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A data assimilation technique is applied to the integrated transport simulation (TASK3D) of a plasma in Large Helical Device (LHD). We use the ensemble Kalman filter (EnKF) as a data assimilation method for the estimation of state variables composed of the electron and ion temperature, density, numerical coefficients of turbulence models, and NBI heat deposition. The time series data of experimentally measured temperature and density profiles are assimilated into TASK3D. The obtained electron and ion temperature profiles and temporal variations by the data assimilation system agree well with measured ones owing to the optimization of the employed turbulent transport model and the heat deposition. These results indicate the effectiveness and validity of the data assimilation approach for accurate prediction of the behavior of fusion plasmas and the possibility of advanced turbulence modeling.

The ensemble Kalman smoother (EnKS) is introduced to the data assimilation system, ASTI, based on the integrated transport simulation code, TASK3D. We use the EnKS to estimate state variables composed of electron and ion temperature, density, and numerical factors of turbulent transport models and neutral beam injection (NBI) heat deposition. The time series data of plasma temperature and density profiles are assimilated into TASK3D. The estimation performance of the EnKS is investigated, and the EnKS is applied to an NBI plasma in the Large Helical Device (LHD) (shot:114053) to estimate the factors of the turbulent heat transport model. The obtained factors can reproduce the experimental temperature data with high accuracy. These results indicate the effectiveness and validity of the EnKS approach for accurate estimation of fusion plasma parameters.

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