arXiv subjects

S. Wiesen

Publications and source records attributed to S. Wiesen.

5 recordsLinked to original sources

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↗

arXiv2025

SOLPS-ITER simulation of an X-point radiator in TCV

SOLPS-ITER simulation is performed to reproduce the X-point radiator recently observed in nitrogen-seeded TCV experiments, which is a scenario that may be favorable to solve the power exhaust problems in future fusion devices. The simulations reveal the transition from the detached regime without XPR to the XPR regime, when increasing the nitrogen seeding rate. A cold X-point core surrounded by ionizing and radiative mentals is formed inside the separatrix and slightly above the X-point, where more than 90% of the total input power is dissipated. The cold X-point core exhibits a temperature of approximately 1eV and features high recombination rate to host the convective fluxes from the ionizing mental. Increasing nitrogen seeding rate also moves the nitrogen ionization front away from the target faster than the nitrogen stagnation point, which enhances the divertor nitrogen leakage to the main chamber and benefits the XPR region cooling. Carbon radiation decreases as the nitrogen seeding increases, and carbon radiation contributes to above 5% of the core impurity radiation before entering the XPR, which decreases to 2.8% when reaching the XPR. Both baffled and unbaffled divertor geometries are simulated and compared, showing that baffles facilitate the access to XPR by increasing the X-point neutral density, but requires higher seeding rate to enter the XPR regime.

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↗

arXiv2023

On learning latent dynamics of the AUG plasma state

In this work, we demonstrate the utility of state representation learning applied to modeling the time evolution of electron density and temperature profiles at ASDEX-Upgrade (AUG). The proposed model is a deep neural network which learns to map the high dimensional profile observations to a lower dimensional state. The mapped states, alongside the original profile's corresponding machine parameters are used to learn a forward model to propagate the state in time. We show that this approach is able to predict AUG discharges using only a selected set of machine parameters. The state is then further conditioned to encode information about the confinement regime, which yields a simple baseline linear classifier, while still retaining the information needed to predict the evolution of profiles. We then discuss the potential use cases and limitations of state representation learning algorithms applied to fusion devices.

physics.plasm-ph↗

arXiv2023

Developing Deep Learning Algorithms for Inferring Upstream Separatrix Density at JET

Predictive and real-time inference capability for the upstream separatrix electron density, $n_\text{e, sep}$, is essential for design and control of core-edge integrated plasma scenarios. In this study, both supervised and semi-supervised machine learning algorithms are explored to establish direct mapping as well as indirect compressed representation of the pedestal profiles for predictions and inference of $n_{\text{e, sep}}$. Based on the EUROfusion pedestal database for JET, a tabular dataset was created, consisting of machine parameters, fraction of ELM cycle, high resolution Thomson scattering profiles of electron density and temperature, and $n_{\text{e, sep}}$ for 608 JET shots. Using the tabular dataset, the direct mapping approach provides a mapping of machine parameters and ELM percentage to $n_{\text{e, sep}}$. Through representation learning, a compressed representation of the experimental pedestal electron density and temperature profiles is established. By conditioning the representation with machine control parameters, a probabilistic generative predictive model is established. For prediction, the machine parameters can be used to establish a conditional distribution of the compressed pedestal profiles, and the decoder that is trained as part of the algorithm can be used to decode the compressed representation back to full pedestal profiles. Although, in this work, a proof-of-principle for predicting and inferring $n_{\text{e, sep}}$ is given, such a representation learning can be used also for many other applications as the full pedestal profile is predicted. An implementation of this work can be found at https://github.com/fusionby2030/moxie.

physics.plasm-ph↗