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Statistical energy analysis of coupled plate systems with low modal density and low modal overlap

机译:低模态密度和低模态重叠的耦合板系统的统计能量分析

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Finite element methods, experimental statistical energy analysis (ESEA) and Monte Carlo methods have been used to determine coupling loss factors for use in statistical energy analysis (SEA). The aim was to use the concept of an ESEA ensemble to facilitate the use of SEA with plate subsystems that have low modal density and low modal overlap. An advantage of the ESEA ensemble approach was that when the matrix inversion failed for a single deterministic analysis, the majority of ensemble members did not encounter problems. Failure of the matrix inversion for a single deterministic analysis may incorrectly lead to the conclusion that SEA is not appropriate. However, when the majority of the ESEA ensemble members have positive coupling loss factors, this provides sufficient motivation to attempt an SEA model. The ensembles were created using the normal distribution to introduce variation into the plate dimensions. For plate systems with low modal density and low modal overlap, it was found that the resulting probability distribution function for the linear coupling loss factor could be considered as lognormal. This allowed statistical confidence limits to be determined for the coupling loss factor. The SEA permutation method was then used to calculate the expected range of the response using these confidence limits in the SEA matrix solution. For plate systems with low modal density and low modal overlap, relatively small variation/uncertainty in the physical properties caused large differences in the coupling parameters. For this reason, a single deterministic analysis is of minimal use. Therefore, the ability to determine both the ensemble average and the expected range with SEA is crucial in allowing a robust assessment of vibration transmission between plate systems with low modal density and low modal overlap. (C) 2002 Elsevier Science Ltd. [References: 30]
机译:有限元方法,实验统计能量分析(ESEA)和蒙特卡洛方法已用于确定耦合损耗因子,以用于统计能量分析(SEA)。目的是使用ESEA集成的概念来促进SEA与低模态密度和低模态重叠的板子系统的结合使用。 ESEA集成方法的优点是,当单个确定性分析的矩阵求逆失败时,大多数集成成员都不会遇到问题。单个确定性分析的矩阵求逆失败可能会错误地得出结论,认为SEA不适当。但是,当大多数ESEA合奏成员具有正的耦合损耗因子时,这将提供足够的动机尝试SEA模型。使用正态分布创建了合奏,以将变化引入板尺寸。对于具有低模态密度和低模态重叠的板系统,发现线性耦合损耗因子的概率分布函数可以视为对数正态。这允许确定耦合损耗因子的统计置信极限。然后,使用SEA矩阵解决方案中的这些置信度限制,使用SEA置换方法来计算预期的响应范围。对于具有低模态密度和低模态重叠的平板系统,相对较小的物理特性变化/不确定性会导致耦合参数存在较大差异。因此,单一确定性分析的用途很少。因此,使用SEA确定总体平均值和预期范围的能力对于允许对具有低模态密度和低模态重叠的板系统之间的振动传递进行可靠的评估至关重要。 (C)2002 Elsevier Science Ltd. [参考:30]

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