The position of vibration sensors influences the modal identification quality of flexible structures for a given number of sensors, and the quality of modal identification is usually estimated in terms of correlation between the natural modes using the modal assurance criterion (MAC). The sensor placement optimization is characterized by the fact that the design variables are not continuous but discrete, implying that the conventional sensitivity-driven optimization methods are not applicable. In this context, this paper presents the application of genetic algorithm to the sensor placement optimization for improving the modal identification quality of flexible structures. A discrete-type optimization problem using genetic algorithm is formulated by defining the sensor positions and the MAC as the design variables and the objective function, respectively. The proposed GA-based evolutionary optimization method is validated through the numerical experiment with a rectangular plate, and its excellence is verified from the comparison with the cases using different modal correlation measures.
The procedure to estimate the sources of noise and vibrations in a typical drum-type washing machine was presented. The sources should be identified to predict the radiated noise with computational model of structure. Source identification techniques based on singular decomposition were implemented using the measured signals of accelerometers and microphones. The finite element analysis and indirect boundary element analysis were implemented to predict the structural vibrations and the acoustic pressures at the field points. The predicted results by only structural sources were compared with those by both structural and acoustical sources. It was verified that not only the structural-borne source but also air-borne source should be considered to predict the radiated noise with better accuracy. The contribution analysis with respect to the transfer path was also preformed.
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