Application of Digital Twin Technology to Aeronautical Combustion: A Case Study on Hydrogen Microinjectors

Abstract The rise of alternative fuels leads to numerous new possible types of injection technology for gas turbine combustion. One promising candidate is microinjection, which relies on the creation of multiple miniaturized flamelets in order to reduce NOx production. From a design and engineering perspective, new sets of tools of various fidelity are needed to make the design screening step faster and more exhaustive. A reduced-order model (ROM) based on the OpenMeasure library and NEXT STEP has been implemented in order to create a digital twin of hydrogen micro-injectors. The ROM is based on either Sparse Sensing or Kriging methodology, both involving a Proper Orthogonal Decomposition. This approach has been carried out on 26 designs, where several geometrical parameters (e.g. number of fuel injection holes, aspect ratio, etc.) and operating conditions (i.e. atmospheric and high pressure, equivalence ratio, and fuel mass flow rate) are varied. The prediction of fields (e.g. temperature, OH mass fraction, etc.) via the reduced model was assessed using 33 RANS simulations, the latter allowing to establish a database of micro-injector behaviour. The RANS approach has been validated against both experimental results and Large-Eddy Simulations. A selection of model inputs was made based on an assessment of the model’s predictive accuracy using the Kriging estimation method. The predictions of the reduced-order model showed qualitative agreement with the reference data.