This has been one of the major projects of my PhD. I started from a low to very low base of knowledge in both the applied methods, so it has taken a long span of time to get anywhere near a product of value. The goal of this project is to build models of the environment (airflow, temperature and moisture) within a clamp. Historical and some new experimental data will be used to populate the model’s parameters. Data from old and new experiments will also be used to verify the results.
The modelling activity is split into two modelling approaches:
- a Computational Fluid Dynamics (CFD) mechanistic model, as used in engineering sciences
- a Machine Learning (ML) statistical model, as used pretty much everywhere
The models will be compared on their accuracy, breadth of information, computational cost, ease of use, etc.
There are two ultimate uses of these models. The CFD model will be used to model new designs and technologies that can be used in sugar beet storage. The ML model will hopefully be used as part of a network of temperature sensors to provide forecasts of clamp temperature and warnings of dangerous conditions.