Prediction of Performance and Turbulence in ITER Burning Plasmas via Nonlinear Gyrokinetic Profile Prediction
arxiv(2024)
摘要
Burning plasma performance, transport, and the effect of hydrogen isotope on
confinement has been predicted for ITER baseline scenario (IBS) conditions
using nonlinear gyrokinetic profile predictions. Accelerated by surrogate
modeling [P. Rodriguez-Fernandez NF 2022], high fidelity, nonlinear gyrokinetic
simulations performed with the CGYRO code [J. Candy JCP 2016], were used to
predict profiles of Ti, Te, and ne while including the effects of alpha
heating, auxiliary power, collisional energy exchange, and radiation losses.
Predicted profiles and resulting energy confinement are found to produce fusion
power and gain that are approximately consistent with mission goals (Pfusion =
500MW at Q=10) for the baseline scenario and exhibit energy confinement that is
within 1 sigma of the H-mode energy confinement scaling. The power of the
surrogate modeling technique is demonstrated through the prediction of
alternative ITER scenarios with reduced computational cost. These scenarios
include conditions with maximized fusion gain and an investigation of potential
Resonant Magnetic Perturbation effects on performance with a minimal number of
gyrokinetic profile iterations required. These predictions highlight the stiff
ITG nature of the core turbulence predicted in the ITER baseline and
demonstrate that Q>17 conditions may be accessible by reducing auxiliary input
power while operating in IBS conditions. Prediction of full kinetic profiles
allowed for the projection of hydrogen isotope effects around ITER baseline
conditions. The gyrokinetic fuel ion species was varied from H, D, and 50/50
D-T and kinetic profiles were predicted. Results indicate that a weak or
negligible isotope effect will be observed to arise from core turbulence in
ITER baseline scenario conditions. The resulting energy confinement,
turbulence, and density peaking, and the implications for ITER operations will
be discussed.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要