Toward Resolvent-Based Estimation and Control of Wavepackets in Supersonic Turbulent Jets

High-speed-jet turbulent mixing noise remains a challenging problem, and here we aim to reduce it using a wavepacket-cancellation strategy. This approach is enabled by the recently developed resolvent-based estimation and control framework, which uses near-nozzle sensors to detect noise-generating wavepackets and suppress them via actuation. This paper presents three main results toward this larger goal: (i) data-driven estimation for a Mach 1.5 supersonic jet using large-eddy simulations to identify coherent structures and inform sensor-target placement; (ii) resolvent-based estimation for the linearized jet, which achieves reasonable accuracy in reconstructing relevant flow features from limited sensor data; and (iii) preliminary resolvent-based control for the linearized jet, demonstrating a 34% reduction in the root mean square of streamwise-momentum fluctuations using only one sensor and one actuator. These findings demonstrate the potential of the resolvent-based framework for mitigating noise-generating wavepacket structures in supersonic jets and provide an important foundation for future computational and experimental investigations.