Application of Kriging Surrogate Modeling for Structural Optimization in Air Duct Noise Reduction Systems

This study investigates the optimization of structural parameters for air duct noise reduction systems based on sound quality, aimed at reducing acoustic finite element simulation time on the COMSOL Multiphysics platform. A Kriging surrogate model was developed to facilitate structural optimization. The iterative pointwise Kriging model established a mapping between muffler structural parameters and sound quality metrics. By integrating optimization algorithms, the dual chamber composite air duct muffler was optimized, resulting in improved insertion loss and reduced sound pressure levels at the outlet, while effectively lowering computational costs and enhancing efficiency.