Flow estimation in supersonic turbulent jets

The turbulent jet of an aircraft can generate intense noise exceeding 140 dB, leading to hearing loss and various health issues, underscoring the importance of reducing jet noise. With flow state estimation as a key enabler, a wavepacket-cancellation noise control approach has emerged as a promising method to reduce jet noise. Our work aims to implement advanced data-driven and resolvent-based estimation for a supersonic jet. The study is structured as follows: first, we generate a supersonic jet database containing flow snapshots and acoustic statistics of an ideally-expanded isothermal jet and an over-expanded overheated jet, generated using the large eddy simulations solver CharLES. Second, we present estimation results obtained using a data-driven implementation of a resolvent-based flow estimation method with and without causality enforcement. Various combinations of sensors and targets are tested and analyzed, including different numbers and types of sensors such as pressure and velocity sensors, as well as different target locations in the flow field and acoustics field. The results reinforce the potential of the wavepacket-cancellation approach for mitigating the noise of supersonic jets.