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Iterative refinement algorithm for efficient velocities and accelerations solutions in closed-loop multibody dynamics

机译:闭环多体动力学有效速度和加速解的迭代细化算法

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摘要

To simulate closed-loop multibody systems in effective and accurate way, the semi-recursive formulations can be used together with optimal numerical integrators. Although numerous contributions have been reported in this field, there still exists a demand to improve the computation efficiency for the faster-than-real-time simulation and hardware-in-the-loop control applications. This paper presents an iterative refinement algorithm to accelerate the numerical integration process of a semi-recursive multibody formulation. By reusing the constraint Jacobian matrix factorization and generalized mass matrix factorization, respectively, the presented algorithm is introduced to enhance the solution of dependent relative velocities and independent relative accelerations. The introduced procedure consists of three steps; initial guess determination, iterative refinement process and termination criteria. The iterative refinement process is designed to find an accurate numerical solution, which contains residual computing, solution increment computing and solution updating. For higher efficiency, the initial guess can be re-determined to repeat the iterative refinement process. Three closed-loop multibody systems with increasing complexity are taken as numerical examples to verify the accuracy and efficiency of the presented iterative refinement algorithm. The results highlight more than 15% efficiency gain. The algorithm also can be implemented in other numerical integrators and multibody formulations.
机译:为了以有效和准确的方式模拟闭环多体系,可以将半递归配方与最佳数值积分器一起使用。尽管在该领域报告了许多贡献,但仍然需要提高更快的实时仿真和环路控制应用的计算效率。本文介绍了一种迭代细化算法,可加速半递归多体配方的数值积分过程。通过重用约束雅比尼亚矩阵分解和广义质量矩阵分解,引入所呈现的算法以增强相关的相对速度和独立相对加速的解决方案。介绍的程序包括三个步骤;初步猜测确定,迭代细化过程和终止标准。迭代细化过程旨在找到一个准确的数字解决方案,其中包含残余计算,解决方案增量计算和解决方案更新。为了更高的效率,可以重新确定初始猜测以重复迭代细化过程。具有越来越复杂性的三种闭环多体系,被认为是数值例子,以验证所提出的迭代细化算法的准确性和效率。结果突出了超过15%的效率增益。该算法还可以在其他数值积分器和多体配方中实现。

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