Coster, D.

Ivanova-Stanik, I.

Jonsson, T.

Kalupin, D.

Kirov, K.

Yadykin, D.

English

MPG.PuRe

Impurity modelling with European Transport Simulator: implementationand verificationD. Yadykin1, I. Ivanova-Stanik2, T.Jonsson3, D. Coster4, K. Kirov5, D. Kalupin6,71 Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden2 Institute of Plasma Physics and Laser Microfusion, Hery 23, 01-497 Warsaw, Poland3 KTH, Royal Institute of Technology, Stockholm, Sweden4 Max-Planck Institut für Plasmaphysik Garching, Germany5 EUROfusion Consortium, JET, Culham Science Centre, Abingdon, OX14 3DB,UK6 IEK-4 Plasmaphysik, Forschungszentrum Jülich GmbH, 52425 Juelich, Germany7 EUROfusion PMU, Boltzmannstrasse 2, 85748 Garching, GermanyAccurate modelling of the impurity evolution is an important part of the scenario modellingfor the present and future fusion devices. Impurity effect on the plasma dynamics (like theimpurity accumulation in the plasma core or the imputity effect on the power exhaust) shouldbe included in the scenario modelling to get reliable predictions [1], [2].European Transport Simulator (ETS) [3] is the integrated modelling tool (workflow) capableto interpret and predict scenarios of the fusion plasma devices. ETS includes several ’build-ing’ blocks describing different plasma processes (transport, heating, etc.) that are combined intransport equations describing evolution of the plasma profiles (poloidal flux, density, tempera-ture). Equations for the evolution of the impurity densities are included in ETS. The verificationof the impurity model for the ETS version using Consistent Physical Objects (CPOs) for thedata transfer was reported in Recently, ETS is updated to use IMAS framework [4]. This wasaccompanied by the substantial modification of the impurity model implementation in the sim-ulator.Implementation in ETSEquations for the evolution of the impurity densities are included in two versions of ETS(CPO based and IMAS based). The general idea of both implementation is the same: the den-sity equations for the impurity species can be treated interpretively or evolved in giving thesources and the transport coefficients calculated for each charge state using ADAS data [5] pro-vided through AMNS library. The boundary conditions and the initial profiles are provided atthe configuration step of the workflow in the case when time evolution is activated. It is possibleto use interpretive profiles or to apply coronal distribution. The density equations are written ingeneralized form (see reference) adopted in ETS to be used by all transport channels. Density48th EPS Conference on Plasma Physics P2a.108Figure 1: Kepler layout of the ETS Convergence Loop with indication of the modules relevant to theimpurity density equationsequations are solved by the scheme proposed by K. Lakner [6] using two consequent iterationswith half time step intervals and going up and down in the charge states on each half step. Theimplementation details though differ in the two ETS versions. This partially is caused by theadoptation of the normalized radial coordinate (x = ρtor/ρator instead of ρtor in CPO based ETSversion), where ρtor is the normalized toroidal flux, a - plasma minor radius. This allows for theFigure 2: Input data used in the verification studies: a) plasma boundary; b) electron density; c)electrontemperature. Solid - profiles used for Ar comparison, dashed - profiles used for Ni, Kr, W comparison.moving plasma boundary that is important for the modelling of the transient discharge phaseslike ramp-up and ramp-down. Implementation logic is also modified in the IMAS based ETSversion.48th EPS Conference on Plasma Physics P2a.108Figure 3: Evolution of the total den-sity of the Kr during simulation time.Solid - after 1 ms; dashed - after 1sec; dotted - after 2 sec.Impurity equations are implemented in the source mod-ule in the CPO based version. Both the transport coef-ficients and the sources for the impurity density equa-tions (ionization and recombination rates) are calculatedin the same module. The numerical solver used to solveimpurity density equation is prescribed and in generalcan be different to that used to solve for other chan-nels.The implementation logic follows the general modularstructure of the workflow for the IMAS based version ofETS. The density equations are solved in the same mod-ule as other channels (T RANSPORT _EQUAT IONS) us-ing the same numerical solver. The transport coefficientsand the source terms for the impurity density equations are calculated in the correspondentmodules of ETS (T RANSPORT and SOURCES respectively) that are separated from theT RANSPORT _EQUAT IONS module (see fig. 1).Verification against the standalone simulation resultsThe verification of the impurity model for the ETS version using Consistent Physical Objects(CPOs) for the data transfer was reported in [7]. The verification of the ETS version based onthe IMAS is also required as substantial modifications of the implementation were introduced(see above). The verification is done against the standalone simulations performed with SANCOcode [8]. The evolution of the impurity density profile is followed for several impurity species(Ar,Ni,Kr,W) starting from the prescribed total density at the boundary.The boundary values ofthe charge state densities are taken from the results obtained with SANCO code. The diffusioncoefficient profiles are set to be radially constant for all charge states with the diffusion coeffi-cient value set to D = 1.0m/s2. The sources (ionisation and recombination rates) are calculatedusing atomic data provided by the AMNS module. JET like geometry is used together withparabolic profiles of the electron/ion density and temperature. The value of on-axis electrondensity is chosen to be ne(0) = 8.0×1019m−3 and the value of the on-axis electron temperatureTe(0) is chosen to be 1keV for Ar comparison and 2keV for Ni, Kr, W comparison. Moreover,temperature equations are not solved and it is set Te = Ti. The density of the main ion (D) iscalculated from quasineutrality. The plasma boundary poloidal cross section and the kinetic pro-files for electrons (ne,Te) are shown on fig. 2.The evolution of the total impurity density profileduring the ETS simulation is shown on fig. 3 for Ar case. Density profile evolves similarly forother impurity species. The density of the selected impurity charge states, the radiation power48th EPS Conference on Plasma Physics P2a.108Figure 4: Results ETS/SANCO for Ar after 2 secof simulation time. a)state densities; b) radiationpower density; c) Zeff. Solid - SANCO, dashed -ETSFigure 5: Results ETS/SANCO for Kr after 2 secof simulation time. a)state densities; b) radiationpower density; c) Zeff. Solid - SANCO, dashed -ETSdensity profile and the profile of effective charge are compared after 2 sec of the simulationtime against the standalone results obtained with SANCO code for different impurity species.The comparison for Ar and W is shown on figs. 4,5. Good agreement between the ETS simula-tion and the standalone results is seen in both cases. This verifies the impurity implementationprocedure adopted in ETS.AcknowledgementThis work has been carried out within the framework of the EUROfusion Consortium, fundedby the European Union via the Euratom Research and Training Programme (Grant AgreementNo 101052200 — EUROfusion). Views and opinions expressed are however those of the au-thor(s) only and do not necessarily reflect those of the European Union or the European Com-mission. Neither the European Union nor the European Commission can be held responsiblefor them.References[1] Luda T. et al, Nucl. Fusion 60 (2020) 036023[2] Wischmeier M. et al, J. Nucl. Mat. 463 (2015) 22[3] Falchetto G. et al, P1-1081, 46th EPS conference, Milan, 2019[4] Imbeaux F. et al., Nucl. Fusion 55 (2015) 123006.[5] Summers H.P. The ADAS User Manual version,www.adas.ac.uk[6] Lackner K. et al., Z. Naturforsch. 37a (1982) 931.[7] Kalupin D. et al., Nucl. Fusion 53 (2013) 123007[8] Lauro-Taroni L. et al., Proc. 21st EPS (1994) vol 1.48th EPS Conference on Plasma Physics P2a.108

Impurity modelling with European Transport Simulator: implementation and verification

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Impurity modelling with European Transport Simulator: implementation and verification

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