English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The safety factor profile is a key property in determining the stability oftokamak plasmas. To improve the safety factor profile in the United Kingdom'sproposed Spherical Tokamak for Energy Production (STEP), we applymulti-objective Bayesian optimisation - a machine learning technique in whichan uncertainty-aware predictive model guides the optimisation process based onthe observed data - to design electron-cyclotron heating profiles. Theresulting procedure generates sets of solutions that represent optimaltradeoffs between objectives, enabling decision makers to understand thecompromises made during the design process. The solutions from our method scoreas well as those generated in previous work by a genetic algorithm; however,our method leads to a greater degree of solution diversity andinterpretability, providing more information to tokamak designers withoutcompromising performance. Our results suggest that the region of reversedcentral safety factor in STEP can be reduced at the cost of letting rationalsafety factor surfaces move slightly inwards, while keeping the minimum safetyfactor above two.

Multi-objective Bayesian optimisation for design of Pareto-optimal current drive profiles in STEP