pygasflow

pygasflow is a Python package that provides a few handful functions to quickly perform:

• Compressible flow computation in the quasi-1D ideal gasdynamic (perfect gas) regime. The following solvers are implemented:

  • isentropic_solver.
  • fanno_solver.
  • rayleigh_solver.
  • shockwave_solver for normal and oblique shock waves.
  • conical_shockwave_solver.
  • De_Laval_solver and the nozzles sub-module, containing functions and classes to understand convergent-divergent nozzles, Rao's TOP nozzles (Thrust Optmizie Parabolic), Minimum Length nozzle with Method of Characteristics. Nozzles can be used to quickly visualize their geometric differences or to solve the isentropic expansion with the De_Laval_Solver class.

• Aerothermodynamic computations (pygasflow.atd module):

  • Correlations to estimate boundary layer thickness, heat flux and wall shear stress over a flat plate or a stagnation region.
  • Newtonian Flow Theory to estimate the pressure distribution around objects and their aerodynamic characteristics.

The following charts has been generated with the functions included in this package:

The following screenshots highlights the interactive capabilities implemented in this module:

Installation

The repository is avaliable on PyPi:

pip install pygasflow

And also on Conda:

conda install conda-forge::pygasflow

Usage

The easiest way is to call a solver. Let's say you need to solve an isentropic flow:

from pygasflow import isentropic_solver
T1 = 290   # K
p1 = 1     # atm

# compute ratios and quantities for an isentropic flow at Mach 2
res = isentropic_solver("m", 2, to_dict=True)
res.show()
# key     quantity
# ----------------------------
# m       M                        2.00000000
# pr      P / P0                   0.12780453
# dr      rho / rho0               0.23004815
# tr      T / T0                   0.55555556
# prs     P / P*                   0.24192491
# drs     rho / rho*               0.36288737
# trs     T / T*                   0.66666667
# urs     U / U*                   1.63299316
# ars     A / A*                   1.68750000
# ma      Mach Angle              30.00000000
# pm      Prandtl-Meyer           26.37976081

# compute total quantities
T0 = (1 / res["tr"]) * T1
P0 = (1 / res["pr"]) * p1
print("---------")
print(f"P0 = {P0} atm")
print(f"T0 = {T0} K")
# ---------
# P0 = 7.824449066867263 atm
# T0 = 522.0 K

Should a solver not be sufficient for your use case, feel free to explore the code implemented inside each flow's type, maybe you'll find a function that suits your needs.

Please:

• visit the documentation page.
• take a look at the notebooks contained in the examples folder. You can also try this package online with Binder.

How to Contribute

Contributions are welcomed. If you find any errors, or would like to provide further examples of usage, open an issue or submit a pull request. Here is how to open a pull request:

1. fork this repository.
2. download the repository: git clone https://github.com/<YOUR-USERNAME>/pygasflow.git
3. enter the project directory: cd pygasflow
4. create a new branch: git branch -d YOUR-BRANCH-NAME
5. do the necessary edits and commit them.
6. push the branch to your remote: git push origin YOUR-BRANCH-NAME
7. open the Pull Request.

NOTE about AI-LLM usage: I have nothing against the use of these tools. However, many people are unable to properly control their outputs. In practice, these tools often modifies too much. With this in mind:

• No vibe coding. I prefer that you code manually and understand exactly what you are doing. If you are unable to code without an LLM, open an issue and clearly explain what's wrong or what could be implemented.
• If there is a comment in the code, it is very likely to be important to me (the maintainer). Equally important are variable names, function names etc. If the LLM is going to change variable names, remove comments or reorganize the code just for the sake of it, I'll close the PR immediately.