This research work deals with the implementation of so-called Dynamic Data-Driven Application Systems (DDDAS) in structural mechanics activities. It aims at designing a real-time numerical feedback loop between a physical system of interest and its numerical simulator, so that (i) the simulation model is dynamically updated from sequential and in situ observations on the system; (ii) the system is appropriately driven and controlled in service using predictions given by the simulator. In order to build such a feedback loop and take various uncertainties into account, a suitable stochastic framework is considered for both data assimilation and control, with the propagation of these uncertainties from model updating up to command synthesis by using a specific and attractive sampling technique. Furthermore, reduced order modeling based on the Proper Generalized Decomposition (PGD) technique is used all along the process in order to reach the real-time constraint. This permits fast multi-query evaluations and predictions, by means of the parametrized physics-based model, in the online phase of the feedback loop. The control of a fusion welding process under various scenarios is considered to illustrate the proposed methodology and to assess the performance of the associated numerical architecture.

中文翻译：

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不确定情况下机械系统的实时数据同化和控制

这项研究工作涉及在结构力学活动中实施所谓的动态数据驱动应用系统（DDDAS）。它旨在设计目标物理系统与其数值模拟器之间的实时数值反馈回路，以便（i）根据对系统的顺序观测和现场观测动态更新模拟模型；（ii）使用模拟器给出的预测对系统进行适当的驱动和控制。为了建立这样的反馈回路并考虑各种不确定性，考虑采用合适的随机框架进行数据同化和控制，并通过使用特定且有吸引力的采样技术将这些不确定性从模型更新传播到命令综合。此外，在整个过程中，始终使用基于适当广义分解（PGD）技术的降阶建模来达到实时约束。通过基于参数化的物理模型，可以在反馈回路的在线阶段进行快速的多查询评估和预测。考虑在各种情况下对熔焊过程的控制，以说明所提出的方法并评估相关数值体系结构的性能。