Modeling Non-planar Coils in a Full-Scale Stellarator

Design and modeling of a stellarator fusion reactor is a multidisciplinary effort that requires a tight integration between simulation of highly nonlinear multi-physics and representation of non-planar complex geometries. The critical current calculation and the design of the mechanical structures a...

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Bibliographic Details
Published in:IEEE transactions on applied superconductivity 2024-05, Vol.34 (3), p.1-5
Main Authors: Backmeyer, M., Riva, N., Lyly, M., Halbach, A., Lahtinen, V.
Format: Article
Language:English
Subjects:
Coils
Critical current (superconductivity)
DDM
Design optimization
Figure of merit
fusion
Fusion reactors
Geometry
Machine learning
Magnetic domains
Magnetic fields
Modelling
non-planar geometry
Plasmas
simulation
stellarator
Stellarators
Superconducting magnets
Three dimensional analysis
Three dimensional models
Tokamaks
Two dimensional analysis
Workflow
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Summary:Design and modeling of a stellarator fusion reactor is a multidisciplinary effort that requires a tight integration between simulation of highly nonlinear multi-physics and representation of non-planar complex geometries. The critical current calculation and the design of the mechanical structures are among the most crucial aspects as they set size, cost, and time to build the stellarator. Because of the asymmetric and non-planar nature of its components the modeling of such figures of merit needs to be carried out at large scale, without the possibility of taking advantage of any particular symmetry. In this work we develop a three-dimensional model for the analysis of the magnetic field and forces, necessary for such considerations, for complex coil geometries, such as stellarators, where a two-dimensional approach can not provide accurate analyses and verification of assumptions. Moreover, this method can quickly generate a large amount of critical modeling data (e.g. Lorentz load, displacement and stresses) that could be integrated into a workflow for coil design optimization based on machine learning or other recent optimization tools.
ISSN:1051-8223
1558-2515
DOI:10.1109/TASC.2024.3356493