Getting started

This page provides instructions for installing your package and running a minimal example.

Installation

The minimal requirements to use femo_alpha for modeling and simulation are FEniCSx and CSDL_alpha. For modeling aircraft design applications, you may install CADDEE_alpha to enable coupling with solvers of other disciplines; for optimization, you will also need ModOpt on top of them for Python bindings of various state-of-the-art optimizers.

Installation instructions

It’s recommended to use conda for installing the module and its dependencies.

Create a conda environment for femo with a specific Python version (Python 3.9) that is compatible with all of the dependencies
```
conda create -n femo python=3.9.10
```
(Python 3.9.7 also works if Python 3.9.10 is unavailable in your conda)
Activate the conda enviroment
```
conda activate femo
```

Install FEniCSx by conda-forge

conda install -c conda-forge fenics-dolfinx=0.5.1

Install CSDL_alpha and ModOpt by

pip install git+https://github.com/LSDOlab/CSDL_alpha.git
pip install git+https://github.com/LSDOlab/modopt.git

Install femo_alpha as a user by

pip install git+https://github.com/LSDOlab/femo_alpha.git

or install femo_alpha as a developer by

git clone https://github.com/LSDOlab/femo_alpha.git
pip install -e ./femo_alpha

Setting up Testing

To test if your installation was successful, run ex_thickness_opt_cantilever_beam.py from /femo_alpha/examples/basic_examples/beam_thickness_opt/. If everything works correctly, the following terminal output will be displayed.

beam_thickness_opt

         ==============
         Scipy summary:
         ==============
         Problem                    : beam_thickness
         Solver                     : scipy_slsqp
         Success                    : True
         Message                    : Optimization terminated successfully
         Objective                  : 23762.153677992977
         Gradient norm              : 100814.27872282796
         Total time                 : 12.408674001693726
         Major iterations           : 111
         Total function evals       : 383
         Total gradient evals       : 111
         ==================================================
Optimization results:
     ['compliance'] [23762.15367799]
     ['volume'] [0.01]
OpenMDAO compliance: 23762.153677443166