Comparison of DIVIMP and EDGE2D-EIRENE tungsten transport predictions in JET edge plasmas. (December 2020)

• Record Type: Journal Article
• Authors: Kumpulainen, H.A.; Groth, M.; Fontell, M.; Jaervinen, A.E.; Corrigan, G.; Harting, D.
• Is Part Of: Nuclear materials and energy. Volume 25(2020)
• Publication Date: 2020-12
• Subjects: Impurity transport -- Tungsten -- Scrape-off layer -- Monte Carlo simulation -- Code validation -- Joint European Torus; Nuclear energy -- Periodicals; Nuclear fuels -- Periodicals; Nuclear reactors -- Materials -- Periodicals; Radioactive substances -- Periodicals; 621.4833
• DOI: 10.1016/j.nme.2020.100784
• Languages: English
• ISSNs: 2352-1791

Abstract

Highlights: Monte Carlo DIVIMP and multi-fluid EDGE2D-EIRENE treatment of tungsten were compared. Tungsten density predictions by DIVIMP and EDGE2D-EIRENE agree within a factor of 2. DIVIMP predicts consistently 50% more tungsten than EDGE2D-EIRENE in attached plasmas. The discrepancy is due to the bundling of tungsten charge states to 6 fluid species. Using 22 tungsten species yields accurate tungsten charge but causes numerical errors. Abstract: The average tungsten concentrations in the pedestal region (cW ) predicted by the Monte Carlo code DIVIMP and the coupled multi-fluid plasma/kinetic neutral code EDGE2D-EIRENE are found to agree within a factor of 2 for a range of JET-ILW L-mode and H-mode plasma conditions. Under attached divertor conditions with cW exceeding 10 −6, the cW predicted by DIVIMP is consistently ~ 50% higher than by EDGE2D-EIRENE. In colder plasma scenarios with cW < 10 - 6, stochastic variations exceed the systematic disagreement between the two codes. The average tungsten charge predicted by EDGE2D-EIRENE in the upstream scrape-off layer is lower by 40–50% due to the bundling of the 74 tungsten ion charge states into 6 fluid species, which explains the reduced tungsten accumulation in the main plasma compared to the DIVIMP predictions.