This paper presents a novel direct Jacobian total Lagrangian explicit dynamics (DJ-TLED) finite element algorithm for real-time nonlinear mechanics simulation. The nodal force contributions are expressed using only the Jacobian operator, instead of the deformation gradient tensor and finite deformation tensor, for fewer computational operations at run-time. Owing to this proposed Jacobian formulation, novel expressions are developed for strain invariants and constant components, which are also based on the Jacobian operator. Results show that the proposed DJ-TLED consumed between 0.70x and 0.88x CPU solution times compared to state-of-the-art TLED and achieved up to 121.72x and 94.26x speed improvements in tetrahedral and hexahedral meshes, respectively, using GPU acceleration. Compared to TLED, the most notable difference is that the notions of stress and strain are not explicitly visible in the proposed DJ-TLED but embedded implicitly in the formulation of nodal forces. Such a force formulation can be beneficial for fast deformation computation and can be particularly useful if the displacement field is of primary interest, which is demonstrated using a neurosurgical simulation of brain deformations for image-guided neurosurgery. The present work contributes towards a comprehensive DJ-TLED algorithm concerning isotropic and anisotropic hyperelastic constitutive models and GPU implementation.
翻译:本文为实时非线性机械模拟提供了一种新型的直导Jacobian 总计Lagrangian 显性动力(DJ-TLED)有限元素算法(DJ-TLED) 。 节点力贡献仅使用雅各布操作器表示, 而不是变形梯度慢速和固定变形慢速, 用于减少运行时的计算操作。 由于这一拟议Jacobian 配方, 为压力和压力概念开发了新的表达方式, 也以雅各布操作器为基础。 结果显示, 拟议的DJ- TLED在0. 70x 和 0.88x CPU 溶解时间之间消费了0. 7x 和 0.88x CPU 的溶解时间, 与最新先进的TREDLED相比, 并分别达到121.72x 和94.26x 速度改进, 使用 GPUPU加速。 与T相比, 最显著的区别是, 压力和紧张的概念概念概念概念概念概念化模型可以用于快速解析定。