Linear and nonlinear local gyrokinetic simulations of the high power high current JT-60SA scenario are presented, based on inputs from predictive transport modelling. Carbon impurities and fast ions are included in the simulations as well as electromagnetic effects. High frequency modes associated with fast ions are found in linear and nonlinear simulations and identified as Toroidal Alfven Eigenmodes (TAEs). In the absence of TAEs, turbulent transport is found to mainly be driven by the ion temperature gradient mode. In this case, fast particles are found not to have a significant effect on the heat flux. The total heat flux at half normalized minor radius is found to be higher than expected based on the assumed total heating power. A modest 10% decrease of the ion and electron temperature gradients (ETGs) is sufficient to match the expected value. On the other hand, simulations at other radial positions demonstrate heat fluxes lower than expected, consistent with the previous study of a similar scenario and an increase of the ion and ETGs by about 20%–30% is necessary to recover the expected heat flux.
This study employs a quasi-1.5D multi-temperature model to investigate the mechanisms governing NOx production and energy costs in microwave plasma reactors operating at 80 mbar, focusing on the interplay of vibrational, chemical and electron kinetics, thermodynamics, and transport processes across the discharge and afterglow. In the plasma discharge zone, non-thermal processes enhance NOx production as electrons transfer energy effectively to the vibrational mode of N2. However, the non-thermal enhancement is found to diminish rapidly within the central-afterglow region. The simulation results show good agreement with experimental data for both the temperature profile and energy cost. Turbulent effects facilitate radial NO diffusion into cooler regions while simultaneously enhancing cooling of the axial region. These findings highlight the potential to improve NOx synthesis efficiency by optimizing turbulence and maintaining non-thermal conditions, offering new opportunities for the advancement of plasma-based chemical processes.
The efficiency of water electrolysis in a photoelectrochemical cell is largely limited by the oxygen evolution reaction (OER) at its semiconductor photoanode. Reaction rate constants are key to investigating the slow kinetics of the multistep OER, as they indicate the rate-determining step. While these rate constants are usually calculated based on first-principles simulations, this research aims to estimate them from experimental electrochemical impedance spectroscopy (EIS) data. Starting from a microkinetic model for charge transfer at the semiconductor-electrolyte interface, an expression for the impedance as a function of the rate constants is derived. At lower potentials, the order of this impedance model is reduced, thus eliminating the rate constants corresponding to the last reaction steps. Moreover, it is shown that EIS data from at least two potentials needs to be combined in order to uniquely identify the rate constants of a particular reduced order model. Therefore, this work details a sample maximum likelihood estimator that integrates not only multiple frequencies, but also multiple potentials simultaneously. Measuring multiple periods of the current density and potential signals, allows this frequency domain estimator to take measurement uncertainty into account. In addition, due to the large numerical range of the rate constants, various scaling methods are implemented to achieve numerical stability. To find suitable initial values for the highly nonlinear optimisation problem, different global estimation methods are compared. The complete estimation procedure of the rate constants is illustrated on simulated EIS data of a hematite photoanode.
Interactions between MHD-scale tearing modes (TMs) and ion-gyroradius-scale trapped-electron modes (TEMs) in a fusion plasma are simulated with global gyrokinetics, using a consistent set of fixed equilibrium profiles. Unstable core TMs nonlinearly couple and transfer energy to smaller-scale stable TMs near the edge, where TEMs are present. Magnetic stochasticity from these edge TMs erodes TEM-generated zonal flows near the edge rational surfaces, leading to strongly increased electrostatic flux. This interaction of macro- and microscale modes suggests it may be possible to control microturbulence through current-profile modifications.
Accurate models of the scrape-off layer are required for the design and operation of tokamak fusion reactors. Scrape-off layer simulations are computationally expensive, difficult to operate and suffer from numerical instabilities. A potential remedy comes in using machine learning models trained on simulations for fast and easy to use predictions. We present a such candidate surrogate model - named SOLPS-NN - to provide recommendations for the methods to construct it. Based on a large dataset of several thousand SOLPS-ITER simulations with reduced neutral fidelity, a variation of machine learning models with differing architectures and scopes are tested. The evaluation shows that simple fully connected neural networks are a suitable architecture. It is demonstrated that the whole spatial domain can be predicted at once, but that it is easier to achieve high accuracy by employing independent models for different observables. The presented surrogate model with reduced neutral fidelity is sufficient to predict access to detachment with trends similar to experiments. A small dataset of higher fidelity ITER baseline SOLPS-ITER simulations is used to (re-)train surrogate models. The smaller extent of the ITER dataset allows for achieving much more accurate predictions. Transfer learning from the previous surrogate model works but has no direct benefits over training a new model from scratch. Future efforts should focus on discovering the potential and the methods for models utilizing simulations with mixtures of fidelity.
