Jul 2 – 6, 2018
Žofín Palace
Europe/Prague timezone

P4.1078 How predict-first will change our approach to experimental planning

Jul 5, 2018, 2:00 PM
2h
Mánes

Mánes

Speaker

Francesca Poli

Description

See the full Abstract at http://ocs.ciemat.es/EPS2018ABS/pdf/P4.1078.pdf How predict-first will change our approach to experimental planning F. Poli1, B. Grierson1, M. Podestà1, Z. Wang1, J. Ferron2, C. Holcomb3, B. Victor3, K. Thome4 1 Princeton Plasma Physics Laboratory, NJ 08543, USA 2 General Atomics, San Diego, CA 92121, USA 3 Lawrence Livermore National Laboratory, Livermore, CA 94551, USA 4 Oak Ridge Associated Universities, Oak Ridge, TN, 37831, USA Time-dependent, transport simulations of experiments ahead of time can improve the efficiency of our experimental studies and might become a game changer. High-fidelity, validated models are critical for the success of the predict-first approach, which relies entirely on the fidelity of the models used to evolve transport and magnetic equilibrium. We are going to show an example of successful application of the predict-first approach on DIII-D, for the optimization of access to steady-state operation with sustained high qmin at mid-radius. A feed-forward scheme has been proposed using free-boundary time-dependent simulations with TRANSP, combining EC and NBI injection in the ramp-up to delay the relaxation of the safety factor profile. Simulations indicate that a combination of Electron Cyclotron heating and current drive for pre-heating in L-mode and Neutral Beam injection sustain a broad and flat safety factor profile in the flattop phase, which has been a posteriori verified in the experiment, a successful demonstration of the predict-first approach towards experimental planning and optimization of runtime resources. The limits of predictive modeling will be discussed and examples will be provided to show what assumptions are critical for the success of the predict-first approach. In particular, self- consistent particle transport, with realistic feedback control on the line-averaged density, like it is done in experiments, is critical for experimental projections. These examples provide directions for improvement of modeling capabilities in time- dependent solvers. This work is supported by the U.S. Department of Energy, Office of Science, Office of Fusion Energy Sciences under contract numbers DE-AC02-09CH11466 and DE-FC02-04ER54698.

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