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Considerations on Stellarator's Optimization from the Perspective of the Energy Confinement Time Scaling Laws

Academic Article
Publication Date:
2022
abstract:
The Stellarator is a magnetic configuration considered a realistic candidate for a future thermonuclear fusion commercial reactor. The most widely accepted scaling law of the energy confinement time for the Stellarator is the ISS04, which employs a renormalisation factor, fren, specific to each device and each level of optimisation for individual machines. The fren coefficient is believed to account for higher order effects not ascribable to variations in the 0D quantities, the only ones included in the database used to derive ISS04, the International Stellarator Confinement database. This hypothesis is put to the test with symbolic regression, which allows relaxing the assumption that the scaling laws must be in power monomial form. Specific and more general scaling laws for the different magnetic configurations have been identified and perform better than ISS04, even without relying on any renormalisation factor. The proposed new scalings typically present a coefficient of determination R2 around 0.9, which indicates that they basically exploit all the information included in the database. More importantly, the different optimisation levels are correctly reproduced and can be traced back to variations in the 0D quantities. These results indicate that fren is not indispensable to interpret the data because the different levels of optimisation leave clear signatures in the 0D quantities. Moreover, the main mechanism dominating transport, in reasonably optimised configurations, is expected to be turbulence, confirmed by a comparative analysis of the Tokamak in L mode, which shows very similar values of the energy confinement time. Not resorting to any renormalisation factor, the new scaling laws can also be extrapolated to the parameter regions of the most important reactor designs available.
Iris type:
01.01 Articolo in rivista
Keywords:
multimachine databases; scaling laws; symbolic regression; genetic programming; energy confinement time; stellarator optimisation
List of contributors:
Murari, Andrea
Authors of the University:
MURARI ANDREA
Handle:
https://iris.cnr.it/handle/20.500.14243/431653
Published in:
APPLIED SCIENCES
Journal
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URL

https://www.mdpi.com/2076-3417/12/6/2862
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