Enabling adaptive pedestals in predictive transport simulations using neural networks
Artikel i vetenskaplig tidskrift, 2022

We present PEdestal Neural Network (PENN) as a machine learning model for tokamak pedestal predictions. Here, the model is trained using the EUROfusion JET pedestal database to predict the electron pedestal temperature and density from a set of global engineering and plasma parameters. Results show that PENN makes accurate predictions on the test set of the database, with R (2) = 0.93 for the temperature, and R (2) = 0.91 for the density. To demonstrate the applicability of the model, PENN is employed in the European transport simulator (ETS) to provide boundary conditions for the core of the plasma. In a case example in the ETS with varied neutral beam injection (NBI) power, results show that the model is consistent with previous studies regarding NBI power dependency on the pedestal. Additionally, we show how an uncertainty estimation method can be used to interpret the reliability of the predictions. Future work includes further analysis of how pedestal models, such as PENN, or other advanced deep learning models, can be more efficiently implemented in integrating modeling frameworks, and also how similar models may be generalized with respect to other tokamaks and future device scenarios.

machine learning

neural networks

AI

pedestal

integrated modeling

fusion

Författare

Andreas Gillgren

Chalmers, Rymd-, geo- och miljövetenskap, Astronomi och plasmafysik

Emil Fransson

Chalmers, Rymd-, geo- och miljövetenskap, Astronomi och plasmafysik

Dimitriy Yadykin

Chalmers, Rymd-, geo- och miljövetenskap, Astronomi och plasmafysik

L. Frassinetti

Kungliga Tekniska Högskolan (KTH)

Pär Strand

Chalmers, Rymd-, geo- och miljövetenskap, Astronomi och plasmafysik

Nuclear Fusion

0029-5515 (ISSN) 1741-4326 (eISSN)

Vol. 62 9 096006

Ämneskategorier

Annan fysik

Bioinformatik (beräkningsbiologi)

Sannolikhetsteori och statistik

DOI

10.1088/1741-4326/ac7536

Mer information

Senast uppdaterat

2022-08-02