Digital coupling of multiscale analyses in modelling and monitoring

建筑基础设施的安全运行需要强大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所考虑的现有结构的现状不断更新，特别是如果预期寿命应涵盖几十年。建模假设与真实的情况的偏差以及不确定或分散的参数只能通过这样的程序来考虑。这就需要，除了一个相应的结构监测系统，一个合适的数值模型，描述可能发生的损坏情况下，具有足够的准确性。在这个项目中，一个多尺度的方法来描述结构损伤。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采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。通过无损检测手段获取中尺度局部模拟所需的当前结构状态信息，监测系统与数值模型之间的信息流需要建立数字耦合。因此，理论驱动的机器学习的方法被应用在这种情况下。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。然而，这些基于数据的模型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的上下文中，该方法旨在开发的项目中，饲料Meta模型不仅与来自测量数据的输入和输出参数的信息，但也与基于物理的关系，用于数值模型。通过这种方式，建立了数字孪生模型的实验研究和数值结构模型之间的耦合。这种耦合不仅允许从测量数据中识别模型参数。它还允许在相反的方向上的信息流，以控制移动的非破坏性检测设备的基础上得到的模拟结果，通过model.The方法来开发需要创建的数值多尺度模型，工具的数值耦合以及实验分析的结构上不同的规模。在这种情况下，该信息链的组成部分首先通过可以在实验室受控条件下进行测试的结构元素进行验证。第二步，将对DFG优先计划2388的参考结构进行验证。

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.