Prediction of Squeal Instabilities of a Finite Element Model Automotive Brake With Uncertain Structural and Environmental Parameters With a Hybrid Surrogate Model - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Journal of Vibration and Acoustics Year : 2022

Prediction of Squeal Instabilities of a Finite Element Model Automotive Brake With Uncertain Structural and Environmental Parameters With a Hybrid Surrogate Model

Abstract

This study focuses on the prediction of the stability behavior of an industrial automotive brake system under structural and environmental uncertainties. Uncertainties are modeled with a random distribution or an interval and are propagated with a hybrid surrogate model associating polynomial chaos and kriging. The objective is to create a surrogate model of each eigenvalue computed with the complex eigenvalue analysis (CEA). As the modes can be tracked only when unstable, the effective size of the training sets can become extremely small. Despite this limitation, it is shown the hybrid meta-model is still able to predict the stability of the brake system. Moreover, the hybrid meta-model gives a direct access to the mean and variance of the eigenvalues with respect to the design parameters without any additional Monte Carlo simulations (MCS). By considering different probability density function for the friction coefficient, it is shown it has a high influence on the stability and the latter should be accurately estimated.
Fichier principal
Vignette du fichier
VIB-21-1055.pdf (1008.09 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03295984 , version 1 (22-07-2021)

Identifiers

Cite

Enora Denimal, Jean-Jacques Sinou, Samuel Nacivet. Prediction of Squeal Instabilities of a Finite Element Model Automotive Brake With Uncertain Structural and Environmental Parameters With a Hybrid Surrogate Model. Journal of Vibration and Acoustics, 2022, 144 (2), ⟨10.1115/1.4051698⟩. ⟨hal-03295984⟩
92 View
145 Download

Altmetric

Share

Gmail Facebook X LinkedIn More