A novel scheme to predict the turbulent transport of ion heat of magnetic confined plasmas is developed by combining mathematical optimization techniques employed in data analysis approaches and first-principle gyrokinetic simulations. Gyrokinetic simulation, as a first-principle approach, is a reliable way to predict turbulent transport. However, in terms of the flux-matching [Candy et al., Phys. Plasmas 16, 060704 (2009)], quantitative transport estimates by gyrokinetic simulations incur extremely heavy computational costs. In order to reduce the costs of quantitative transport prediction based on the gyrokinetic simulations, we develop a scheme with the aid of a reduced transport model. In the scheme, optimization techniques are applied to find relevant input parameters for nonlinear gyrokinetic simulations, which should be performed to obtain relevant transport fluxes and to optimize the reduced transport model for a target plasma. The developed scheme can reduce the numbers of the gyrokinetic simulations to perform the quantitative estimate of the turbulent transport levels and plasma profiles. Utilizing the scheme, the predictions for the turbulent transport can be realized by performing the first-principle simulations once for each radial position.

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