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Validation of edge turbulence codes against the TCV-X21 diverted L-mode reference case

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arxiv 2109.01618 v2 pith:PP7ONJWE submitted 2021-09-03 physics.plasm-ph

classification physics.plasm-ph
keywords simulationsturbulencedatasetexperimentalmodelsvalidationedgematch
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Self-consistent full-size turbulent-transport simulations of the divertor and SOL of existing tokamaks have recently become feasible. This enables the direct comparison of turbulence simulations against experimental measurements. In this work, we perform a series of diverted Ohmic L-mode discharges on the TCV tokamak, building a first-of-a-kind dataset for the validation of edge turbulence models. This dataset, referred to as TCV-X21, contains measurements from 5 diagnostic systems -- giving a total of 45 1- and 2-D comparison observables in two toroidal magnetic field directions. The dataset is used to validate three flux-driven 3D fluid-turbulence models: GBS, GRILLIX and TOKAM3X. With each model, we perform simulations of the TCV-X21 scenario, tuning the particle and power source rates to achieve a reasonable match of the upstream separatrix value of density and electron temperature. We find that the simulations match the experimental profiles for most observables at the OMP -- both in terms of profile shape and absolute magnitude -- while a poorer agreement is found towards the divertor targets. The match between simulation and experiment is seen to be sensitive to the value of the resistivity, the heat conductivities, the power injection rate and the choice of sheath boundary conditions. Additionally, despite targeting a sheath-limited regime, the discrepancy between simulations and experiment also suggests that the neutral dynamics should be included. The results of this validation show that turbulence models are able to perform simulations of existing devices and achieve reasonable agreement with experimental measurements. Where disagreement is found, the validation helps to identify how the models can be improved. By publicly releasing the experimental dataset, this work should help to guide and accelerate the development of predictive turbulence simulations of the edge and SOL.

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