As part of the ITER Design Review, the physics requirements were reviewed and as appropriate updated. The focus of this paper will be on recent work affecting the ITER design with special emphasis on topics affecting near-term procurement arrangements. This paper will describe results on: design sensitivity studies, poloidal field coil requirements, vertical stability, effect of toroidal field ripple on thermal confinement, heat load requirements for plasma-facing components, edge localized modes control, resistive wall mode control, disruptions and disruption mitigation.
CREATE-L, a simple and reliable linearized plasma response model for the control of the plasma current, position and shape in tokamaks, is presented. The basic assumption made is that the plasma behaviour is described using three degrees of freedom, related to the total plasma current, internal inductance and poloidal beta. The state variables are the coil and plasma currents; the inputs are the applied voltages, whereas poloidal beta and internal inductance play the role of disturbances. The outputs are field and flux values and some basic plasma parameters. The model is tested against non-linear codes and validated via comparison with experimental results.
In the European fusion roadmap, ITER is followed by a demonstration fusion power reactor (DEMO), for which a conceptual design is under development. This paper reports the first results of a coherent effort to develop the relevant physics knowledge for that (DEMO Physics Basis), carried out by European experts. The program currently includes investigations in the areas of scenario modeling, transport, MHD, heating & current drive, fast particles, plasma wall interaction and disruptions.
Heavy Neutral Leptons (HNLs) are hypothetical particles predicted by many extensions of the Standard Model. These particles can, among other things, explain the origin of neutrino masses, generate the observed matter-antimatter asymmetry in the Universe and provide a dark matter candidate. The SHiP experiment will be able to search for HNLs produced in decays of heavy mesons and travelling distances ranging between O(50 m) and tens of kilometers before decaying. We present the sensitivity of the SHiP experiment to a number of HNL's benchmark models and provide a way to calculate the SHiP's sensitivity to HNLs for arbitrary patterns of flavour mixings. The corresponding tools and data files are also made publicly available.
In this paper, a coupling strategy based on the control surface concept is used to self-consistently couple linear MHD solvers to 3D codes for the eddy current computation of eddy currents in the metallic structures surrounding the plasma. The coupling is performed by assuming that the plasma inertia (and, with it, all Alfven wave-like phenomena) can be neglected on the time scale of interest, which is dictated by the relevant electromagnetic time of the metallic structures. As is shown, plasma coupling with the metallic structures results in perturbations to the inductance matrix operator. In particular, by adopting the Fourier decomposition in poloidal and toroidal modes, it turns out that each toroidal mode can be associated with a matrix (additively) perturbing the inductance matrix that commonly describes the magnetic coupling of currents in vacuum. In this way, the treatment of resistive wall modes instabilities of various toroidal mode numbers and their possible cross-talk through the currents induced in the metallic structures can be easily studied.
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.
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