J
Jean-Claude Golinval
Researcher at University of Liège
Publications - 220
Citations - 5159
Jean-Claude Golinval is an academic researcher from University of Liège. The author has contributed to research in topics: Finite element method & Nonlinear system. The author has an hindex of 30, co-authored 218 publications receiving 4698 citations.
Papers
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Journal ArticleDOI
A micro-model for elasto-plastic adhesive–contact in micro-switches: Application to cyclic loading
TL;DR: In this article, the adhesion between rough surfaces is studied considering the elasto-plastic deformations of the asperities, and a model predicting the resulting micro-adhesive contact forces is derived.
Experimental modal analysis using blind source separation techniques
TL;DR: This dissertation deals with dynamics of engineering structures and principally discusses the identification of the modal parameters using output-only information, the excitation sources being considered as unknown and unmeasurable, and a new modal parameter estimation approach is developed.
Journal ArticleDOI
Energy Transfer and Dissipation in a Duffing Oscillator Coupled to a Nonlinear Attachment
TL;DR: In this paper, the dynamics of a two-degree-of-freedom nonlinear system consisting of a grounded Duffing oscillator coupled to an essentially nonlinear attachment is examined, and the basic mechanisms for energy transfer and dissipation are analyzed.
Journal ArticleDOI
The generalized‐α method in mechatronic applications
TL;DR: An extension of the generalized-α time-integrator to mechatronic systems represented by coupled first and second-order DAEs allows the implementation of a monolithic integration scheme, so that the numerical dissipation properties are preserved, andsecond-order accuracy is obtained at least in the unconstrained case.
Book ChapterDOI
Damage Detection in Civil Engineering Structure Considering Temperature Effect
TL;DR: This paper concerns damage identification of a bridge located in Luxembourg, and modal parameters are identified from the response data through Principal Component Analysis (PCA) and Kernel PCA.