|Title:||Data-driven stability maps for friction induced vibrations||Language:||English||Authors:||Geier, Charlotte Magdalena
|Other contributor:||Hitachi Astemo France S.A.S., Drancy, France||Keywords:||deep learning; friction-induced vibrations; data science; bifurcation; nonlinear dynamics||Issue Date:||Aug-2022||Source:||92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM 2022)||Abstract (english):||
Friction-induced vibrations, apparent for example as squeal noise in car braking systems, have seen a significant amount of research effort in the past and remain an area of active research today. Due to the huge bifurcation parameter space and model parameter uncertainties, the instability mechanisms of these vibrations remain elusive. Particularly, bifurcation parameters and critical sensitivities vary from brake system to brake system. Numerical simulations cannot resolve all aspects for robust instability predictions today.
Recent work has illustrated how data-driven approaches to friction-induced noise modeling can be an alternative approach to conventional physics-based modeling: A digital twin representing a specific brake system was shown to predict instabilities and mode coupling associated with squeal occurrence with high accuracy. This work builds upon previous data-driven approaches to obtain a digital twin from measurement data of a real-world disc brake system. After validation, this data-based model is exploited to study the effect of various loading conditions on the predicted squealing behavior of the system. By gradually altering the loading conditions, we generate stability maps that encode information about the stability regions of the respective brake system. These stability maps provide a data-driven perspective on the mechanisms underlying frictioninduced noise, fostering a deeper understanding of the bifurcation parameter space of the underlying system.
|Conference:||92nd Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2022||URI:||http://hdl.handle.net/11420/13472||DOI:||10.15480/882.4563||Institute:||Strukturdynamik M-14||Document Type:||Presentation||Project:||Verbundprojekt PI-CUBE: Physikalische KI-Methoden zur Reduzierung von Radbrems-Emissionen elektrischer Fahrzeuge||Funded by:||Bundesministerium für Bildung und Forschung (BMBF)||License:||CC BY 4.0 (Attribution)|
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