Fast transport simulations with higher-fidelity surrogate models for ITER

A fast and accurate turbulence transport model based on quasilinear gyrokinetics is developed. The model consists of a set of neural networks trained on a bespoke quasilinear GENE dataset, with a saturation rule calibrated to dedicated nonlinear simulations. The resultant neural network is approximately eight orders of magnitude faster than the original GENE quasilinear calculations. ITER predictions with the new model project a fusion gain in line with ITER targets. While the dataset is currently limited to the ITER baseline regime, this approach illustrates a pathway to develop reduced-order turbulence models both faster and more accurate than the current state-of-the-art.</p

Rapid optimization of stationary tokamak plasmas in RAPTOR: demonstration for the ITER hybrid scenario with neural network surrogate transport model QLKNN

This work presents a fast and robust method for optimizing the stationary radial distribution of temperature, density and parallel current density in a tokamak plasma and its application to first-principle-based modeling of the ITER hybrid scenario. A new solver is implemented in the RAPTOR transport code, enabling direct evaluation of the stationary solution to which the radial plasma profiles evolve. Coupled to a neural network emulation of the quasi-linear gyrokinetic QuaLiKiz transport model (QLKNN-hyper-10D), a first-principle-based estimate of the stationary state of the core plasma can be found at unprecedented computational speed (typically a few seconds on standard hardware). The stationary state solver is then embedded in a numerical optimization scheme, allowing the optimization of tokamak plasma scenarios in only a few minutes. The proposed method is applied to investigate the performance of ITER hybrid scenarios at different values of total plasma current, plasma density and pedestal height and for different power contributions in a heating mix consisting of electron cyclotron and neutral beam heating. Optimizing the radial distribution of electron cyclotron current drive (ECCD) deposition, the q profile is tailored to maximize the fusion gain Q, by maximizing the energy confinement predicted through the first-principles-based transport model, while satisfying q &gt; 1, avoiding sawtooth oscillations. It is found that optimal use of ECCD in ITER hybrid scenarios is to deposit power as close to the core as possible, while maintaining sufficient off-axis current drive to keep q above 1. Upper limits for the fusion gain Q are shown to be constrained either by minimum power requirements for the separatrix power flow to maintain H-mode or by minimum current drive requirements for q profile tailoring. Finally, it is shown that the ITER hybrid scenario operating window is significantly extended by an upgrade of the electron cyclotron power to 40 MW.</p

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

Within integrated tokamak plasma modeling, 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 and by comparing its impact within the integrated model, JINTRAC. The profile-averaged RMS of the integrated modeling simulations is &lt;10% for each of the five 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, 104 times faster than the original QuaLiKiz model. Within the JINTRAC integrated modeling 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.</p

Optical transceiver ICs based on 3D die-stacking of opto-electronic devices

Wafer scale fabrication of the 3D stacked transceivers is discussed. Uniform open eye patterns at 10 Gb/s/channel of both 3D stacked transmitter and receiver ICs indicates that the interconnection technology is robust

Overview of JET results for optimising ITER operation

The JET 2019-2020 scientific and technological programme exploited the results of years of concerted scientific and engineering work, including the ITER-like wall (ILW: Be wall and W divertor) installed in 2010, improved diagnostic capabilities now fully available, a major neutral beam injection upgrade providing record power in 2019-2020, and tested the technical and procedural preparation for safe operation with tritium. Research along three complementary axes yielded a wealth of new results. Firstly, the JET plasma programme delivered scenarios suitable for high fusion power and alpha particle (α) physics in the coming D-T campaign (DTE2), with record sustained neutron rates, as well as plasmas for clarifying the impact of isotope mass on plasma core, edge and plasma-wall interactions, and for ITER pre-fusion power operation. The efficacy of the newly installed shattered pellet injector for mitigating disruption forces and runaway electrons was demonstrated. Secondly, research on the consequences of long-term exposure to JET-ILW plasma was completed, with emphasis on wall damage and fuel retention, and with analyses of wall materials and dust particles that will help validate assumptions and codes for design and operation of ITER and DEMO. Thirdly, the nuclear technology programme aiming to deliver maximum technological return from operations in D, T and D-T benefited from the highest D-D neutron yield in years, securing results for validating radiation transport and activation codes, and nuclear data for ITER

Disruption prediction with artificial intelligence techniques in tokamak plasmas

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures

Quasilinear gyrokinetic theory: a derivation of QuaLiKiz

In order to predict and analyse turbulent transport in tokamaks, it is important to model transport that arises from microinstabilities. For this task, quasilinear codes have been developed that seek to calculate particle, angular momentum and heat fluxes, both quickly and accurately. In this tutorial, we present a derivation of one such code known as QuaLiKiz, a quasilinear gyrokinetic transport code. The goal of this derivation is to provide a self-contained and complete description of the underlying physics and mathematics of QuaLiKiz from first principles. This work serves both as a comprehensive overview of QuaLiKiz specifically as well as an illustration for deriving quasilinear models in general

Rapid optimization of stationary tokamak plasmas in RAPTOR: demonstration for the ITER hybrid scenario with neural network surrogate transport model QLKNN

This work presents a fast and robust method for optimizing the stationary radial distribution of temperature, density and parallel current density in a tokamak plasma and its application to first-principle-based modeling of the ITER hybrid scenario. A new solver is implemented in the RAPTOR transport code, enabling direct evaluation of the stationary solution to which the radial plasma profiles evolve. Coupled to a neural network emulation of the quasi-linear gyrokinetic QuaLiKiz transport model (QLKNN-hyper-10D), a first-principle-based estimate of the stationary state of the core plasma can be found at unprecedented computational speed (typically a few seconds on standard hardware). The stationary state solver is then embedded in a numerical optimization scheme, allowing the optimization of tokamak plasma scenarios in only a few minutes. The proposed method is applied to investigate the performance of ITER hybrid scenarios at different values of total plasma current, plasma density and pedestal height and for different power contributions in a heating mix consisting of electron cyclotron and neutral beam heating. Optimizing the radial distribution of electron cyclotron current drive (ECCD) deposition, the q profile is tailored to maximize the fusion gain Q, by maximizing the energy confinement predicted through the first-principles-based transport model, while satisfying q &amp;gt; 1, avoiding sawtooth oscillations. It is found that optimal use of ECCD in ITER hybrid scenarios is to deposit power as close to the core as possible, while maintaining sufficient off-axis current drive to keep q above 1. Upper limits for the fusion gain Q are shown to be constrained either by minimum power requirements for the separatrix power flow to maintain H-mode or by minimum current drive requirements for q profile tailoring. Finally, it is shown that the ITER hybrid scenario operating window is significantly extended by an upgrade of the electron cyclotron power to 40 MW

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

Within integrated tokamak plasma modeling, 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 and by comparing its impact within the integrated model, JINTRAC. The profile-averaged RMS of the integrated modeling simulations is &lt;10% for each of the five 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, 104 times faster than the original QuaLiKiz model. Within the JINTRAC integrated modeling 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.</p