基于协同克里格的梁振动多保真度不确定性量化,采用粗、细有限元网格

R. Rohit, R. Ganguli
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引用次数: 3

摘要

多保真度模型因其在不牺牲精度的前提下规避高保真度模型的计算复杂性而迅速流行起来。在本文中,我们展示了建立多保真度模型的过程,并通过对梁振动问题的不确定性量化研究说明了它的优点。利用低保真度和高保真度的有限元模型,分别进行粗离散化和精细离散化,建立了多保真度的协同克里格模型。co-kriging模型的预测能力非常出色,在不确定性量化研究中,其准确度在高保真度模型的1%以内,同时比高保真度模型节省98%的计算量。
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Co-kriging based multi-fidelity uncertainty quantification of beam vibration using coarse and fine finite element meshes
Abstract Multi-fidelity models have exploded in popularity as they promise to circumvent the computational complexity of a high-fidelity model without sacrificing accuracy. In this paper, we demonstrate the process of building a multi-fidelity model and illustrate its advantage through an uncertainty quantification study using the beam vibration problem. A multi-fidelity co-kriging model is built with data from low- and high-fidelity models, which are finite element models with coarse and fine discretization, respectively. The co-kriging model’s predictive capabilities are excellent, achieving accuracy within 1% of the high-fidelity model while providing 98% computational savings over the high-fidelity model in the uncertainty quantification study.
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