Interaction of multiple steps in hybrid rocket engines: Experimental investigation

Abstract

In this article, we investigate the interaction of multiple steps in hybrid rocket fuel grains. Previous studies have shown the potential of single steps to increase the regression rate. In this study, we evaluate if the single step results can be translated to a multi-step approach. Of special interest are the interactions between the different step configurations. Four grain profiles are assembled by using fuel grain segments with different inner diameters, therefore forming a multi-step approximation of the profiles that can easily be manufactured and scaled up. The multi-step grains enhance the regression rate because of increased mixing and convective heat transfer induced by the recirculation zones of the steps. The experimentally obtained regression rate profiles are similar to the single step experimental data. This signifies that they are reproducible and therefore predictable. Most importantly, multiple steps do not interfere negatively with each other, which proves that steps can be used to approximate different fuel grain profiles. We show experimentally that the regression rate can be increased up to 81% by accumulating the regression rate enhancing potential of single steps. Moreover, we developed a genetic algorithm to estimate the spatially resolved Marxman parameters with only one single burn.