Reengineering Aircraft Structural Life Prediction Using a Digital Twin

Reengineering of the aircraft structural life prediction process to fully exploit advances in very high performance digital computing is proposed. The proposed process utilizes an ultrahigh fidelity model of individual aircraft by tail number, a Digital Twin, to integrate computation of structural deflections and temperatures in response to flight conditions, with resulting local damage and material state evolution. A conceptual model of how the Digital Twin can be used for predicting the life of aircraft structure and assuring its structural integrity is presented. The technical challenges to developing and deploying a Digital Twin are discussed in detail

On key technologies for realising digital twins for structural dynamics applications

The term digital twin has gained increasing popularity over the last few years. The concept, loosely based on a virtual model framework that can replicate a particular system for contexts of interest over time, will require the development and integration of several key technologies in order to be fully realised. This paper, focusing on vibration-related problems in mechanical systems, discusses these key technologies as the building blocks of a digital twin. The example of a simulation digital twin that can be used for asset management is then considered. After briefly discussing the building blocks required, the process of data-augmented modelling is selected for detailed investigation. This concept is one of the defining characteristics of the digital twin idea, and using a simple numerical example, it is shown how augmenting a model with data can be used to compensate for the inherent model discrepancy. Finally the implications of this type of data augmentation for future digital twin technology is discussed

Language support for multi agent reinforcement learning

Software Engineering must increasingly address the issues of complexity and uncertainty that arise when systems are to be deployed into a dynamic software ecosystem. There is also interest in using digital twins of systems in order to design, adapt and control them when faced with such issues. The use of multi-agent systems in combination with reinforcement learning is an approach that will allow software to intelligently adapt to respond to changes in the environment. This paper proposes a language extension that encapsulates learning-based agents and system building operations and shows how it is implemented in ESL. The paper includes examples the key features and describes the application of agent-based learning implemented in ESL applied to a real-world supply chain

A digital twin model for enhancing performance measurement in assembly lines

Dynamic manufacturing processes are characterized by a lack of coordination, complexity and sheer volumes of data. Digital transformation technologies offer the manufacturers the capability to better monitor and control both assets and production. This provides also an ever-improving ability to investigate new products and production concepts in the virtual world while optimizing future production with IoT-captured data from different devices and shop floor machine centres. In this study, a digital twin is presented for an assembly line, where IoT-captured data is fed back into the digital twin enabling manufacturers to interface, analyse and measure the performance in real-time of a manufacturing process. The digital twin concept is then applied to an assembly production plan found in the automotive industry, where actual data is considered to analyse how the digital duplicate can be used to review activities and improve productivity within all production shifts