arXiv subjects

JET Contributors

Publications and source records attributed to JET Contributors.

At least 19 recordsLinked to original sources

arXiv2026

Improved n=1 Empirical Error Field Penetration Threshold Scaling with Ohmic and L-Mode Conventional Tokamak Plasma Discharges

This paper presents an updated n=1 error field penetration threshold scaling, which increases fit quality compared to previous error field scaling laws, is produced from an expanded database, and exhibits reduced uncertainty in projections to future conventional tokamaks. It improves confidence in tokamak engineering tolerances, which are a significant driver of cost and time constraints on device construction. We add J-TEXT data, new JET data, and create the scaling using only conventional tokamak Ohmic and L-mode experiments. Since H-mode plasmas are more resilient to error field penetration, this scaling predicts what is likely the most dangerous regime of error field penetration for new tokamak designs. These decisions improve confidence in the error field penetration threshold scaling and its application in the construction and design decisions of any future conventional tokamak or FPP.

physics.plasm-ph↗

arXiv2026

Variability of MHD Instabilities in Benign Termination of High-Current Runaway Electron Beams in the JET and DIII-D Tokamaks

Benign termination, in which magnetohydrodynamic (MHD) instabilities deconfine runaway electrons (REs) following hydrogenic injections, is a promising strategy for mitigating dangerous RE loads after disruptions. Recent experiments on the Joint European Torus (JET) have explored this scenario at higher pre-disruptive plasma currents than are achievable on other devices, revealing challenges in obtaining benign terminations at $I_p \geq 2.5$ MA. This work analyzes the evolution of these high-current RE beams and their terminating MHD events using fast magnetic sensor measurements and EFIT equilibrium reconstructions for approximately $40$ JET and $20$ DIII-D tokamak discharges. On JET, unsuccessful non-benign terminations occur at low edge safety factor ($q_{\text{edge}} \approx 2$), and are preceded by intermittent, non-terminating MHD events at higher rational $q_{\text{edge}}$. Trends in the internal inductance $l_i$ indicate more peaked RE current profiles in the high-$I_p$ non-benign population, which may hinder successful recombination through re-ionization. In contrast, benign terminations on JET typically occur at higher $q_{\text{edge}} \geq 3$ and exhibit less peaked RE current profiles. DIII-D displays a range of terminating edge safety factors, correlated with the measured $l_i$ values. Across both tokamaks, the RE current peaking is therefore found to determine which MHD instability boundary is encountered, confirmed by linear resistive MHD modeling with the CASTOR3D code. Measured growth rates are similar for benign and non-benign cases, indicating that ideal MHD timescales at low density after hydrogenic injection do not alone explain efficient RE deconfinement. Instead, non-benign cases are characterized by their lower MHD perturbation amplitudes $δB$. These observations suggest that the interplay between ideal and resistive dynamics governs the termination process.

physics.plasm-ph↗

arXiv2025

Reconstructions of electron-temperature profiles from EUROfusion Pedestal Database using turbulence models and machine learning

This study uses plasma-profile data from the EUROfusion pedestal database, focusing on the electron-temperature and electron-density profiles in the edge region of H-mode ELMy JET ITER-Like-Wall (ILW) pulses. We make systematic predictions of the electron-temperature pedestal, using the density profiles and engineering parameters of the pulses as inputs. We first present a machine-learning algorithm that, given more inputs than theory-based modelling and 80\% of the database as training data, can reconstruct the remaining 20\% of temperature profiles within 20\% of the experimental values, including accurate estimates of the pedestal width and location. The most important engineering parameters for these predictions are magnetic field strength, particle fuelling rate, plasma current, and strike-point configuration. This confirms the potential of accurate pedestal prediction using large databases. Next, we take a simple theoretical approach assuming a local power-law relationship between the gradients of density ($R/L_{n_e}$) and temperature ($R/L_{T_e}$): $R/L_{T_e}=A\left(R/L_{n_e}\right)^α$ with $α\approx 0.4$ fits well in the steep-gradient region. When $A$ and $α$ are fit independently for each pedestal, a one-to-one correlation emerges, also valid for JET-C data. For $α= 1$, $A \equiv η_e$, a known control parameter for turbulence in slab-ETG theory. Measured values of $η_e$ in the steep-gradient region lie well above the slab-ETG stability threshold, suggesting a nonlinear threshold shift or a supercritical turbulent state. Finally, we test heat-flux scalings motivated by gyrokinetic simulations, and we provide best-fit parameters for reconstructing JET-ILW pedestals. These models require additional experimental inputs to reach the accuracy of the machine-learning reconstructions.

physics.plasm-ph↗

arXiv2025

Towards Transparent and Accurate Plasma State Monitoring at JET

Controlling and monitoring plasma within a tokamak device is complex and challenging. Plasma off-normal events, such as disruptions, are hindering steady-state operation. For large devices, they can even endanger the machine's integrity and it represents in general one of the most serious concerns for the exploitation of the tokamak concept for future power plants. Effective plasma state monitoring carries the potential to enable an understanding of such phenomena and their evolution which is crucial for the successful operation of tokamaks. This paper presents the application of a transparent and data-driven methodology to monitor the plasma state in a tokamak. Compared to previous studies in the field, supervised and unsupervised learning techniques are combined. The dataset consisted of 520 expert-validated discharges from JET. The goal was to provide an interpretable plasma state representation for the JET operational space by leveraging multi-task learning for the first time in the context of plasma state monitoring. When evaluated as disruption predictors, a sequence-based approach showed significant improvements compared to the state-based models. The best resulting network achieved a promising cross-validated success rate when combined with a physical indicator and accounting for nearby instabilities. Qualitative evaluations of the learned latent space uncovered operational and disruptive regions as well as patterns related to learned dynamics and global feature importance. The applied methodology provides novel possibilities for the definition of triggers to switch between different control scenarios, data analysis, and learning as well as exploring latent dynamics for plasma state monitoring. It also showed promising quantitative and qualitative results with warning times suitable for avoidance purposes and distributions that are consistent with known physical mechanisms.

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↗

arXiv2024

Isotope effects and Alfven eigenmode stability in JET H, D, T, DT, and He plasmas

While much about Alfven eigenmode (AE) stability has been explored in previous and current tokamaks, open questions remain for future burning plasma experiments, especially regarding exact stability threshold conditions and related isotope effects; the latter, of course, requiring good knowledge of the plasma ion composition. In the JET tokamak, eight in-vessel antennas actively excite stable AEs, from which their frequencies, toroidal mode numbers, and net damping rates are assessed. The effective ion mass can also be inferred using measurements of the plasma density and magnetic geometry. Thousands of AE stability measurements have been collected by the Alfven Eigenmode Active Diagnostic in hundreds of JET plasmas during the recent Hydrogen, Deuterium, Tritium, DT, and Helium-4 campaigns. In this novel AE stability database, spanning all four main ion species, damping is observed to decrease with increasing Hydrogenic mass, but increase for Helium, a trend consistent with radiative damping as the dominant damping mechanism. These data are important for confident predictions of AE stability in both non-nuclear (H/He) and nuclear (D/T) operations in future devices. In particular, if radiative damping plays a significant role in overall stability, some AEs could be more easily destabilized in D/T plasmas than their H/He reference pulses, even before considering fast ion and alpha particle drive. Active MHD spectroscopy is also employed on select HD, HT, and DT plasmas to infer the effective ion mass, thereby closing the loop on isotope analysis and demonstrating a complementary method to typical diagnosis of the isotope ratio.

physics.plasm-ph↗

arXiv2023

High-order coupling of shear and sonic continua in JET plasmas

A recent model coupling the shear-Alfvén and acoustic continua, which depends strongly on the equilibrium shaping and on elongation in particular, is employed to explain the properties of Alfvénic activity observed on JET plasmas below but close to the typical frequency of toroidicity-induced Alfvén eigenmodes (TAEs). The frequency gaps predicted by the model result from high-order harmonics of the geodesic field-line curvature caused by plasma shaping (as opposed to lower-order toroidicity) and give rise to high-order geodesic acoustic eigenmodes (HOGAEs), their frequency value being close to one-half of the TAEs one. The theoretical predictions of HOGAE frequency and radial location are found to be in fair agreement with measurements in JET experiments, including magnetic, reflectometry and soft x-ray data. The stability of the observed HOGAEs is evaluated with the linear hybrid MHD/drift-kinetic code CASTOR-K, taking into account the energetic-ion populations produced by the NBI and ICRH heating systems. Wave-particle resonances, along with drive/damping mechanisms, are also discussed in order to understand the conditions leading to HOGAEs destabilization in JET plasmas.

physics.plasm-ph↗

arXiv2022

Integrated modelling and multiscale gyrokinetic validation study of ETG turbulence in a JET hybrid H-mode scenario

Previous studies with first-principle-based integrated modelling suggested that ETG turbulence may lead to an anti-GyroBohm isotope scaling in JET high-performance hybrid H-mode scenarios. A dedicated comparison study against higher-fidelity turbulence modelling invalidates this claim. Ion-scale turbulence with magnetic field perturbations included, can match the power balance fluxes within temperature gradient error margins. Multiscale gyrokinetic simulations from two distinct codes produce no significant ETG heat flux, demonstrating that simple rules-of-thumb are insufficient criteria for its onset.

physics.plasm-ph↗

arXiv2022

Deep or Not Deep: Supervised Learning Approaches to Modeling the Pedestal Density

Pedestal is the key to conventional high performance plasma scenarios in tokamaks. However, high fidelity simulations of pedestal plasmas are extremely challenging due to the multiple physical processes and scales that are encompassed by tokamak pedestals. The leading paradigm for predicting the pedestal top pressure is encompassed by EPED-like models. However, EPED does not predict the pedestal top density, $n_\text{e,ped}$, but requires it as an input. EUROPED employs simplified models, such as log-linear regression, to constrain $n_\text{e,ped}$ with tokamak machine control parameters in an EPED-like model. However, these simplified models for $n_\text{e,ped}$ often show disagreements with experimental observations and do not use all of the available numerical and categorical machine control information. In this work it is observed that using the same input parameters, decision tree ensembles and deep learning models improve the predictive quality of $n_\text{e,ped}$ by about 23% relative to that obtained with log-linear scaling laws, measured by root mean square error. Including all of the available tokamak machine control parameters, both numerical and categorical, leads to further improvement of about 13%. Finally, predictive quality was tested when including global normalized plasma pressure and effective charge state as inputs, as these parameters are known to impact pedestals. Surprisingly, these parameters lead to only a few percent further improvement of the predictive quality.

physics.plasm-ph↗

arXiv2022

Simultaneous measurements of unstable and stable Alfvén Eigenmodes in JET

In this paper, we report the novel experimental observation of both unstable and stable Toroidicity-induced Alfvén Eigenmodes (TAEs) measured simultaneously in a JET tokamak plasma. The three-ion-heating scheme (D-DNBI-3He) is employed to accelerate deuterons to MeV energies, thereby destabilizing TAEs with toroidal mode numbers n = 3-5, each decreasing in mode amplitude. At the same time, the Alfvén Eigenmode Active Diagnostic resonantly excites a stable n = 6 TAE with total normalized damping rate $-γ/ω_0 \approx$ 1%-4%. Hybrid kinetic-MHD modeling with codes NOVA-K and MEGA both find eigenmodes with similar frequencies, mode structures, and radial locations as in experiment. NOVA-K demonstrates good agreement with the n = 3, 4, and 6 TAEs, matching the damping rate of the n = 6 mode within uncertainties and identifying radiative damping as the dominant contribution. Improved agreement is found with MEGA for all modes: the unstable n = 3-5 and stable n = 2, 6 modes, with the latter two stabilized by higher intrinsic damping and lower fast ion drive, respectively. While some discrepancies remain to be resolved, this unique validation effort gives us confidence in TAE stability predictions for future fusion devices.

physics.plasm-ph↗

arXiv2022

Bayesian equilibria of axisymmetric plasmas

We present a Bayesian method for inferring axisymmetric plasma equilibria from the magnetic field and plasma pressure measurements. The method calculates all possible solutions for plasma current and pressure distributions consistent with the measurements and magnetohydrodynamic (MHD) force balance. Toroidal plasma current and magnetic field coils are modelled as a set of axisymmetric current-carrying solid beams. The other parameters such as plasma pressure and poloidal current flux are given as a function of poloidal magnetic flux, which is determined given a 2D current distribution. Plasma pressure and poloidal current flux profiles are modelled as Gaussian processes whose smoothness is optimally chosen based on the principle of Occam's razor. To find equilibrium solutions, we introduce an MHD force balance constraint at every plasma current beam as a part of the prior knowledge. Given all these physical quantities, predictions calculated by the predictive (forward) models for diagnostics are compared to the observations. The high dimensional complex posterior probability distribution is explored by a new algorithm based on the Gibbs sampling scheme.

physics.plasm-ph↗

arXiv2022

Runaway electron velocity-space observation regions of bremsstrahlung hard X-ray spectroscopy

The reconstruction of the distribution function of runaway electrons (RE) in magnetically confined fusion plasmas gives insights on the runaway electron beam dynamics during plasma disruptions. In view of enabling a two-dimensional, energy-pitch reconstruction of the RE velocity space, in this work we present a calculation of the weight functions for the bremsstrahlung emission by the REs. The weight functions allow bridging the bremsstrahlung spectrum with the RE velocity space, as they tell the region of the velocity space that contributes to a particular spectral measurement. The results are applied to investigate the RE velocity-space sensitivity of the hard X-ray diagnostic installed at the Joint European Torus.

physics.plasm-ph↗

arXiv2022

Energy-selective confinement of fusion-born alpha particles during internal relaxations in a tokamak plasma

Long-pulse operation of a self-sustained fusion reactor using toroidal magnetic containment requires control over the content of alpha particles produced by D-T fusion reactions. On the one hand, MeV-class alpha particles must stay confined to heat the plasma. On the other hand, decelerated helium ash must be expelled before diluting the fusion fuel. Our kinetic-magnetohydrodynamic hybrid simulations of a large tokamak plasma confirm the existence of a parameter window where such energy-selective confinement can be accomplished by exploiting internal relaxation events known as `sawtooth crashes'. The physical picture -- consisting of a synergy between magnetic geometry, optimal crash duration and rapid particle motion -- is completed by clarifying the role played by magnetic drifts. Besides causing asymmetry between co- and counter-going particle populations, magnetic drifts determine the size of the confinement window by dictating where and how much `reconnection' occurs in particle orbit topology.

physics.plasm-ph↗

arXiv2021

A novel measurement of marginal Alfvén Eigenmode stability during high power auxiliary heating in JET

The interaction of Alfvén Eigenmodes (AEs) and energetic particles is one of many important factors determining the success of future tokamaks. In JET, eight in-vessel antennas were installed to actively probe stable AEs with frequencies ranging 25-250 kHz and toroidal mode numbers $\vert n \vert < 20$. During the 2019-2020 deuterium campaign, almost 7500 resonances and their frequencies $f_0$, net damping rates $γ< 0$, and toroidal mode numbers were measured in almost 800 plasma discharges. From a statistical analysis of this database, continuum and radiative damping are inferred to increase with edge safety factor, edge magnetic shear, and when including non-ideal effects. Both stable AE observations and their associated damping rates are found to decrease with $\vert n \vert$. Active antenna excitation is also found to be ineffective in H-mode as opposed to L-mode; this is likely due to the increased edge density gradient's effect on accessibility and ELM-related noise's impact on mode identification. A novel measurement is reported of a marginally stable, edge-localized Ellipticity-induced AE probed by the antennas during high-power auxiliary heating (ICRH and NBI) up to 25 MW. NOVA-K kinetic-MHD simulations show good agreement with experimental measurements of $f_0$, $γ$, and $n$, indicating the dominance of continuum and electron Landau damping in this case. Similar experimental and computational studies are planned for the recent hydrogen and ongoing tritium campaigns, in preparation for the upcoming DT campaign.

physics.plasm-ph↗

arXiv2021

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

Within integrated tokamak plasma modelling, turbulent transport codes are typically the computational bottleneck limiting their routine use outside of post-discharge 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 QuaLiKiz model, both by comparing individual transport quantity predictions as well as comparing its impact within the integrated model, JINTRAC. The profile-averaged RMS of the integrated modelling simulations is <10% for each of the 5 scenarios tested. This is non-trivial given the potential numerical instabilities 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 $\sim$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.

physics.plasm-ph↗

arXiv2021

Application of Gaussian process regression to plasma turbulent transport model validation via integrated modelling

This paper outlines an approach towards improved rigour in tokamak turbulence transport model validation within integrated modelling. Gaussian process regression (GPR) techniques were applied for profile fitting during the preparation of integrated modelling simulations allowing for rigourous sensitivity tests of prescribed initial and boundary conditions as both fit and derivative uncertainties are provided. This was demonstrated by a JETTO integrated modelling simulation of the JET ITER-like-wall H-mode baseline discharge #92436 with the QuaLiKiz quasilinear turbulent transport model, which is the subject of extrapolation towards a deuterium-tritium plasma. The simulation simultaneously evaluates the time evolution of heat, particle, and momentum fluxes over $\sim10$ confinement times, with a simulation boundary condition at $ρ_{tor} = 0.85$. Routine inclusion of momentum transport prediction in multi-channel flux-driven transport modelling is not standard and is facilitated here by recent developments within the QuaLiKiz model. Excellent agreement was achieved between the fitted and simulated profiles for $n_e$, $T_e$, $T_i$, and $Ω_{tor}$ within $2σ$, but the simulation underpredicts the mid-radius $T_i$ and overpredicts the core $n_e$ and $T_e$ profiles for this discharge. Despite this, it was shown that this approach is capable of deriving reasonable inputs, including derivative quantities, to tokamak models from experimental data. Furthermore, multiple figures-of-merit were defined to quantitatively assess the agreement of integrated modelling predictions to experimental data within the GPR profile fitting framework.

physics.plasm-ph↗

arXiv2021

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

Core turbulent particle transport with multiple isotopes can display observable differences in behaviour between the electron and ion particle channels. Experimental observations at JET with mixed H-D plasmas and varying NBI and gas-puff sources [M. Maslov et al., Nucl. Fusion 7 076022 (2018)] inferred source dominated electron peaking, but transport dominated isotope peaking. In this work, we apply the QuaLiKiz quasilinear gyrokinetic transport model within JINTRAC flux-driven integrated modelling, for core transport validation in this multiple-isotope regime. The experiments are successfully reproduced, predicting self consistently $ j $, $ n_{e} $, $ n_{Be} $, $ T_{e} $, $ T_{i} $, $ω_{tor}$ and the isotope composition. As seen in the experiments, both H and D profiles are predicted to be peaked regardless of the core isotope source. An extensive sensitivity study confirmed that this result does not depend on the specific choices made for the boundary conditions and physics settings. While kinetic profiles and electron density peaking did vary depending on the simulation parameters, the isotope ratio remained nearly invariant, and tied to the electron density profile. These findings have positive ramifications for multiple-isotope fuelling, burn control, and helium ash removal.

physics.plasm-ph↗

arXiv2021

Multiple-isotope pellet cycles captured by turbulent transport modelling in the JET tokamak

For the first time the pellet cycle of a multiple-isotope plasma is successfully reproduced with reduced turbulent transport modelling, within an integrated simulation framework. Future nuclear fusion reactors are likely to be fuelled by cryogenic pellet injection, due to higher penetration and faster response times. Accurate pellet cycle modelling is crucial to assess fuelling efficiency and burn control. In recent JET tokamak experiments, deuterium pellets with reactor-relevant deposition characteristics were injected into a pure hydrogen plasma. Measurements of the isotope ratio profile inferred a Deuterium penetration time comparable to the energy confinement time. The modelling successfully reproduces the plasma thermodynamic profiles and the fast deuterium penetration timescale. The predictions of the reduced turbulence model QuaLiKiz in the presence of a negative density gradient following pellet deposition are compared with GENE linear and nonlinear higher fidelity modelling. The results are encouraging with regard to reactor fuelling capability and burn control.

physics.plasm-ph↗