Interior Noise Design of a Light Rail Vehicle Using Statistical Energy Analysis

Paper #:
  • 2015-01-2300

Published:
  • 2015-06-15
DOI:
  • 10.4271/2015-01-2300
Citation:
Fiedler, R., Musser, C., and Cuchý, P., "Interior Noise Design of a Light Rail Vehicle Using Statistical Energy Analysis," SAE Technical Paper 2015-01-2300, 2015, https://doi.org/10.4271/2015-01-2300.
Pages:
6
Abstract:
This paper addresses the NVH design of a light rail vehicle whose maximum allowable interior SPL levels at certain speeds are regulated and may vary between countries, states, and cities. The objective of this study was to predict sound pressure levels (SPL) at several interior locations across a wide range of frequencies and estimate if the current design configuration will meet the noise level limits. Statistical Energy Analysis (SEA) was used to predict interior SPL and to understand and rank the various noise contribution paths and give a better understanding of the physics of transmission and what types of design changes are most effective to reduce the overall interior SPL to meet targets. A typical light rail vehicle is composed of a frame-like structure covered by lightweight panels and with interior panels that are increasingly made from composites, sandwich, laminated, or honeycomb materials or extruded panels. These lightweight structures made from materials that have previously been used primarily in aerospace applications have modal characteristics and transmission properties that require more advanced modeling than for simpler monolithic panels. Evaluation of the dominant noise transmission paths from the dominant sources allows sensitivity studies to evaluate which noise paths are the best candidates for improvement to overall vehicle NVH and which parts may be candidates for cost and weight reduction without significant degradation of the acoustic performance. This paper describes the motivation for this study and the details of the light rail vehicle construction. The SEA modeling approach is discussed, including the modeling of the structures and the main contributing sources. The contribution path ranking and the interior SPL predicted by the SEA model at several target locations are presented and compared to measured interior SPL data. Conclusions about the results and recommendations for future work are given.
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