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Topology Optimization with Python and code_aster

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# TOPyCA - Topology Optimization with Python and code_aster

The idea is to use code_aster to solve a simple static analysis and print VMIS and INVA_2 of a node group called "REGION" to a .txt file. 
Next, use a python code to calculate a new density using this file, and this density distribution then map as Young's modulus 
onto a mesh in next analysis, and solve.
And so on...

The whole SIMP method is written in python, thus it is basically code_aster + python or more precisely bash + code_aster + python coupling.
Therefore, every stage is done separately, and the loops are not done inside one code_aster run. This approach leads to:

- Postprocessing can be done when optimization is running.
- Code is more comprehensible.
- It is possible to change parameters manually when simulation is running. For example target volume fraction, penalization factor etc..
- It is possible to restart optimization from a specified iteration.
- It is possible to easily add a new manufacturing constrains.
- Code can be modified and used for shells or 2D/axisymmetry as well (DKT tested so far).
- For small cases the most time consuming part is start up of code_aster, the solution of linear static and calculation of densities takes only a second.

**NOTES**:

• Element volume is considered constant so far. However, creating a uniform mesh is quite easy for optimization domain, since it usually has a simple shape. Moreover, a uniform high mesh density is needed everywhere anyway...

• Stamping and casting manufacturing constraints are implemented only for cartesian directions to increase performance, since in 99% of cases an arbitrary direction is not needed (but with a little effort it could be implemented as well). However, user has to switch manually COOR_X, COOR_Y, COOR_Z in the function.

• For symmetry manufacturing constraint an arbitrary direction is implemented.

• To use more than one symmetry constraint, user has to copy the function, rename it for example to symmetry_constraint_2, and apply the constraints sequentially. However, I have not tested it yet. If more manufacturing constraints are used, it is treated this way.

More on: https://forum.code-aster.org/public/d/20385-topological-optimization/13

## Prerequisites

- code_aster (tested version is 15.4, 16.2, 16.4)
- python3 with all necessary libraries, for example Anaconda distribution

After downloading, you have to change all lines containing
```bash
$ singularity run ~/salome_meca-lgpl-2022.1.0-1-20221225-scibian-9.sif shell << END
```
according to your container name in file **run.sh**.
If you use code_aster without salome_meca, then delete these lines (lines with END also).

## How to run

Just change directory to ./beam and enter

```bash
$ bash run.sh
```
or

```bash
$ bash run.sh       | tee log.run
```

## Post-Process

- In Paraview go to File -> Load State
- open state_1.pvsm

or do it manually

- In Paraview click on Open File, RESULTS, all_density.csv
- select Temporal CSV Reader
- in Field Delimeter Characters enter ,
- in Time Column Name enter INST
- apply filter TableToPoints
- change X Column to COOR_X, Y Column to COOR_Y, Z Column to COOR_Z

- open file MESH_GROUPS.med
- apply filter ExtractGroup, select UNAFFECTED
- apply filter ExtractGroup, select REGION
- apply filter PointDatasetInterpolator - Input > TableToPoints, Source > ExtractGroup (REGION), or apply filter ResampleWithDataset - Source Data Arrays > TableToPoints, Destination Mesh > ExtractGroup (REGION), then tick Snap To Cell With Closest Point, untick Compute Tolerance (much slower)

How to extract 3D shape:
- apply filter Clip
- Clip Type -> Scalar -> density
- value -> for example 0.5

Eventually you can smooth the final shape:
- apply filter Extract Surface
- apply filter Smooth -> Number of Iterations -> 500

**NOTE**: For large studies, instead of selecting all_density.csv select density_{iteration}.csv. 
For updating: right click - Change File - select different density_{iteration}.csv.

## How to cite 
If you've used TOPyCA in your research or work, or find it useful in any way, please consider to cite:
```
@misc{code-jacob,
  title = {TOPyCA - Topology Optimization with Python and code_aster},
  author = {Jakub Trušina}, 
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/code-jacob/TOPyCA}},
}
```

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