Citrin, J.

Ho, A.

English

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 Near-realtime tokamak scenario simulation with neuralnetworksCitation for published version (APA):JET Contributors, Citrin, J., & Ho, A. (2019). Near-realtime tokamak scenario simulation with neural networks.Paper presented at 46th European Physical Society Conference on Plasma Physics (EPS 2019), Milan, Italy.Document status and date:Published: 01/01/2019Document Version:Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)Please check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can beimportant differences between the submitted version and the official published version of record. Peopleinterested in the research are advised to contact the author for the final version of the publication, or visit theDOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and pagenumbers.Link to publicationGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, pleasefollow below link for the End User Agreement:www.tue.nl/taverneTake down policyIf you believe that this document breaches copyright please contact us at:openaccess@tue.nlproviding details and we will investigate your claim.Download date: 04. Oct. 2023Near-realtime tokamak scenario simulation with neural networksK.L. van de Plassche1, J. Citrin1, C. Bourdelle2, Y. Camenen3, F. J. Casson4,V. I. Dagnelie1, F. Felici5, A. Ho1, and JET Contributors∗1 DIFFER, PO Box 6336, 5600 HH Eindhoven, The Netherlands, 2 CEA, IRFM, F-13108 Saint-Paul-lez-Durance, France3 CNRS, Aix-Marseille Univ., PIIM UMR7345, Marseille, France, 4 CCFE, Culham Science Centre, Abingdon, UK5 EPFL-SPC, CH-1015, Lausanne, Switzerland*See the author list of ”Overview of the JET preparation for Deuterium-Tritium Operation” by E. Joffrin et al. to be published in NuclearFusion Special issue: overview and summary reports from the 27th Fusion Energy Conference (Ahmedabad, India, 22-27 October 2018)Accurate prediction of turbulent transport is essential for interpretation of current-day fusion ex-periments, designing future devices, and optimization of plasma scenarios. Turbulent transportin the core of the plasma is well-described by quasilinear theory, which can be leveraged to cre-ate reduced models. These are then applied within flux driven integrated modelling to predicttime evolution of temperature, density, and rotation profiles in fusion devices. Recent devel-opments in the QuaLiKiz gyrokinetic quasilinear transport model [1, 2] within the JINTRACintegrated modelling suite [3] has provided validated prediction of JET and AUG scenarios,with 1s of plasma evolution predicted in ∼ 24 hours using ∼ 10 cores [4, 5].We provide further significant speedup of first-principle-based turbulent transport modellingsufficient for large scale reactor optimization and control oriented applications. We apply feed-forward neural networks (FFNNs) for regression of a pre-generated QuaLiKiz database consist-ing of 108 flux calculations. The resultant neural network surrogate model is 5 orders of mag-nitude faster than QuaLiKiz itself. Generic neural network training is not sufficient to correctlycapture known physical features of tokamak turbulence, such as sharp instability thresholdscommon to all transport channels. We show how we can incorporate these features directly inthe training process.The surrogate turbulent transport model is applied within the rapid plasma transport simu-lator RAPTOR [6, 7] and JINTRAC. We show that the predictions of temperature and densityevolution of JET plasmas are in excellent agreement with the original QuaLiKiz model, yetorders of magnitude faster. This allows us to simulate one second of plasma evolution in lessthan 10 seconds, a speed that is unprecedented for first-principle based transport simulations,opening up new avenues for tokamak scenario optimization and realtime control applications.References[1] Bourdelle, C. et al. 2016 Plasma Physics and Controlled Fusion 58 014036[2] Citrin, J. et al. 2017 Plasma Physics and Controlled Fusion 59 12400[3] Romanelli, M. et al. 2014 Plasma and Fusion research 9 3403023[4] Ho, A. et al. 2019 Nuclear Fusion[5] Linder, O. et al. 2018 Nuclear Fusion 59 016003[6] Felici, F. et al. 2012 Plasma Physics and Controlled Fusion 54 025002[7] Felici, F. et al. 2018 Nuclear Fusion 58 09600646th EPS Conference on Plasma Physics P1.1089

Near-realtime tokamak scenario simulation with neural networks

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Near-realtime tokamak scenario simulation with neural networks

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