Recurrence network analysis for uncovering dynamic transition of thermo-acoustic instability of supercritical hydrocarbon fuel flow

Abstract

Characterizing the transition of thermo-acoustic instability of supercritical hydrocarbon fuel flow is a fundamental problem eliciting a great deal of attention from different disciplines. We experimentally and theoretically investigate the transition process between thermo-acoustic stability and instability. The method of recurrence network is applied to analyze the pressure time series of supercritical hydrocarbon fuel flow. As a result, we can distinguish a thermo-acoustic transition process from normal signals by real-time detecting the complexity variation of pressure signals of fuel in cooling channels with the recurrence network method. Then, we construct the recurrence network from experimental data under the stable, transition and unstable states, and investigate the degree distribution and motifs distribution. We find that the degree distribution and motifs distribution allow quantitatively uncovering the complexity dynamic process. We investigate the during time of transition process, and find that the mass flow rate and the inlet pressure will make an influence on the transition time. These findings present a first step towards an improved understanding on the transition of thermo-acoustic instability from a complex network perspective. Moreover, the investigation on the transition of thermo-acoustic instability under supercritical pressure condition offers guidance on the control of scramjet fuel supply, which can secure stable fuel flowing in regenerative cooling system.