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Acceleration of uncertainty propagation through Lagrange multipliers in partitioned stochastic method

机译:随机分配方法中拉格朗日乘数的不确定性传播加速

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A partitioned stochastic method (PSM) is proposed for the solution of static structural mechanics problems with uncertainties, whose solution vectors are the displacements for each partition and Lagrange multipliers along with the partition interfaces. The proposed partitioned stochastic method employs three stochastic basis selection steps: an arbitrary initial choice of displacement random bases, a set of conjugate bases for the Lagrange multipliers, and finally modification of the displacement bases affected by those of the Lagrange multipliers. The present PSM thus propagates the uncertainties instantly across partitioned substructures, resulting in an improved rate of convergence. Numerical experiments illustrate the proposed PSM outperforms a conventional partitioned solution method for structural mechanics problems. (C) 2020 Elsevier B.V. All rights reserved.
机译:提出了一种求解不确定性静态结构力学问题的分区随机方法(PSM),其求解向量为每个分区的位移和拉格朗日乘数以及分区界面。提出的分区随机方法采用三个随机基础选择步骤:位移随机基的任意初始选择,拉格朗日乘数的一组共轭底,最后修改受拉格朗日乘数影响的位移底。因此,当前的PSM将不确定性立即传播到分区的子结构中,从而提高了收敛速度。数值实验表明,所提出的PSM优于传统的针对结构力学问题的分区求解方法。 (C)2020 Elsevier B.V.保留所有权利。

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