Optimization of a Hydrogen-Fueled Parametric Strut Injector Using an Automated Workflow Computational Fluid Dynamics Method

Abstract

A strut injector for burning hydrogen has been optimized using an automated workflow system. In order to choose the optimal set of injectors to cover the design space, an optimized 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 was used to generate a series of stereolithography (STL) files for each design. The STL files were then uploaded to a supercomputer for computational fluid dynamics analysis. For each of the 100 designs, a four step process was followed to generate the required data, and this included an automated mesh generation step, a field initialization step, a mesh adaptation step, and finally an large eddy simulation all within the yales2 numerical framework. In order to reduce the time required for postprocessing and the amount of data required, the simulations relied heavily on an on-the-fly postprocessing 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 optimization and surrogate modeling. Several surrogate modeling methods were attempted with mixed results; however, a simple classification methodology quickly identified the parameters of interest.