Investigation of Mixing Characteristics in Strut Injectors using Modal Decomposition

TL;DR

This study numerically investigates how large-scale vortical structures influence mixing in strut injectors, using modal decomposition techniques to analyze flow dynamics across different configurations and Mach numbers.

Contribution

It introduces a combined use of Proper Orthogonal Decomposition and Dynamic Mode Decomposition to analyze vortex behavior and mixing efficiency in different strut injector geometries.

Findings

Higher shear strength enhances vortex generation and mixing.

SS-0.6 configuration achieves the highest mixing efficiency.

Modal decomposition confirms dominant flow structures and vortex interactions.

Abstract

Effect of large-scale vortical structure on mixing and spreading of shear layer is numerically investigated. Two strut configurations namely Straight & Tapered strut at two convective Mach numbers (Mc = 1.4 & 0.37) for two jet heights (0.6 & 1mm) are investigated. Hydrogen jet is injected through a two-dimensional slot in oncoming coflow at Mach 2. Excellent agreement between simulated and experimental data is witnessed, whereas the instantaneous data reveal the presence of various large-scale structures in the flow field. From the instantaneous field, it becomes apparent that both the geometries have different vortical breakdown locations. It is also noticed that an early onset of vortex breakdown manifests itself into the mixing layer thickness enhancement, effect of which is reflected in overall mixing characteristics. It becomes evident that the shear strength plays an important role in the near field mixing. The higher shear strength promotes the generation of large vortices. The analysis shows that SS-0.6 case offers highest mixing efficiency being dominated by relatively large-scale structures. Eigenmodes obtained through Proper Orthogonal Decomposition (POD) confirm the presence of dominating structures and shed light into the series of events involved in vortex pairing/merging and breakdown. Dynamic modal decomposition (DMD) also strengthens the observation made through the POD.