Far-Field Aeroacoustic Shape Optimization Using Large Eddy Simulation

TL;DR

This paper introduces a comprehensive shape optimization framework combining LES, FW-H, and MADS to effectively reduce far-field noise and drag in airfoil design, demonstrating significant improvements in aerodynamic performance.

Contribution

The study develops a parallel, gradient-free optimization framework integrating LES and FW-H for accurate far-field noise prediction in aeroacoustic shape optimization.

Findings

Achieved 5.9 dB reduction in OASPL

Reduced mean drag by over 14%

Maintained mean lift coefficient

Abstract

This study presents a shape optimization framework that combines a Flux Reconstruction (FR) spatial discretization, Large Eddy Simulation (LES), the Ffowcs-Williams and Hawkings (FW-H) formulation, and the gradient-free Mesh Adaptive Direct Search (MADS) optimization algorithm. We emphasize the necessity of duplicating the data surface to achieve accurate far-field noise prediction in spanwise periodic problems using the FW-H formulation. The proposed parallel implementation of the optimization framework ensures consistent runtime per optimization iteration, regardless of the number of design parameters, thereby addressing a common limitation of many gradient-free algorithms. The framework is demonstrated through far-field aeroacoustic shape optimization of NACA 4-digit airfoils at a Reynolds number of $23,000$. The objective function minimizes the Overall Sound Pressure Level (OASPL) at a far-field observer positioned $10$ unit chords below the trailing edge, while preserving the mean lift coefficient and reducing the mean drag coefficient. The optimized airfoil achieves an OASPL reduction of $5.9~dB$ and over $14\%$ decrease in mean drag, while maintaining the mean lift coefficient. These results underscore the feasibility and effectiveness of the proposed approach for practical shape optimization applications.