Autonomous Modelling Solutions for Operational Structural Dynamic Systems

确定和理解工程结构如何随时间响应不同的载荷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、生产和管理创造数字孪生兄弟目前是学术界和工业界发展的一个关键领域。该项目旨在扩大这一可能的操作方案的范围，并进一步使这一过程自动化。海上能源部门是一个很好的应用领域，在该领域，该项目正在开发的技术可以支持对工程系统的更深入的了解。环境的性质往往意味着直接测量结构载荷是不可行的。例如，风力对涡轮叶片或波浪对单桩结构产生的力很难测量，但对结构的运行很重要。这一困难推动了方法的发展，这些方法不仅可以推断这些系统的动态特性，而且可以从可用测量中推断未测量的输入(负载)。该项目还将进一步扩展识别任务，以包括作出自主建模决定的能力。换句话说，从收集的数据中收集的信息与先前的工程判断相融合，以学习适当的方法来自动表示系统。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。通过对工程结构的性能和运行条件提供更好的估计，可以更有信心地做出决定。其次，自动化工程师建立模型的方式减少了过程中的主观性，解放了工程师，让他们专注于他们的干预将带来最大利益的地方。这个项目的关键成果将是让工程师更好地理解感兴趣的结构的行为，开发出对它们更好的表示--例如数字双胞胎--并提高他们根据测量做出操作和维护决策的能力。总体而言，增加对工程系统的洞察力将使管理更加有针对性和有效。其结果是减少了不必要的维护，从而减少了必须在这些恶劣环境中执行维护的工作人员的成本和风险。这一提议中存在挑战；第一是加强现有的方法，以学习动态系统的输入和参数，以包括更复杂的加载场景。这些场景包括对多个相互关联的力、分布载荷和未知位置的载荷进行建模。第二个挑战是使为动态系统确定适当模型的过程进一步自动化。假设通过确定多个/分布未知载荷和动态模型结构的自动学习这两个挑战，增加了可以稳健考虑的情况的范围。这种加深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.