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This paper describes how use of multi-objective optimization of pulsating noise and backpressure improved an exhaust silencer for diesel drive equipment. Low frequency pulsating noise and backpressure were simultaneously predicted using one-dimensional fluid dynamics and acoustic analysis by BEM. In addition, an experiment was done to investigate the relation between high frequency noise including flow-induced noise and the dimensions of perforations in silencer pipes. Finally, a prototype of the exhaust silencer was built and examined in order to confirm the effects of these design methods mentioned. As predicted, exhaust noise was reduced without increasing backpressure.

This paper describes the application of statistical energy analysis (SEA) to predicting sound power radiated from co-generation system enclosure. To predict vibration and noise accurately by using SEA, it is important to estimate parameter called loss factors. In this study, loss factors were estimated by power injection method. Next, the noise radiated from enclosure surface was predicted by the obtained vibration and radiation efficiency of enclosure panels. As a result, the calculated sound power was relatively corresponding to measured sound power. Finally, the sound power from modified enclosure was predicted. Coupling loss factors related to a modified subsystem were estimated by ratio of the number of structure modes. By using these steps, the noise from the system was reduced.

This paper describes damping loss factor prediction in statistical energy analysis (SEA) for co-generation system (CGS) enclosures. To accurately predict vibration and noise by SEA, it is important to estimate parameters called the damping and coupling loss factors. In this study, the damping loss factors were estimated by the decay ratio method and a technique for calculating the modal damping ratio that uses a multi-degree of freedom curve fit. The calculated loss factor was applied to the vibration prediction of the co-generation system, and the influence of the internal loss factor calculation method on prediction accuracy was verified.