- 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.
- You can install by
$ git clone https://github.com/jaem-seo/KSTAR_tokamak_simulator.git
$ cd KSTAR_tokamak_simulator
- 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!
- 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.
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
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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© 2022 GitHub, Inc.
Terms
- 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.