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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The European DEMO power reactor is currently under conceptual design within the EUROfusion Consortium. One of the most critical activities is the engineering of the plasma-facing components (PFCs) covering the plasma chamber wall, which must operate reliably in an extreme environment of neutron irradiation and surface heat and particle flux, while also allowing sufficient neutron transmission to the tritium breeding blankets. A systems approach using advanced numerical analysis is vital to realising viable solutions for these first wall and divertor PFCs. Here, we present the system requirements and describe bespoke thermo-mechanical and thermo-hydraulic assessment procedures which have been used as tools for design. The current first wall and divertor designs are overviewed along with supporting analyses. The PFC solutions employed will necessarily vary around the wall, depending on local conditions, and must be designed in an integrated manner by analysis and physical testing.

The design and development of a novel plasma facing component (for fusion power plants) is described. The component uses the existing "monoblock" construction which consists of a tungsten "block" joined via a copper interlayer to a through CuCrZr cooling pipe. In the new concept the interlayer stiffness and conductivity properties are tuned so that stress in the principal structural element of the component (the cooling pipe) is reduced. Following initial trials with off-the-shelf materials, the concept was realized by machined features in an otherwise solid copper interlayer. The shape and distribution of the features were tuned by Finite Element (FE) analyses subject to ITER Structural Design Criterion In-Vessel Components (SDC-IC) design rules. Proof of concept mock-ups were manufactured using a two stage brazing process verified by tomography and micrographic inspection. Full assemblies were inspected using ultrasound and thermographic (SATIR) test methods at ENEA and CEA respectively. High heat flux tests using IPP's GLADIS facility showed that 200 cycles at 20 MW/m 2 and five cycles at 25 MW/m 2 could be sustained without apparent component damage. Further testing and component development is planned.

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