JET DT Scenario Extrapolation and Optimization with METIS

0484263 - ÚFP 2018 RIV US eng A - Abstrakt
Urban, Jakub - Jaulmes, Fabien - Artaud, J.
JET DT Scenario Extrapolation and Optimization with METIS.
Bulletin of the American Physical Society. vol. 62, 12/2017. San Jose: American Physical Society, 2017. BAPS.2017.DPP.TP11.76. ISSN 0003-0503.
[APS: 59th Annual Meeting of the APS Division of Plasma Physics 2017. 23.10.2017-27.10.2017, Milwaukee, Wisconsin]
Grant CEP: GA ČR(CZ) GA16-24724S
Institucionální podpora: RVO:61389021
Klíčová slova: JET * METIS * scenario * optimization
Obor OECD: Fluids and plasma physics (including surface physics)
http://meetings.aps.org/link/BAPS.2017.DPP.TP11.76

Bayesian optimization is applied to tokamak scenario optimizations. The key advantages are 1) a reduced number of objective function evaluations, 2) no need for derivatives, and 3) the possibility to include a prior knowledge. This is of a great value for optimizing tokamak scenarios, where several (competing) objectives with often unknown magnitudes exist and the number of parameters is large (>10). The first two properties imply that Bayesian optimization is well suited for heavy, complex objective functions. Reusing previous iterations as priors for next optimization steps effectively enables interactive, multiobjective optimizations, regardless of whether a human decision maker is included or not. We show that these features make Bayesian optimization an outstanding tool for optimizing tokamak scenarios. Objective functions and constraints, targeting, e.g., fusion gain, flux consumption, coils currents limits or q-profile, can be assembled interactively. The optimized parameter vector may include actuators like plasma current or heating waveforms. We demonstrate the capabilities on optimizing ITER and DEMO-like scenarios, simulated by the METIS code
Trvalý link: http://hdl.handle.net/11104/0279416