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.

The use of 13C isotopic distribution as an efficient means to determine the origin of vanillin has been substantiated. Using quantitative 13C NMR, the 13C/12C ratios at all eight carbon positions can be exploited. On a set of 21 samples of vanillin from five different origins, complete discrimination can be achieved. It is shown that, for many purposes, a rapid analysis in which only five sites are used is sufficient. However, improved discrimination using all eight sites is preferable to differentiate between different methods of production from natural ferulic acid or between natural and lignin-derived vanillin on the basis of the 13C/12C ratios characteristic of different origins. The C1 and C8 positions are demonstrated to be the most significant sites for discrimination using principle component analysis. However, aromatic carbon positions make an essential contribution, notably in differentiating between natural and lignin-derived vanillin.

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