Model-free data-driven computational mechanics, first proposed by Kirchdoerfer and Ortiz, replaces phenomenological models with numerical simulations based on sample data sets in strain-stress space. Recent literature extended the approach to inelastic problems using structured data sets, tangent space information, and transition rules. From an application perspective, the coverage of qualified data states and calculating the corresponding tangent space is crucial. In this respect, material symmetry significantly helps to reduce the amount of necessary data. This study applies the data-driven paradigm to elasto-plasticity with isotropic hardening. We formulate our approach employing Haigh-Westergaard coordinates, providing information on the underlying material yield surface. Based on this, we use a combined tension-torsion test to cover the knowledge of the yield surface and a single tensile test to calculate the corresponding tangent space. The resulting data-driven method minimizes the distance over the Haigh-Westergaard space augmented with directions in the tangent space subject to compatibility and equilibrium constraints.
翻译:模型无关的数据驱动计算机力学是基于应变-应力数据集的数值模拟,用于替代基于现象的模型。Kirchdoerfer和Ortiz首次提出了这种方法。最近的文献已经将这种方法扩展到使用结构化数据集、切空间信息和转移规则的非弹性问题。从应用角度来看,覆盖合格数据状态并计算相应的切空间是至关重要的。在这方面,材料对称性极大地有助于减少所需数据的数量。本研究将数据驱动范式应用于各向同性硬化的弹塑性。我们采用Haigh-Westergaard坐标系来构建我们的方法,提供了有关材料屈服面的信息。基于此,我们使用联合张力-扭转试验来覆盖关于屈服面的知识,并使用单轴拉伸试验来计算相应的切空间。结果表明,所得到的数据驱动方法最小化了在Haigh-Westergaard空间中增加切空间方向的距离(同时满足兼容性和平衡约束)。