Real-time-capable prediction of temperature and density profiles in a tokamak using RAPTOR and a first-principle-based transport model

The ASDEX Upgrade, MAST and TCV Teams; JET contributors

doi:10.1088/1741-4326/aac8f0

Real-time-capable prediction of temperature and density profiles in a tokamak using RAPTOR and a first-principle-based transport model

The ASDEX Upgrade, MAST and TCV Teams, JET contributors

Research output: Contribution to journal › Article › peer-review

34 Scopus citations

Abstract

The RAPTOR code is a control-oriented core plasma profile simulator with various applications in control design and verification, discharge optimization and real-time plasma simulation. To date, RAPTOR was capable of simulating the evolution of poloidal flux and electron temperature using empirical transport models, and required the user to input assumptions on the other profiles and plasma parameters. We present an extension of the code to simulate the temperature evolution of both ions and electrons, as well as the particle density transport. A proof-of-principle neural-network emulation of the quasilinear gyrokinetic QuaLiKiz transport model is coupled to RAPTOR for the calculation of first-principle-based heat and particle turbulent transport. These extended capabilities are demonstrated in a simulation of a JET discharge. The multi-channel simulation requires ∼0.2 s to simulate 1 second of a JET plasma, corresponding to ∼20 energy confinement times, while predicting experimental profiles within the limits of the transport model. The transport model requires no external inputs except for the boundary condition at the top of the H-mode pedestal. This marks the first time that simultaneous, accurate predictions of T _e, T _i and n _e have been obtained using a first-principle-based transport code that can run in faster-than-real-time for present-day tokamaks.

Original language	English
Article number	096006
Journal	Nuclear Fusion
Volume	58
Issue number	9
DOIs	https://doi.org/10.1088/1741-4326/aac8f0
State	Published - 3 Jul 2018
Externally published	Yes

Keywords

integrated tokamak simulation
machine learning
real-time control
tokamak profiles
tokamak transport

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10.1088/1741-4326/aac8f0

Cite this

@article{d8325efe91f24ecc8eb454c282127fee,

title = "Real-time-capable prediction of temperature and density profiles in a tokamak using RAPTOR and a first-principle-based transport model",

abstract = "The RAPTOR code is a control-oriented core plasma profile simulator with various applications in control design and verification, discharge optimization and real-time plasma simulation. To date, RAPTOR was capable of simulating the evolution of poloidal flux and electron temperature using empirical transport models, and required the user to input assumptions on the other profiles and plasma parameters. We present an extension of the code to simulate the temperature evolution of both ions and electrons, as well as the particle density transport. A proof-of-principle neural-network emulation of the quasilinear gyrokinetic QuaLiKiz transport model is coupled to RAPTOR for the calculation of first-principle-based heat and particle turbulent transport. These extended capabilities are demonstrated in a simulation of a JET discharge. The multi-channel simulation requires ∼0.2 s to simulate 1 second of a JET plasma, corresponding to ∼20 energy confinement times, while predicting experimental profiles within the limits of the transport model. The transport model requires no external inputs except for the boundary condition at the top of the H-mode pedestal. This marks the first time that simultaneous, accurate predictions of T e, T i and n e have been obtained using a first-principle-based transport code that can run in faster-than-real-time for present-day tokamaks.",

keywords = "integrated tokamak simulation, machine learning, real-time control, tokamak profiles, tokamak transport",

author = "{The ASDEX Upgrade, MAST and TCV Teams} and {JET contributors} and J. Redondo and H. Meyer and Th Eich and M. Beurskens and S. Coda and A. Hakola and P. Martin and J. Adamek and M. Agostini and D. Aguiam and J. Ahn and L. Aho-Mantila and R. Akers and R. Albanese and R. Aledda and E. Alessi and S. Allan and D. Alves and R. Ambrosino and L. Amicucci and H. Anand and G. Anastassiou and Y. Andr{\`e}be and C. Angioni and G. Apruzzese and M. Ariola and H. Arnichand and W. Arter and A. Baciero and M. Barnes and L. Barrera and R. Behn and A. Bencze and J. Bernardo and M. Bernert and P. Bettini and P. Bilkov{\'a} and W. Bin and G. Birkenmeier and Bizarro, {J. P.S.} and P. Blanchard and T. Blanken and M. Bluteau and V. Bobkov and O. Bogar and P. B{\"o}hm and T. Bolzonella and L. Boncagni and A. Botrugno and Wiechec, {A. Baron}",

note = "Publisher Copyright: {\textcopyright} EURATOM 2018.",

year = "2018",

month = jul,

day = "3",

doi = "10.1088/1741-4326/aac8f0",

language = "英语",

volume = "58",

journal = "Nuclear Fusion",

issn = "0029-5515",

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number = "9",

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T1 - Real-time-capable prediction of temperature and density profiles in a tokamak using RAPTOR and a first-principle-based transport model

AU - The ASDEX Upgrade, MAST and TCV Teams

AU - JET contributors

AU - Redondo, J.

AU - Meyer, H.

AU - Eich, Th

AU - Beurskens, M.

AU - Coda, S.

AU - Hakola, A.

AU - Martin, P.

AU - Adamek, J.

AU - Agostini, M.

AU - Aguiam, D.

AU - Ahn, J.

AU - Aho-Mantila, L.

AU - Akers, R.

AU - Albanese, R.

AU - Aledda, R.

AU - Alessi, E.

AU - Allan, S.

AU - Alves, D.

AU - Ambrosino, R.

AU - Amicucci, L.

AU - Anand, H.

AU - Anastassiou, G.

AU - Andrèbe, Y.

AU - Angioni, C.

AU - Apruzzese, G.

AU - Ariola, M.

AU - Arnichand, H.

AU - Arter, W.

AU - Baciero, A.

AU - Barnes, M.

AU - Barrera, L.

AU - Behn, R.

AU - Bencze, A.

AU - Bernardo, J.

AU - Bernert, M.

AU - Bettini, P.

AU - Bilková, P.

AU - Bin, W.

AU - Birkenmeier, G.

AU - Bizarro, J. P.S.

AU - Blanchard, P.

AU - Blanken, T.

AU - Bluteau, M.

AU - Bobkov, V.

AU - Bogar, O.

AU - Böhm, P.

AU - Bolzonella, T.

AU - Boncagni, L.

AU - Botrugno, A.

AU - Wiechec, A. Baron

PY - 2018/7/3

Y1 - 2018/7/3

N2 - The RAPTOR code is a control-oriented core plasma profile simulator with various applications in control design and verification, discharge optimization and real-time plasma simulation. To date, RAPTOR was capable of simulating the evolution of poloidal flux and electron temperature using empirical transport models, and required the user to input assumptions on the other profiles and plasma parameters. We present an extension of the code to simulate the temperature evolution of both ions and electrons, as well as the particle density transport. A proof-of-principle neural-network emulation of the quasilinear gyrokinetic QuaLiKiz transport model is coupled to RAPTOR for the calculation of first-principle-based heat and particle turbulent transport. These extended capabilities are demonstrated in a simulation of a JET discharge. The multi-channel simulation requires ∼0.2 s to simulate 1 second of a JET plasma, corresponding to ∼20 energy confinement times, while predicting experimental profiles within the limits of the transport model. The transport model requires no external inputs except for the boundary condition at the top of the H-mode pedestal. This marks the first time that simultaneous, accurate predictions of T e, T i and n e have been obtained using a first-principle-based transport code that can run in faster-than-real-time for present-day tokamaks.

AB - The RAPTOR code is a control-oriented core plasma profile simulator with various applications in control design and verification, discharge optimization and real-time plasma simulation. To date, RAPTOR was capable of simulating the evolution of poloidal flux and electron temperature using empirical transport models, and required the user to input assumptions on the other profiles and plasma parameters. We present an extension of the code to simulate the temperature evolution of both ions and electrons, as well as the particle density transport. A proof-of-principle neural-network emulation of the quasilinear gyrokinetic QuaLiKiz transport model is coupled to RAPTOR for the calculation of first-principle-based heat and particle turbulent transport. These extended capabilities are demonstrated in a simulation of a JET discharge. The multi-channel simulation requires ∼0.2 s to simulate 1 second of a JET plasma, corresponding to ∼20 energy confinement times, while predicting experimental profiles within the limits of the transport model. The transport model requires no external inputs except for the boundary condition at the top of the H-mode pedestal. This marks the first time that simultaneous, accurate predictions of T e, T i and n e have been obtained using a first-principle-based transport code that can run in faster-than-real-time for present-day tokamaks.

KW - integrated tokamak simulation

KW - machine learning

KW - real-time control

KW - tokamak profiles

KW - tokamak transport

UR - http://www.scopus.com/inward/record.url?scp=85051239087&partnerID=8YFLogxK

U2 - 10.1088/1741-4326/aac8f0

DO - 10.1088/1741-4326/aac8f0

M3 - 文章

AN - SCOPUS:85051239087

SN - 0029-5515

VL - 58

JO - Nuclear Fusion

JF - Nuclear Fusion

IS - 9

M1 - 096006

ER -

Real-time-capable prediction of temperature and density profiles in a tokamak using RAPTOR and a first-principle-based transport model

Abstract

Keywords

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