The evaluation of hydrogenic retention in present tokamaks is of crucial importance to estimate the expected tritium (T) vessel inventory in ITER, limited from safety considerations to 350g. In the framework of the European Task Force on Plasma Wall Interaction (EU TF on PWI) efforts are underway to investigate gas balance and fuel retention during discharges, and to compare the data obtained with those from post-mortem analysis of in-vessel components exposed over whole experimental campaigns. This paper summarizes the principal findings from coordinated studies on gas balance and fuel retention from a number of European tokamaks, viz. ASDEX-Upgrade (AUG), JET, TEXTOR and Tore Supra (TS). For most devices, the long-term retention fraction deduced from integrated particle balance is ∼ 10-20 %. This is larger than the ~3-4% deduced from post mortem analysis of plasma facing components (PFCs). However, from the database available for tokamaks with their main PFCs made of carbon, the important conclusion is that the T inventory limit (set by the working guideline for operations) could be reached in ITER within fewer than 100 discharges. This, therefore, would seriously impact on operation of the device unless efficient T removal processes were developed.
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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