Acoustic mufflers play a critical role in controlling noise across various industries, including automotive, aerospace, and medical systems. Effective noise reduction is essential not only for performance and regulatory compliance but also for user comfort, safety, and well-being. In many applications, muffler design is constrained by strict limitations on size, weight, and geometry. This is particularly the case in designing life support systems for aerospace applications. This study explores the use of artificial intelligence (AI) algorithms to accelerate and streamline the design and optimization of acoustic mufflers for aerospace applications. Focusing on improving transmission loss, the AI model adjusts key geometric parameters of expansion chambers within predefined constraints. The optimization process targets dominant machinery noise frequencies as well as frequency ranges important to human auditory perception. Results indicate that AI-assisted design can significantly enhance noise attenuation performance, reduce design time, and meet space constraints without adding unnecessary complexity.
Zhang et al. (Wed,) studied this question.