2023/12/07 by Mohsen Hamedi, Brian C. Vermeire, Hamedi, Mohsen +1 · 1 citation
Engineering · Physics and Astronomy · #Aerodynamics and Acoustics in Jet Flows #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Fluid Dynamics and Turbulent Flows #Model Reduction and Neural Networks
paper · pdf · doi:10.48550/arxiv.2312.14167
openalex publication_date 2023/12/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
We present an aeroacoustic shape optimization framework that relies on high-order Flux Reconstruction (FR), the gradient-free Mesh Adaptive Direct Search (MADS) optimization algorithm, and Large Eddy Simulation (LES). Our parallel implementation ensures consistent runtime for each optimization iteration, regardless of the number of design parameters, provided sufficient resources are available. The objective is to minimize the Overall Sound Pressure Level (OASPL) at a near-field observer by computing it directly from the flow field. We evaluate this framework across three problems. First, an open deep cavity is considered at a free-stream Mach number of M_∞=0.15 and Reynolds number of Re=1500, reducing the OASPL by 12.9~dB. Next, we considered tandem cylinders at Re=1000 and M_∞=0.2, achieving over 11~dB noise reduction by optimizing cylinder spacing and diameter ratio. Lastly, a baseline NACA0012 airfoil at Re=23000 and M_∞=0.2 is optimized to generate a new 4-digit NACA airfoil at an appropriate angle of attack to minimize the OASPL while ensuring the baseline time-averaged lift coefficient is maintained and prevent any increase in the baseline time-averaged drag coefficient. The OASPL and mean drag coefficient are reduced by 5.7~dB and more than 7%, respectively. These results highlight the feasibility and effectiveness of our aeroacoustic shape optimization framework.