Burn control simulation experiments in JT-60U

Shimomura, K.; Takenaga, H.; Tsutsui, Hiroaki; Mimata, Hideyuki; Tsuji‐Iio, S.; Miura, Y.; Tani, K.; Kubo, H.; Sakamoto, Y.; Hiratsuka, H.; Ichige, H.; Sueoka, M.; Kawamata, Y.; Sakamoto, R.; Kobayashi, S.; Shimada, Ryuichi

doi:10.1016/j.fusengdes.2007.07.059

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…Although the burn controllability was not lost even with P ex = 0, these results indicated that the control margin was smaller with a higher fusion gain. In addition, oscillation of P ex for keeping the stored energy constant was larger in the RS plasma than that in the ELMy H-mode plasma [2]. The analysis using the 1.5 dimension transport code TOPICS [3] indicated that the larger oscillation of P ex experimentally observed in the RS plasma cannot be explained by the difference in the thermal diffusivity profiles or temperature dependence of the thermal diffusivities.…”

Section: Introductionmentioning

confidence: 83%

“…However, considerations of various physics among the strong linkages are limited in the present simulation model. In JT-60U, a burning plasma simulation scheme has been developed [1,2], in order to experimentally introduce in DD non-burning plasmas the linkage between pressure and heating profiles similar to alpha particle heating. In the scheme used in [1,2], the neutral beam (NB) heating power proportional to the DD neutron yield rate was applied as P α = G × S n using a real-time control system for the simulation of alpha particle heating.…”

Section: Introductionmentioning

confidence: 99%

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Response of fusion gain to density in burning plasma simulation on JT-60U

Takenaga¹,

Kubo²,

Sueoka³

et al. 2008

Nucl. Fusion

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A burning plasma simulation scheme has been developed with consideration for temperature dependence of the DT fusion reaction rate in JT-60U. The heating power for the simulation of alpha particle heating was calculated using real-time measurements of density and ion temperature. The response of a simulated fusion gain to the density was investigated in this scheme where a constant heating power was used for the simulation of external heating, in order to understand burn controllability by the fuel density in a fusion reactor. When temperature dependence of the fusion reaction rate was assumed as a square of ion temperature, density dependence of the simulated fusion gain stronger than the square of density was observed. Transport analysis using a 1.5 dimension transport code indicated that the strong density dependence is induced by changes in a confinement improvement factor and changes in a pressure profile.

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Section: Introductionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Response of fusion gain to density in burning plasma simulation on JT-60U

Takenaga¹,

Kubo²,

Sueoka³

et al. 2008

Nucl. Fusion

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“…Similar analyses are done in some researchers; e.g., the current drive simulation with the measurement of current profile peaking factor [4], the current drive and control simulation in ITER [5,6], the density profile control analysis in ASDEX [7]. The plasma burn control simulation is done in JT-60U [8][9][10] and in Ref [11]. In most of these researches, they control one parameter with one actuator for moderate performance plasma.…”

Section: Introductionmentioning

confidence: 87%

Research on Burn Control of Core Plasma with the Transport Code

Miyoshi

Ogawa

Nakamura

2012

Plasma and Fusion Research

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For the fusion reactors or experimental devices, one will be required to control several plasma parameters, like the fusion power, the heat flux, the neutron flux, the beta-value and so on. To control these parameters, many diagnostics and actuators are needed, but the diagnostics and actuators available in DEMO/commercial reactors are limited because of the high heat or neutron flux. For these reasons, to realize the fusion reactors, the construction of the reactor control logic is required. We are developing the burn control logic in the core plasma with a 1.5D transport code, and discussing on the relationship between control parameters and actuators. To demonstrate the feasibility of the core plasma control, we have demonstrated the simultaneous control of the fusion power and the safety factor profile with the gas-puff and NBI.

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“…In the latter half of this section, we show an example for which control using the trained NN outperforms the fixedgain PID controller. We introduce additional NB injection that is proportional to the neutron yield rate, which simulates the effect of alpha heating as seen in the burning plasma simulation experiments in JT-60U [15][16][17]. In this case, the non-linearity of the ΔT i response to the injected NB power used for control (excluding the NB power used to simulate alpha heating) becomes much stronger, especially for models with high confinement quality, and control with one fixed gain is thought to be more difficult.…”

Section: Validation Of the Control System For Jt-60u Plasmas Of Jt-60umentioning

confidence: 99%

Ion temperature gradient control using reinforcement learning technique

Wakatsuki¹,

Suzuki²,

Oyama³

et al. 2021

Nucl. Fusion

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Plasma with an internal transport barrier (ITB) is desirable for a steady-state tokamak reactor because of its high confinement quality and high bootstrap current fraction. However, the local pressure gradient tends to be steep and the plasma often becomes unstable. In this study, an ion temperature gradient control system based on neutral beam injection (NBI) is developed using the reinforcement learning technique. The response characteristics of an ion temperature gradient to NBI are non-linear and sensitive to experimental conditions, which makes it difficult to develop a robust control system. Our control system is trained for plasmas with a wide range of ITB strengths. Using the reinforcement learning technique, the system acquires a robust control feature through several thousand iterations of trial and error in an integrated transport simulation hosted by TOPICS. The control system is composed of neural networks (NNs) whose input variables are the ion temperature gradient, the current NBI power, and the NBI powers for several previous control time steps. The trained system can determine a control output which is suitable for the response characteristics inferred from the input variables. The trained control system is tested in the TOPICS simulation using plasma models based on two experimental plasmas of JT-60U with different ITB strengths. It is shown that the ion temperature gradient can be appropriately controlled for both plasmas, which supports the expectation that this system is applicable to real experiments.

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Burn control simulation experiments in JT-60U

Cited by 5 publications

References 8 publications

Response of fusion gain to density in burning plasma simulation on JT-60U

Response of fusion gain to density in burning plasma simulation on JT-60U

Research on Burn Control of Core Plasma with the Transport Code

Ion temperature gradient control using reinforcement learning technique

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