Abstract
A strut injector for burning hydrogen has been optimised using an automated workflow system. The design process began with a parametric CAD model that allowed for various design features to be altered and various different designs to be generated from a given list of parameters. In order to choose the optimal set of injectors to cover the design space, an optimised design of experiments (DOE) method was used to automatically choose the parameters that best spanned the design space. One hundred candidate designs were chosen and a script used to generate a series of stereolithography (STL) files for each design. The STL files were then uploaded to a supercomputer for CFD analysis. For each of the 100 designs, a 4 step process was followed to generate the required data, this included a automated mesh generation step, field initialisation step, mesh adaptation step and finally an LES all within the YALES2 numerical framework. These 400 simulations were automated using an automatic workflow management process which limited the quantity of human intervention required and massively boosted productivity. In order to reduce the time required for post-processing and the amount of data required, the simulations relied heavily on a on-the-fly post-processing methodology which reduced the complex time-unsteady flow fields to a small number of quantified outputs of interest that measured the suitability of each design such as the pressure drop across the injector and the efficiency of the mixing process. At the conclusion of these simulations, automated scripts translated these outputs into a smaller set of parameters that could be used to compare each design and allow subsequent optimisation and surrogate modelling. Several surrogate modelling methods were attempted with mixed results however a simple classification methodology quickly identified the parameters of interest.
