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Quantifying uncertainties in contact mechanics of rough surfaces using the multilevel Monte Carlo method

机译:使用多级蒙特卡洛方法量化粗糙表面的接触力学不确定性

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摘要

We 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. Then, we introduce the multilevel Monte Carlo method which is a computationally efficient sampling method for the computation of the expectation and higher 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. (C) 2019 Elsevier Ltd. All rights reserved.
机译:我们量化不确定性对与粗糙表面接触力学有关的感兴趣量的影响。具体来说,我们考虑两个半无限线性弹性固体之间存在不确定性的无摩擦非粘合法向接触问题。这些不确定性例如可能源于不完整的表面描述。为了解决表面不确定性问题,我们将粗糙表面建模为合适的高斯随机场,其协方差函数编码表面的粗糙度,这在实验上是可以测量的。然后,我们介绍了多级蒙特卡洛方法,这是一种计算效率高的采样方法,用于计算不确定性系统输出的期望值和较高的统计矩,例如从接触模拟得出的结果。特别地,我们考虑了两个不同的关注量,即接触面积和接触簇数,并通过数值实验表明,与经典的蒙特卡洛采样法相比,多层蒙特卡洛方法提供了显着的计算增益。 (C)2019 Elsevier Ltd.保留所有权利。

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