Direct data-driven computational mechanics for anelastic material behaviours

用于迟弹性材料行为的直接数据驱动计算力学

• 批准号: 431386925
• 负责人: Professorin Dr.-Ing. Stefanie Reese
• 金额: --
• 依托单位: Rektor der Universität Siegen
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/431386925?language=en
• 关键词: Direct; data; driven; computational; mechanics

Due to the volatile development of storage capacities as well as suitable soft- and hardware, the amount of available data has increased by many orders of magnitude over the last decades. This influences almost all parts of practical life but also science and technology. In particular, the area of engineering and many fields of applied sciences have a significant role in it, since they require to provide, in addition to fundamental conservation principles, relations between fields of interest, for example stress and strain. Traditionally, these relations are derived from constitutive models, relying on a number of assumptions, and prone to significant epistemic uncertainty. Additionally, these models involve parameters, which might be difficult to be identified, especially with relatively simple experiments such as uniaxial tests. The objective of this project is thus to develop methods allowing to perform numerical simulations of the behavior of structures directly from available data (either experimental or coming from fine scale computations), eliminating the necessity to formulate phenomenological constitutive models and the uncertainty associated with them. The methodology has already been demonstrated for elastic materials. The project is based on the research hypothesis that the framework can be extended to inelastic material behaviors, such as elasto-plasticity or visco-elasticity, overcoming the challenge of the resulting increased dimen-sionality of phase space.A general view of a data-driven approach to inelasticity has already been given by the proposers in a recent paper. The objectives of this project are to implement and assess the methodology in a framework able to handle cases oriented towards industrial applications: use of actual experimental and eventually incomplete data, complex 3D geometries and loading, efficiency and robustness of solvers, uncertainty quantification. An additional objective is to provide an online platform allowing to share the methodology and the associated data with the scientific community.

由于存储能力的波动发展以及合适的软件和硬件，在过去的几十年中，可用数据的量增加了许多数量级。这影响了实际生活的几乎所有部分，也影响了科学和技术。特别是，工程领域和许多应用科学领域在其中具有重要的作用，因为除了基本的保护原则以及感兴趣的领域之间的关系，例如压力和压力，它们还需要提供。传统上，这些关系源自本构模型，依靠许多假设，并且容易出现明显的认知不确定性。此外，这些模型涉及参数，可能难以识别，尤其是在相对简单的实验（例如单轴测试）中。因此，该项目的目的是开发方法，允许直接从可用数据（实验性或来自精细规模计算的实验性或来自精细的计算）进行结构行为的数值模拟，从而消除了制定现象学本构成模型及其相关的不确定性的必要性。该方法已经被证明用于弹性材料。该项目基于研究假设，即该框架可以扩展到非弹性材料行为，例如弹性塑性或粘弹性，克服了相位空间的增加范围的挑战。在最近的论文中，预知者已经给出了数据驱动的弹性方法的一般观点。该项目的目标是在能够处理针对工业应用的案例的框架中实施和评估方法：使用实际实验和最终不完整的数据，复杂的3D几何形状以及求解器的加载，效率和鲁棒性，不确定性量化。另一个目标是提供一个在线平台，允许与科学界共享方法和相关数据。