Digital coupling of multiscale analyses in modelling and monitoring

建模和监测中多尺度分析的数字耦合

• 批准号: 501805504
• 负责人: Professor Dr.-Ing. Carsten Könke
• 金额: --
• 依托单位: Institut für Strukturmechanik (ISM)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/501805504?language=en
• 关键词: Digital; coupling; multiscale; analyses; modelling

A safe operation of built infrastructure requires robust prognosis models for the prediction of system or structural properties. These prognosis models have continuously to be updated with respect to the current state of the considered existing structures especially if the expected lifetime should cover several decades. Deviations of the modelling assumptions from real situation and uncertain or scattering parameters can only be taken into consideration by such a procedure. This requires, beside a respective structural monitoring system, a suitable numerical model that describes possibly occurring damage scenarios with sufficient accuracy. In this project, a multiscale approach is applied to describe structural damage. Starting point is a numerical model of the complete structure on macroscale. Critical zones that were identified based on monitored data are modelled with a higher resolution on the mesoscale using a hybrid multiscale modelling strategy. The information related to the current structural state that is necessary for local modelling on the mesoscale is obtained by the application of non-destructive testing methods.For the information flow between monitoring system and numerical model, digital coupling has to be established. Therefore the method of theory-driven machine learning is applied in this context. Models describing relations between input and output data can be created from experimental data by means of neuronal networks. However, these data-based models are not necessarily related to physical phenomena. And therefore, can not directly be put into context with the structural behaviour of the considered system.The approach intended to be developed within the project, feeds the meta models not only with information about input and output parameters derived from measured data but also with physics-based relations that are used for the numerical models. In this way, the coupling between experimental investigations and the numerical structural model of a digital twin is established. This coupling does not only allow for the identification of model parameters from measured data. It allows also an information flow in reverse direction to control mobile non-destructive testing equipment based on simulation results obtained by means of the model.The methodology to be developed requires the creation of numerical multiscale models, tools for the numerical coupling as well as experimental analyses of the structure on different scales. In this context, the components of this information chain are first validated with structural elements that can be tested under controlled conditions in the laboratory. In a second step, a validation will be carried out on the reference structure of the DFG priority programme 2388.

已建成基础设施的安全运行需要可靠的预测模型来预测系统或结构特性。这些预测模型必须根据考虑的现有结构的当前状态不断更新，特别是如果预期寿命应该覆盖几十年。这种程序只能考虑建模假设与实际情况的偏差以及不确定或散射参数。除了各自的结构监测系统外，这需要一个合适的数值模型，以足够的精度描述可能发生的损坏情况。本项目采用多尺度方法描述结构损伤。出发点是宏观尺度上完整结构的数值模型。根据监测数据确定的关键区域使用混合多尺度建模策略在中尺度上以更高的分辨率进行建模。通过应用无损检测方法获得了与当前结构状态有关的信息，这些信息是在中尺度上进行局部建模所必需的。对于监测系统与数值模型之间的信息流，必须建立数字耦合。因此，理论驱动的机器学习方法在这种情况下被应用。描述输入和输出数据之间关系的模型可以通过神经网络从实验数据中创建出来。然而，这些基于数据的模型并不一定与物理现象相关。因此，不能直接与所考虑的系统的结构行为联系起来。该方法旨在在项目中开发，不仅为元模型提供来自测量数据的输入和输出参数信息，还提供用于数值模型的基于物理的关系。通过这种方式，建立了实验研究与数字孪生数值结构模型之间的耦合关系。这种耦合不仅允许从测量数据中识别模型参数。基于该模型所获得的仿真结果，还允许反向信息流来控制移动无损检测设备。要开发的方法需要创建数值多尺度模型，用于数值耦合的工具以及不同尺度结构的实验分析。在这种情况下，该信息链的组成部分首先用结构元素进行验证，这些结构元素可以在实验室受控条件下进行测试。在第二步中，将对DFG优先方案2388的参考结构进行验证。