gaspype 1.1.1

Performant library for thermodynamic calculations

Gaspype

The python package provides an performant library for thermodynamic calculations like equilibrium reactions for several hundred gas species and their mixtures - written in Python/Numpy.

Species are treated as ideal gases. Therefore the application is limited to moderate pressures or high temperature applications.

Its designed with goal to be portable to Numpy-style GPU frameworks like JAX and PyTorch.

Key features

• Tensor operations to prevent bottlenecks by the python interpreter
• Immutable types
• Elegant pythonic interface
• Great readable and compact syntax when using this package
• Good usability in Jupyter Notebook
• High performance for multidimensional fluid arrays

Installation

Installation with pip:

pip install gaspype

Installation with conda:

conda install conda-forge::gaspype

Getting started

Gaspype provides two main classes: fluid and elements.

Fluid

A fluid class describes a mixture of molecular species and their individual molar amounts.

import gaspype as gp
fl = gp.fluid({'H2O': 1, 'H2': 2})
fl

Total            3.000e+00 mol
H2O              33.33 %
H2               66.67 %

Its' functions provides thermodynamic, mass balance and ideal gas properties of the mixture.

cp = fl.get_cp(t=800+273.15)
mass = fl.get_mass()
gas_volume = fl.get_v(t=800+273.15, p=1e5)

The arguments can be provided as numpy-arrays:

import numpy as np
t_range = np.linspace(600, 800, 5) + 273.15
fl.get_density(t=t_range, p=1e5)

array([0.10122906, 0.09574625, 0.09082685, 0.08638827, 0.08236328])

A fluid object can have multiple compositions. A multidimensional fluid object can be created for example by multiplication with a numpy array:

fl2 = gp.fluid({'H2O': 1, 'N2': 2}) + \
      np.linspace(0, 10, 4) * gp.fluid({'H2': 1})
fl2

Total mol:
array([ 3.        ,  6.33333333,  9.66666667, 13.        ])
Species:
              H2        H2O         N2
Molar fractions:
array([[0.        , 0.33333333, 0.66666667],
       [0.52631579, 0.15789474, 0.31578947],
       [0.68965517, 0.10344828, 0.20689655],
       [0.76923077, 0.07692308, 0.15384615]])

A fluid object can be converted to a pandas dataframe:

import pandas as pd
pd.DataFrame(list(fl2))

	H2O	N2	H2
0	1.0	2.0	0.000000
1	1.0	2.0	3.333333
2	1.0	2.0	6.666667
3	1.0	2.0	10.000000

The broadcasting behavior is not limited to 1D-arrays:

fl3 = gp.fluid({'H2O': 1}) + \
      np.linspace(0, 10, 4) * gp.fluid({'H2': 1}) + \
      np.expand_dims(np.linspace(1, 3, 3), axis=1) * gp.fluid({'N2': 1})
fl3

Total mol:
array([[ 2.        ,  5.33333333,  8.66666667, 12.        ],
       [ 3.        ,  6.33333333,  9.66666667, 13.        ],
       [ 4.        ,  7.33333333, 10.66666667, 14.        ]])
Species:
              H2        H2O         N2
Molar fractions:
array([[[0.        , 0.5       , 0.5       ],
        [0.625     , 0.1875    , 0.1875    ],
        [0.76923077, 0.11538462, 0.11538462],
        [0.83333333, 0.08333333, 0.08333333]],

       [[0.        , 0.33333333, 0.66666667],
        [0.52631579, 0.15789474, 0.31578947],
        [0.68965517, 0.10344828, 0.20689655],
        [0.76923077, 0.07692308, 0.15384615]],

       [[0.        , 0.25      , 0.75      ],
        [0.45454545, 0.13636364, 0.40909091],
        [0.625     , 0.09375   , 0.28125   ],
        [0.71428571, 0.07142857, 0.21428571]]])

Elements

In some cases not the molecular but the atomic composition is of interest. The elements class can be used for atom based balances and works similar:

el = gp.elements({'N': 1, 'Cl': 2})
el.get_mass()

np.float64(0.08490700000000001)

A elements object can be as well instantiated from a fluid object. Arithmetic operations between elements and fluid result in an elements object:

el2 = gp.elements(fl) + el - 0.3 * fl
el2

Cl               2.000e+00 mol
H                4.200e+00 mol
N                1.000e+00 mol
O                7.000e-01 mol

Going from an atomic composition to an molecular composition is possible as well. One way is to calculate the thermodynamic equilibrium for a mixture:

fs = gp.fluid_system('CH4, H2, CO, CO2, O2')
el3 = gp.elements({'C': 1, 'H': 2, 'O':1}, fs)
fl3 = gp.equilibrium(el3, t=800)
fl3

Total            1.204e+00 mol
CH4              33.07 %
H2               16.93 %
CO               16.93 %
CO2              33.07 %
O2                0.00 %

The equilibrium function can be called with a fluid or elements object as first argument. fluid and elements referencing a fluid_system object witch can be be set as shown above during the object instantiation. If not provided, a new one will be created automatically. Providing a fluid_system gives more control over which molecular species are included in derived fluid objects. Furthermore arithmetic operations between objects with the same fluid_system are potentially faster:

fl3 + gp.fluid({'CH4': 1}, fs)

Total            2.204e+00 mol
CH4              63.44 %
H2                9.24 %
CO                9.24 %
CO2              18.07 %
O2                0.00 %

Especially if the fluid_system of one of the operants has not a subset of molecular species of the other fluid_system a new fluid_system will be created for the operation which might degrade performance:

fl3 + gp.fluid({'NH3': 1})

Total            2.204e+00 mol
CH4              18.07 %
CO                9.24 %
CO2              18.07 %
H2                9.24 %
NH3              45.38 %
O2                0.00 %

Developer Guide

Contributions are welcome, please open an issue or submit a pull request on GitHub.

To get started with developing the gaspype package, follow these steps.

First, clone the repository to your local machine using Git:

git clone https://github.com/DLR-Institute-of-Future-Fuels/gaspype.git
cd gaspype

It's recommended to setup an venv:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Install the package and dev-dependencies while keeping the package files in the current directory:

pip install -e .[dev]

Compile binary property database from text based files:

python thermo_data/combine_data.py thermo_data/combined_data.yaml thermo_data/nasa9*.yaml thermo_data/nasa9*.xml
python thermo_data/compile_to_bin.py thermo_data/combined_data.yaml src/gaspype/data/therm_data.bin

Ensure that everything is set up correctly by running the tests:

pytest

License

This project is licensed under the MIT License - see the LICENSE file for details.