Characterizing and predicting bluff-body solid fuel ramjet performances via shape design and multi-objective optimization model

Abstract

In this work, we propose a rapid optimization approach to examine its application potential for the design and performance prediction and optimization of a solid fuel ramjet (SFRJ) with a bluff body. For this, the shape of the bluff body is parameterized first using the non-uniform rational B-spline method. We then develop a model for predicting SFRJ performances by incorporating both levy motion-gradient descent and support vector regression methods. It is found that a faster prediction is achievable, while the average error is maintained to be less than 5%. We then develop a multi-objective optimization model by considering the full thrust and minimum total pressure loss (TPL). The optimization model is examined using the non-dominated sorting genetic algorithm. A cost parameter is also created to facilitate the tradeoffs between the thrust and TPL in the Pareto front, when different bluff-body design configurations are considered. The present results reveal that an increase in the cost parameter will elevate the turbulence intensity within the SFRJs while drawing the incoming air closer to the fuel surface, resulting in an increase in thrust and regression rate, but the TPL will also increase. When prioritizing the TPL reduction in the design stage, the optimized solution reduces TPL by 50%. Meanwhile, the net thrust is shown to be decreased by less than 3.5%. Furthermore, flow-field investigation reveals that the improved performance of the optimized SFRJ is due to more uniform flow velocity gradients around the bluff body and a reduced rear vortex, resulting in reduced momentum loss. Our proposed optimization approach's robustness has been further confirmed with consistent performances, as the ramjet inlet speed varies over a broad range. It shows that our approach has great potential to be applied for the SFRJ performance prediction and optimization, being operated under various conditions.