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A minimal grid convergence analysis toolkit

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GCAT: grid convergence analysis toolkit

Code style: black Imports: isort black

Note

The implementation is completed. Thus, do not expect updates to the code anymore.

Installation

Rye

  1. Clone the repository

    git clone https://github.com/gabrielbdsantos/gcat
    cd gcat
    
  2. Pin a specific Python version (optional)

    rye pin 3.x
    
  3. Install

    rye sync
    
  4. Activate the virtual environment.

    source .venv/bin/activate
    

Pip

  1. Clone the repository

    git clone https://github.com/gabrielbdsantos/gcat
    cd gcat
    
  2. Create a virtual environment (optional)

    python3 -m venv .venv --clear
    
  3. Install

    pip install -r requirements.lock
    
  4. Activate it

    source .venv/bin/activate
    

Quick start

  1. Check the refinement ratios and representative grid sizes.

    $ gcat check --n1 1000 --n2 2000 --n3 3000 --area 0.5
    # Grid summary
    + ------------
      N1 = 2900 elements
      N2 = 1700 elements
      N3 = 1000 elements
      Area = 0.2 m^2
    
    # Representative grid size
    + ------------------------
      h1 = 8.304548 mm
      h2 = 10.846523 mm
      h3 = 14.142136 mm
    
    # Refinement ratio
    + ----------------
      r21 = 1.306094
      r32 = 1.303840
    
  2. Compute the grid convergence index

    $ gcat gci --h1 8.30 --h2 10.84 --h3 14.14 --f1 1 --f2 1.02 --f3 1.08
    # Grid summary
    + ---------------------------------------
      h1 = 8.300000e+00 m, f1 = 1.000000e+00
      h2 = 1.084000e+01 m, f2 = 1.020000e+00
      h3 = 1.414000e+01 m, f3 = 1.080000e+00
    
    # GCI (safety factor = 1.25)
    + ---------------------------------------
      GCI21_fine   = 1.562500e-02
      GCI21_coarse = 4.687500e-02
    
      GCI32_fine   = 4.630449e-02
      GCI32_coarse = 1.382163e-01
    
      Asymptotic ratio = 1.012321
    

References

  1. I. B. Celik, U. Ghia, P. J. Roache, C. J. Freitas, H. Coleman, and P. E. Raad, Procedure for Estimation and Reporting of Uncertainty Due to Discretization in CFD Applications,” J. Fluids Eng., vol. 130, no. 7, p. 078001, Jul. 2008, doi: 10.1115/1.2960953.

  2. P. J. Roache, Quantification of Uncertainty in Computational Fluid Dynamics, Annu. Rev. Fluid Mech., vol. 29, no. 1, pp. 123–160, Jan. 1997, doi: 10.1146/annurev.fluid.29.1.123.

  3. Examining Spatial (Grid) Convergence. https://www.grc.nasa.gov/WWW/wind/valid/tutorial/spatconv.html (accessed Oct. 22, 2020).

License

The code is licensed under the terms of the MIT license. For further information refer to LICENSE.