This paper presents a model-free data-driven strategy for linear and non-linear finite element computations of open-cell foam. Employing sets of material data, the data-driven problem is formulated as the minimization of a distance function subjected to the physical constraints from the kinematics and the balance laws of the mechanical problem. The material data sets of the foam are deduced here from representative microscopic material volumes. These volume elements capture the stochastic characteristics of the open-cell microstructure and the properties of the polyurethane material. Their computation provides the required stress-strain data used in the macroscopic finite element computations without postulating a specific constitutive model. The paper shows how to efficiently derive suitable material data sets for the different (non-)linear and (an-)isotropic material behavior cases. Exemplarily, we compare data-driven finite element computations with linearized and finite deformations and show that a linear kinematic is sufficiently accurate to capture the material's non-linearity up to 50% of straining. The numerical example of a rubber sealing illustrates possible areas of application, the expenditure, and the proposed strategy's versatility.
翻译:本文为开放细胞泡沫的线性和非线性限量元素计算提供了一个无模型数据驱动战略。 使用成套材料数据,数据驱动的问题被表述为尽量减少受运动学和机械问题平衡法物理限制的距离功能。 泡沫的材料数据集在这里从具有代表性的微小材料量中推导出来。 这些量元素捕捉开细胞微结构的随机特征和聚氨酯材料的特性。 它们的计算提供了在宏观统计有限元素计算中使用的所需压力- 压力- 压力- 压力- 度数据,而没有设定一个具体的构成模型。 本文展示了如何高效地为不同( 非线性) 和( 单线性) 物质行为案例获取合适的材料数据集。 我们将数据驱动的有限要素计算与线性和有限性变形进行对比,并表明线性运动足够准确,可以捕捉到材料的非线性至50%的临界值。 橡胶密封的数值示例了可能的应用领域、 支出和拟议的战略性。