Abstract
The results of transport modeling codes, e.g., GENE for the plasma core or SOLPS-ITER for the plasma edge, depend critically on reliable profile and equilibrium estimates. The propagation of uncertainties (UP) of input quantities to the results of modeling codes, e.g., power and particle exhaust and plasma stability, is frequently neglected because of the costs of running the codes as well as because of the missing uncertainty quantification of input quantities. The situation becomes even more cumbersome if profile gradients and their uncertainties are of major concern for transport analyses. Two different techniques are presented to estimate profiles, profile gradients, their uncertainties, and candidate profiles for UP in modeling codes. Markov Chain Monte Carlo sampling of the posterior probability density of an integrated data analysis approach is applied to estimate electron density and temperature profiles. Nonstationary Gaussian process regression is applied to estimate ion temperature and angular velocity profiles. Both methods provide in a natural way profile gradients, profile logarithmic gradients, and their uncertainties. Modeling codes benefit also from reliable equilibrium reconstructions and quantification of the uncertainty of various equilibrium parameters. For the analysis of diagnostics data, the position and uncertainty of flux surfaces as well as of the magnetic axis are important. For plasma transport and stability codes, the estimation of uncertainties of current and q-profiles is presented. For plasma edge codes the position of the separatrix contour and its uncertainty at various poloidal positions is of primary interest especially if steep profile gradients are present. Examples of uncertainties and their sources in magnetic scalar quantities, profiles, and separatrix contours are shown.
Highlights
For the analysis and prediction of the plasma performance, plasma stability, and power and particle exhaust sophisticated transport and stability codes, e.g. GENE1 and SOLPS-ITER2, are routinely used
Markov chain Monte Carlo sampling of the posterior probability density of an Integrated Data Analysis (IDA) approach is applied to estimate electron density and temperature profiles
For plasma edge codes the position of the separatrix contour and its uncertainty at various poloidal positions is of primary interest especially if steep profile gradients are present
Summary
For the analysis and prediction of the plasma performance, plasma stability, and power and particle exhaust sophisticated transport and stability codes, e.g. GENE1 and SOLPS-ITER2, are routinely used. The present work addresses the uncertainty quantification (UQ) of input quantities applying different methods of estimating profiles and equilibria and their uncertainties for the use in modelling codes. Gaussian process regression (GPR) was applied to estimate ion temperature and angular velocity profiles Both methods, MCMC and GPR, provide in a natural way profile gradients and profile logarithmic gradients and their uncertainties.
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