Focus July 2020

However, there are several enabling factors that are coming together to accelerate the implementation of twins. The first of these is cheap, high bandwidth sensing availability - IOT is a key enabler for twins. The second is ‘big data’, there is now an abundance of cheap data storage and associated advances in data management and mining. The third is the increased accessibility of the high-performance computing (HPC) required to manage the computing burden of a functioning twin. Finally, advances in integrated design and analysis environments are creating key digital threads at the start of the lifecycle, enabling the later creation of a twin. These design digital threads link 3D information and multi-physics with integrated data management to create a configuration managed environment that can be maintained and augmented through production. Digital twins: Why now? The concept of a digital twin isn’t a new one - the term was first used in 2002 and the original information concepts date back to the 1980s. 17 16 The complexity of twins To provide benefits across an entire enterprise, a digital twin must be more than a single entity. Fully-featured digital twins will be realised as a family of twins, each of which matures and grows in depth and value through its life. The models used to support asset performance may differ from those used in design and development, but there will be clear data linkages across all of these model sets that need to be managed. Twins will both consume and create high volumes of data throughout their lives. Several coherent twins will be created in the design phase, from basic 3D geometric and design information through to complex high order simulation models and wider design information management. This “Design Twin” set will mature through the build phase to create a “Manufacturing Twin” - a key enabler for the future digital shipyard. This evolution will continue through to the in-service phase, where sensor inputs from the physical asset will help realise the final “Digital Twin.” Further models will need to be created at this point, typically fast surrogate models that include a measure of their own uncertainty to effectively provide predictive and prognostic outputs in response to real-time input data. The creation of a twin is therefore a complex process and relies upon a continuous digital thread throughout the lifecycle. Although IoT is a key enabling technology for digital twins, twins are much more than a simple IoT implementation, which may integrate an intelligent sensor with a data platform. Twins bring together on-platform sensing with other data sources (e.g. environmental data, historical maintenance data) and match this data with a set of models, applying analytics and machine learning to enable predictive and prognostic insights. These insights provide effective decision support in an intelligent asset management context. Due to the complexity and volume of data, the effective visualisation of twin outputs is critical to enable usability and layered consumption of data. Cont’d