Sensitivity Analysis of NVH Simulations with Stochastic Input Parameters for a Car Body

Features
Event
12th International Styrian Noise, Vibration & Harshness Congress: The European Automotive Noise Conference
Authors Abstract
Content
Uncertainties play a major role in vibroacoustics - especially in car body design in the preliminary development because of the overall spread in the production that should be covered with one simulation model. Therefore, we use uncertain input parameters to determine the stochastically distributed admittance of the car body before each part of the car is fully designed. To gain a stochastic result - the stochastically distributed admittance curve - we calculate a deterministic finite element simulation several times with sets of stochastically distributed input parameter values. To reduce simulation time and cost of the car model with many million degrees of freedom we focus on the uncertain parameters that show a significant influence on the admittance curve. It is therefore necessary to be able to accurately estimate for each parameter if its influence on the admittance of the car body plays a major role for the noise vibration harshness simulation. A sensitivity analysis describes the connection between model input and output and the influence of the input on the output. We conduct a two-step global sensitivity analysis which is based on the generalized polynomial chaos expansion to determine the sensitivity of the parameters. Since the less sensitive parameters hardly influence the admittance curve of the car body, we can simulate them as deterministic values. In the further research, we will focus on the most sensitive parameters.
Meta TagsDetails
DOI
https://doi.org/10.4271/2022-01-0951
Citation
Cram, S., Luegmair, M., Schmid, J., and Marburg, S., "Sensitivity Analysis of NVH Simulations with Stochastic Input Parameters for a Car Body," SAE Int. J. Adv. & Curr. Prac. in Mobility 5(2):876-887, 2023, https://doi.org/10.4271/2022-01-0951.
Additional Details
Publisher
Published
Jun 15, 2022
Product Code
2022-01-0951
Content Type
Journal Article
Language
English