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
Published in: