MACHINE LEARNING REGRESSION OF UNDER-EXPANDED HYDROGEN JETS

The introduction of hydrogen-methane blends as fuel in gas turbines rises concerns on the capability of state-of-art ventilation systems to dilute possible fuel leaks in the enclosures. Traditional numerical methods to perform leak analysis are limited by the number of factors involved, i.e. location and direction of the leak, cross-section area, gas pressure in the pipelines, gas composition, and location of external objects. Hence, this arise the need for novel and fast tools capable for the accurate prediction of fuel dispersion in leak scenarios. To this extent, we propose a novel machine learning approach to model gas leaks. The model is trained on a dataset of numerical simulations accounting for several hydrogen/methane concentrations in the fuel, different storage to ambient pressure ratios at the leak section, and a set of cross-flow ventilation velocities. The architecture of the machine learning model is based on graph neural networks, to solve a node-level regression task predicting fuel concentration in space for different high pressure leak scenarios. The model shows a significant speed-up in predicting fuel dispersion with respect to conventional methodology (0.1 s vs 3.5 h) but the GPU memory requirements proved to be a problem when dealing with 3D domains.