Autonomous Modelling Solutions for Operational Structural Dynamic Systems

识别和理解工程结构如何随时间对不同负载作出反应是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孪生是目前学术界和工业界发展的一个关键领域。该项目旨在扩展操作场景的范围，使其成为可能，并进一步实现流程的自动化。海上能源领域是应用领域的一个很好的例子，在该项目中开发的技术可以支持对工程系统的增强洞察力。环境的性质往往意味着直接测量结构荷载是不可行的。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。这一困难促使人们开发方法，不仅可以推断这些系统的动态特性，还可以从可用的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，包括自主建模决策的能力。换句话说，从收集到的数据信息与先前的工程判断相融合，以自动学习适当的方式来表示系统。该项目开发的技术将以两种关键方式支持操作和维护活动。通过提供对工程结构特性和运行条件的改进估计，可以提高决策的可信度。其次，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。该项目的关键成果将使工程师能够更好地理解感兴趣的结构的行为，开发出更好的结构表示——比如数字双胞胎——并提高他们根据测量结果做出操作和维护决策的能力。总的来说，这种对工程系统的深入了解将使管理更有针对性和更有效。其结果是减少了不必要的维护，从而降低了必须在这些恶劣环境中进行维护的工作人员的成本和风险。这一提议存在挑战；首先是增强现有的学习动态系统输入和参数的方法，以包括更复杂的加载场景。这些场景包括建模多个相关力，分布载荷和未知位置的载荷。第二个挑战是为动态系统决定合适模型的过程带来进一步的自动化。假设通过解决这两个挑战，确定多/分布未知负载和动态模型结构的自动学习，可以鲁棒考虑的情况范围增加。这种增强的理解将极大地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发展。

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.