The python validation engine (pyvale): An all-in-one package for sensor simulation, sensor uncertainty quantification, sensor placement optimisation and simulation calibration/validation. Used to simulate experimental data from an input multi-physics simulation by explicitly modelling sensors with realistic uncertainties. Useful for experimental design, sensor placement optimisation, testing simulation validation metrics and virtually testing digital shadows/twins.
Here we demonstrate how pyvale can be used to simulate thermocouples and strain gauges applied to a MOOSE thermo-mechanical simulation of a fusion divertor armour heatsink. The figures below show visualisations of the virtual thermocouple and strain gauge locations on the simualtion mesh as well as time traces for each sensor over a series of simulated experiments. The code to run the simulated experiments and produce the output shown here comes from this example.
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| Visualisation of the thermcouple locations. | Visualisation of the strain gauge locations. |
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| Thermocouples time traces over a series of simulated experiments. | Strain gauge time traces over a series of simulated experiments. |
To be compatible with bpy (the Blender python interface), pyvale uses python 3.11. To install python 3.11 without corrupting your operating systems python installation first add the deadsnakes repository to apt:
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt update && sudo apt upgrade -yInstall python 3.11:
sudo apt install python3.11Add venv to your python 3.11 install:
sudo apt install python3.11-venvCheck your python 3.11 install is working using the following command which should open an interactive python interpreter:
python3.11We recommend installing pyvale in a virtual environment using venv or pyvale can be installed into an existing environment of your choice. To create a specific virtual environment for pyvale navigate to the directory you want to install the environment and use:
python3.11 -m venv .pyvale-env
source .pyvale-env/bin/activateClone pyvale to your local system along with submodules using
git clone --recurse-submodules [email protected]:Computer-Aided-Validation-Laboratory/pyvale.git
cd to the root directory of pyvale. Ensure you virtual environment is activated and run the following commmand from the pyvale directory:
pip install .
pip install ./dependencies/mooseherder
To create an editable/developer installation of pyvale and mooseherder - follow the instructions for a standard installation but run:
pip install -e .
pip install -e ./dependencies/mooseherder
pyvale come pre-packaged with example moose physics simulation outputs (as *.e exodus files) to demonstrate its functionality. If you need to run additional simulation cases we recommend proteus (https://github.com/aurora-multiphysics/proteus) which has build scripts for common linux distributions.
The examples folder in "src/pyvale/examples" includes a sequence of examples of increasing complexity that demonstrate the functionality of pyvale.
The Computer Aided Validation Team at UKAEA:
- Lloyd Fletcher (ScepticalRabbit), UK Atomic Energy Authority
- John Charlton, UK Atomic Energy Authority
- Joel Hirst, UK Atomic Energy Authority
- Lorna Sibson, UK Atomic Energy Authority
- Adel Tayeb, UK Atomic Energy Authority
- Alex Marsh, UK Atomic Energy Authority
- Rory Spencer, UK Atomic Energy Authority
- Michael Atkinson, UK Atomic Energy Authority