Frontloading Approach for Sound Package Design for Noise Reduction and Weight Optimization Using Statistical Energy Analysis

Paper #:
  • 2017-26-0222

Published:
  • 2017-01-10
Citation:
Chaudhari, V., Radhika, V., and Vijay, R., "Frontloading Approach for Sound Package Design for Noise Reduction and Weight Optimization Using Statistical Energy Analysis," SAE Int. J. Veh. Dyn., Stab., and NVH 1(1):66-72, 2017, https://doi.org/10.4271/2017-26-0222.
Pages:
7
Abstract:
First time right vehicle performance and time to market, remains all automotive OEMs top priority, to remain competitive. NVH performance of product communicates impression to customer, remains one of the most important and complex attribute to meet, considering performances to be met for 20 Hz -6000 Hz. Frontloading techniques (FEM/BEM/SEA/MBD) for NVH are critical and necessary to achieve first time right NVH performance.Objective of this paper is to present a frontloading approach for automotive sound package optimization (absorber, barrier and damper elements) for SUV vehicle. Current process of designing sound package is mainly based on experience, competitive benchmarking of predecessor products. This process (current process) heavily depend on testing and validation at physical prototype and happens at later stages of program, especially on tooled up body. This is because, structure borne noise development, sealing and leakage path treatment refinement assume priority over sound package development. This way of working has impacted on validation timelines, late changes in peripheral system, leading to cost increase and time delays. High frequency simulations (using statistical energy analysis) provides opportunity in terms of being able to evaluate design options and frontload sound package design in parallel with structure borne NVH development.In this exercise, SEA (statistical energy analysis) was used to predict full vehicle cabin noise (400 Hz-6000 Hz) by modelling all major paths (structure borne and air borne) present in vehicle. All major sources of excitation such as powertrain vibrations, suspension vibrations and engine bay noise, tyre patch noise, exhaust noise were used to predict cabin noise. Static and dynamic load cases were used to validate modelling confidence and model updating. Contribution analysis was used to identify dominant sources, transfer paths for optimization of sound package for performance and weight.
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