Prediction of Acoustic Natural Modes and Natural Frequencies Using Deep Learning - Cho, Jae Ho; Lee, Jin Woo (Transactions of the Korean Society of Mechanical Engineers, A, 2022)
In this study, a deep learning-based acoustic analysis method is proposed to predict the acoustic natural modes and natural frequencies of a structure given only its shape information. The effectiveness of the proposed method is proved by applying it to identification of the acoustic characteristics of a vehicle. The acoustic characteristics of a closed space vary depending on the shape, size, and location of the partitions existing therein. Although a designer may possess no knowledge of acoustic theory or acoustic analysis programs, the redesigning time of a mechanical structure, such as a vehicle, can be dramatically shortened if the acoustic characteristics of the candidate shape can be identified. A deep learning model is developed to perform this task on a two-dimensional acoustic cavity. It is trained with appropriate input and output data to verify the feasibility, and subsequently applied to the two-dimensional vehicle model to demonstrate its validity.
Keywords: Acoustic analysis; Acoustic characteristic; Acoustic natural frequency; Acoustic natural mode; Convolutional neural network; Deep learning; Natural modes; Partition; Two-dimensional; Vehicle compartment; Acoustic Natural Frequency; Acoustic Natural Mode; Convolutional Neural Network; Deep Learning; Partition; Vehicle Compartment