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The theory, development, and validation of reduced quasilinear models of gyrokinetic turbulent transport in the closed flux surface core of tokamaks is reviewed. In combination with neoclassical collisional transport, these models are successful in accurately predicting core tokamak plasma temperature, density, rotation, and impurity profiles in a variety of confinement regimes. Refined experimental tests have been performed to validate the predictions of the quasilinear models, probing changes in the dominant gyrokinetic instabilities, as reflected in fluctuation measurements, cross-phases, and transport properties. These tests continue to produce a deeper understanding of the complex mix of instabilities at both electron and ion gyroradius scales.

A long standing shortfall in the predicted L-mode edge energy transport by reduced quasi-linear models of gyrokinetic turbulent transport has been resolved. The improved model TGLF-SAT2 has higher fidelity to gyrokinetic simulations of the electron-scale contribution to the electron energy transport and the ion-scale flux surface shape dependence of energy transport. The success of TGLF-SAT2 in predicting the L-mode and Ohmic edge profiles is critical to whole pulse simulation and opens the door to prediction of the H-mode power threshold.

The increased low to high confinement mode (L to H-mode) power threshold P L H in DIII-D low collisionality hydrogen plasmas (compared to deuterium) is shown to result from lower impurity (carbon) content, consistent with reduced (mass-dependent) physical and chemical sputtering of graphite. Trapped gyro-Landau fluid (TGLF) quasilinear calculations and local non-linear gyrokinetic CGYRO simulations confirm stabilization of ion temperature gradient (ITG) driven turbulence by increased carbon ion dilution as the most important isotope effect. In the plasma edge, electron non-adiabaticity is also predicted to contribute to the isotope dependence of thermal transport and P L H , however its effect is subdominant compared to changes from impurity isotopic behavior. This L-H power threshold reduction with increasing carbon content at low collisionality is in stark contrast to high collisionality results, where additional impurity content appears to increase the power necessary for H-mode access.

The JET Deuterium-Tritium-Experiment Campaign 2 (DTE2) has demonstrated the highest-ever fusion energy production. To forecast the transport dynamics within these discharges, the TGLF and NEO models within the TGYRO transport code were employed. A critical development in this study is the new quasilinear transport model, TGLF-SAT2, specifically designed to resolve discrepancies identified in JET deuterium discharges. This model accurately describes the saturated three-dimensional (3D) fluctuation spectrum, aligning closely with a database of nonlinear CGYRO turbulence simulations, thereby enhancing the predictive accuracy of TGYRO simulations. In validating against the JET DTE2 discharges across two primary operating scenarios, TGYRO effectively predicted the temperature profiles within a broad radial window (ρ ∼ 0.2–0.85), though with minor ion temperature discrepancies near the core. However, a consistent underprediction of electron density profiles by 20% across the simulation domain was noted, indicating areas for future refinement. To achieve a self-consistent steady-state solution based on the JET DTE2 discharges, an integrated modeling workflow TGYRO-STEP within the OMFIT framework was introduced. This workflow iterates among the core transport, the pedestal pressure and the MHD equilibrium, ultimately yielding a converged solution that significantly reduces dependence on experimental boundary conditions for temperature and density profiles. The integrated simulation results show negligible differences in electron density and temperature profiles compared to standalone TGYRO modeling, while the ion temperature profile is lower due to the updated boundary condition in TGYRO-STEP. The application of the TGYRO-STEP workflow to JET DTE2 discharges serves as a crucial test to validate its robustness and highlights its limitations, providing valuable insights for its potential future application in ITER and Fusion Power Plant deuterium and tritium prediction modeling.

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.

The increased low to high confinement mode (L to H-mode) power threshold PLH in DIII-D hydrogen plasmas (compared to deuterium) is shown to result from lower impurity (carbon) content, consistent with reduced (mass-dependent) physical and chemical sputtering of graphite. Trapped Gyro-Landau Fluid (TGLF) quasilinear calculations and local non-linear gyrokinetic CGYRO simulations confirm stabilization of Ion Temperature Gradient (ITG) driven turbulence by increased carbon ion dilution as the most important isotope effect. In the plasma edge, electron non-adiabaticity also contributes to the isotope dependence of thermal transport and PLH, however its effect is subdominant compared to changes from impurity isotopic behavior.

A new neural network model for a quasilinear saturation rule has been developed to map linear gyrokinetic data to nonlinear saturated potential magnitudes to predict the total energy and particle fluxes. The training dataset is taken from the high resolution simulation database generated from nonlinear gyrokinetic turbulence simulations with the CGYRO code for developing the SAT3 model. This new model, named SAT3-NN, overall is able to capture the 1D saturated potential magnitudes of the dataset more accurately than SAT3, as depicted by lower percentage errors in the peak locations and peak values of the 1D saturated potentials. The resulting fluxes also had smaller deviations from the nonlinear CGYRO data as compared to previous saturation models such as SAT0 - SAT2. Consistent with SAT3, SAT3-NN is able to recreate the anti-gyroBohm scaling of fluxes seen for the TEM-dominated cases considered.

Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project

This is the six month progress report to Fusion Energy Science (FES) and the American Science Cloud (AmSC) on the MPEX AI Digtial Twins project that was started in October 2025. There are two milestones to demonstrate the Artificial Intelligence (AI) advantage for MPEX operations and scientific discovery, that will be completed by June 2026. The first is a Helicon AI Hot-Spot Controller (Sec. 3.1), which is the helicon heating component of the more comprehensive planned MPEX AI Hot Spot Digital Twin (Sec. 3). The second is an E-beam Damage Assessment Digital Twin (Sec. 4.1), which is a reduced electron beam damage modality prototype for the MPEX AI Damage Assessment Digital Twin (Sec. 4). These two phase I milestones are on track for the June demonstration. In addition to these two milestones, progress on configuring the Galaxy software interface for automation, validation and data analysis is reported (Sec. 5). This interface now connects a subset of the main physics simulation codes to DOE HPC resources and will connect to the MPEX data acquisition system so that analysis of data, validation and execution of simulations can be performed by the scientist or by AI-Agents. When AmSC is ready to accept connections and data, Galaxy will be the MPEX interface to AmSC

Based on the community input at the National Academy of Sciences (NAS) Madison and Austin workshops in July and December 2017, respectively, this whitepaper was prepared and submitted to the NAS in the category of US fusion theory and computation. This whitepaper was submitted to NAS as one of five community-approved whitepapers. The revised version was also submitted for the Knoxville American Physical Society Division of Plasma Physics Community Planning Process (APS-DPP-CPP) workshop in September 2019.