Closed-loop jet noise control via resolvent-based wavepacket models
Abstract
The objective of this project is evaluate the potential of a new wavepacket-cancellation jet noise mitigation strategy using a state-of-the-art estimation and control framework paired with high-fidelity simulations. Tactical military aircraft produce intense noise levels, often in excess of 140 dB, that are dangerous to airstrip and flight deck personnel and disruptive in communities adjacent to military bases. A primary source of this noise is the high-speed exhaust from jet engines, and numerous investigations spanning over fifty years have demonstrated the key role of organized motions within the jet, called wavepackets, in producing the most intense portions of the overall noise levels. Despite this insight, previous passive and active noise control efforts have achieved reductions of only a few dB by indirectly modifying the wavepacket structures. This suggests the need for new closed-loop control strategies that directly target the noise-producing wavepackets. To this end, we propose a wavepacket-cancellation noise control strategy. The feasibility of such an approach is supported by recent experiments showing that time-varying actuation generates new wavepackets that superimpose linearly with the natural wavepackets already in the jet. Thus, the natural wavepackets could be linearly canceled by applying near-nozzle actuation that produces wavepackets with the opposite sign. We will evaluate this approach for a series of round and rectangular supersonic jets by using a recently developed optimal estimation and control framework based on resolvent analysis of the linearized Navier-Stokes equations to determine the best-case noise reduction as a function of sensor and actuator type and arrangement, and we will subsequently test the optimal solution within large-eddy simulations of the controlled jets. If successful, this work will enable real-time, closed-loop control of turbulent jet noise for the first time
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 13, 2022
- Source ID
- N000142212561
Entities
People
- Aaron Towne
Organizations
- Board of Regents of the University of Michigan
- Office of Naval Research
- United States Navy