[Abstract] Statistical Energy Analysis (SEA) is a solution method for approaching vibroacoustic problems in the medium-high frequency range. SEA system is described via an energy balance equations set, from which the energy associated to the subsystems modelling the structure can be evaluated. Indication on the preferential paths along which energy is transferred can not be promptly obtained by relying only on the data coming from usual SEA analysis, if not throughout a very slow, lengthy and not consistent post-processing work. A fast and reliable knowledge on this matter is known to be quite valuable to identify and design the most efficient control techniques able to reduce interior noise in a closed environment. Contrary to existing techniques, the approach followed herein is based on the detection of the main energy transmission paths by taking into account the actual direction of Energy Flow, for which is known that power flows from subsystems with higher modal energy density to subsystems with lower modal energy density. To this purpose, the original SEA system is transformed into an equivalent suitable digraph with a number of nodes equal to the SEA subsystems and connections with weight factors reflecting the actual energy flow ratio coming out from each node. A path identification algorithm based on Eppstein shortest path detection method has been developed in Matlab environment to extrapolate all possible paths in the global system in order to classify the main paths according to the total energy transferred to a target subsystem. The emphasis on this new approach is aimed at the identification of the principal contributors to cabin noise built up when structureborne path is the main mechanism involved in the fluid-structure interaction. This information can be regarded as valuable to start a cabin noise reduction process performed by blocking or modifying at the origin the most important paths. The path identification technique has been applied within Friendcopter European Project on the SEA model of Agusta A109 helicopter mock-up.
[1]
Juha Plunt,et al.
Variation of Vehicle NVH Properties due to Component Eigenfrequency Shifting - Basic Limits of Predictability
,
1995
.
[2]
Robert J. Bernhard,et al.
Measurement of the Statistical Variation of Structural-Acoustic Characteristics of Automotive Vehicles
,
1993
.
[3]
Evan B. Davis,et al.
By Air by SEA
,
2004
.
[4]
Robert J.M. Craik.
Sound transmission paths through a statistical energy analysis model
,
1990
.
[5]
David Eppstein,et al.
Finding the k Shortest Paths
,
1999,
SIAM J. Comput..