TRANSP-TGLF core predictive modeling of the JET DT baseline scenario

Abstract In recent years, an intense modeling activity has been focused on preparing and analyzing the second JET Deuterium–Tritium (D–T) experimental campaign DTE2. Among the numerous scientific outcomes of this campaign was the unique opportunity to test and validate the state of the art of modeling tools with fusion-relevant DT plasmas using the full metallic ITER-like wall in different scenarios. This work reports on the core predictive modeling of plasma density, electron and ion temperatures ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>n</mml:mi> <mml:mrow> <mml:mi mathvariant="normal">e</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>T</mml:mi> <mml:mrow> <mml:mi mathvariant="normal">e</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>T</mml:mi> <mml:mrow> <mml:mi mathvariant="normal">i</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> ) performed using TRANSP (Pankin et al 2025 Comput. Phys. Commun. 312 109611) coupled with Trapped Gyro-Landau Fluid (TGLF)-SAT2 (Kinsey et al 2008 Phys. Plasmas 15 055908), (Staebler et al 2021 Nucl. Fusion 61 116007) for the JET D–T baseline scenario ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>I</mml:mi> <mml:mrow> <mml:mi mathvariant="normal">p</mml:mi> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>3.5</mml:mn> </mml:mrow> </mml:math> MA, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>q</mml:mi> <mml:mrow> <mml:mn>95</mml:mn> </mml:mrow> </mml:msub> <mml:mo>=</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>β</mml:mi> <mml:mrow> <mml:mi mathvariant="normal">N</mml:mi> </mml:mrow> </mml:msub> <mml:mo>&lt;</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> , with pellet pacing) (Garzotti et al 2025 Plasma Phys. Control. Fusion 67 075011). The sensitivity to different input parameters, as the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mover> <mml:mi>E</mml:mi> <mml:mo stretchy="false">→</mml:mo> </mml:mover> </mml:mrow> <mml:mo>×</mml:mo> <mml:mrow> <mml:mover> <mml:mi>B</mml:mi> <mml:mo stretchy="false">→</mml:mo> </mml:mover> </mml:mrow> </mml:mrow> </mml:math> shear parameterization and the values of the kinetic quantities at the boundary of the prediction domain ( ρ = 0.85) has been assessed, identifying the confidence interval of the prediction results. In particular, the dependence of the electron density profile on the particle source parameters has been studied, identifying the ionization source as the main cause for the density gradient under-prediction obtained by TRANSP-TGLF, reported in (Hyun-Tae et al 2023 Nucl. Fusion 63 112004).