Data acquisition for AI-aided identification of mapped acoustic radiation modes (vor Ort)
* Presenting author
Abstract:
Acoustic radiation modes (ARMs) and their corresponding radiation efficiencies characterize the emission of sound from the surface of a vibrating structure to the air. This gives a more accurate prediction of sound power than using the hypothesis of monopole radiator. Therefore, ARM is widely studied relating to structural dynamics, evaluation of sound power, and active noise control. Previous research shows that the ARMs of different geometries share similar forms. Thus, the assumption is made that the ARMs of a three-dimensional convex geometry can be obtained from the known ARMs of simple geometries, for example spheres. Other than the traditional mapped ARM using Boundary Element Method (BEM), the booming Artificial Intelligence (AI) techniques gain our attention to optimize and accelerate the identification process. In this work a set of virtual data from numerical simulations are acquired for the purpose of AI-aided identification of mapped ARMs. Besides, the numerical simulation is validated with theoretical knowledge.