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arXiv 1506.00532cond-mat.mtrl-sci
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粗糙表面无摩擦法向接触问题的优化算法

Optimization algorithms for the solution of the frictionless normal contact between rough surfaces

A. Bemporad, M. Paggi

摘要:

本文重新讨论了用边界元法(BEM)求解粗糙刚性表面与线弹性半平面之间无摩擦单侧法向接触问题的基本方程。在将得到的线性互补问题(LCP)转换为具有非负约束的凸二次规划(QP)之后,比较了不同的优化算法的解:(i)基于无约束线性系统的不同求解器的贪婪方法(共轭梯度CG,Gauss-Seidel,Cholesky因式分解),(ii)约束CG算法,(iii)交替方向乘法(ADMM),以及($iv$)非负最小二乘(NNLS)算法,可能通过加速梯度投影步骤或利用加载历史来热启动。后一种方法比贪婪CG方法快两个数量级,比约束CG算法快一个数量级。最后,我们提出了另一种类型的热启动的基础上,可以与所有以前的优化算法结合使用的初始试验接触域的识别的细化标准。这种方法被称为级联多分辨率(CMR),它利用了通过改变表面分辨率来缩放接触预测的物理考虑。该方法是非常有效和准确的,当应用到真实或数值生成的粗糙表面,提供他们的功率谱密度函数是幂律型,在自相似分形表面的情况下。

英文摘要:

This paper revisits the fundamental equations for the solution of the frictionless unilateral normal contact problem between a rough rigid surface and a linear elastic half-plane using the boundary element method (BEM). After recasting the resulting Linear Complementarity Problem (LCP) as a convex quadratic program (QP) with nonnegative constraints, different optimization algorithms are compared for its solution: (i) a Greedy method, based on different solvers for the unconstrained linear system (Conjugate Gradient CG, Gauss-Seidel, Cholesky factorization), (ii) a constrained CG algorithm, (iii) the Alternating Direction Method of Multipliers (ADMM), and ($iv$) the Non-Negative Least Squares (NNLS) algorithm, possibly warm-started by accelerated gradient projection steps or taking advantage of a loading history. The latter method is two orders of magnitude faster than the Greedy CG method and one order of magnitude faster than the constrained CG algorithm. Finally, we propose another type of warm start based on a refined criterion for the identification of the initial trial contact domain that can be used in conjunction with all the previous optimization algorithms. This method, called Cascade Multi-Resolution (CMR), takes advantage of physical considerations regarding the scaling of the contact predictions by changing the surface resolution. The method is very efficient and accurate when applied to real or numerically generated rough surfaces, provided that their power spectral density function is of power-law type, as in case of self-similar fractal surfaces.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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