Optimization of Supersonic Jet Noise Using a Reynolds-Averaged Navier-Stokes Approach

The objective of this proposal is the development and demonstration of a cost-effective high-fidelity aeroacoustic design tool for future commercial supersonic nozzle designs and installations. Although eddy-resolving CFDmethods for computing high-speed jet noise are available, such methods are computationally expensive and are currently deemed impractical for use in a design optimization loop. On the other hand, the prediction of turbulence generated noise using the RANSequations provides a less accurate but more cost-effective approach for practical design problems, wherein the turbulence length and time scales needed to model the local noise source terms can be extracted from the RANS turbulence model solution, as performed by the NASA JeNo code. In this Phase 1 proposal, we seek to demonstrate the feasibility of using the exact discrete adjoint of a coupled RANS-JeNo turbulent noise prediction methodology for optimizing far-field acoustic objectives of jet noise. Based on our previous experience developing an unsteady RANS-FWH (Ffowcs Williams-Hawkings) far-field acoustic optimization capability, the Phase 1 proposal targets the formulation of the turbulence noise source terms used by the JeNo code, along with the discrete adjoint of these terms within an existing adjoint-enabled RANS solver. The immediate goal will be to demonstrate the possibility of reducing these noise sources through nozzle shape optimization. In Phase 2, this capability will be linked with the NASA JeNo code, and the remaining terms for the formulation of the discrete adjoint of the coupled RANS-JeNo simulation capability will be implemented and used to perform optimization of far-field noise signatures for realistic nozzle configurations. By targeting the specific terms that drive the noise propagation in the JeNo formulation, our Phase 1 approach will demonstrate the feasibility of using a fully coupled RANS-JeNo code for cost-effective gradient-based jet noise optimization.