An acoustic trade-off chart for the design of multilayer acoustic packages

Abstract

This paper presents a novel trade-off chart to support the design of multilayer acoustic packages. In this multi-objective problem, a designer has to specify a combination of layers from a set of available acoustic materials and thicknesses. Material types may include porous, mass-weighted, facing, among others. The combination must meet requirements in terms of sound absorption, sound transmission loss, cluttering, mass, etc. While predictions and analyses can be made on predetermined multilayer acoustic packages using the transfer matrix method, statistical energy analysis, finite elements methods or modal analysis, comparing a large number of possible combinations is cumbersome. On the other hand, optimization methods can be used to identify optimal thicknesses or material properties for a given layer combination, but the obtained solution may not be industrially relevant since, in general, only a limited set of acoustic materials and layer thicknesses exist commercially. In this paper, a new design methodology is proposed, which takes into account only the feasible combinations and provides guidelines for compromises between different performance parameters. The three-step methodology is demonstrated through a case study inspired by the automotive industry. First, relevant categories of layer configurations are defined, and following these patterns, all possible combinations of materials from a given inventory are calculated and stored in a database. Then, for selected performance parameters, the Pareto set of "better combinations" is identified. Finally, the "better solutions" are displayed on a trade-off chart through utility functions that allow weighing the different performance parameters. The tool developed for doing so is applied to the case study, and two example situations are presented. For each situation, the trade-off chart provides several suitable solutions, which are discussed. The use of this new tool effectively induces gains of time at the early stage of design, when it is most crucial.

A practical procedure for vehicle sound package design using statistical energy analysis

“…In such circumstances, on a macroscopic scale, porous materials can be presented by effective density and effective bulk modulus. In the extensively used Johnson-Champoux-Allard (JCA) model, [28][29][30] these two effective values are expressed in the form of equations ( 5) and (6).…”

“…On the other hand, multi-layer insulators are designed to provide both absorption and sound transmission loss properties simultaneously. 6,7 Typically, two or more layers of porous and polymeric materials are used in the configuration of multi-layer acoustic insulators. Since transmission loss is often contradictory with absorption, layers combination are arranged to compromise between these two objectives and create the best acoustic insulation performance.…”

Multi-Objective Prediction of the Sound Insulation Performance of a Vehicle Body System Using Multiple Kernel Learning–Support Vector Regression

“…Su et al [13] pointed out that experimental statistical energy analysis, finite element statistical energy analysis, statistical modal energy distribution analysis, and waveguide analysis in SEA are powerful tools for solving the problem of high-frequency acoustic vibration. Hugues et al [14] proposed a new trade-off diagram for the design of multi-layer acoustic packages. The practical application proves that this method effectively shortens the time in the early stage of design.…”