Bio Reactor Design (BiRD): a toolbox to simulate and analyze different designs of bioreactors in OpenFOAM
Project description
Bio Reactor Design (BiRD) Toolbox
Installation for developers
conda create --name bird python=3.10
conda activate bird
git clone https://github.com/NREL/BioReactorDesign.git
cd BioReactorDesign
pip install -e .
Installation for users
conda create --name bird python=3.10
conda activate bird
pip install nrel-bird
OpenFOAM solvers
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
Meshing
Generate Stir tank mesh
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
Block cylindrical meshing
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
How to change the dimensions or mesh refinement
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
How to change the arrangement of concentric cylinders
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}
]
}
How to change boundaries
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}
],
...
Manual
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
Block rectangular meshing
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
How to change the block rectangular geometry
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
Preprocess
Generate STL mesh
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
Manual
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
How to change the set of shapes in the boundary patch
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}
],
}
...
Postprocess
Perform early prediction
python applications/early_prediction.py -df bird/postprocess/data_early
Generates
Manual
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
Plot conditional means
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
Formatting
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
References
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}
}
Acknowledgments
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.
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