Bayesian optimization of disruption scenarios with fluid-kinetic models
Paper i proceeding, 2023

Tokamak disruptions can damage the machine due to localized heat
loads, mechanical stresses and impact of energetic runaway
electron beams. We use a Bayesian optimization framework to
optimize massive material injection of deuterium and neon in an
ITER-like tokamak set up. The optimization is performed using both
fluid and kinetic plasma models. The fluid model allows the
exploration of a large parameter space. Once promising parameter
regions are located, these are studied in higher physics fidelity using
kinetic simulations. The kinetic model predicts more optimistic results
regarding the success of the disruption mitigation.

kinetic

runaway electron

fluid

disruptions

massive material injection

Författare

Ida Ekmark

Chalmers, Fysik, Subatomär, högenergi- och plasmafysik

Istvan Pusztai

Chalmers, Fysik, Subatomär, högenergi- och plasmafysik

M. Hoppe

Kungliga Tekniska Högskolan (KTH)

Patrik Jansson

Chalmers, Data- och informationsteknik, Funktionell programmering

Tünde-Maria Fülöp

Chalmers, Fysik, Subatomär, högenergi- och plasmafysik

49th EPS Conference on Plasma Physics, EPS 2023


978-171389867-2 (ISBN)

49th EPS Conference on Plasma Physics, EPS 2023
Bordeaux, France,

Ämneskategorier

Fusion, plasma och rymdfysik

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Senast uppdaterat

2024-07-18