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JET experiments using the fuel mixture envisaged for fusion power plants, deuterium and tritium (D–T), provide a unique opportunity to validate existing D–T fusion power prediction capabilities in support of future device design and operation preparation. The 2021 JET D–T experimental campaign has achieved D–T fusion powers sustained over 5 s in ITER-relevant conditions i.e. operation with the baseline or hybrid scenario in the full metallic wall. In preparation of the 2021 JET D–T experimental campaign, extensive D–T predictive modelling was carried out with several assumptions based on D discharges. To improve the validity of ITER D–T predictive modelling in the future, it is important to use the input data measured from 2021 JET D–T discharges in the present core predictive modelling, and to specify the accuracy of the D–T fusion power prediction in comparison with the experiments. This paper reports on the validation of the core integrated modelling with TRANSP, JINTRAC, and ETS coupled with a quasilinear turbulent transport model (Trapped Gyro Landau Fluid or QualLiKiz) against the measured data in 2021 JET D–T discharges. Detailed simulation settings and the heating and transport models used are described. The D–T fusion power calculated with the interpretive TRANSP runs for 38 D–T discharges (12 baseline and 26 hybrid discharges) reproduced the measured values within 20 % . This indicates the additional uncertainties, that could result from the measurement error bars in kinetic profiles, impurity contents and neutron rates, and also from the beam-thermal fusion reaction modelling, are less than 20 % in total. The good statistical agreement confirms that we have the capability to accurately calculate the D–T fusion power if correct kinetic profiles are predicted, and indicates that any larger deviation of the D–T fusion power prediction from the measured fusion power could be attributed to the deviation of the predicted kinetic profiles from the measured kinetic profiles in these plasma scenarios. Without any posterior adjustment of the simulation settings, the ratio of predicted D–T fusion power to the measured fusion power was found as 65%–96% for the D–T baseline and 81%–97% for D–T hybrid discharge. Possible reasons for the lower D–T prediction are discussed and future works to improve the fusion power prediction capability are suggested. The D–T predictive modelling results have also been compared to the predictive modelling of the counterpart D discharges, where the key engineering parameters are similar. Features in the predicted kinetic profiles of D–T discharges such as underprediction of n e are also found in the prediction results of the counterpart D discharges, and it leads to similar levels of the normalized neutron rate prediction between the modelling results of D–T and the counterpart D discharges. This implies that the credibility of D–T fusion power prediction could be a priori estimated by the prediction quality of the preparatory D discharges, which will be attempted before actual D–T experiments.

We present herein the results of a linear gyrokinetic analysis of electromagnetic microinstabilites in the conceptual high-\\(\\), reactor-scale, tight-aspect-ratio tokamak STEP (Spherical Tokamak for Energy Production, https://step.ukaea.uk). We examine a range of flux surfaces between the deep core and the pedestal top for two candidate flat-top operating points of the prototype device. Local linear gyrokinetic analysis is performed to determine the type of microinstabilities that arise under these reactor-relevant conditions. We find that the equilibria are dominated at ion binormal scales by a hybrid version of the Kinetic Ballooning Mode (KBM) instability that has significant linear drive contributions from the ion temperature gradient and from trapped electrons, while collisional Microtearing Modes (MTMs) are sub-dominantly also unstable at similar binormal scales. The hybrid-KBM and MTM exhibit very different radial scales. We study the sensitivity of these instabilities to physics parameters, and discuss potential mechanisms for mitigating them. The results of this investigation are compared to a small set of similar conceptual reactor designs in the literature. A detailed benchmark of the linear results is performed using three gyrokinetic codes; alongside extensive resolution testing and sensitivity to numerical parameters providing confidence in the results of our calculations, and paving the way for detailed nonlinear studies in a companion article.

In this work, we present first-of-their-kind nonlinear local gyrokinetic simulations of electromagnetic turbulence at mid-radius in the burning plasma phase of the conceptual high-\\(\\), reactor-scale, tight-aspect-ratio tokamak STEP (Spherical Tokamak for Energy Production). A prior linear analysis in D. Kennedy et al. 2023 Nucl. Fusion 63 126061 reveals the presence of unstable hybrid kinetic ballooning modes, where inclusion of the compressional magnetic field fluctuation, \\( B_\\), is crucial, and subdominant microtearing modes are found at binormal scales approaching the ion-Larmor radius. Local nonlinear gyrokinetic simulations on the selected surface in the central core region suggest that hybrid kinetic ballooning modes can drive large turbulent transport, and that there is negligible turbulent transport from subdominant microtearing modes when hybrid kinetic ballooning modes are artificially suppressed (through the omission of \\( B_\\)). Nonlinear simulations that include perpendicular equilibrium flow shear can saturate at lower fluxes that are more consistent with the available sources in STEP. This analysis suggests that hybrid kinetic ballooning modes could play an important role in setting the turbulent transport in STEP, and possible mechanisms to mitigate turbulent transport are discussed. Increasing the safety factor or the pressure gradient strongly reduces turbulent transport from hybrid kinetic ballooning modes in the cases considered here. Challenges of simulating electromagnetic turbulence in this high-\\(\\) regime are highlighted. In particular the observation of radially extended turbulent structures in the absence of equilibrium flow shear motivates future advanced global gyrokinetic simulations that include \\( B_\\).

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 (\\(\\)), 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 \\(\\)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.

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 \\(10\\) confinement times, with a simulation boundary condition at \\(_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 \\(_tor\\) within \\(2\\), 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.

We present an ultrafast neural network (NN) model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 300 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The database covers a wide range of realistic tokamak core parameters. Physical features such as the existence of a critical gradient for the onset of turbulent transport were integrated into the neural network training methodology. We have coupled QLKNN to the tokamak modelling framework JINTRAC and rapid control-oriented tokamak transport solver RAPTOR. The coupled frameworks are demonstrated and validated through application to three JET shots covering a representative spread of H-mode operating space, predicting turbulent transport of energy and particles in the plasma core. JINTRAC-QLKNN and RAPTOR-QLKNN are able to accurately reproduce JINTRAC-QuaLiKiz T i,e and n e profiles, but 3 to 5 orders of magnitude faster. Simulations which take hours are reduced down to only a few tens of seconds. The discrepancy in the final source-driven predicted profiles between QLKNN and QuaLiKiz is on the order 1%-15%. Also the dynamic behaviour was well captured by QLKNN, with differences of only 4%-10% compared to JINTRAC-QuaLiKiz observed at mid-radius, for a study of density buildup following the L-H transition. Deployment of neural network surrogate models in multi-physics integrated tokamak modelling is a promising route towards enabling accurate and fast tokamak scenario optimization, Uncertainty Quantification, and control applications.

The non-linear evolution of a magnetic island is studied using the Vlasov gyro-kinetic code GKW. The interaction of electromagnetic turbulence with a self-consistently growing magnetic island, generated by a tearing unstable \\(' > 0\\) current profile, is considered. The turbulence is able to seed the magnetic island and bypass the linear growth phase by generating structures that are approximately an ion gyro-radius in width. The non-linear evolution of the island width and its rotation frequency, after this seeding phase, is found to be modified and is dependent on the value of the plasma beta and equilibrium pressure gradients. At low values of beta the island evolves largely independent of the turbulence, while at higher values the interaction has a dramatic effect on island growth, causing the island to grow exponentially at the growth rate of its linear phase, even though the island is larger than linear theory validity. The turbulence forces the island to rotate in the ion-diamagnetic direction as opposed to the electron diamagnetic direction in which it rotates when no turbulence is present. In addition, it is found that the mode rotation slows as the island grows in size.

The isotope effect on intrinsic rotation was studied at the Joint European Torus (JET) tokamak. With the unique capability of JET to operate with tritium (T), for the first time, experiments in hydrogen (H), deuterium (D) and T in Ohmic plasmas were compared. Two rotation reversals per isotope type are observed in plasma density scans spanning the linear and the saturated Ohmic confinement regimes. A clear isotope mass dependence is observed at the higher densities. The magnitude of the core rotation was found to depend on isotope mass, with stronger co-current rotation observed in H. Change on intrinsic rotation characteristics coexist with a stronger thermal energy confinement in T.

Objectives To assess how long the UK's National Institute for Health and Clinical Excellence's (NICE) Technology Appraisal Programme has taken to produce guidance and to determine independent predictors of time to guidance. Design Retrospective time to event (survival) analysis. Setting Technology Appraisal guidance produced by NICE. Datasource All appraisals referred to NICE by February 2010 were included, except those referred prior to 2001 and a number that were suspended. Outcome measure Duration from the start of an appraisal (when the scope document was released) until publication of guidance. Results Single Technology Appraisals (STAs) were published significantly faster than Multiple Technology Appraisals (MTAs) with median durations of 48.0 (IQR; 44.3–75.4) and 74.0 (IQR; 60.9–114.0) weeks, respectively (p <0.0001). Median time to publication exceeded published process timelines, even after adjusting for appeals. Results from the modelling suggest that STAs published guidance significantly faster than MTAs after adjusting for other covariates (by 36.2 weeks (95% CI −46.05 to −26.42 weeks)) and that appeals against provisional guidance significantly increased the time to publication (by 42.83 weeks (95% CI 35.50 to 50.17 weeks)). There was no evidence that STAs of cancer-related technologies took longer to complete compared with STAs of other technologies after adjusting for potentially confounding variables and only weak evidence suggesting that the time to produce guidance is increasing each year (by 1.40 weeks (95% CI −0.35 to 2.94 weeks)). Conclusions The results from this study suggest that the STA process has resulted in significantly faster guidance compared with the MTA process irrespective of the topic, but that these gains are lost if appeals are made against provisional guidance. While NICE processes continue to evolve over time, a trade-off might be that decisions take longer but at present there is no evidence of a significant increase in duration.