https://dlr-institute-of-future-fuels.github.io/gaspype/ Skip to main content Back to top [ ] Ctrl+K gaspype documentation * Classes and functions * Gaspype examples Search Ctrl+K Search Ctrl+K * Classes and functions * Gaspype examples Gaspype# The python package provides a 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. It is designed with goal to be portable to Numpy-style GPU frameworks like JAX and PyTorch. Key Features# * Pure Python implementation with NumPy vectorization for high performance * Immutable types and comprehensive type hints for reliability * Intuitive, Pythonic API for both rapid prototyping and complex multidimensional models * Ready for Jupyter Notebook and educational use * Designed for future GPU support (JAX, PyTorch) * Ships with a comprehensive NASA9-based species database 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. next Classes and functions On this page * Key Features * Installation * Getting started + Fluid + Elements * Developer Guide * License This Page * Show Source (c) Copyright 2025, Nicolas Kruse. 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