A. Panera Alvarez | Searcharxiv

arXiv2026

Integrated full pulse modeling for pellet injection in tokamaks: HPI2 model improvement and validation in WEST

Reliable modeling and control of core density is essential for reactor-relevant magnetic confinement fusion operation, motivating cryogenic pellet injection as a primary fueling actuator and the need for predictive pellet source models in integrated modeling. Here we present an upgrade of the physics-based pellet code HPI2 in which the plasmoid release spatial step is determined self-consistently from ablation physics, $dx_{var}=v_{\mathrm{pel}}\,t_{\mathrm{exit}}$ (optionally rescaled to trade accuracy for computational cost), removing an ad-hoc discretization parameter and improving numerical robustness across injection conditions. The upgraded model is first validated in stand-alone against a high-field-side pellet-fueled, ohmic, WEST discharge (#58656) by comparing synthetic and measured interferometry line-integrated density increments, obtaining a mean error of $\sim 10\%$. We then perform full-radius, time-dependent integrated modeling validation by coupling the new HPI2 within the High Fidelity Pulse Simulator (HFPS) workflow (JINTRAC/IMAS), combining JETTO with SANCO for the impurity/radiation evolution and TGLF-SAT2 for the turbulent transport. The coupled simulations reproduce the main density rise and relaxation after pellet injection and the associated electron-temperature transient, while taking into account the strong influence of tungsten radiation in WEST, supporting the consistency of HPI2 as a predictive pellet particle source in integrated modeling frameworks. Ultimately, this validation study supports the use of pellet modeling tools in integrated modeling studies for larger devices such as ITER.

physics.plasm-ph↗

arXiv2024

EuroPED-NN: Uncertainty aware surrogate model

This work successfully generates an uncertainty-aware surrogate model of the EuroPED plasma pedestal model using the Bayesian neural network with noise contrastive prior (BNN-NCP) technique. This model is trained using data from the JET-ILW pedestal database and subsequent model evaluations, conforming to EuroPED-NN. The BNN-NCP technique has been proven to be a suitable method for generating uncertainty-aware surrogate models. It matches the output results of a regular neural network while providing confidence estimates for predictions as uncertainties. Additionally, it highlights out-of-distribution (OOD) regions using surrogate model uncertainties. This provides critical insights into model robustness and reliability. EuroPED-NN has been physically validated, first, analyzing electron density $n_e\!\left(ψ_{\text{pol}}=0.94\right)$ with respect to increasing plasma current, $I_p$, and second, validating the $Δ-β_{p,ped}$ relation associated with the EuroPED model. This affirms the robustness of the underlying physics learned by the surrogate model. On top of that, the method was used to develop a EuroPED-like model fed with experimental data, i.e. an uncertainty aware experimental model, which is functional in JET database. Both models have been also tested in $\sim 50$ AUG shots.

physics.plasm-ph↗