conda create --name bird python=3.10
conda activate bird
git clone https://github.com/NREL/BioReactorDesign.git
cd BioReactorDesign
pip install -e .

conda create --name bird python=3.10
conda activate bird
pip install nrel-bird

Place the attached models in OFsolvers into the same arborescence in your $FOAM_APP directory. These compile with openFOAM-9

We provide a new drag model Grace, a new interfacial composition model Higbie and various other models which magnitude can be controlled via an efficiency factor *_limited

inp=bird/meshing/stir_tank_mesh_templates/base_tank/tank_par.yaml
out=bird/meshing/stir_tank_case_templates/base/system/blockMeshDict

python applications/write_stir_tank_mesh.py -i $inp -o $out

Generates a blockMeshDict

Then activate openFoam environment (tested with OpenFoam9) and mesh with

blockMesh -dict system/blockMeshDict
stitchMesh -perfect -overwrite inside_to_hub inside_to_hub_copy
stitchMesh -perfect -overwrite hub_to_rotor hub_to_rotor_copy
transformPoints "rotate=((0 0 1)(0 1 0))":

Mesh visualized in Paraview

Generates blockMeshDict in system

root=`pwd`
caseFolder=bird/meshing/block_cyl_cases_templates/case
mesh_temp=bird/meshing/block_cyl_mesh_templates/sideSparger

python applications/write_block_cyl_mesh.py -i $mesh_temp/input.json -t $mesh_temp/topology.json -o $caseFolder/system

Then activate openFoam environment (tested with OpenFoam9) and mesh with

cd $caseFolder
blockMesh
transformPoints "scale=(0.001 0.001 0.001)"
transformPoints "rotate=((0 0 1) (0 1 0))"
cd $root

Will generate this

All dimensions and mesh are controlled by the input file input.json. The input file can also be in .yaml format. The parser will decide the file format based on its extension. See bird/meshing/block_cyl_mesh_templates/baseColumn/ for an example of .yaml

The block topology is controlled by the topology.json Always work with a schematic. Here is the schematic for this case

The purple blocks are walls (not meshed) and the white blocks are fluid blocks (meshed). The symmetry axis is indicated as a dashed line

In the topology.json, the purple blocks are defined as

"Walls": {
                "Support": [
                            {"R": 0, "L": 3},
                            {"R": 1, "L": 3}
                           ],
                "Sparger": [
                            {"R": 0, "L": 2},
                            {"R": 1, "L": 2},
                            {"R": 2, "L": 2}
                           ]
        }

Boundaries are defined with three types, top, bottom and lateral

In the case of sparger walls shown below with the red lines

the boundary is defined in the topology.json as

"Boundary": {
                "wall_sparger":[
                           {"type": "bottom", "Rmin": 2, "Rmax": 2, "Lmin": 2, "Lmax": 3},
                           {"type": "top", "Rmin": 0, "Rmax": 0, "Lmin": 1, "Lmax": 2},
                           {"type": "top", "Rmin": 1, "Rmax": 1, "Lmin": 1, "Lmax": 2},
                           {"type": "top", "Rmin": 2, "Rmax": 2, "Lmin": 1, "Lmax": 2}
                         ],
...

In the case of sparger inlet shown below with the red line

the boundary is defined in the topology.json as

"Boundary": {
                "inlet": [
                           {"type": "lateral", "Rmin": 2, "Rmax": 3, "Lmin": 2, "Lmax": 2}
                         ],
...

usage: write_block_cyl_mesh.py [-h] -i  -t  -o

Block cylindrical meshing

options:
  -h, --help            show this help message and exit
  -i , --input_file     Input file for meshing and geometry parameters
  -t , --topo_file      Block description of the configuration
  -o , --output_folder 
                        Output folder for blockMeshDict

Generates blockMeshDict in system

root=`pwd`
caseFolder=bird/meshing/block_rect_cases_templates/case
mesh_temp=bird/meshing/block_rect_mesh_templates/loopReactor

python applications/write_block_rect_mesh.py -i $mesh_temp/input.json -o $caseFolder/system

Then activate openFoam environment (tested with OpenFoam9) and mesh with

cd $caseFolder
blockMesh
cd $root

Will generate this

The geometry of the block cylindrical mesh is defined within a 3D domain (X,Y,Z). The blocks that represent the fluid domain are a subset of a block rectangular background domain. The fluid blocks are defined using the geometry corners. For the mesh shown above, the geometry corners are the red blocks shown below

The corners are defined in the input.json

"Geometry": {
        "Fluids": [
                [ [0,0,0], [9,0,0], [9,0,4], [0,0,4] ],
                [ [0,1,4], [0,4,4], [0,4,0], [0,1,0] ]
        ]
}
...

To see how to use this on an actual case see OFsolvers/tutorial_cases/loop_reactor

Boundaries may be specified with surfaceToPatch utility in OpenFOAM, based on STL files that can be generated with

python applications/write_stl_patch.py -v

Generates

To see how to use this on an actual case see OFsolvers/tutorial_cases/loop_reactor

usage: write_stl_patch.py [-h] [-i] [-v]

Generate boundary patch

options:
  -h, --help     show this help message and exit
  -i , --input   Boundary patch Json input
  -v, --verbose  plot on screen

Edit the json files read when generating the mesh. In the case below, the boundary condition inlets consists of 3 discs

{
    "inlets": [
        {"type": "circle", "centx": 5.0, "centy": 0.0, "centz": 0.5, "radius": 0.4, "normal_dir": 1,"nelements": 50},
        {"type": "circle", "centx": 2.5, "centy": 0.0, "centz": 0.5, "radius": 0.4, "normal_dir": 1,"nelements": 50},
        {"type": "circle", "centx": 7.5, "centy": 0.0, "centz": 0.5, "radius": 0.4, "normal_dir": 1,"nelements": 50}
    ],
}
...

python applications/early_prediction.py -df bird/postprocess/data_early

Generates

usage: early_prediction.py [-h] -df  [-func]

Early prediction

options:
  -h, --help            show this help message and exit
  -df , --dataFolder    Data folder containing multiple QOI time histories
  -func , --functionalForm 
                        functional form used to perform extrapolation

python applications/compute_conditional_mean.py -f bird/postprocess/data_conditional_mean -avg 2

Generates (among others)

usage: compute_conditional_mean.py [-h] -f  [-vert] [-avg] [--fl FL [FL ...]] [-n  [...]]

Compute conditional means of OpenFOAM fields

options:
  -h, --help            show this help message and exit
  -f , --caseFolder     caseFolder to analyze
  -vert , --verticalDirection 
                        Index of vertical direction
  -avg , --windowAve    Window Average
  --fl FL [FL ...], --field_list FL [FL ...]
                        List of fields to plot
  -n  [ ...], --names  [ ...]
                        names of cases

Code formatting and import sorting are done automatically with black and isort.

Fix imports and format : pip install black isort; bash fixFormat.sh

Spelling is checked but not automatically fixed using codespell

Software record SWR 24-35

To cite BioReactorDesign use these articles on CO2 interphase mass transfer (open access) on aerobic bioreactors and on butanediol synthesis

@article{hassanaly2024inverse,
  title={Inverse modeling of bubble size dynamics for interphase mass transfer and gas holdup in CO2 bubble column reactors},
  author={Hassanaly, Malik and Parra-Alvarez, John M. and Rahimi, Mohammad J. and Sitaraman, Hariswaran},
  journal={Under Review},
  year={2024},
}


@article{rahimi2018computational,
  title={Computational fluid dynamics study of full-scale aerobic bioreactors: Evaluation of gas--liquid mass transfer, oxygen uptake, and dynamic oxygen distribution},
  author={Rahimi, Mohammad J and Sitaraman, Hariswaran and Humbird, David and Stickel, Jonathan J},
  journal={Chemical Engineering Research and Design},
  volume={139},
  pages={283--295},
  year={2018},
  publisher={Elsevier}
}

@article{sitaraman2023reacting,
  title={A reacting multiphase computational flow model for 2, 3-butanediol synthesis in industrial-scale bioreactors},
  author={Sitaraman, Hariswaran and Lischeske, James and Lu, Yimin and Stickel, Jonathan},
  journal={Chemical Engineering Research and Design},
  volume={197},
  pages={38--52},
  year={2023},
  publisher={Elsevier}
}

This work was authored by the National Renewable Energy Laboratory (NREL), operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. This work was supported by funding from DOE Bioenergy Technologies Office (BETO) CO2RUe consortium. The research was performed using computational resources sponsored by the Department of Energy's Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.