6,442 research outputs found
ASCOT: solving the kinetic equation of minority particle species in tokamak plasmas
A comprehensive description of methods, suitable for solving the kinetic
equation for fast ions and impurity species in tokamak plasmas using Monte
Carlo approach, is presented. The described methods include Hamiltonian
orbit-following in particle and guiding center phase space, test particle or
guiding center solution of the kinetic equation applying stochastic
differential equations in the presence of Coulomb collisions, neoclassical
tearing modes and Alfv\'en eigenmodes as electromagnetic perturbations relevant
to fast ions, together with plasma flow and atomic reactions relevant to
impurity studies. Applying the methods, a complete reimplementation of the
well-established minority species code ASCOT is carried out as a response both
to the increase in computing power during the last twenty years and to the
weakly structured growth of the code, which has made implementation of
additional models impractical. Also, a benchmark between the previous code and
the reimplementation is accomplished, showing good agreement between the codes.Comment: 13 pages, 9 figures, submitted to Computer Physics Communication
Improving PWR core simulations by Monte Carlo uncertainty analysis and Bayesian inference
A Monte Carlo-based Bayesian inference model is applied to the prediction of
reactor operation parameters of a PWR nuclear power plant. In this
non-perturbative framework, high-dimensional covariance information describing
the uncertainty of microscopic nuclear data is combined with measured reactor
operation data in order to provide statistically sound, well founded
uncertainty estimates of integral parameters, such as the boron letdown curve
and the burnup-dependent reactor power distribution. The performance of this
methodology is assessed in a blind test approach, where we use measurements of
a given reactor cycle to improve the prediction of the subsequent cycle. As it
turns out, the resulting improvement of the prediction quality is impressive.
In particular, the prediction uncertainty of the boron letdown curve, which is
of utmost importance for the planning of the reactor cycle length, can be
reduced by one order of magnitude by including the boron concentration
measurement information of the previous cycle in the analysis. Additionally, we
present first results of non-perturbative nuclear-data updating and show that
predictions obtained with the updated libraries are consistent with those
induced by Bayesian inference applied directly to the integral observables.Comment: 10 pages, 11 figure
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