Gyrokinetic modelling of the quasilinear particle flux for plasmas with neutral-beam fuelling
Gyrokinetic modelling of the quasilinear particle flux for plasmas with neutral-beam fuelling
复制标题
中性束燃料等离子体准线性粒子通量的回旋动力学建模
DOI：
10.1088/1361-6587/aaa02d
复制
发表时间：
2018
影响因子：
2.2
通讯作者：
Hayashi N.
中科院分区：
文献类型：
--
作者：
Narita E.;Honda M.;Nakata M.;Yoshida M.;Takenaga H.;Hayashi N.
一个准线性粒子通量建模的基础上的陀螺动力学计算。颗粒通量的估计由决定因素，即，非对角项和颗粒扩散系数的系数。在本文中，提出了使用JT-60 U等离子体的一个子集的方法来估计的因素。首先，非对角项的系数估计的线性gyrokinetic计算。接下来，为了获得颗粒扩散率，采用半经验方法。大多数粒子输运的实验分析都假设湍流粒子通量在核心区域为零。另一方面，即使在静止状态，等离子体的问题有一个有限的湍流粒子通量由于中性束燃料。通过结合实验湍流粒子通量的估计和先前计算的非对角项的系数，得到粒子扩散率。粒子扩散系数应反映不稳定性的饱和幅度。颗粒扩散系数的线性不稳定性和线性纬向流响应的影响进行了研究，它被发现，一个公式，包括这些影响大致再现的颗粒扩散系数。所开发的粒子通量预测框架是灵活的，可以添加在当前模型中忽略的项。估计准线性粒子通量的方法需要如此低的计算成本，可以构建由非对角项和粒子扩散率的合成系数组成的数据库来训练神经网络。该方法的发展是第一步，以神经网络为基础的粒子输运模型的粒子通量的快速预测。
A quasilinear particle flux is modelled based on gyrokinetic calculations. The particle flux is estimated by determining factors, namely, coefficients of off-diagonal terms and a particle diffusivity. In this paper, the methodology to estimate the factors is presented using a subset of JT-60U plasmas. First, the coefficients of off-diagonal terms are estimated by linear gyrokinetic calculations. Next, to obtain the particle diffusivity, a semi-empirical approach is taken. Most experimental analyses for particle transport have assumed that turbulent particle fluxes are zero in the core region. On the other hand, even in the stationary state, the plasmas in question have a finite turbulent particle flux due to neutral-beam fuelling. By combining estimates of the experimental turbulent particle flux and the coefficients of off-diagonal terms calculated earlier, the particle diffusivity is obtained. The particle diffusivity should reflect a saturation amplitude of instabilities. The particle diffusivity is investigated in terms of the effects of the linear instability and linear zonal flow response, and it is found that a formula including these effects roughly reproduces the particle diffusivity. The developed framework for prediction of the particle flux is flexible to add terms neglected in the current model. The methodology to estimate the quasilinear particle flux requires so low computational cost that a database consisting of the resultant coefficients of off-diagonal terms and particle diffusivity can be constructed to train a neural network. The development of the methodology is the first step towards a neural-network-based particle transport model for fast prediction of the particle flux.