Feasibility of charge exchange spectroscopy fast helium measurements on ITER

The feasibility to measure fast alpha particles using Active Charge Exchange Recombination Spectroscopy (CXRS) on ITER is investigated. Through modelling of the charge exchange spectral line for fast ions together with the expected background emission, the signal-to-noise ratio has been calculated as a function of the diagnostic design parameters. Combining the CXRS data from both the heating and the diagnostic neutral beams on ITER, information on the fast ion energy spectrum up to 1 MeV can be obtained for the parameters of the ITER core CXRS diagnostic design, provided that the signal is binned in 100 keV bins and a time resolution of Isec is used.</p

An optimization approach coupling pre-processing with model regression for enhanced chemometrics

Chemometric methods are broadly used in the chemical and biochemical sectors. Typically, derivation of a regression model follows data preprocessing in a sequential manner. Yet, preprocessing can significantly influence the regression model and eventually its predictive ability. In this work, we investigate the coupling of preprocessing and model parameter estimation by incorporating them simultaneously in an optimization step. Common model selection techniques rely almost exclusively on the performance of some accuracy metric, yet having a quantitative metric for model robustness can prolong model up-time. Our approach is applied to optimize for model accuracy and robustness. This requires the introduction of a novel mathematical definition for robustness. We test our method in a simulated set up and with industrial case studies from multivariate calibration. The results highlight the importance of both accuracy and robustness properties and illustrate the potential of the proposed optimization approach toward automating the generation of efficient chemometric models

Probabilistic predictions for partial least squares using bootstrap

Modeling the uncertainty in partial least squares (PLS) is made difficult because of the nonlinear effect of the observed data on the latent space that the method finds. We present an approach, based on bootstrapping, that automatically accounts for these nonlinearities in the parameter uncertainty, allowing us to equally well represent confidence intervals for points lying close to or far away from the latent space. To show the opportunities of this approach, we develop applications in determining the Design Space for industrial processes and model the uncertainty of spectroscopy data. Our results show the benefits of our method for accounting for uncertainty far from the latent space for the purposes of Design Space identification, and match the performance of well established methods for spectroscopy data

Method to obtain absolute impurity density profiles combining charge exchange and beam emission spectroscopy without absolute intensity calibration

Investigation of impurity transport properties in tokamak plasmas is essential and a diagnostic that can provide information on the impurity content is required. Combining charge exchange recombination spectroscopy (CXRS) and beam emission spectroscopy (BES), absolute radial profiles of impurity densities can be obtained from the CXRS and BES intensities, electron density and CXRS and BES emission rates, without requiring any absolute calibration of the spectra. The technique is demonstrated here with absolute impurity density radial profiles obtained in TEXTOR plasmas, using a high efficiency charge exchange spectrometer with high etendue, that measures the CXRS and BES spectra along the same lines-of-sight, offering an additional advantage for the determination of absolute impurity densities