Data acquisition for AI-aided identification of mapped acoustic radiation modes

Acoustic Radiation Modes (ARMs) and their corresponding radiation efficiencies characterize the emission of sound from the surface of a vibrating structure to the air [1]. This gives a more accurate prediction of sound power than using the hypothesis of monopole radiator. Previous research shows that the ARMs of different geometries are similar. 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 [2]. Other than the traditional mapped ARM using Boundary Element Method (BEM) [3], 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 AI-aided identification of mapped ARMs. Besides, the numerical simulation is validated with theoretical knowledge.