Near-Field Aeroacoustic Shape Optimization at Low Reynolds Numbers

What is it about?

In our study, we investigated a method for reducing noise from aircraft and similar objects by optimizing their shapes. We employed the Mesh Adaptive Direct Search (MADS) technique to achieve this. Our research involved testing this approach on various objects, including deep cavities, cylinders, and airfoils. Through shape optimization, we achieved substantial noise reductions, up to 16.5 decibels in some cases. Notably, we successfully transformed a common airfoil into a completely silent configuration with reduced drag. These findings highlight the effectiveness of our method in enhancing aerodynamic and aeroacoustic design.

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Why is it important?

In this work, we combined the gradient-free MADS optimization algorithm with the high-order flux reconstruction numerical method. We tested the framework for low Reynolds two-dimensional problems. This research is proof of technology for larger-scale industrial problems. Our framework achieved significant noise reduction in a wide range of problems.