First-principles-based multiple-isotope particle transport modelling at JET

Marin, Michèle; Citrin, J.; Bourdelle, C.; Camenen, Y.; Casson, F. J.; Ho, A.; Koechl, F.; Maslov, M.; Contributors, Jet

doi:10.1088/1741-4326/ab60d1

Cited by 16 publications

(15 citation statements)

References 52 publications

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“…prising that option 5 gives reasonable results in this scenario. This is because it conflicts with the expectation that the electron D and V can be very different from the ion D and V in mixed-isotope plasmas [46]. Since this simulation was designed for analyzing longtimescale transport behaviour, it is suspected that this difference between electron and ion transport properties becomes overshadowed by the ambipolarity constraint in the plasma.…”

Section: Integrated Modelling Comparisonsmentioning

confidence: 90%

“…Due to the stiffness of heat transport and strength of the equipartition coupling, the differences in the heat transport properties of the different ion species are typically neglected. However, this cannot generally be assumed for particle transport [46]. In simulations where the density profiles of these additional species are not fixed, further assumptions are required to provide the transport coefficients for these species.…”

Section: Integrated Modelling Comparisonsmentioning

confidence: 99%

“…where i0 represents the deuterium ion and i represents a generic ion species. This is the ideal option to transfer deuterium transport quantities to other hydrogenic species [46]. The performance of this option is questionable with QLKNN-jetexp-15D due to absence of ion particle transport consistency filters.…”

Section: Integrated Modelling Comparisonsmentioning

confidence: 99%

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Neural network surrogate of QuaLiKiz using JET experimental data to populate training space

Citrin

Bourdelle

et al. 2021

Physics of Plasmas

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Within integrated tokamak plasma modelling, turbulent transport codes are typically the computational bottleneck limiting their routine use outside of postdischarge analysis. Neural network (NN) surrogates have been used to accelerate these calculations while retaining the desired accuracy of the physics-based models. This paper extends a previous NN model, known as QLKNN-hyper-10D, by incorporating the impact of impurities, plasma rotation and magnetic equilibrium effects. This is achieved by adding a light impurity fractional density (n imp,light /n e ) and its normalized gradient, the normalized pressure gradient (α), the toroidal Mach number (M tor ) and the normalized toroidal flow velocity gradient. The input space was sampled based on experimental data from the JET tokamak to avoid the curse of dimensionality. The resulting networks, named QLKNN-jetexp-15D, show good agreement with the original Qua-LiKiz model, both by comparing individual transport quantity predictions as well as comparing its impact within the integrated model, JINTRAC. The profileaveraged RMS of the integrated modelling simulations is <10% for each of the 5 scenarios tested. This is non-trivial given the potential numerical instabil-ities present within the highly nonlinear system of equations governing plasma transport, especially considering the novel addition of momentum flux predictions to the model proposed here. An evaluation of all 25 NN output quantities at one radial location takes ∼0.1 ms, 10 4 times faster than the original QuaLiKiz model. Within the JINTRAC integrated modelling tests performed in this study, using QLKNN-jetexp-15D resulted in a speed increase of only 60-100 as other physics modules outside of turbulent transport become the bottleneck.

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Section: Integrated Modelling Comparisonsmentioning

confidence: 90%

Section: Integrated Modelling Comparisonsmentioning

confidence: 99%

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Neural network surrogate of QuaLiKiz using JET experimental data to populate training space

Citrin

Bourdelle

et al. 2021

Physics of Plasmas

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“…This limits QLKNN, which is then not applicable for impurity transport or for multiple-isotope particle physics as in Ref. 28. The next generation of QLKNN will include multiple-ion transport.…”

Section: Application In Transport Codesmentioning

confidence: 99%

Fast modeling of turbulent transport in fusion plasmas using neural networks

et al. 2020

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“…2018; Marin et al . 2020). This is in stark contrast with the electron particle transport, which governs the electron density profile and is characterised by transport coefficients an order of magnitude smaller than those for the ions.…”

Section: Isotope Dependence In Jet-ilw Type I Elmy H-modesmentioning

confidence: 99%

Isotope dependence of energy, momentum and particle confinement in tokamaks

Weisen

Maggi²,

Oberparleiter³

et al. 2020

J. Plasma Phys.

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The isotope dependence of plasma transport will have a significant impact on the performance of future D-T experiments in JET and ITER and eventually on the fusion gain and economics of future reactors. In preparation for future D-T operation on JET, dedicated experiments and comprehensive transport analyses were performed in H, D and H-D mixed plasmas. The analysis of the data has demonstrated an unexpectedly strong and favourable dependence of the global confinement of energy, momentum and particles in ELMy H-mode plasmas on the atomic mass of the main ion species, the energy confinement time scaling as ${\tau _E}\sim {A^{0.5}}$ (Maggi et al., Plasma Phys. Control. Fusion, vol. 60, 2018, 014045; JET Team, Nucl. Fusion, vol. 39, 1999, pp. 1227–1244), i.e. opposite to the expectations based only on local gyro-Bohm (GB) scaling, ${\tau _E}\sim {A^{ - 0.5}}$ , and stronger than in the commonly used H-mode scaling for the energy confinement (Saibene et al., Nucl. Fusion, vol. 39, 1999, 1133; ITER Physics Basis, Nucl. Fusion, vol. 39, 1999, 2175). The scaling of momentum transport and particle confinement with isotope mass is very similar to that of energy transport. Nonlinear local GENE gyrokinetic analysis shows that the observed anti-GB heat flux is accounted for if collisions, E × B shear and plasma dilution with low-Z impurities (9Be) are included in the analysis (E and B are, respectively the electric and magnetic fields). For L-mode plasmas a weaker positive isotope scaling ${\tau _E}\sim {A^{0.14}}$ has been found in JET (Maggi et al., Plasma Phys. Control. Fusion, vol. 60, 2018, 014045), similar to ITER97-L scaling (Kaye et al., Nucl. Fusion, vol. 37, 1997, 1303). Flux-driven quasi-linear gyrofluid calculations using JETTO-TGLF in L-mode show that local GB scaling is not followed when stiff transport (as is generally the case for ion temperature gradient modes) is combined with an imposed boundary condition taken from the experiment, in this case predicting no isotope dependence. A dimensionless identity plasma pair in hydrogen and deuterium L-mode plasmas has demonstrated scale invariance, confirming that core transport physics is governed, as expected, by the 4 dimensionless parameters ρ*, ν*, β, q (normalised ion Larmor radius, collisionality, plasma pressure and safety factor) consistently with global quasi-linear gyrokinetic TGLF calculations (Maggi et al., Nucl. Fusion, vol. 59, 2019, 076028). We compare findings in JET with those in different devices and discuss the possible reasons for the different isotope scalings reported from different devices. The diversity of observations suggests that the differences may result not only from differences affecting the core, e.g. heating schemes, but are to a large part due to differences in device-specific edge and wall conditions, pointing to the importance of better understanding and controlling pedestal and edge processes.

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First-principles-based multiple-isotope particle transport modelling at JET

Cited by 16 publications

References 52 publications

Neural network surrogate of QuaLiKiz using JET experimental data to populate training space

Neural network surrogate of QuaLiKiz using JET experimental data to populate training space

Fast modeling of turbulent transport in fusion plasmas using neural networks

Isotope dependence of energy, momentum and particle confinement in tokamaks

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