000263569 001__ 263569
000263569 005__ 20190509131806.0
000263569 0247_ $$2doi$$a10.5075/epfl-MATHICSE-263569
000263569 037__ $$aREP_WORK
000263569 245__ $$aMATHICSE Technical Report : Quantifying uncertainties in contact mechanics of rough surfaces using the Multilevel Monte Carlo method
000263569 260__ $$c2018-04-10
000263569 269__ $$a2018-04-10
000263569 336__ $$aWorking Papers
000263569 500__ $$aMATHICSE Technical Report Nr. 05.2018 April 2018
000263569 520__ $$aWe quantify the effect of uncertainties on quantities of interest related to contact mechanics of rough surfaces. Specifically, we consider the problem of frictionless non adhesive normal contact between two semi infinite linear elastic solids subject to uncertainties. These uncertainties may for example originate from an incomplete surface description. To account for surface uncertainties, we model a rough surface as a suitable Gaussian random field whose covariance function encodes the surface's roughness, which is experimentally measurable. Within this stochastic framework, we first introduce the complete random contact model, which includes the precise definition of the considered class of rough random surfaces as well as the study of a practical random surface generator. Then, we introduce the multilevel Monte Carlo method which is a computationally efficient sampling method for the computation of statistical moments of uncertain system output's, such as those derived from contact simulations. In particular, we consider two different quantities of interest, namely the contact area and the number of contact clusters, and show via numerical experiments that the multilevel Monte Carlo method offers significant computational gains compared to an approximation via a classic Monte Carlo sampling.
000263569 700__ $$0249567$$aRey, Valentine
000263569 700__ $$0248679$$aKrumscheid, Sebastian
000263569 700__ $$0241873$$aNobile, Fabio
000263569 710__ $$aMATHICSE-Group
000263569 787__ $$whttps://infoscience.epfl.ch/record/254864$$eIs Previous Version Of
000263569 8560_ $$ffabio.nobile@epfl.ch
000263569 8564_ $$yVersion 1$$zVersion 1$$uhttps://infoscience.epfl.ch/record/263569/files/Report-05.2018_VR_SK_FN.pdf$$s2332351
000263569 909C0 $$zJunod, Julien$$xU12495$$pCSQI$$mrachel.bordelais@epfl.ch$$0252411
000263569 909CO $$qGLOBAL_SET$$pworking$$pDOI$$pSB$$ooai:infoscience.epfl.ch:263569
000263569 960__ $$afabio.nobile@epfl.ch
000263569 961__ $$ajulien.junod@epfl.ch
000263569 973__ $$aEPFL
000263569 981__ $$aoverwrite
000263569 980__ $$aREP_WORK