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. The source code is available at https://github.com/jinaojakezhang/DJTLED.
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Lagrangeian 清晰动态(DJ-TLED) 的限定要素算法。 节点部队贡献仅使用雅各布运算器表示, 而不是使用变形梯度梯度高温和固定变异度高压, 用于减少运行时的计算操作。 由于这一拟议Jacobian 配方, 也以雅各布运算器为基础, 为压力和压力概念开发了新表达方式, 这些新表达方式也以Jacobian 操作器为基础。 结果显示, 拟议的DJ- TLED在0. 0. 70x 和 0. 0. 08x CPU 中消耗了0. 8 CPU 的解决方案时间, 与最新技术含量TLELED相比, 并分别达到121.72x 和94.26x 速度改进, 使用GPU 加速。 与 TREG/DLED相比,压力和压力概念分析仪的模型为当前G- dromacial- dalationalationalislevoral 提供了一个可使用的GILELEBILED/ drual 的模拟。 。 和GILELD- daldaldaldaldaldaldaldaldaldaldaldal 的模拟工作。 的模拟, 。