newton method to solve these equations, and does a few other things to generate a good starting guess and to produce a reasonably robust algorithm. If you want to know more about the details, look at the on- line documented source code of Cantera C++ class ‘ChemEquil.h’.
Solution objects are also Kinetics objects, and provide all of the methods necessary to compute the thermodynamic quantities associated with each reaction, reaction rates, and species creation and destruction rates. They also provide methods to inspect the quantities that define each reaction such as the rate constants and the stoichiometric coefficients. The rate calculation functions are used extensively within Cantera’s reactor network model and 1D flame model.
Information about individual reactions that is independent of the thermodynamic state can be obtained by accessing Reaction objects with the Kinetics.reaction method:
>>> g = ct.Solution('gri30.cti')
>>> r = g.reaction(2) # get a Reaction object
>>> r
<ElementaryReaction: H2 + O <=> H + OH>
>>> r.reactants
{'H2': 1.0, 'O': 1.0}
>>> r.products
{'H': 1.0, 'OH': 1.0}
>>> r.rate
Arrhenius(A=38.7, b=2.7, E=2.61918e+07)
If we are interested in only certain types of reactions, we can use this information to filter the full list of reactions to find the just the ones of interest. For example, here we find the indices of just those reactions which convert CO into CO2:
>>> II = [i for i,r in enumerate(g.reactions())
if 'CO' in r.reactants and 'CO2' in r.products]
>>> for i in II:
... print(g.reaction(i).equation)
CO + O (+M) <=> CO2 (+M)
CO + O2 <=> CO2 + O
CO + OH <=> CO2 + H
CO + HO2 <=> CO2 + OH
(Actually, we should also include reactions where the reaction is written such that CO2 is a reactant and CO is a product, but for this example, we’ll just stick to this smaller set of reactions.) Now, let’s set the composition to an interesting equilibrium state:
>>> g.TPX = 300, 101325, {'CH4':0.6, 'O2':1.0, 'N2':3.76}
>>> g.equilibrate('HP')
We can verify that this is an equilibrium state by seeing that the net reaction rates are essentially zero:
>>> g.net_rates_of_progress[II]
array([ 4.06576e-20, -5.50571e-21, 0.00000e+00, -4.91279e-20])
Now, let’s see what happens if we decrease the temperature of the mixture:
>>> g.TP = g.T-100, None
>>> g.net_rates_of_progress[II]
array([ 3.18645e-05, 5.00490e-08, 1.05965e-01, 2.89503e-06])
All of the reaction rates are positive, favoring the formation of CO2 from CO, with the third reaction, CO + OH <=> CO2 + H proceeding the fastest. If we look at the enthalpy change associated with each of these reactions:
>>> g.delta_enthalpy[II]
array([ -5.33035e+08, -2.23249e+07, -8.76650e+07, -2.49170e+08])
we see that the change is negative in each case, indicating a net release of thermal energy. The total heat release rate can be computed either from the reaction rates:
>>> np.dot(g.net_rates_of_progress, g.delta_enthalpy)
-58013370.720881931
or from the species production rates:
>>> np.dot(g.net_production_rates, g.partial_molar_enthalpies)
-58013370.720881805
The contribution from just the selected reactions is:
>>> np.dot(g.net_rates_of_progress[II], g.delta_enthalpy[II])
-9307123.2625651453
Or about 16% of the total heat release rate.