Autonomous Modelling Solutions for Operational Structural Dynamic Systems

识别和理解工程结构如何随着时间的推移对不同的载荷做出响应是一项支持许多其他活动的关键任务。例如，为工程系统的设计、生产和管理创建数字双胞胎是目前学术界和工业界发展的一个关键领域。该项目旨在扩大可能的操作场景范围，并进一步实现过程自动化。海上能源部门是一个很好的应用领域，该项目开发的技术可以支持对工程系统的深入了解。环境的性质通常意味着直接测量结构载荷是不可行的。例如，由风在涡轮机叶片上产生的力或由波浪在单桩结构上产生的力难以测量，但在结构的操作中是重要的。这种困难促使开发的方法，可以推断，不仅这些系统的动态特性，但未测量的输入（负载）从可用的测量。该项目还将进一步扩展识别任务，以包括自主建模决策的能力。换句话说，从收集的数据中获得的信息与先前的工程判断相融合，以学习自动表示系统的适当方法。本项目开发的技术将在两个关键方面支持运营和维护活动。通过提供对工程结构的特性及其运行条件的改进估计，可以以更高的置信度做出决策。第二，自动化工程师构建模型的方式减少了过程中的主观性，并使工程师能够专注于他们的干预将带来最大利益的地方。该项目的主要成果将使工程师能够更好地了解感兴趣的结构的行为，开发更好的表示方法-例如数字孪生模型-并提高他们基于测量做出运营和维护决策的能力。总的来说，对工程系统的深入了解将有助于更有针对性和更有效的管理。其结果是减少了不必要的维护，从而降低了成本，降低了必须在这些恶劣环境中执行维护的工作人员的风险。该提案中存在挑战;首先是加强现有的用于学习动态系统的输入和参数的方法，以包括更复杂的加载场景。这些场景包括对多个相关力、分布载荷和未知位置处的载荷进行建模。第二个挑战是使决定动态系统的适当模型的过程进一步自动化。假设通过解决这两个挑战，确定多个/分布未知负载和动态模型结构的自动学习，可以鲁棒地考虑的情况的范围增加。这种增强的理解将大大有助于将动态模型纳入工程系统的虚拟化和智能基础设施的开发。

Identifying and understanding how engineering structures respond over time to different loads is a key task which supports many other activities. For example, the creation of digital twins for design, production and management of engineering systems is currently a key area of development across academia and industry. This project aims to extend the range of operational scenarios in which this is possible and to further automate the process.The offshore energy sector is a good example of an application area where the technology being developed in this project can support enhanced insight into an engineering system. The nature of the environment often means direct measurements of structural loads is infeasible. For example, the forces generated by the wind on a turbine blade or by the waves on a mono-pile structure are difficult to measure but important in the structure's operation. This difficulty motivates the development of methods which can infer, not only the dynamic properties of these systems, but the unmeasured inputs (loads) from the available measurements. The project will also extend the identification task further to include the ability to make autonomous modelling decisions. In other words, the information from collected data is fused with prior engineering judgement to learn appropriate ways to represent the system automatically.The technology developed in this project will support operations and maintenance activities in two key ways. By providing improved estimates of the properties of engineering structures and the conditions they have operated in, decisions can be made with an increased level of confidence. Secondly, automating the way engineers build models reduces subjectivity in the process and frees up engineers to focus on where their intervention will bring greatest benefit.The key outcome of this project will be allowing engineers better understanding of the behaviour of structures of interest, developing better representations of them - such as digital twins - and improving their ability to make operations and maintenance decisions based on measurements. Overall, this increased insight into engineering systems will allow for more targeted and effective management. The result of which is reduced unnecessary maintenance, hence a reduction in costs and reduction in risk to the staff that have to perform maintenance in these harsh environments.There are challenges in this proposal; the first is to enhance existing methodologies for learning the inputs and parameters of a dynamic system to include more complex loading scenarios. These scenarios include modelling multiple correlated forces, distributed loads and loads at unknown locations. The second challenge is to bring further automation to the process of deciding an appropriate model for the dynamic system. It is hypothesised that by addressing these two challenges, determination of multiple/distribution unknown loads and automated learning of the dynamic model structure, the range of situations which can be robustly considered is increased. This enhanced understanding will greatly aid the incorporation of dynamic models in virtualisation of engineering systems and the development of smart infrastructure.