Optimal Eigenvectors Design for Structural Noise Reduction Through Adaptive Sandwich Algorithm

Abstract

The purpose of this research is to investigate the feasibility of utilizing the adaptive sandwich algorithm to find the optimal left and right eigenvectors for active structural noise reduction. As depicted in the previous studies, the structural acoustic radiation depends on the structural vibration behavior, which is strongly related to both the left eigenvectors (concept of disturbance rejection capability) and right eigenvectors (concept of mode shape distributions) of the system, respectively. In this research, a novel adaptive sandwich algorithm is developed for determining the optimal combination of left and right eigenvectors of the structural system. The sound suppression performance index (SSPI) is defined by combining the orthogonality index of left eigenvectors and the modal radiation index of right eigenvectors. Through the proposed adaptive sandwich algorithm, both the left and right eigenvectors are adjusted such that the SSPI decreases, and therefore one can find the optimal combination of left and right eigenvectors of the closed-loop system for structural noise reduction purpose. The optimal combination of left-right eigenvectors is then synthesized to determine the feedback gain matrix of the closed-loop system. The result of the active noise control shows that the proposed method can significantly suppress the sound pressure radiated from the vibrating structure.