High-pressure jet injection into quiescent air is a challenging fluid dynamics problem in the field of aerospace engineering. Although plenty of experimental, theoretical, and numerical studies have been conducted to explore this flow, there is a dearth of literature detailing the flow evolution and instability characteristics, which is vital to the mixing enhancement design and jet noise reduction. In this paper, a density-based solver for compressible supersonic flow, astroFoam, is developed based on the OpenFOAM library. Large-eddy simulations of highly underexpanded jets with nozzle pressure ratios from 5.60 to 11.21 at a Reynolds number around 10(5) are carried out with a highresolution grid. A grid-convergence study has been conducted to confirm the fidelity of the large-eddy simulation results. The large-eddy simulation results have also been validated against available literature data in terms of the time-averaged near-field properties of underexpanded jets. The turbulent transition processes are revealed based on the instantaneous flow features and are quantitatively resolved according to the jet penetration and maximum width. The vorticity analysis is conducted to understand the turbulent transition mechanism, and it is found that the vortex stretching term plays a leading role on the distortion of the vortex rings in the near field of the jets. The dominant instability modes of jets, visualized by helicity, are quantitatively revealed based on the spectrum and relative phase of pressure fluctuation. The single helical modes corresponding to a phase angle close to +/- 180 deg with the 1 + 1 helices are dominant for nozzle pressure ratios of 5.60 and 7.47, whereas the complex and multiple helices for the other two higher nozzle pressure ratios are due to the superposition of the single and double helical modes. In addition, the performance of the coarse mesh and different subgrid-scale models on capturing the dominant instability characteristics in large-eddy simulation of underexpanded jets is investigated.

The project was supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (grant 10621202) and the National Natural Science Foundation of China (grant 11502270). The authors are also grateful to National Supercomputer Center in Tianjin for providing computational resources.