Computer-aided design (CAD) models play an important role in fusion and fission reactor design and operation. They are used in the construction, upgrading of devices, and component placement. They play a crucial role in component manufacturing, system integration, and various simulations, including simulations of neutron and gamma ray transport using the Monte Carlo method. These simulations ensure operation within parameters, identify potential issues, and assess key features of a reactor or component.
The preparation of a CAD model for a reactor or individual components typically requires a significant amount of user time to reach a usable form. Models usually undergo a cyclical process of refinement and analysis, which can be time-consuming. As changes in the model during this process are typically progressively smaller, it makes sense to consider at what point analysts themselves should perform the model refinement process and what tools would aid them in that effort.
To address this problem, we utilized the Python library CadQuery, which enabled us to prepare models with a fully parametric approach and automate this process. We collected these functions into a module called STOK, which we validated using Monte Carlo programs. The results of Monte Carlo analyses of the STOK model showed comparable results to those models that were designed in a more conventional way.
Our approach to automating the modeling and model optimization process was initially created and then validated, providing a foundation for further research in the field of optimisation of fusion reactor models in terms of neutronics.
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