Plasmon dispersion in metal nanoparticle chains from angle-resolved scattering

We present angle and frequency resolved optical extinction measurements to determine the dispersion relation of plasmon modes on Ag and Au nanoparticle chains with pitches down to 75 nm. The large splitting between transverse and longitudinal modes and the band curvature are inconsistent with reported electrostatic near-field models, and confirm that far-field retarded interactions are important, even for $\lambda/5$-sized structures. The data imply that lower propagation losses, larger signal bandwidth and larger maximum group velocity then expected can be achieved for wave vectors below the light line. We conclude that for the design of optical nanocircuits coherent far-field couplings across the entire circuit need to be considered, even at subwavelength feature sizes.Comment: 4 pages, 4 figures, colo

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

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

Quasilinear gyrokinetic theory: A derivation of QuaLiKiz

In order to predict and analyze 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.Comment: 52 page

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

Mathematical equivalence of non-local transport models and broadened deposition profiles

Old and recent experiments show that there is a direct response to the heating power of transport observed in modulated electron cyclotron heating (ECH) experiments both in tokamaks and stellarators, which is commonly known as non-local transport. This is most apparent for modulated experiments in stellarators such as LHD and W7-AS. We show that this power dependence and its corresponding experimental observations such as the so-called hysteresis in flux [Inagaki, NF, 113006, 2013] can be reproduced by broadened ECH deposition profiles. In other words, many mathematical models proposed to describe non-local transport are equivalent to an deposition (effective) profile in its linearized forms [vanBerkel, NF, 106042, 2018]. This also connects with new insights on microwave scattering due to density fluctuations in the edge plasma which shows that in reality the deposition profiles are much broader than expected [Chellai, PRL, 105001, 2018] but it is unclear if this effect is sufficient to explain non-local transport. These relationships can be further studied by separating the transport in a slow (diffusive) and a fast (heating/non-local) time-scale using perturbative experiments

Unusual Pseudogap-like Features Observed in Iron Oxypnictide Superconductors

We have performed a temperature-dependent angle-integrated laser photoemission study of iron oxypnictide superconductors LaFeAsO:F and LaFePO:F exhibiting critical transition temperatures (Tc's) of 26 K and 5 K, respectively. We find that high-Tc LaFeAsO:F exhibits a temperature-dependent pseudogap-like feature extending over ~0.1 eV about the Fermi level at 250 K, whereas such a feature is absent in low-Tc LaFePO:F. We also find ~20-meV pseudogap-like features and signatures of superconducting gaps both in LaFeAsO:F and LaFePO:F. We discuss the possible origins of the unusual pseudogap-like features through comparison with the high-Tc cuprates

Fast modeling of turbulent transport in fusion plasmas using neural networks

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.Comment: 18 pages, 11 figures, Physics of Plasmas, ICDDPS 2019 conference pape