Data Driven Models for the Design of Rocket Injector Elements

Abstract

Improving the predictive capabilities of reduced-order models for the design of injector and chamber elements of rocket engines could greatly improve the quality of early rocket chamber designs. In the present work, we propose an innovative methodology that uses high-fidelity numerical simulations of turbulent reactive flows and artificial intelligence for the generation of surrogate models. The surrogate models that were generated and analyzed are deep learning networks trained on a dataset of 100 large eddy simulations of a single-shear coaxial injector chamber. The design of experiments was created considering three design parameters: chamber diameter, recess length, and oxidizer–fuel ratio. The paper presents the methodology developed for training and optimizing the data-driven models. Fully connected neural networks (FCNNs) and U-Nets were utilized as surrogate-modeling technology. Eventually, the surrogate models for the global quantity, average, and root mean square fields were used in order to analyze the impact of the length of the post’s recess on the performances obtained and the behavior of the flow.