Far-Field Aeroacoustic Shape Optimization Using Large Eddy Simulation
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arXiv
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| Format: | Preprint |
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2025
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| author | Hamedi, Mohsen Vermeire, Brian |
| author_facet | Hamedi, Mohsen Vermeire, Brian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05709 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Far-Field Aeroacoustic Shape Optimization Using Large Eddy Simulation Hamedi, Mohsen Vermeire, Brian Fluid Dynamics 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. |
| title | Far-Field Aeroacoustic Shape Optimization Using Large Eddy Simulation |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2501.05709 |