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.

建筑基础设施的安全操作需要强大的预后模型来预测系统或结构特性。这些预后模型一直在不断更新有关现有结构的当前状态，特别是如果预期的寿命应涵盖数十年。建模假设与真实情况以及不确定或散射参数的偏差只能通过这种过程来考虑。除了相应的结构监测系统外，这还需要一个合适的数值模型，该模型描述了可能以足够准确的损害情况。在这个项目中，采用多尺度方法来描述结构损害。起点是宏观上完整结构的数值模型。使用混合多尺度建模策略对基于监视数据进行识别的关键区域以更高的分辨率进行建模。通过应用非破坏性测试方法获得了与当前结构状态有关的当前结构状态有关的信息。对于监视系统和数值模型之间的信息流，必须建立数字耦合。因此，在这种情况下采用了理论驱动的机器学习方法。描述输入和输出数据之间关系的模型可以通过神经元网络从实验数据创建。但是，这些基于数据的模型不一定与物理现象有关。因此，不能直接将所考虑系统的结构行为直接置于上下文中。该方法旨在在项目中开发的方法，不仅要使用来自测量数据的输入和输出参数的信息，还可以用物理学来喂养元模型。 - 用于数值模型的基于基于关系。这样，建立了实验研究与数字双胞胎的数值结构模型之间的耦合。这种耦合不仅允许从测量数据识别模型参数。它还允许向反向的信息流以基于模型获得的仿真结果来控制移动非破坏性测试设备。开发的方法需要创建数值多尺度模型，数值耦合的工具以及实验性的工具在不同尺度上对结构的分析。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的受控条件下测试的结构元素来验证。在第二步中，将对DFG优先计划2388的参考结构进行验证。