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A superlinear convegent augmented Lagrangian procedure for contact problems

机译:接触问题的超线性对流扩充拉格朗日过程

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摘要

The numerical solution of contact problems via the penalty method yields approximate satisfaction of contact constraints. The solution can be improved using augmentation schemes. However their efficiency is strongly dependent on the value of the penalty parameter and usually results in a poor rate of convergence to the exact solution. In this paper we propose a new method to perform the augmentations. It is based on estimated values of the augmented Lagrangians. At each augmentation the converged state is used to extract some data. Such information updates a database used for the Lagrangian estimation. The prediction is primarily based on the evolution of the constraint violation with respect to the evolution of the contact forces. The proposed method is characterised by a noticeable efficiency in detecting nearly exact contact forces, and by superlinear convergence for the subsequent minimisation of the residual of constraints. Remarkably, the method is relatively insensitive to the penalty parameter. This allows a solution which fulfils the constraints very rapidly, even when using penalty values close to zero.
机译:通过惩罚方法对接触问题进行数值求解,可以得出接触约束的近似满足条件。可以使用扩充方案来改进该解决方案。但是,它们的效率在很大程度上取决于惩罚参数的值,通常会导致收敛到精确解的速度很慢。在本文中,我们提出了一种执行增强的新方法。它基于增强的拉格朗日估计值。在每次扩充时,会聚状态都用于提取一些数据。这样的信息更新了用于拉格朗日估计的数据库。该预测主要基于相对于接触力的发展的约束违反的发展。所提出的方法的特征在于,在检测几乎精确的接触力方面具有显着的效率,并且其特征在于,超线性收敛用于随后最小化约束残差。值得注意的是,该方法对惩罚参数相对不敏感。即使使用惩罚值接近零,这也允许解决方案非常迅速地满足约束条件。

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