Data driven prediction of the neutral gas pressure in the stellarator Wendelstein 7-X

A machine learning approach, namely symbolic regression (SR), is applied in the stellarator Wendelstein 7-X (W7-X), to investigate the effect of six plasma parameters (line integrated electron density, heating power, toroidal plasma current, fraction of radiated power, core and edge ion temperatures) on the sub-divertor neutral gas pressure. Based on the data from the OP1.2b experimental campaign, closed-form expressions of the neutral gas pressure in terms of the plasma parameters are deduced for the standard, high iota and high mirror magnetic configurations at three different ports of the exhaust system. While common regression schemes assume a predetermined functional form, SR autonomously discovers, via genetic programming, the functional structure of the model, purely from data. In all cases, the optimized data driven SR framework clearly points out that, in estimating the neutral gas pressure, the most dominant parameters are the electron density and the heating power, while the remaining plasma parameters have minor impact, at least from the statistical point of view and may not be included in the correlations. Balancing model generality, complexity(COMP) and accuracy for all considered magnetic configurations and ports, the proposed closed form expressions contain only the product of electron density and heating power raised at some powers, times a constant. The proposed two-parameter symbolic expressions, exhibiting low COMP and excellent accuracy metrics, provide a practical and analytical tool for the acceleration of the neutral gas pressure calculations, that are otherwise computationally very expensive and for the overall performance assessment of the W7-X exhaust system. They may also contribute to more efficient experimental design and operation. performance assessment of the W7-X exhaust system. They may also contribute to more efficient experimental design and operation.