jaem-seo / KSTAR_tokamak_simulator

A tokamak (nuclear fusion reactor) simulator with LSTM-based neural network (KSTAR-NN)

KSTAR tokamak simulator (KSTAR-NN)

• KSTAR is a tokamak (donut-shaped nuclear fusion device) located in South Korea.
• This repository provides a KSTAR tokamak simulation tool with LSTM-based neural network.
• See also AI Tokamak Control where the AI replaces the manual control of this simulator.

Installation

• You can install by

$ git clone https://github.com/jaem-seo/KSTAR_tokamak_simulator.git
$ cd KSTAR_tokamak_simulator

Try it out

• Open the GUI by typing below. It might take a bit depending on your environment.

$ python kstar_simulator_v0.py

• Slide the toggles in the left side and see the fusion plasma evolution in the right side.

• I hope you get insight with this virtual experiment!

Note

• This simulation has been tested with many real discharges, and shows acceptable prediction accuracy.

• But it does not always guarantee perfect prediction since it doesn't account for all unknown factors.
• For example, the experiments #18672 and #22671 were conducted under almost the same setting, but showed quite different behaviors.
• In this case, the simulation shows quite a reasonable, average prediction as shown below.

License

MIT License

Copyright (c) 2022 Jaemin Seo

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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The above copyright notice and this permission notice shall be included in all
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References

• J. Seo, et al. "Feedforward beta control in the KSTAR tokamak by deep reinforcement learning." Nuclear Fusion 61 (2021): 106010.
• J. Seo, et al. "Development of an operation trajectory design algorithm for control of multiple 0D parameters using deep reinforcement learning in KSTAR." Nuclear Fusion 62 (2022): 086049.